Water conducting fracture zone height prediction method, device, equipment, medium and product

Abstract

This disclosure relates to a method, apparatus, equipment, medium, and product for predicting the height of water-conducting fracture zones, applicable to the field of coal mining technology. In this disclosure, the overburden of the area to be processed is classified based on geological information, resulting in overburden classification results, each including at least one overburden type. For each overburden type, the predicted height of the water-conducting fracture zone is determined based on the actual bedrock thickness parameter of the overburden type and a pre-constructed water-conducting fracture zone prediction mapping relationship. The water-conducting fracture zone prediction mapping relationship is related to fracture parameters and bedrock thickness parameters. The predicted heights of the water-conducting fracture zones corresponding to each overburden type are then fused to obtain the predicted height of the area to be processed. By performing geological clustering on multiple overburden types, inaccurate prediction results caused by uneven geological conditions in the same area can be avoided. Therefore, the accuracy of water-conducting fracture zone height prediction can be improved.

Description

Technical Field

[0001] This disclosure belongs to the field of coal mining technology, specifically relating to a method, apparatus, equipment, medium, and product for predicting the height of water-conducting fracture zones. Background Technology

[0002] Water-conducting fracture zones in coal mines are areas of longitudinal water-conducting fractures formed by the fracturing of overlying strata after coal seam mining. As crucial channels connecting aquifers and goaf areas, they directly threaten mine safety and impact the regional ecological environment. Accurate prediction of the development height of water-conducting fracture zones is a core prerequisite for preventing water hazards, enabling synergistic control of safe mining, economic benefits, and ecological protection. It is a key technological foundation for the sustainable development of coal mines.

[0003] In related technologies, numerical simulation or physical similarity simulation can be used to predict the evolution of fractures. However, such prediction methods are highly dependent on existing geological exploration data, and the mechanical properties, lithological combinations, and bedrock thickness of the overlying bedrock vary greatly in different regions. Given the significant dispersion of bedrock structure in the Northwest mining area, it is difficult to cover the prediction needs of the entire region. Therefore, the accuracy of predicting the height of water-conducting fracture zones is relatively low. Summary of the Invention

[0004] This disclosure addresses some deficiencies mentioned in the background art by providing a method, apparatus, device, medium, and product for predicting the height of water-conducting fracture zones, which helps to improve the accuracy of water-conducting fracture zone height prediction.

[0005] In a first aspect, embodiments of this disclosure provide a method for predicting the height of a water-conducting fracture zone, comprising: Based on the geological information of the area to be processed, the overlying rocks of the area to be processed are classified to obtain overlying rock classification results, which include at least one type of overlying rock. For each of the aforementioned overburden types, based on the actual bedrock thickness parameters of the overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone, the predicted height of the water-conducting fracture zone corresponding to the overburden type is determined. The predicted mapping relationship of the water-conducting fracture zone is related to the fracture parameters and the bedrock thickness parameters. The height prediction results of the water-conducting fracture zone corresponding to each of the aforementioned overburden types are fused to obtain the height prediction results of the area to be processed.

[0006] Optionally, for each overburden type, determining the predicted height of the water-conducting fracture zone corresponding to that overburden type based on the actual bedrock thickness parameter of that overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone includes: The actual bedrock thickness parameter is input into the water-conducting fracture zone prediction mapping relationship to calculate the predicted height of the water-conducting fracture zone corresponding to the overburden type. The water-conducting fracture zone prediction mapping relationship includes a logarithmic sub-function, and the actual bedrock thickness parameter is the independent variable of the logarithmic sub-function.

[0007] Optionally, the method further includes: Based on multiple simulated parameters of water-conducting fracture zone height and simulated bedrock thickness, the first mapping relationship for the prediction of water-conducting fracture zones is determined; Based on the actual parameters of multiple water-conducting fracture zone heights and the actual bedrock thickness parameters, a second mapping relationship for the prediction of water-conducting fracture zones is determined; By comparing the first mapping relationship and the second mapping relationship, the predicted mapping relationship of the water-conducting fracture zone is obtained.

[0008] Optionally, comparing the first mapping relationship and the second mapping relationship to obtain the predicted mapping relationship of the water-conducting fracture zone includes: Based on the first mapping relationship and the second mapping relationship, the prediction error value is determined; If the prediction error value is less than or equal to the first threshold, then the first mapping relationship is determined to be the predicted mapping relationship for the water-conducting fracture zone; If the prediction error value is greater than the first threshold, the overlying rock classification result is adjusted and the first mapping relationship is re-determined until the prediction error value is less than the first threshold and the first mapping relationship is determined to be the water-conducting fracture zone prediction mapping relationship.

[0009] Optionally, the area to be processed includes a multi-layered geological structure, which includes at least a bedrock layer. Determining the first mapping relationship for the predicted water-conducting fracture zones based on multiple simulated parameters of fracture zone height and simulated bedrock thickness includes: For each type of overburden, based on the initial model parameters of the preset coal seam mining model, the simulation material parameters of each geological structure are determined, and the initial model parameters include at least the simulation bedrock thickness parameter; Based on the simulated material parameters and the coal seam mining model, the simulated rock movement parameters are determined; Based on the simulated rock movement parameters and the corresponding actual rock movement parameters, the model is verified to obtain the target coal seam mining model; Based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple simulated parameters of water-conducting fracture zone height. The first mapping relationship is determined based on the simulated parameters of the height of each water-conducting fracture zone and the corresponding simulated bedrock thickness parameters.

[0010] Optionally, based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple simulated parameters for the height of the water-conducting fracture zone, including: Based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple sets of overburden fracture parameters. When any set of the overburden fracture parameters meets the preset fracture rules, the overburden location to which the overburden fracture parameters belong is determined as the simulated parameter for the height of the water-conducting fracture zone.

[0011] Optionally, the classification of the overburden rocks in the area to be processed based on the geological information of the area to be processed, to obtain the overburden rock classification result, includes: The weight of each geological sample is determined based on the information entropy of each geological sample, wherein the geological information includes at least one geological sample; Dimension elimination processing is performed based on the weights of each geological sample to obtain the geological sample to be processed. Based on the total number of geological samples to be processed, the first average distance and the second average distance of each geological sample to be processed, the number of target clusters is determined. The first average distance represents the average distance of the geological sample to be processed to all samples in the same cluster, and the second average distance represents the average distance of the geological sample to be processed to all samples in the nearest heterogeneous cluster. Based on the target number of clusters, the overburden classification result is determined with the goal of minimizing the intra-cluster Mahalanobis distance function.

[0012] In a second aspect, embodiments of this disclosure provide a device for predicting the height of a water-conducting fracture zone, comprising: The classification module is used to classify the overburden of the area to be processed based on the geological information of the area to be processed, and to obtain the overburden classification result, wherein the overburden classification result includes at least one type of overburden; The first determining module is used to determine the predicted height of the water-conducting fracture zone corresponding to each of the overburden types, based on the actual bedrock thickness parameters of the overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone. The predicted mapping relationship of the water-conducting fracture zone is related to the fracture parameters and the bedrock thickness parameters. The fusion module is used to fuse the height prediction results of the water-conducting fracture zone corresponding to each of the overburden types to obtain the height prediction result of the area to be processed.

[0013] In a third aspect, embodiments of this disclosure provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for predicting the height of water-conducting fracture zones.

[0014] In a fourth aspect, embodiments of this disclosure provide a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the above-described method for predicting the height of water-conducting fracture zones.

[0015] In a fifth aspect, embodiments of this disclosure provide a computer program product including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device executes the above-described method for predicting the height of water-conducting fracture zones.

[0016] In this disclosure, the overburden of the area to be processed is classified based on geological information, resulting in overburden classification results, each including at least one overburden type. For each overburden type, the predicted height of the water-conducting fracture zone is determined based on the actual bedrock thickness parameter and a pre-constructed prediction mapping relationship. This prediction mapping relationship is related to fracture parameters and bedrock thickness parameters. The predicted heights of the water-conducting fracture zones for each overburden type are then fused to obtain the predicted height for the area to be processed. By geologically clustering multiple overburden types, inaccurate predictions due to uneven geological conditions in the same area can be avoided. Predicting various overburden types after clustering using a pre-constructed prediction mapping relationship improves prediction efficiency and reduces computational load and parameter acquisition difficulty. Therefore, the accuracy of water-conducting fracture zone height prediction can be improved.

[0017] It should be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further illustration of the claimed technology. Attached Figure Description

[0018] Figure 1 A flowchart of a method for predicting the height of a water-conducting fracture zone provided in this disclosure.

[0019] Figure 2 This is a schematic diagram of the structure of the coal seam mining model provided in this disclosure.

[0020] Figure 3 This is a schematic diagram of the fitting of the prediction mapping relationship provided in this disclosure.

[0021] Figure 4 This is a schematic diagram of the overburden classification results provided in this disclosure.

[0022] Figure 5 This is a schematic diagram of the structure of a water-conducting fracture zone height prediction device provided in this disclosure.

[0023] Figure 6 This is a hardware block diagram of an electronic device provided in this disclosure.

[0024] Figure 7 This is a schematic diagram of a computer program product provided in this disclosure. Detailed Implementation

[0025] To enable those skilled in the art to better understand the technical solution of this application, the application scenario of this application will be described first below.

[0026] Water-conducting fracture zones in coal mines are longitudinally developed water-conducting fracture zones formed by the fracturing of overlying strata after coal seam mining. As a crucial channel connecting aquifers and goaf areas, they directly threaten mine safety and impact the regional ecological environment. Accurate prediction of the development height of water-conducting fracture zones is a core prerequisite for preventing water hazards: it can avoid water inrush accidents caused by fracture networks connecting to strong aquifers (such as Ordovician limestone water) (accounting for over 65% of major water inrush accidents in my country), guide the scientific placement of waterproof coal pillars (requiring the aquitard thickness to be ≥ fracture zone height + protective layer thickness); simultaneously support resource protection and green mining. For example, in the North China mining area, controlling fracture height reduces groundwater resource loss caused by mining tens of thousands of tons of coal; the Shendong mining area implements a height-limiting mining strategy to confine fracture zones within the bedrock weathering zone to protect the unconfined strata, and provides a basis for the application of technologies such as delamination grouting. Ultimately, it achieves synergistic control of safe mining, economic benefits, and ecological protection, and is a key technological foundation for sustainable coal mine development.

[0027] Generally, the evolution of rock height can be predicted using empirical formulas, physical similarity simulations, numerical simulations, field measurements, and intelligent prediction methods. However, empirical formulas (such as those recommended in standards) are only applicable to shallow, gently dipping coal seams with simple geological structures (mining depth <500m). While calculations are simple, they neglect tectonic stress and time-dependent deformation, resulting in errors as high as 15%-30%. Physical similarity simulations can reproduce the fracture propagation process under complex geological conditions, but the scaling effect leads to distortion of small fractures, and the cost is high. Numerical simulations (such as FLAC³D) can couple stress-seepage field analysis to analyze fracture evolution in deep mining, with an accuracy error of about 10%-15%, but they are sensitive to parameters and require large computational resources. Field measurement methods (borehole imaging, microseismic monitoring, fiber optic sensing) can acquire millimeter-resolution data, but they only reflect local point information or require dense sensor deployment, making it difficult to cover the entire area. Intelligent prediction methods (machine learning) can reduce errors to below 8% with the support of big data, but they heavily depend on sample quality and have insufficient generalization ability.

[0028] In the Northwest mining area, coal seams are generally thick and shallow, overlying with thin bedrock layers and thick loose soil layers. Geological surveys of some mines indicate that while the overall coal seam occurrence conditions are generally consistent across the region, the mechanical properties, lithological combinations, and bedrock thickness of the overlying bedrock vary significantly across different areas. The "thick sand-thin bedrock" structure is affected by intense mining, resulting in well-developed fractures throughout the bedrock layer and the absence of a "critical layer." This makes empirical formula methods insufficient for predictive needs due to their limitations. Furthermore, the significant differences in overlying strata characteristics across different mining areas mean that existing geological survey data cannot cover the entire region's water-conducting fracture zone height prediction requirements. Physical similarity simulation and numerical simulation methods are insufficient for rapid and accurate prediction of water-conducting fracture zone height across all working faces within the mine. While field measurement and intelligent prediction methods can achieve accurate prediction of water-conducting fracture zone height, they involve large engineering projects, long construction periods, and are only applicable to specific areas, making it difficult to conduct measurements across the entire region. Therefore, the accuracy rate of water-conducting fracture zone height prediction remains low.

[0029] To address the aforementioned technical problems, this disclosure provides an inventive concept: by geologically clustering multiple overburden types, inaccurate prediction results caused by uneven geological conditions in the same area can be avoided. Predicting various overburden types after clustering using a pre-constructed water-conducting fracture zone prediction mapping relationship can improve prediction efficiency and reduce computational load and parameter acquisition difficulty. In other words, based on the unique geological conditions of the Northwest mining area and existing mine geological data, this disclosure obtains the water-conducting fracture zone prediction mapping relationship through geological clustering, numerical simulation, and verification with measured data, achieving rapid and accurate prediction of the height of water-conducting fracture zones across the entire region, thus ensuring the protection of groundwater resources and safe mine production in the Northwest mining area.

[0030] The present disclosure will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present disclosure and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the present disclosure are shown in the drawings, not the entire structure.

[0031] Figure 1 This is a flowchart illustrating a method for predicting the height of a water-conducting fracture zone provided in this disclosure. Figure 1 As shown, the method includes: S101: Based on the geological information of the area to be processed, the overburden of the area to be processed is classified to obtain the overburden classification results.

[0032] Specifically, the overburden classification results include at least one overburden type. This embodiment, tailored to the unique geological conditions of the Northwest mining area, employs a hybrid clustering algorithm to geologically cluster multiple overburden types within the divided mining area. This effectively addresses the challenges of heterogeneous geological conditions in the same region and the large workload involved in predicting the height of water-conducting fracture zones in a single working face. Furthermore, the hybrid clustering algorithm avoids the local optima problem of the traditional K-means algorithm, improving the reliability of the clustering results.

[0033] S102: For each overburden type, based on the actual bedrock thickness parameters of the overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone, determine the predicted height of the water-conducting fracture zone corresponding to the overburden type.

[0034] Specifically, in this embodiment, considering that the overburden of coal seams in the Northwest mining area is mostly of a "thick sandy soil - thin bedrock" structure, the bedrock thickness, a strongly correlated variable, is selected to establish a predictive mapping relationship for water-conducting fracture zones. This variable is easy to obtain numerically, the overall method is convenient and quick to calculate, and the technical requirements are low. It can achieve prediction of the height of water-conducting fracture zones throughout the entire mine area using only a small amount of data, making it suitable for actual production work. After clustering the bedrock layers, a predictive mapping relationship for water-conducting fracture zones is pre-constructed using numerical simulation methods. By substituting the actual bedrock thickness parameters of the overburden type into the mapping relationship, the corresponding height prediction result can be determined. The predictive mapping relationship for water-conducting fracture zones is related to both fracture parameters and bedrock thickness parameters.

[0035] This embodiment overcomes the dependence of traditional numerical simulation on complete geological data by combining geological clustering with numerical simulation. Through clustering and grouping, it proposes an equivalent parameter conversion system for bedrock layers, significantly simplifying the model and reducing computational load. This enables efficient and precise calculation of the UDEC (Universal Distinct Element Code, a tool model that provides accurate and effective analysis for geotechnical engineering using explicit problem-solving schemes) model. Simultaneously, in the process of pre-constructing the predicted mapping relationship of water-conducting fracture zones, a multi-verification mechanism is established, including posterior verification (displacement error ≤5%) and measured data verification (height error ≤10m). This effectively solves the problems of parameter sensitivity, large computational load, and difficult verification in traditional methods, significantly improving efficiency while ensuring accuracy and meeting the rapid prediction needs of the entire mine.

[0036] S103: The height prediction results of the water-conducting fracture zone corresponding to each overburden type are fused to obtain the height prediction results of the area to be processed.

[0037] Specifically, after predicting the height of the water-conducting fracture zone for each type of overburden, the height prediction results of each overburden type are integrated to obtain the predicted height of the water-conducting fracture zone in the working face of the area to be treated.

[0038] In this disclosure, the overburden of the area to be processed is classified based on geological information, resulting in overburden classification results, each including at least one overburden type. For each overburden type, the predicted height of the water-conducting fracture zone is determined based on the actual bedrock thickness parameter and a pre-constructed prediction mapping relationship. This prediction mapping relationship is related to fracture parameters and bedrock thickness parameters. The predicted heights of the water-conducting fracture zones for each overburden type are then fused to obtain the predicted height for the area to be processed. By geologically clustering multiple overburden types, inaccurate predictions due to uneven geological conditions in the same area can be avoided. Predicting various overburden types after clustering using a pre-constructed prediction mapping relationship improves prediction efficiency and reduces computational load and parameter acquisition difficulty. Therefore, the accuracy of water-conducting fracture zone height prediction can be improved.

[0039] In one possible implementation, an exemplary method for determining the predicted height of the water-conducting fracture zone corresponding to each overburden type, based on the actual bedrock thickness parameter of the overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone, includes: By inputting the actual bedrock thickness parameter into the water-conducting fracture zone prediction mapping relationship, the predicted height of the water-conducting fracture zone corresponding to the overburden type is calculated.

[0040] Specifically, the predicted mapping relationship for water-conducting fracture zones includes a logarithmic sub-function, with the actual bedrock thickness parameter being the independent variable of the logarithmic sub-function. In this embodiment, an exemplary expression for the predicted mapping relationship for water-conducting fracture zones is shown below:

[0041] in, The height of the water-conducting fracture zone; is the actual bedrock layer thickness parameter; C is a constant term.

[0042] For example, assuming the actual bedrock thickness at the working face is 124.5m, the predicted height of the water-conducting fracture zone is 125.68m.

[0043] In one possible implementation, the method further includes: Based on multiple simulated parameters of water-conducting fracture zone height and simulated bedrock thickness, the first mapping relationship for the prediction of water-conducting fracture zones is determined.

[0044] Specifically, in this embodiment, the first mapping relationship can be a fitted curve. Constructing the first mapping relationship requires statistical analysis of data obtained from numerical simulations to establish a mapping dataset of bedrock thickness parameters—water-conducting fracture zones under the current type, and then performing regression analysis to obtain the fitted curve. In this embodiment, the fitted curve is shown below:

[0045] in, The height of the water-conducting fracture zone; This refers to the actual bedrock layer thickness parameter; This is a constant term.

[0046] For example, the area to be processed includes a multi-layered geological structure, which includes at least a bedrock layer; based on multiple simulated parameters of water-conducting fracture zone height and simulated bedrock thickness, a first mapping relationship for the predicted water-conducting fracture zones is determined, including: For each overburden type, based on the initial model parameters of the preset coal seam mining model, the simulated material parameters of each geological structure are determined. The initial model parameters include at least the simulated bedrock thickness parameters. Based on the simulated material parameters and the coal seam mining model, the simulated rock movement parameters are determined. Based on the simulated rock movement parameters and the corresponding actual rock movement parameters, the model is verified to obtain the target coal seam mining model. Based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple simulated parameters of water-conducting fracture zone height. Based on each simulated parameter of water-conducting fracture zone height and the corresponding simulated bedrock thickness parameters, the first mapping relationship is determined.

[0047] Specifically, a coal seam mining model is established by selecting a specific overburden type, and the model is then calibrated to ensure its accuracy. In this embodiment, the coal seam mining model can be a discrete element method (DEM) model implemented using UDEC numerical simulation software. Figure 2 This is a schematic diagram of the structure of the coal seam mining model provided in this disclosure, such as... Figure 2 As shown. The initial model parameters of the coal seam mining model can be set to a width of 400m, with 100m coal pillars left on the left and right sides to eliminate boundary effects. From top to bottom, the model consists of loose soil (140m), bedrock (100m), coal seam (6m), and floor rock (40m). The blocks adopt the Mohr-Coulomb model, and the rock joints are simplified to horizontal and vertical joints. The loose layer uses the Voronoi command to generate random polygonal joints, and all joints adopt the surface contact Coulomb slip model.

[0048] After constructing the initial coal seam mining model, the model was calibrated by standardizing the simulation material parameters used in the simulation. The loose soil layer, coal seam, and floor strata all have consistent properties, and the simulation material parameters were taken as the average of all samples from the corresponding strata for this overburden type.

[0049] The simulated material parameters include at least one of the following: equivalent thickness, equivalent elastic modulus, equivalent density, equivalent compressive strength, equivalent tensile strength, joint normal stiffness, joint tangential stiffness, equivalent internal friction angle, and equivalent cohesion. The simulated material parameters for each layer are determined using a sample mean calculation method. In this embodiment, the multi-layered structure within the bedrock layer is treated as a whole when calculating the bedrock layer.

[0050] An exemplary formula for determining the equivalent thickness is shown below:

[0051] in, The number of bedrock layers. The layer number is 1 to n; The thickness of the i-th layer of bedrock preceding the geological cluster. This represents the equivalent thickness of the bedrock layer.

[0052] An exemplary formula for determining the equivalent elastic modulus is shown below:

[0053] in, The number of bedrock layers. For the floor number, The thickness of the i-th layer of bedrock preceding the geological cluster. Let be the neutral axis moment of inertia of the i-th bedrock layer before geological clustering. Let be the elastic modulus of the bedrock layer before geological clustering of the i-th bedrock layer. It is the equivalent elastic modulus of the bedrock layer.

[0054] An exemplary formula for determining the equivalent density is shown below:

[0055] in, The number of bedrock layers. For the floor number, The thickness of the i-th layer of bedrock preceding the geological cluster. The density of the bedrock layer before geological clustering is the i-th bedrock layer. This is the equivalent density of the bedrock layer.

[0056] An exemplary formula for determining the equivalent compressive strength is shown below:

[0057] in, The number of bedrock layers. For the floor number, The thickness of the i-th layer of bedrock preceding the geological cluster. Let be the elastic modulus of the bedrock layer before geological clustering of the i-th bedrock layer. The compressive strength of the bedrock layer before geological clustering is given by the i-th bedrock layer. It represents the equivalent compressive strength of the bedrock layer.

[0058] An exemplary formula for determining the equivalent tensile strength is shown below:

[0059] in, The number of bedrock layers. For the floor number, The thickness of the i-th layer of bedrock preceding the geological cluster. For equivalent compressive strength, The tensile strength of the bedrock layer before geological clustering of the i-th bedrock layer. The compressive strength of the bedrock layer before geological clustering is given by the i-th bedrock layer. It represents the equivalent tensile strength of the bedrock layer.

[0060] An exemplary formula for determining the normal stiffness of a joint is shown below:

[0061] in, The number of bedrock layers. For the floor number, The thickness of the i-th layer of bedrock preceding the geological cluster. For the joint-based phase stiffness of the i-th bedrock layer before geological clustering, The joint development index is the geological clustering index of the i-th bedrock layer. This represents the normal stiffness of the joints in the bedrock layer.

[0062] An exemplary formula for determining the tangential stiffness of a joint is shown below:

[0063] in, The number of bedrock layers. For the floor number, For joint normal stiffness, This represents the tangential stiffness of the joints in the bedrock layer.

[0064] An exemplary formula for determining the equivalent internal friction angle is shown below:

[0065] in, The number of bedrock layers. For the floor number, The thickness of the i-th layer of bedrock preceding the geological cluster. The internal friction angle before geological clustering of the i-th bedrock layer. This represents the rock quality index before geological clustering of the i-th bedrock layer. This is the equivalent internal friction angle of the bedrock layer.

[0066] An exemplary formula for determining equivalent cohesion is shown below:

[0067] in, The number of bedrock layers. For the floor number, The thickness of the i-th layer of bedrock preceding the geological cluster. The cohesion of the bedrock strata before geological clustering of the i-th bedrock layer. It represents the equivalent cohesion of the bedrock layer.

[0068] After calculating the simulated material parameters of loose soil layer, bedrock layer, coal seam and floor rock layer after geological clustering, the simulated material parameters are input into the numerical calculation model constructed based on the coal seam mining model and run to obtain the simulated rock movement parameters, as shown in Table 1. Table 1 is the simulated material parameter table.

[0069] Table 1 Simulation Material Parameters

[0070] By comparing simulated rock movement parameters with actual rock movement parameters in the area with the overlying strata type, the accuracy of the model verification is determined. The coal seam mining model with accurate verification is used as the target coal seam mining model. Multiple sets of different simulated bedrock thickness parameters are input into the target coal seam mining model to calculate and determine the height of the water-conducting fracture zone corresponding to different bedrock thicknesses, thereby establishing the first mapping relationship.

[0071] For example, based on the example data above, an exemplary expression for the first mapping relationship is shown below:

[0072] in, The height of the water-conducting fracture zone; To simulate bedrock thickness parameters, The degree of fit.

[0073] Furthermore, the model can be verified using a preset displacement error. In this embodiment, when the displacement error is less than or equal to 10%, the model is considered to be verified accurately; when the displacement error is greater than 10%, the bedrock material parameters are adjusted until the error meets the requirements.

[0074] An example formula for displacement error is shown below:

[0075] in, To simulate rock movement parameters, These are the actual rock movement parameters.

[0076] For example, based on multiple sets of simulated bedrock thickness parameters, advance mining processing is performed on the target coal seam mining model to obtain multiple simulated parameters for the height of water-conducting fracture zones, including: Based on multiple sets of simulated bedrock thickness parameters, advance mining processing is carried out in the target coal seam mining model to obtain multiple sets of overburden fracture parameters; when any set of overburden fracture parameters meets the preset fracture rules, the overburden location to which the overburden fracture parameter belongs is determined as the simulated parameter of the height of the water-conducting fracture zone.

[0077] Specifically, a numerical calculation model based on the target coal seam mining model is run to extract overburden fracture parameters during the simulated working face advance. The height of the water-conducting fracture zone is determined based on the extracted fracture parameters. During the simulated working face advance, a vector map of the overburden fracture distribution is extracted every 20m. The output image is then converted to grayscale and binarized. After noise removal, the fracture parameters are obtained through algorithmic processing. The fracture parameters include at least one of fracture density, fracture length, and fracture connectivity.

[0078] An exemplary formula for determining the fracture density is shown below:

[0079] in, Fracture density; This represents the number of gap pixels in the image (i.e., the number of black pixels in the binary image). This represents the total number of pixels in the image.

[0080] An exemplary formula for determining the crack length is shown below:

[0081] in, The crack length; The number of cracks; Let be the pixel length of the i-th crack; The actual length represented by each pixel.

[0082] An example formula for determining connectivity is shown below:

[0083] in, For fracture connectivity; The area of ​​the largest connected component; This represents the total area of ​​all cracks.

[0084] After determining the fracture parameters, starting from the overlying strata and working upwards, when a certain stratum satisfies the fracture rules, the location of that stratum is determined as the simulated parameter for the height of the water-conducting fracture zone. In this embodiment, the fracture rules include at least one of the following: fracture density less than 0.02, fracture length greater than 2, and fracture connectivity less than 0.1.

[0085] By varying the simulated bedrock thickness parameters, numerical simulations were performed on multiple sets of simulated bedrock thickness parameters, and overlying fracture parameters were extracted to determine the corresponding simulated parameters for the height of the water-conducting fracture zone. For example, with simulated bedrock thickness parameters of 80m, 120m, 140m, 160m, 180m, and 200m, the corresponding simulated parameters for the height of the water-conducting fracture zone are 110.28m, 124.65m, 129.06m, 132.80m, 134.54m, 136.15m, and 137.79m, respectively.

[0086] Subsequently, based on the actual parameters of multiple water-conducting fracture zone heights and actual bedrock thicknesses, a second mapping relationship for the predicted water-conducting fracture zones was determined.

[0087] Specifically, measured data on bedrock thickness and water-conducting fracture zones under similar conditions and in adjacent mining areas are obtained. A second mapping relationship, i.e., the measured fitting curve, is derived using the actual height parameter and the actual bedrock thickness parameter. In this embodiment, an exemplary expression for the actual fitting curve is shown below;

[0088] in, This refers to the actual parameters of the height of the water-conducting fracture zone; This is the actual bedrock thickness parameter. The degree of fit.

[0089] Then, the first mapping relationship and the second mapping relationship are compared to obtain the predicted mapping relationship of the water-conducting fracture zone.

[0090] Example methods include: Based on the first mapping relationship and the second mapping relationship, the prediction error value is determined; if the prediction error value is less than or equal to the first threshold, the first mapping relationship is determined as the prediction mapping relationship for the water-conducting fracture zone; if the prediction error value is greater than the first threshold, the overburden classification result is adjusted and the first mapping relationship is re-determined until the prediction error value is less than the first threshold and the first mapping relationship is determined as the prediction mapping relationship for the water-conducting fracture zone.

[0091] Specifically, the fitting curves obtained from numerical simulation and measured data are compared. If the error is within a preset range, the curve obtained from numerical simulation, i.e. the first mapping relationship, is determined as the predicted mapping relationship for the water-conducting fracture zone; otherwise, the clustering parameters are adjusted and the first mapping relationship is re-determined.

[0092] In this embodiment, if the prediction error of the fitted curve obtained from numerical simulation and measured data is less than or equal to the first threshold (10m), then the error is considered to be within an acceptable range.

[0093] Figure 3 This is a schematic diagram of the fitting of the prediction mapping relationship provided in this disclosure, as shown below. Figure 3 As shown. The maximum difference between the first and second mapping relationships is 6.52m (x=95.42m). With an error of 10m, which is within an acceptable range, the predicted mapping relationship of the water-conducting fracture zone is determined to be the first mapping relationship.

[0094] In one possible implementation, an exemplary method for classifying the overburden of the area to be processed based on geological information of the area to be processed, and obtaining the overburden classification results, includes: Based on the information entropy of each geological sample, the weights of each geological sample are determined; based on the weights of each geological sample, dimension elimination processing is performed to obtain the geological samples to be processed; based on the total number of geological samples to be processed and the first and second average distances of each geological sample to be processed, the target number of clusters is determined; based on the target number of clusters, the overburden classification result is determined with the goal of minimizing the intra-cluster Mahalanobis distance function.

[0095] Specifically, existing geological exploration data of the mine are collected, and information on overlying rock types (such as rock layer thickness, rock mass strength, rock layer combination form, etc.) is statistically analyzed to form geological information. Cluster analysis methods are then used to perform geological clustering, and similar overlying rock types are grouped into one category.

[0096] In this embodiment, geological information was collected and statistically analyzed from 37 groups of overburden types in the mine, including at least one geological sample. A hybrid clustering algorithm was used for geological clustering, classifying the overburden types of the mine into four categories.

[0097] The weights of geological samples in the geological information are calculated using the entropy weight method, and the Mahalanobis distance is used to eliminate interference from dimensional differences and parameter correlations among geological information.

[0098] An example formula for determining the weight of a geological sample is shown below:

[0099]

[0100] in, is the weight of the k-th geological sample; p is the number of geological samples; The information entropy of the k-th geological sample; This is the standardized value of the kth geological sample of the i-th sample.

[0101] An exemplary formula for determining the Mahalanobis distance is as follows:

[0102] in, Geological samples and The weighted Mahalanobis distance; Let S be the geological parameter vector of the i-th geological sample; S be the covariance matrix of the geological information; and W be the weight diagonal matrix.

[0103] Then, the number of target clusters is dynamically determined based on the silhouette coefficient and the elbow rule. An exemplary expression for the silhouette coefficient is shown below:

[0104] in, is the target cluster size; n is the total number of geological samples. ; i is the geological sample number; The first average distance; The second average distance is the average distance from the geological sample to be processed to all samples in the same cluster. The first average distance represents the average distance from the geological sample to be processed to all samples in the nearest dissimilar cluster.

[0105] Subsequently, K target cluster numbers are generated through hierarchical clustering. The adaptive particle swarm optimization algorithm is then used to globally optimize the cluster center update process, i.e., to minimize the weighted Mahalanobis distance within the cluster, until geological clustering is completed and the overlying rock classification results are obtained. Figure 4 This is a schematic diagram of the overburden classification results provided in this disclosure, as shown below. Figure 4 As shown. An exemplary formula for minimizing the objective function is shown below:

[0106] in, This represents the k-th cluster; Let be the center vector of the k-th cluster.

[0107] Figure 5 This is a schematic diagram of a device for predicting the height of a water-conducting fracture zone provided in this disclosure. Figure 5 As shown, the device 500 includes: a classification module 510, a first determination module 520, and a fusion module 530.

[0108] The classification module 510 is used to classify the overburden of the area to be processed based on the geological information of the area to be processed, and to obtain the overburden classification result, wherein the overburden classification result includes at least one type of overburden. The first determining module 520 is used to determine the predicted height of the water-conducting fracture zone corresponding to each of the overburden types, based on the actual bedrock thickness parameters of the overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone. The predicted mapping relationship of the water-conducting fracture zone is related to the fracture parameters and the bedrock thickness parameters. The fusion module 530 is used to fuse the height prediction results of the water-conducting fracture zone corresponding to each of the overburden types to obtain the height prediction results of the area to be processed.

[0109] Optionally, the first determining module is used to: The actual bedrock thickness parameter is input into the water-conducting fracture zone prediction mapping relationship to calculate the predicted height of the water-conducting fracture zone corresponding to the overburden type. The water-conducting fracture zone prediction mapping relationship includes a logarithmic sub-function, and the actual bedrock thickness parameter is the independent variable of the logarithmic sub-function.

[0110] Optionally, the device further includes: The second determining module is used to determine the first mapping relationship of the predicted water-conducting fracture zone based on multiple simulated parameters of the height of the water-conducting fracture zone and simulated parameters of the bedrock thickness. The third determining module is used to determine the second mapping relationship of the predicted water-conducting fracture zone based on multiple actual parameters of the height of the water-conducting fracture zone and the actual bedrock thickness parameter; The comparison module is used to compare the first mapping relationship and the second mapping relationship to obtain the predicted mapping relationship of the water-conducting fracture zone.

[0111] Optionally, the comparison module is used for: Based on the first mapping relationship and the second mapping relationship, the prediction error value is determined; If the prediction error value is less than or equal to the first threshold, then the first mapping relationship is determined to be the predicted mapping relationship for the water-conducting fracture zone; If the prediction error value is greater than the first threshold, the overlying rock classification result is adjusted and the first mapping relationship is re-determined until the prediction error value is less than the first threshold and the first mapping relationship is determined to be the water-conducting fracture zone prediction mapping relationship.

[0112] Optionally, the area to be processed includes a multi-layered geological structure, the geological structure including at least a bedrock layer, and the second determining module is used to: For each type of overburden, based on the initial model parameters of the preset coal seam mining model, the simulation material parameters of each geological structure are determined, and the initial model parameters include at least the simulation bedrock thickness parameter; Based on the simulated material parameters and the coal seam mining model, the simulated rock movement parameters are determined; Based on the simulated rock movement parameters and the corresponding actual rock movement parameters, the model is verified to obtain the target coal seam mining model; Based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple simulated parameters of water-conducting fracture zone height. The first mapping relationship is determined based on the simulated parameters of the height of each water-conducting fracture zone and the corresponding simulated bedrock thickness parameters.

[0113] Optionally, the second determining module is used to: Based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple sets of overburden fracture parameters. When any set of the overburden fracture parameters meets the preset fracture rules, the overburden location to which the overburden fracture parameters belong is determined as the simulated parameter for the height of the water-conducting fracture zone.

[0114] Optionally, the classification module is used for: The weight of each geological sample is determined based on the information entropy of each geological sample, wherein the geological information includes at least one geological sample; Dimension elimination processing is performed based on the weights of each geological sample to obtain the geological sample to be processed. Based on the total number of geological samples to be processed, the first average distance and the second average distance of each geological sample to be processed, the number of target clusters is determined. The first average distance represents the average distance of the geological sample to be processed to all samples in the same cluster, and the second average distance represents the average distance of the geological sample to be processed to all samples in the nearest heterogeneous cluster. Based on the target number of clusters, the overburden classification result is determined with the goal of minimizing the intra-cluster Mahalanobis distance function.

[0115] This application also provides an electronic device for performing the above-described method for predicting the height of water-conducting fracture zones. Please refer to... Figure 6 It illustrates a schematic diagram of an electronic device provided by some embodiments of this application. For example... Figure 6 As shown, the electronic device 60 includes: a processor 600, a memory 601, a bus 602, and a communication interface 603. The processor 600, the communication interface 603, and the memory 601 are connected via the bus 602. The memory 601 stores a computer program that can run on the processor 600. When the processor 600 runs the computer program, it executes the water-conducting fracture zone height prediction method provided in any of the foregoing embodiments of this application.

[0116] The memory 601 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between the device network element and at least one other network element is achieved through at least one communication interface 603 (which can be wired or wireless), such as the Internet, wide area network, local area network, metropolitan area network, etc.

[0117] Bus 602 can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory 601 is used to store programs. After receiving an execution instruction, the processor 600 executes the program. The water-conducting fracture zone height prediction method disclosed in any of the foregoing embodiments of this application can be applied to the processor 600, or implemented by the processor 600.

[0118] The processor 600 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 600 or by instructions in software form. The processor 600 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules may reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory 601. Processor 600 reads the information in memory 601 and, in conjunction with its hardware, completes the steps of the above method.

[0119] The electronic device provided in this application embodiment and the water-conducting fracture zone height prediction method provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0120] This application also provides a computer-readable storage medium corresponding to the water-conducting fracture zone height prediction method provided in the foregoing embodiments. The computer-readable storage medium shown can be an optical disc, on which a computer program is stored. When the computer program is run by a processor, it executes the water-conducting fracture zone height prediction method provided in any of the foregoing embodiments.

[0121] It should be noted that examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical and magnetic storage media, which will not be elaborated here.

[0122] The computer-readable storage medium provided in the above embodiments of this application and the water-conducting fracture zone height prediction method provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the applications stored therein.

[0123] This application also provides a computer program product 700, such as... Figure 7 As shown. This computer program product carries a computer program 701, the instructions of which can be used to execute the steps of the water-conducting fracture zone height prediction method described in the above method embodiments. For details, please refer to the above method embodiments, which will not be repeated here.

[0124] The aforementioned computer program product can be implemented through hardware, software, or a combination thereof. In one optional embodiment, the computer program product is specifically embodied in a computer storage medium; in another optional embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0125] The basic principles of this disclosure have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this disclosure are merely examples and not limitations, and should not be considered as essential features of each embodiment of this disclosure. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the scope of this disclosure to the necessity of employing the aforementioned specific details for implementation.

[0126] The block diagrams of devices, apparatuses, devices, and systems disclosed herein are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0127] Additionally, as used herein, the "or" used in a list of items beginning with "at least one" indicates a separate list, such that a list of, for example, "at least one of A, B, or C" means A or B or C, or AB or AC or BC, or ABC (i.e., A and B and C). Furthermore, the word "exemplary" does not imply that the described example is preferred or better than other examples.

[0128] It should also be noted that in the systems and methods of this disclosure, the components or steps can be decomposed and / or recombined. These decompositions and / or recombinations should be considered as equivalent solutions to this disclosure.

[0129] Various changes, substitutions, and modifications can be made to the technology described herein without departing from the teachings defined by the appended claims. Furthermore, the scope of the claims of this disclosure is not limited to the specific aspects of the processes, machines, manufactures, events, means, methods, and actions described above. Currently existing or later-developed processes, machines, manufactures, events, means, methods, or actions that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein can be utilized. Therefore, the appended claims include such processes, machines, manufactures, events, means, methods, or actions within their scope.

[0130] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of this disclosure. Therefore, this disclosure is not intended to be limited to the aspects shown herein, but rather to be carried out within the widest scope consistent with the principles and novel features disclosed herein.

[0131] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this disclosure to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations therein.

Claims

1. A method for predicting the height of a water-conducting fracture zone, characterized in that, include: Based on the geological information of the area to be processed, the overlying rocks of the area to be processed are classified to obtain overlying rock classification results, which include at least one type of overlying rock. For each of the aforementioned overburden types, based on the actual bedrock thickness parameters of the overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone, the predicted height of the water-conducting fracture zone corresponding to the overburden type is determined. The predicted mapping relationship of the water-conducting fracture zone is related to the fracture parameters and the bedrock thickness parameters. The height prediction results of the water-conducting fracture zone corresponding to each of the aforementioned overburden types are fused to obtain the height prediction results of the area to be processed.

2. The method according to claim 1, characterized in that, For each of the aforementioned overburden types, based on the actual bedrock thickness parameters of the overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone, the predicted height of the water-conducting fracture zone corresponding to the overburden type is determined, including: The actual bedrock thickness parameter is input into the water-conducting fracture zone prediction mapping relationship to calculate the predicted height of the water-conducting fracture zone corresponding to the overburden type. The water-conducting fracture zone prediction mapping relationship includes a logarithmic sub-function, and the actual bedrock thickness parameter is the independent variable of the logarithmic sub-function.

3. The method according to claim 1, characterized in that, The method further includes: Based on multiple simulated parameters of water-conducting fracture zone height and simulated bedrock thickness, the first mapping relationship for the prediction of water-conducting fracture zones is determined; Based on the actual parameters of multiple water-conducting fracture zone heights and the actual bedrock thickness parameters, a second mapping relationship for the prediction of water-conducting fracture zones is determined; By comparing the first mapping relationship and the second mapping relationship, the predicted mapping relationship of the water-conducting fracture zone is obtained.

4. The method according to claim 3, characterized in that, The step of comparing the first mapping relationship and the second mapping relationship to obtain the predicted mapping relationship of the water-conducting fracture zone includes: Based on the first mapping relationship and the second mapping relationship, the prediction error value is determined; If the prediction error value is less than or equal to the first threshold, then the first mapping relationship is determined to be the predicted mapping relationship for the water-conducting fracture zone; If the prediction error value is greater than the first threshold, the overlying rock classification result is adjusted and the first mapping relationship is re-determined until the prediction error value is less than the first threshold and the first mapping relationship is determined to be the water-conducting fracture zone prediction mapping relationship.

5. The method according to claim 3, characterized in that, The area to be processed includes a multi-layered geological structure, which includes at least a bedrock layer. The determination of the first mapping relationship for the predicted water-conducting fracture zones based on multiple simulated parameters of fracture zone height and simulated bedrock thickness includes: For each type of overburden, based on the initial model parameters of the preset coal seam mining model, the simulation material parameters of each geological structure are determined, and the initial model parameters include at least the simulation bedrock thickness parameter; Based on the simulated material parameters and the coal seam mining model, the simulated rock movement parameters are determined; Based on the simulated rock movement parameters and the corresponding actual rock movement parameters, the model is verified to obtain the target coal seam mining model; Based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple simulated parameters of water-conducting fracture zone height. The first mapping relationship is determined based on the simulated parameters of the height of each water-conducting fracture zone and the corresponding simulated bedrock thickness parameters.

6. The method according to claim 5, characterized in that, Based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple simulated parameters for the height of the water-conducting fracture zone, including: Based on multiple sets of simulated bedrock thickness parameters, the target coal seam mining model is subjected to advanced mining processing to obtain multiple sets of overburden fracture parameters. When any set of the overburden fracture parameters meets the preset fracture rules, the overburden location to which the overburden fracture parameters belong is determined as the simulated parameter for the height of the water-conducting fracture zone.

7. The method according to claim 1, characterized in that, The overburden rocks of the area to be processed are classified based on the geological information of the area to be processed, and the overburden rock classification results are obtained, including: The weight of each geological sample is determined based on the information entropy of each geological sample, wherein the geological information includes at least one geological sample; Dimension elimination processing is performed based on the weights of each geological sample to obtain the geological sample to be processed. Based on the total number of geological samples to be processed, the first average distance and the second average distance of each geological sample to be processed, the number of target clusters is determined. The first average distance represents the average distance of the geological sample to be processed to all samples in the same cluster, and the second average distance represents the average distance of the geological sample to be processed to all samples in the nearest heterogeneous cluster. Based on the target number of clusters, the overburden classification result is determined with the goal of minimizing the intra-cluster Mahalanobis distance function.

8. A device for predicting the height of a water-conducting fracture zone, characterized in that, include: The classification module is used to classify the overburden of the area to be processed based on the geological information of the area to be processed, and to obtain the overburden classification result, wherein the overburden classification result includes at least one type of overburden; The first determining module is used to determine the predicted height of the water-conducting fracture zone corresponding to each of the overburden types, based on the actual bedrock thickness parameters of the overburden type and the pre-constructed predicted mapping relationship of the water-conducting fracture zone. The predicted mapping relationship of the water-conducting fracture zone is related to the fracture parameters and the bedrock thickness parameters. The fusion module is used to fuse the height prediction results of the water-conducting fracture zone corresponding to each of the overburden types to obtain the height prediction result of the area to be processed.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by a processor to implement the method as described in any one of claims 1-7.

11. A computer program product, characterized in that, Includes computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the method as described in any one of claims 1-7.

Images