Water disaster prevention, monitoring and early warning method for underground goaf of coal mine

Abstract

The invention provides a coal mine underground goaf water disaster prevention and control and monitoring early warning method, and relates to the technical field of water disaster prevention and control and intelligent monitoring in coal mine engineering. The method comprises the steps that physical parameters, used for reflecting the water disaster evolution process, of a goaf monitoring area are obtained; constructing a three-dimensional hydrological and mechanical coupling inversion model of the goaf; the physical parameters are input into a three-dimensional hydrological and mechanical coupling inversion model, and the water pressure change rate, the seepage flux change rate and the fracture expansion rate, output by the three-dimensional hydrological and mechanical coupling inversion model, of the goaf are obtained; calculating a water disaster disturbance risk value of the goaf according to the water pressure change rate, the seepage flux change rate, the fracture expansion rate and a preset water disaster disturbance risk function; and according to the water disaster disturbance risk value and a preset risk level division threshold, determining a risk level of the water disaster disturbance risk value and calling an early warning strategy corresponding to the risk level. According to the technical scheme disclosed by the invention, the water disaster prediction and early warning capability is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water disaster prevention and control and intelligent monitoring in coal mine engineering, in particular to a coal mine underground goaf water disaster prevention and monitoring and early warning method. BACKGROUND

[0002] In the process of deep mine mining, with the intensification of working face advance and mining disturbance, the goaf water accumulation problem is increasingly prominent, which has become a key obstacle to the safety, continuity and green transformation of deep resource development. The structure in the goaf is broken, and the hydraulic connection is complex, which is easy to form hidden high-pressure water body. Once the seepage path is penetrated, it is likely to induce water inrush, water collapse and other disasters in a short time, especially in mines with water storage coal pillars or natural water storage space, the risk is particularly serious.

[0003] The traditional water disaster prevention method mainly relies on local pumping, static exploration and experience prediction, and lacks spatial cognition ability and dynamic intervention means for the complex seepage structure of the goaf. In addition, the current monitoring system is mostly point-shaped deployment, with single parameter, which cannot establish continuous water pressure field or identify dynamic fracture connection structure; the early warning mechanism is generally based on fixed threshold, which cannot reflect the disturbance evolution trend; the response measures lack intelligent linkage, and cannot realize rapid control and closed-loop adjustment. The above technical bottlenecks directly limit the predictability and active prevention and control ability of sudden water disaster, resulting in high risk of mine water inrush accidents for a long time. SUMMARY

[0004] The technical problem to be solved by the present application is that the existing water disaster prevention method has low early warning accuracy and efficiency, and further provides a coal mine underground goaf water disaster prevention and monitoring and early warning method.

[0005] The technical scheme of the present application provides a coal mine underground goaf water disaster prevention and monitoring and early warning method, comprising: Obtaining physical parameters of a monitoring area of a goaf for reflecting a water disaster evolution process; wherein the monitoring area of the goaf includes a water storage space and its adjacent boundary area, a surrounding rock damage zone area, a fracture development and seepage path area; the physical parameters include water pressure dynamic monitoring data, surrounding rock micro-deformation monitoring data, electrical anomaly monitoring data and structure fracture monitoring data; A three-dimensional hydrology and mechanics coupled inversion model of the goaf is constructed, the three-dimensional hydrology and mechanics coupled inversion model includes a non-steady-state Darcy seepage control model for inverting pore water pressure field and seepage velocity field, and a fracture strain evolution model for describing the process of tensile and shear fractures induced by structure disturbance, the non-steady-state Darcy seepage control model and the fracture strain evolution model realize physical field linkage feedback through permeability and strain coupling relationship; input the physical parameters into the three-dimensional hydrology and mechanics coupling inversion model to obtain a water pressure change rate, a seepage flux change rate and a crack expansion rate of the goaf output by the three-dimensional hydrology and mechanics coupling inversion model; a water disaster disturbance risk value of the goaf is calculated according to the water pressure change rate, the seepage flux change rate, the crack expansion rate and a preset water disaster disturbance risk function; According to the water disaster disturbance risk value and the preset risk level division threshold, the risk level where the water disaster disturbance risk value is located is determined, and the early warning strategy corresponding to the risk level is called.

[0006] Preferably, in the coal mine goaf water disaster prevention and monitoring and early warning method, the three-dimensional hydrology and mechanics coupling inversion model of the goaf is constructed, the three-dimensional hydrology and mechanics coupling inversion model includes a non-steady-state Darcy seepage control model for inverting a pore water pressure field and a seepage velocity field and a crack strain evolution model for describing a tensile and shear crack process induced by structure disturbance, the non-steady-state Darcy seepage control model and the crack strain evolution model realize physical field linkage feedback through a permeability and strain coupling relationship, and the permeability and strain coupling relationship includes: The non-steady-state Darcy seepage control model is constructed as follows: ; wherein, represents a time step, represents a center point spatial coordinate of a small volume unit, is a seepage velocity vector of the small volume unit, is an equivalent permeability tensor, is a water head of the small volume unit, is a unit volume water content, is an instantaneous water pressure field of the small volume unit, is a water specific weight, is an elevation; The crack strain evolution model is constructed as follows: ; wherein, is a crack strain of the small volume unit, is a crack expansion and diffusion coefficient, is a Laplace operator of a strain field, is a crack source term of the small volume unit, is a parameter calibration coefficient, is a collection event frequency; The permeability and strain coupling relationship is represented as follows: ; wherein, is a basic permeability field of the small volume unit, The sensitivity coefficient for enhancing permeability during fracture propagation; The seepage velocity vector, hydraulic head, and instantaneous water pressure field are determined by the dynamic water pressure monitoring data; the fracture strain and fracture source term are determined by the surrounding rock micro-deformation monitoring data; the frequency of the collected events is determined by the structural rupture monitoring data; and the foundation permeability field is determined by the electrical anomaly monitoring data.

[0007] Preferably, in the method for prevention, monitoring, and early warning of water hazards in underground goaf areas of coal mines, the unsteady Darcy flow control model and the fracture strain evolution model are configured with the following boundary conditions: Seepage field boundary conditions: Apply no-flow boundary conditions at the outer boundary of the water storage space. n is the normal vector outside the boundary; at the boundary of the surrounding rock failure zone, a constant water head or constant water pressure boundary condition is applied. , This refers to the real-time water pressure in the goaf area. Structural disturbance field boundary conditions: Apply fixed displacement boundary conditions at the boundary of the stable region of the surrounding rock. At the boundary of the surrounding rock failure zone, apply free tensile crack propagation conditions. .

[0008] Preferably, the method for prevention, monitoring, and early warning of water hazards in underground goaf areas of coal mines, wherein the construction of the unsteady Darcy flow control model and the construction of the fracture strain evolution model further include: If the dynamic water pressure monitoring data indicates that the water pressure fluctuation exceeds the static threshold, then the real-time collected dynamic water pressure monitoring data will be loaded into the boundary input of the unsteady Darcy seepage control model according to the time step, for use in... Perform dynamic updates; If the structural rupture monitoring data represents the microseismic frequency If the set threshold is exceeded, the strain evolution model of the crack will be... Switch from a conventional linear growth mode to an exponential amplification mode; adjust the sensitivity coefficient of the penetration rate-strain coupling relationship. This is to characterize the increased water conductivity of the fractures; If the monitoring data on micro-deformation of the surrounding rock indicates the presence of fracture structures within the goaf, then this is the fracture source term in the fracture strain evolution model. Give a higher initial value or weaken the crack propagation diffusion coefficient .

[0009] Preferably, in the method for prevention, monitoring, and early warning of water hazards in underground goaf areas of coal mines, the water hazard disturbance risk value of the goaf area is calculated based on the water pressure change rate, the seepage flow change rate, the fracture propagation rate, and a preset water hazard disturbance risk function: Preset water hazard disturbance risk function This can be achieved using the following function: ; The water pressure change rate is... The rate of change of the permeation flow is... The crack propagation rate, , , These are the corresponding risk weight coefficients; The preset water hazard disturbance risk function is normalized: ; For the same time step t The mean of the calculated results of the water hazard disturbance risk function corresponding to all small volume units. For the same time step t Standard deviation of the water hazard disturbance risk function calculation results for all small volume units; The calculated result of the preset water hazard disturbance risk function after normalization is used as the water hazard disturbance risk value.

[0010] Preferably, in the method for prevention, monitoring, and early warning of water hazards in underground goaf areas of coal mines, the step of determining the risk level of the water hazard disturbance risk value based on the water hazard disturbance risk value and a preset risk level classification threshold, and then calling the early warning strategy corresponding to the risk level, is achieved by obtaining the risk level of the water hazard disturbance risk value in the following manner: When the risk value of water-related disturbance exceeds the first threshold, it is classified as Level 1 risk. When the second threshold is less than the water hazard disturbance risk value and less than the first threshold, it is classified as a level two risk. When the third threshold is less than the water hazard disturbance risk value and less than or equal to the second threshold, it is a level three risk level. When the risk value of water hazard disturbance is less than or equal to the third threshold, it is classified as a level four risk level, corresponding to a safe and stable zone.

[0011] Preferably, the method for preventing and monitoring water hazards in underground goaf areas of coal mines, wherein the risk level of the water hazard disturbance risk value is obtained further includes: If continuous The small volume units are spatially adjacent and all belong to If the risk level is specified, then the aggregation area is calculated: , For the first The volume of a small volume unit, when At that time, it is determined that the aggregation region has formed. An effective early warning aggregation system for risk levels; for a minimum effective volume threshold corresponding to the risk level; the above is one, two, three or four; writing the effective early warning aggregate into a dynamic risk index table: , represents the i-th effective early warning aggregate identified as a risk level of i , is a risk region set index table of the risk level of is a threshold value corresponding to the risk level of is the volume of the effective early warning aggregate, is a volume determination threshold of the lowest effective early warning aggregate; generating a dynamic risk map in real time according to the dynamic risk index table; calling an early warning strategy according to the risk level of each effective early warning aggregate in the dynamic risk map: the early warning strategy of the first risk level is to execute a forced response; the early warning strategy of the second risk level is to implement a pre-set early warning control measure; the early warning strategy of the third risk level is to adjust the monitoring frequency and expand the monitoring range; the early warning strategy of the fourth risk level is to maintain regular monitoring.

[0012] Preferably, the coal mine goaf water disaster prevention and monitoring and early warning method, after calling the early warning strategy corresponding to the risk level, the method further comprises: for the effective early warning aggregates of the first risk level and the second risk level, if the water disaster disturbance risk value thereof changes by more than a set threshold within consecutive time steps, the monitoring intensity of the effective early warning aggregate is increased.

[0013] Preferably, the coal mine goaf water disaster prevention and monitoring and early warning method, the calling of the early warning strategy according to the risk level of each effective early warning aggregate in the dynamic risk map comprises: generating a trigger signal according to the risk level of the i-th effective early warning aggregate , , is the response priority corresponding to the risk level of is used for resource scheduling sorting mechanism in response queue, the mapping rule is as follows: ; based on the response priority, a response execution criterion is constructed: if​​​​ then the i-th effective early warning aggregate joins the response scheduling queue with the trigger signal; for the set minimum response priority threshold; according to each of the trigger signal , call the response strategy function corresponding to the risk level F k , output the control parameter set: , for the response instruction set corresponding to the risk level, including forced response, early warning control measures, adjustment of monitoring frequency and expansion of monitoring range and range adjustment, and maintenance of regular monitoring.

[0014] Preferably, in the coal mine underground goaf water disaster prevention and monitoring and early warning method, the physical parameters for reflecting the water disaster evolution process of the goaf monitoring area are: The physical parameters are all normalized to dimensionless parameters.

[0015] The above technical solutions provided by the present application have the following technical effects compared with the prior art: The coal mine underground goaf water disaster prevention and monitoring and early warning method provided by the present application collects multi-dimensional physical parameters of the goaf monitoring area, which includes the water storage space and its adjacent boundary area, the surrounding rock damage zone, the fracture development and seepage path area, and the physical parameters include water pressure dynamic monitoring data, surrounding rock micro-deformation monitoring data, electrical anomaly monitoring data and structural fracture monitoring data. A three-dimensional hydrology and mechanics coupling inversion model is constructed, which includes a non-steady-state Darcy seepage control model and a fracture strain evolution model, and a permeability and strain coupling relationship coupling the two models. According to the physical parameters, the three-dimensional hydrology and mechanics coupling inversion model calculates the water pressure change rate, the seepage flux change rate and the fracture expansion rate of the goaf, calculates the water disaster disturbance risk value of the goaf according to the water pressure change rate, the seepage flux change rate, the fracture expansion rate and the preset water disaster disturbance risk function; according to the water disaster disturbance risk value and the preset risk level division threshold, the risk level of the water disaster disturbance risk value is determined and the corresponding early warning strategy is called. Through the scheme, multi-physical field monitoring, three-dimensional hydrology and mechanics coupling inversion model modeling, risk level division and early warning strategy calling are realized, solving the problems of traditional coal mine water disaster monitoring response lag, unclear regional control and low prediction accuracy, and fundamentally improving the water disaster prediction and early warning capability and system response rate. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a flowchart of the coal mine underground goaf water disaster prevention and monitoring and early warning method according to an embodiment of the present application; Figure 2 The flowchart according to an embodiment of the present application is described as determining the water disaster disturbance risk value and calling the corresponding early warning strategy of the risk level. DETAILED DESCRIPTION

[0017] The specific embodiments of the present application are further illustrated below with reference to the accompanying drawings.

[0018] It is easy to understand that, according to the technical solution of the present application, a person skilled in the art can replace various structural modes and implementation modes without changing the essential spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solution of the present application, and should not be regarded as the whole or as a limitation or restriction on the technical solution of the application.

[0019] The technical solution of the following embodiments of the present application provides a coal mine underground goaf water disaster prevention and monitoring and early warning method based on multi-source perception, three-dimensional identification, dynamic early warning and automatic response coupling. A whole process prevention and control mechanism from "monitoring and identification" to "risk control" is established for the underground goaf, and the water disaster prediction and early warning capability and system response rate are fundamentally improved.

[0020] The present embodiment provides a coal mine underground goaf water disaster prevention and monitoring and early warning method, which is applied to a monitoring system configured with an operating system, as shown in FIG. 1, which comprises: Figure 1 As shown in the figure, it comprises: S100: Obtain the physical parameters of the goaf monitoring area for reflecting the water disaster evolution process; wherein the goaf monitoring area comprises a water storage space and its adjacent boundary area, a surrounding rock damage zone area, a fracture development and seepage path area; the physical parameters include water pressure dynamic monitoring data, surrounding rock micro-deformation monitoring data, electrical anomaly monitoring data and structural rupture monitoring data.

[0021] In specific application, the above-mentioned physical parameters can be collected by sensors arranged in the goaf monitoring area, and can be obtained by drilling sampling and the like. Among them, multi-source heterogeneous sensors are arranged in the key structure zone of the goaf, the water storage area and the fracture channel, forming a spatially continuous, physically multidimensional and structured data acquisition network, and real-time acquisition of key parameters such as water pressure, strain, seepage, electrical property and microseismic signal.

[0022] Based on the geological structure model and the distribution characteristics of water storage risk, the monitoring area is mainly composed of the following three types: water storage space and its adjacent area, including representative old goaf, goaf residual water zone, etc.; surrounding rock failure zone area: including coal pillar dam body boundary, fault intersection, working face advancing influence zone, etc.; fissure development and seepage path area, which is the potential high pressure water inrush channel. In the three types of monitoring areas, according to the principle of function priority, regional representation and data complementarity, the sensor is laid out, and the laying interval is generally 10-50 m, which can be adjusted according to the underground space conditions. The types of deployed sensors include: Water pressure and water level monitoring unit, which can collect water pressure and water level of goaf water storage area in real time ; ; Distributed optical fiber strain array, which is laid in the key position of surrounding rock and coal pillar to monitor the total strain for identifying the crack opening area and correcting the crack strain of the model ; ; Resistivity sensing module, which uses multi-point switchable quadrupole method to measure resistivity , and inverses the change of water-rich body and conductive channel; Microseismic and acoustic emission identification device, which is laid in the fault or stress concentration area to collect event frequency and energy , and is used to identify the instability precursors such as crack slip and local breakage; In addition, the accurate values of the permeability parameters of surrounding rock such as porosity , water content , and basic permeability can be obtained by drilling sampling and indoor test correction.

[0023] Various sensors form a local wired network through RS485 bus or optical fiber transmission, supplemented by LoRa or ZigBee wireless relay nodes to form a hybrid networking structure. All data is aggregated to the underground relay gateway and then uploaded to the ground monitoring system through industrial Ethernet or 4G / 5G channel for data visualization and remote control.

[0024] In actual application, the whole monitoring system adopts time synchronization mechanism, supports unified timing and structured processing of multi-source data when acquiring physical parameters, and has local preprocessing and abnormal filtering functions to improve the collection efficiency and anti-interference ability. The monitoring system of the application can realize the whole process, spatialization and continuity perception of the state evolution characteristics such as water pressure mutation of goaf water storage unit, structural disturbance in the process of crack conduction, resistivity reduction trend of water conduction path and microseismic signal frequency change, and provide original basic data support for subsequent three-dimensional hydrology and mechanics coupling inversion model.

[0025] S200: Construct a three-dimensional hydro-mechanical coupling inversion model of the goaf, the three-dimensional hydro-mechanical coupling inversion model comprising a non-steady-state Darcy seepage control model for inverting a pore water pressure field and a seepage velocity field and a crack strain evolution model for describing a tensile and shear crack process induced by structure disturbance, the non-steady-state Darcy seepage control model and the crack strain evolution model realizing physical field linkage feedback through a permeability and strain coupling relationship.

[0026] In this step, on the basis of the multiple-source physical parameters obtained in step S100, a three-dimensional hydro-mechanical coupling inversion model based on small volume elements is established, the pore water pressure field and the seepage velocity field are inverted, the tensile and shear crack process induced by structure disturbance is described, and coupling is realized through the permeability and strain coupling relationship, that is, the three-dimensional hydro-mechanical coupling inversion model can be used to dynamically reconstruct the seepage structure, the water pressure evolution state and the crack propagation process in the goaf.

[0027] S300: Input the physical parameters into the three-dimensional hydro-mechanical coupling inversion model to obtain the water pressure change rate, the seepage flux change rate and the crack propagation rate of the goaf output by the three-dimensional hydro-mechanical coupling inversion model.

[0028] The three-dimensional hydro-mechanical coupling inversion model is obtained from the multiple-source, real-time physical parameters in step S100, and the synchronous solution of the seepage behavior and the crack evolution in the same three-dimensional physical field is realized, so that the water pressure change rate, the seepage flux change rate and the crack propagation rate can be obtained.

[0029] S400: Calculate the water hazard disturbance risk value of the goaf according to the water pressure change rate, the seepage flux change rate, the crack propagation rate and a preset water hazard disturbance risk function.

[0030] After obtaining the dynamic output results of the three-dimensional coupling model, the water hazard disturbance risk value is calculated. The preset water hazard disturbance risk function is generated and stored in the monitoring system in advance.

[0031] S500: According to the water hazard disturbance risk value and the preset risk level division threshold, determine the risk level of the water hazard disturbance risk value and call the early warning strategy corresponding to the risk level.

[0032] In specific implementation, the calculated water hazard disturbance risk value and the pre-defined risk level division threshold can be directly compared to determine the risk level corresponding to the water hazard disturbance risk value, and the early warning strategy corresponding to the risk level can be directly called.

[0033] The above scheme of the embodiment is aimed at collecting multi-dimensional physical parameters of the goaf monitoring area, which includes the water storage space and its adjacent boundary area, the surrounding rock damage zone, the fracture development and seepage path area, and the physical parameters include water pressure dynamic monitoring data, surrounding rock micro-deformation monitoring data, electrical anomaly monitoring data and structural fracture monitoring data. A three-dimensional hydrology and mechanics coupling inversion model is constructed, which includes an unsteady Darcy seepage control model and a fracture strain evolution model, and a permeability and strain coupling relationship coupling the two models. According to the physical parameters, the three-dimensional hydrology and mechanics coupling inversion model calculates the water pressure change rate, the seepage flux change rate and the fracture expansion rate of the goaf, calculates the water hazard disturbance risk value of the goaf according to the water pressure change rate, the seepage flux change rate, the fracture expansion rate and the preset water hazard disturbance risk function; according to the water hazard disturbance risk value and the preset risk grade division threshold, the risk grade of the water hazard disturbance risk value is determined and the corresponding early warning strategy is called. Through the scheme, multi-physical field monitoring, three-dimensional hydrology and mechanics coupling inversion model modeling, risk grade division and early warning strategy calling are realized, solving the problems of traditional coal mine water hazard monitoring response lag, unclear regional control and low prediction accuracy, and fundamentally improving the water hazard prediction and early warning capability and system response rate.

[0034] Preferably, in the step S200, the three-dimensional hydrology and mechanics coupling inversion model of the goaf is constructed, including: S201: constructing the unsteady Darcy seepage control model: ; wherein t represents a time step, x, y and z represent the spatial coordinates of the center point of the small volume unit, is the seepage velocity vector of the small volume unit, is the equivalent permeability tensor (m²), which is determined by the fracture morphology, opening and connectivity; is the water head of the small volume unit, is the unit volume water content, is the instantaneous water pressure field of the small volume unit, is the specific weight of water, and Z is the elevation, which is defined along the vertical direction in the model and is used to calculate the gravity potential energy term. In the relationship, the water head gradient is obtained by the water head difference of adjacent monitoring points or weighted least squares fitting, which is used to represent the change rate and direction of the water head in the three-dimensional space. In this relationship, the measured water pressure of the goaf is the instantaneous water pressure field of the small volume unit is the observation value at the boundary or monitoring point, and for each small volume unit, the continuous water pressure distribution field is reconstructed in a certain three-dimensional space through interpolation and smoothing processing.

[0035] S202: constructing the crack strain evolution model; ; wherein, is the crack strain of the small volume unit, representing the relative deformation of crack opening or shear dilation within the unit volume, obtained by real-time monitoring of sensors such as fiber optic strain arrays; is the crack propagation diffusion coefficient (m² / s), reflecting the ability of crack strain to propagate to adjacent units; is the Laplacian of the strain field (m²), describing the spatial variation characteristics of crack propagation; is the crack source term of the small volume unit, reflecting the difference of crack inducing mechanisms at different spatial positions; is the parameter calibration coefficient, is the acquisition event frequency.

[0036] The permeability and strain coupling relationship is expressed as: ; wherein, is the basic permeability field of the small volume unit, which can be obtained using lithology and drilling data or estimated by inversion of measured static resistivity, and λ is the sensitivity coefficient of crack propagation enhancing permeability, generally taking 0.5-2, obtained based on field experience.

[0037] wherein, the seepage velocity vector , the water head , the instantaneous water pressure field are determined by the water pressure dynamic monitoring data, from the measured results of the field sensors; the crack strain and the crack source term are determined by the surrounding rock micro-deformation monitoring data; the acquisition event frequency is determined by the structure rupture monitoring data; and the basic permeability field is determined by the electrical anomaly monitoring data. Each partial differential equation group in the above model provided in the embodiment is discretized using a space-time coupling method, a small volume unit grid (such as 1×1×1 m³) is constructed in three-dimensional space, the input physical parameters are discretized at equal intervals on the time interval, and periodic coupling iteration is performed. During the calculation process, the multiple-source physical parameters are input into the above model: input after being detected by the water pressure and water level monitoring unit, input after being detected by the distributed fiber optic strain array, calculated and input after detecting the resistivity by the resistivity sensing module, input after being detected by the microseismic and acoustic emission identification device. According to the above formula, the instantaneous water pressure field , the seepage velocity field , the crack evolution field and the equivalent permeability field can be calculated, is the water pressure change rate; is the seepage flux change rate; is the propagation rate.

[0038] Further preferably, to avoid the reflection interference or dissipation error of the boundary condition distortion on the internal solution of the model, the unsteady Darcy seepage control model and the fracture strain evolution model are configured with the following boundary conditions: Seepage field boundary condition: at the outer boundary of the water storage space, a no-flow boundary condition (Neumann condition) is applied , indicating that the water body does not seep through at the boundary, and n is the outer normal vector of the boundary, i.e. the unit directional vector perpendicular to the boundary surface in three-dimensional space; at the boundary of the surrounding rock damage zone, a constant water head or constant water pressure boundary condition (Dirichlet condition) is applied , is the real-time water pressure of the goaf; Structural disturbance field boundary condition: at the boundary of the surrounding rock stable area, a fixed displacement boundary condition is applied , indicating that the fracture in the stable area no longer expands or deforms; at the boundary of the surrounding rock damage zone, a free tensile crack expansion condition is applied , indicating that the fracture can freely expand along the stress gradient direction without external constraints.

[0039] Through the above boundary setting, the unsteady Darcy seepage control model and the fracture strain evolution model can realize the coupled response calculation of the seepage-fracture field in the goaf, surrounding rock and fault intersection area, and provide a physical boundary condition basis for subsequent risk identification.

[0040] Further, to improve the accuracy and practicality of the results of the unsteady Darcy seepage control model and the fracture strain evolution model, field measured data is dynamically embedded, specifically, in the construction of the unsteady Darcy seepage control model in S201 and the construction of the fracture strain evolution model in S202, further comprising: (1) Boundary value update: if the water pressure dynamic monitoring data indicates that the water pressure fluctuation exceeds the static threshold, the real-time water pressure dynamic monitoring data is loaded to the boundary input of the unsteady Darcy seepage control model according to the time step, for dynamic update. Specifically, through the sensors arranged at the boundary of the water storage space adjacent area and the surrounding rock damage zone, the water pressure and water level are collected in real time. The monitoring system is provided with a static threshold . When the water pressure of the monitoring point changes beyond the static threshold, the system automatically triggers the boundary reloading instruction to read the real-time time series data The boundary conditions are then input into the unsteady Darcy flow control model at time steps, and the boundary conditions are updated before each solution iteration to achieve dynamic time updating of the boundary conditions of the unsteady Darcy flow control model.

[0041] (2) Crack activation condition correction: If the structural fracture monitoring data represents the microseismic frequency If the set threshold is exceeded, the strain evolution model of the crack will be... Switch from a conventional linear growth mode to an exponential amplification mode; adjust the sensitivity coefficient of the penetration rate-strain coupling relationship. This is to characterize the increased water conductivity of fractures. Specifically, event frequencies are collected in real time using deployed microseismic and acoustic emission identification devices. With energy The monitoring system has a preset micro-vibration trigger threshold. When the frequency of events collected at any monitoring point Multiple consecutive sampling periods exceeding the preset micro-vibration trigger threshold At that time, the monitoring system automatically determines that the area has entered the fracture activation stage and automatically performs the following operations: adjusting the fracture source term parameters... The process switches from a conventional linear growth model to an exponential growth model, correspondingly increasing the fracture growth rate; then the permeability update coefficient is adjusted. The simulation simulates a sudden increase in the water conductivity of fractures; relevant areas are marked as high-risk units for subsequent risk indicator calculation and early warning.

[0042] (3) Fault structure embedding: If the monitoring data of microdeformation of the surrounding rock indicates the existence of fracture structures in the goaf, then it is the fracture source term in the fracture strain evolution model. Give a higher initial value or weaken the crack propagation diffusion coefficient This is to simulate the preferred propagation path of the fracture.

[0043] Specifically, the above three embedding mechanisms (1)-(3) are uniformly managed by the system: physical parameters are transmitted to the monitoring system in real time; the monitoring system judges whether to trigger boundary value update, fracture activation condition correction or fault structure embedding operation according to the above-set logic; physical parameters and boundary conditions are automatically updated at each time step, forming a monitoring, calculation and correction operation mechanism.

[0044] In the above scheme, preferably, in step S400, the water hazard disturbance risk value of the goaf is calculated based on the water pressure change rate, the seepage flow change rate, the fracture propagation rate, and a preset water hazard disturbance risk function. The preset water hazard disturbance risk function is implemented through the following function: ; As mentioned above, The water pressure change rate is... a change rate of the seepage flux, a crack propagation rate, , , respectively, are corresponding risk weight coefficients, which are set according to lithology, buried depth and microseismic response sensitivity experience; normalizing the preset water damage disturbance risk function: ; is the mean value of the calculation results of the water damage disturbance risk function corresponding to all small volume units at the same time step t, is the standard deviation of the calculation results of the water damage disturbance risk function corresponding to all small volume units at the same time step t; the calculation result of the normalized preset water damage disturbance risk function is used as the water damage disturbance risk value, and the normalization processing can avoid the imbalance of the risk degree caused by different dimensions.

[0045] Due to the nonlinear superposition property of the disturbance process, the statistical distribution of the value tends to be a normal distribution, so the standard quantile method can be used for risk level division, and in step S500, the risk level in which the water damage disturbance risk value is located is determined according to the water damage disturbance risk value and the preset risk level division threshold, and the corresponding early warning strategy is called, as shown in Figure 2 , including: S501: water damage early warning grading: When the water damage disturbance risk value is greater than the first threshold value, it is a first risk level, which is a high disturbance area and needs to be responded immediately. In specific implementation, the first threshold value can be selected as 2.5; when the second threshold value is less than the water damage disturbance risk value and is less than or equal to the first threshold value, it is a second risk level, which belongs to a strong disturbance early warning area. In specific implementation, the second threshold value can be selected as 1.5; when the third threshold value is less than the water damage disturbance risk value and is less than or equal to the second threshold value, it is a third risk level, which belongs to a medium disturbance warning area. In specific implementation, the third threshold value can be selected as 0.5; when the water damage disturbance risk value is less than or equal to the third threshold value, it is a fourth risk level, which corresponds to a safe and stable area. In this scheme, the selection of each threshold value is derived from the standard deviation layering theory in the statistical analysis method. Under the assumption that the normalized index obeys the approximate normal distribution, the disturbance exceeding 2.5 is in the upper 0.6% interval of the sample distribution, representing an extreme abnormal disturbance; the data in the interval of 1.5-2.5 accounts for about 5% of the distribution, corresponding to a risk area that deviates from the stable state but does not reach the burst level; 0.5 to 1.5 belongs to a slight fluctuation segment; and the area below 0.5 is a disturbance background noise area.

[0046] Further preferably, in order to improve the accuracy of early warning and suppress occasional false positives, a spatial connectivity constraint mechanism is introduced, and the way to obtain the risk level in which the water damage disturbance risk value is located in the above scheme further includes: S502: Effective early warning aggregate division If the consecutive small volume units are adjacent in space and belong to the same risk level, calculate the aggregate area: , The volume of the small volume unit, when , determine that the aggregate area forms an effective early warning aggregate of the risk level; The minimum effective volume threshold value corresponding to the risk level; wherein the risk level may be one, two, three, or four.

[0047] S503: Store in dynamic risk index table Write the effective early warning aggregate into the dynamic risk index table: , The i-th effective early warning aggregate identified as the risk level at time , The risk area set index table of the risk level, The threshold value corresponding to the risk level, The volume of the effective early warning aggregate, The volume determination threshold value of the minimum effective early warning aggregate; S504: Real-time generation of dynamic risk map according to the dynamic risk index table; S505: According to the risk level of each effective early warning aggregate in the dynamic risk map, call the early warning strategy: the early warning strategy of the first risk level is to execute forced response; the early warning strategy of the second risk level is to implement the pre-set early warning control measures; the early warning strategy of the third risk level is to adjust the monitoring frequency and expand the monitoring range; the early warning strategy of the fourth risk level is to maintain regular monitoring.

[0048] Further, the step S500 in the above scheme further comprises, after calling the early warning strategy corresponding to the risk level: for the effective early warning aggregates of the first risk level and the second risk level, if the water disaster disturbance risk value thereof changes by more than a set threshold value within consecutive time steps, increase the monitoring intensity of the effective early warning aggregate. That is, determine whether is satisfied, wherein is the current time step risk index, is the weighted average disturbance index of the previous time step of the same area, is the disturbance change threshold value, is determined. When the above criterion is met, the region is marked as a monitoring enhancement unit , and the single-parameter acquisition mode is switched to a multi-channel collaborative mode. The final risk level region is output in a structured form through an index table.

[0049] In the above scheme, the step S504 includes: S5041: generating a trigger signal according to the risk level of the i-th effective early warning aggregate , is the response priority corresponding to the risk level of the i-th effective early warning aggregate ; S5042: constructing a response execution criterion based on the response priority: if , the trigger signal of the i-th effective early warning aggregate is added to the response scheduling queue. is the minimum response priority threshold, for example, set to 1, indicating that only regions with risk levels of three or above execute responses.

[0050] S5043: according to each trigger signal , calling the response strategy function F corresponding to the risk level of the i-th effective early warning aggregate k , outputting a set of control parameters: , is the response instruction set corresponding to the risk level, including forced response, early warning control measures, adjustment of monitoring frequency and expansion of monitoring range, range adjustment, and maintenance of regular monitoring. Specifically, according to the risk level , a preset response strategy function F k is called to output a set of response control parameters . ​​​The response instruction set corresponding to the risk level includes four types of forced response, early warning control measures, adjustment of monitoring frequency and extension of monitoring range, and maintenance of regular monitoring. According to the risk level, the first level risk (which can be represented by red) area needs to perform forced response, including emergency pumping system activation, area closure and operation suspension, grouting plugging system activation, etc.; the second level risk (which can be represented by orange) area implements early warning control measures, including starting local limited mining, real-time monitoring of frequency sampling, remote personnel evacuation preparation; the third level risk (which can be represented by yellow) area adjusts the monitoring frequency and extends the monitoring range; the fourth level risk (which can be represented by green) area maintains regular monitoring.

[0051] In the above scheme, after the early warning strategy response to the risk area of each level, it further includes: In the set window period Δ T The disturbance variable monitoring is carried out on the original risk area , the risk index is recalculated, and a new round of risk assessment is carried out. The recalculation of the risk index can be expressed by the following formula: ; To ensure that the modeling always fits the on-site evolution process, the system takes the measured data of the disturbance after the response as the initialization parameters of the non-steady-state Darcy seepage control model and the fracture strain evolution model in the next round of calculation process, and thus the cycle is repeated.

[0052] In the above scheme, in the step S100, the physical parameters sent by the goaf monitoring area sensor for reflecting the water disaster evolution process are all normalized to dimensionless parameters.

[0053] To realize the long-term online operation of the goaf water disaster prevention system and the dynamic self-correction of the risk identification model, the data flow from various sensors and the three-dimensional hydrological and mechanical coupling inversion model needs to be uniformly managed and standardized. Different types of data have large differences in dimensions (Pa, m, m / s, S / m, etc.). After normalization, the model solving weight imbalance and feature correlation reduction can be avoided, and the calculation stability of multi-dimensional coupling analysis can be ensured. The normalization processing proposed in this step uniformly converts the related data into a dimensionless parameter set , to realize cross-scale fusion, dynamic correction and online optimization of multi-source information. Specifically, various types of sensor data (including water pressure, water level, optical fiber strain, resistivity, microseismic frequency and energy, etc.) are uniformly converted into a standardized format to form a comparable input matrix. Specifically, the following steps are included: S101: all physical parameters are uniformly converted into a standardized small volume unit space-time index structure, using the following mode: ; wherein, For the first i standardized parameter indicators, representing the spatial unit and the time step acquired physical parameters, including hydrological indicators (such as water pressure , seepage velocity , water head gradient ), rock mechanics indicators (such as fracture strain , diffusion coefficient ), medium indicators (such as porosity , equivalent permeability ) and disturbance risk indicators , etc.

[0054] S102: All standardized indicators are processed by zero-mean normalization, with cross-dimension comparability. The data standardization function form is: ; represent the mean value of different standardized indicators; S103: The system automatically records the input data set , control strategy function and response result in each calculation period, forming a training sample library S T : ; where, represents the input physical parameter data set in the first time period, including water pressure , water head gradient , fracture strain , resistivity and disturbance risk indicators , etc. is the control strategy function executed by the system in this period, including risk classification response parameters, pumping and grouting control instructions, monitoring frequency adjustment strategies, etc. is the system feedback result after executing the strategy, used to quantify the strategy effect, mainly including risk level change rate , water pressure recovery rate and fracture expansion attenuation rate , etc.

[0055] The physical parameter data set is composed of multiple physical parameters, forming a multi-dimensional input vector structure; the corresponding system feedback result contains multiple characteristic indicators reflecting the response effect, thus forming a multi-input-multiple-output dynamic mapping relationship, which is used for model self-learning and strategy optimization.

[0056] Training sample library The sample library is dynamically updated in a rolling window manner indexed by time period, i.e. new data is automatically written in and old data is replaced in chronological order. When a significant change in environmental conditions, seepage state or risk level is detected, the sample library automatically triggers a weight update and strategy correction program to adaptively adjust key parameters such as risk determination thresholds and response priorities.

[0057] Through the above scheme, the correspondence between "input-strategy-result" can be continuously accumulated, and the bidirectional update of data and strategy is realized, providing data support for online optimization and intelligent evolution of subsequent models. The sample library in this scheme not only has real-time and continuity, but also has traceability and self-learning features, providing a long-term stable data foundation for water disaster risk prediction and dynamic control.

[0058] According to the needs, the above technical solutions can be combined to achieve the best technical effect.

[0059] The above is only the principle and preferred embodiment of the present application. It should be noted that for those skilled in the art, on the basis of the principles of the present application, a number of other variations can also be made, which should be considered as the protection scope of the present application.

Claims

1. A method for preventing and monitoring water disasters in a coal mine goaf, characterized in that, The method comprises: acquiring physical parameters of a goaf monitoring area for a water disaster evolution process; wherein the goaf monitoring area comprises a water storage space and its adjacent boundary area, a surrounding rock damage zone area, a fracture development and seepage path area; the physical parameters comprise water pressure dynamic monitoring data, surrounding rock micro-deformation monitoring data, electrical anomaly monitoring data and structural fracture monitoring data; constructing a three-dimensional hydro-mechanical coupling inversion model of the goaf, the three-dimensional hydro-mechanical coupling inversion model comprising a non-steady-state Darcy seepage control model for inverting a pore water pressure field and a seepage velocity field and a fracture strain evolution model for describing a tensile and shear fracture process induced by structural disturbance, the non-steady-state Darcy seepage control model and the fracture strain evolution model realizing physical field linkage feedback through a permeability and strain coupling relationship; inputting the physical parameters into the three-dimensional hydro-mechanical coupling inversion model to obtain a water pressure change rate, a seepage flux change rate and a fracture expansion rate of the goaf output by the three-dimensional hydro-mechanical coupling inversion model; calculating a water disaster disturbance risk value of the goaf according to the water pressure change rate, the seepage flux change rate, the fracture expansion rate and a preset water disaster disturbance risk function; determining a risk level of the water disaster disturbance risk value according to the water disaster disturbance risk value and a preset risk level division threshold and calling a corresponding early warning strategy of the risk level.

2. The coal mine goaf water disaster prevention and monitoring and early warning method according to claim 1, characterized in that, The method of constructing a three-dimensional hydro-mechanical coupling inversion model of the goaf, the three-dimensional hydro-mechanical coupling inversion model comprising a non-steady-state Darcy seepage control model for inverting a pore water pressure field and a seepage velocity field and a fracture strain evolution model for describing a tensile and shear fracture process induced by structural disturbance, the non-steady-state Darcy seepage control model and the fracture strain evolution model realizing physical field linkage feedback through a permeability and strain coupling relationship, comprises: constructing the non-steady-state Darcy seepage control model: ； wherein, denotes the time step, denotes the spatial coordinates of the center point of the small volume element, is the seepage velocity vector of the small volume element, is the equivalent permeability tensor, is the water head of the small volume element, is the water content per unit volume, is the instantaneous water pressure field of the small volume element, is the specific weight of water, is the elevation; constructing the fracture strain evolution model: ； wherein, is the crack strain of the small volume element, is the crack propagation diffusion coefficient, is the Laplacian of the strain field, is the crack source term of the small volume element, is the parameter calibration coefficient, is the acquisition event frequency; the permeability and strain coupling relationship is expressed as: ； wherein, is the base permeability field of the small volume element, is the sensitivity coefficient of the fracture propagation enhancing permeability; wherein a seepage velocity vector, a water head and an instantaneous water pressure field are determined by the water pressure dynamic monitoring data; a fracture strain and a fracture source term are determined by the surrounding rock micro-deformation monitoring data; an acquisition event frequency is determined by the structural fracture monitoring data; and a basic permeability field is determined by the electrical anomaly monitoring data.

3. The coal mine goaf water disaster prevention and monitoring and early warning method according to claim 2, characterized in that, The non-steady-state Darcy seepage control model and the fracture strain evolution model are configured with the following boundary conditions: Seepage field boundary condition: no-flow boundary condition is applied at the outer boundary of the water storage space , is the outer normal vector of the boundary; a constant water head or water pressure boundary condition is applied at the boundary of the surrounding rock failure zone area , is the real-time water pressure of the goaf Structural disturbance field boundary condition: fixed displacement boundary condition is applied at the boundary of the stable zone of the surrounding rock ; free tension crack propagation condition is applied at the boundary of the failure zone of the surrounding rock .

4. The coal mine goaf water disaster prevention and monitoring and early warning method according to claim 3, characterized in that, In the method of constructing the non-steady-state Darcy seepage control model and the fracture strain evolution model, the method further comprises: If the water pressure dynamic monitoring data represents that the water pressure fluctuation exceeds the static threshold value, the real-time collected water pressure dynamic monitoring data is loaded to the boundary input of the unsteady Darcy seepage control model according to time steps, for dynamic updating ; if the structural breakage monitoring data indicates microseismic frequencies exceeding a set threshold, switching the fracture strain evolution model from a conventional linear growth pattern to an exponential amplification pattern; adjusting a sensitivity coefficient in the permeability and strain coupling relationship to characterize an increase in fracture hydraulic conductivity; If the micro-deformation monitoring data of the surrounding rock indicates that there is a fracture structure in the goaf, a fracture source term in the fracture strain evolution model Assign a higher initial value or weaken the fracture propagation diffusion coefficient .

5. The coal mine goaf water disaster prevention and monitoring and early warning method according to claim 1, characterized in that, In the method of calculating a water disaster disturbance risk value of the goaf according to the water pressure change rate, the seepage flux change rate, the fracture expansion rate and a preset water disaster disturbance risk function, the method further comprises: Pre-set water damage disturbance risk function By the following function: ； is the water pressure change rate, is the seepage flux change rate, is the crack propagation rate, , , are the corresponding risk weight coefficients, respectively. normalizing the preset water disaster disturbance risk function: ； for the same time step t the mean of the results of the calculation of the water hazard disturbance risk function for all the small volume units, for the same time step t the standard deviation of the results of the calculation of the water hazard disturbance risk function for all the small volume units; taking a calculation result of the normalized preset water disaster disturbance risk function as the water disaster disturbance risk value.

6. The coal mine goaf water disaster prevention and monitoring and early warning method according to claim 5, characterized in that, In the method of determining a risk level of the water disaster disturbance risk value according to the water disaster disturbance risk value and a preset risk level division threshold and calling a corresponding early warning strategy of the risk level, the risk level of the water disaster disturbance risk value is determined in the following manner: When the water hazard disturbance risk value is greater than the first threshold value, the risk level is level one; When the second threshold value is less than the water hazard disturbance risk value and the water hazard disturbance risk value is less than or equal to the first threshold value, the risk level is level two; When the third threshold value is less than the water hazard disturbance risk value and the water hazard disturbance risk value is less than or equal to the second threshold value, the risk level is level three; When the water hazard disturbance risk value is less than or equal to the third threshold value, the risk level is level four, corresponding to a safe and stable region.

7. The coal mine goaf water disaster prevention and monitoring and early warning method according to claim 6, characterized in that, The method for obtaining the risk level of the water hazard disturbance risk value further comprises: If consecutive small volume units are spatially adjacent and belong to the same risk level, calculate the aggregation area: , the volume of the small volume unit, when , determine that the aggregation area forms an effective early warning aggregation of the risk level; is the minimum effective volume threshold corresponding to the risk level of ; the above is one, two, three or four; writing the effective early warning aggregate into a dynamic risk index table: , representing the th effective early warning aggregate identified as a risk level of , , a risk region set index table of a risk level of , a threshold value corresponding to a risk level of , a volume of the effective early warning aggregate, a volume determination threshold value of the lowest effective early warning aggregate; generating a dynamic risk map in real time according to the dynamic risk index table; calling an early warning strategy according to the risk level of each effective early warning aggregate in the dynamic risk map; the early warning strategy for the level one risk level is to execute a forced response; the early warning strategy for the level two risk level is to implement a pre-set early warning control measure; the early warning strategy for the level three risk level is to adjust the monitoring frequency and expand the monitoring range; the early warning strategy for the level four risk level is to maintain regular monitoring.

8. The coal mine goaf water disaster prevention and monitoring and early warning method according to claim 7, characterized in that, After the early warning strategy corresponding to the risk level is called, the method further comprises: For the effective early warning aggregation body of the first risk level and the second risk level, if the change range of the water disaster disturbance risk value of the effective early warning aggregation body exceeds the set threshold value in continuous time steps, the monitoring strength of the effective early warning aggregation body is increased. ​ 9. The coal mine goaf water disaster prevention and monitoring and early warning method according to claim 7, characterized in that, the calling of the early warning strategy according to the risk level of each effective early warning aggregate in the dynamic risk map comprises: According to the first effective early warning aggregate risk level generation trigger signal: , For the response priority corresponding to the risk level, the resource scheduling sorting mechanism in the response queue is as follows: ； Based on the response priority, a response execution criterion is constructed: if the triggering signal of the first effective early warning aggregate is added to the response scheduling queue; is the minimum response priority threshold set.​ According to each of the trigger signals , call level risk response strategy function corresponding to the policy , output control parameter set: , corresponding to the response instruction set for risk level, including mandatory response, early warning control measures, adjustment of monitoring frequency and expansion of monitoring range and range adjustment, and maintenance of regular monitoring.

10. The coal mine goaf water disaster prevention and monitoring and early warning method according to any one of claims 1-9, characterized in that, in the acquisition of the physical parameters of the goaf monitoring area for reflecting the water hazard evolution process, all the physical parameters are normalized to be dimensionless parameters.

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