A method for predicting coal production based on fine 3D modeling of coal mining faces

Abstract

This application provides a coal production prediction method based on a detailed 3D model of a coal mining face, relating to the field of coal production prediction. The method includes: collecting boundary data of the exposed dip profile behind the strike of the coal mining face; extracting discontinuity boundary lines from the boundary data; performing polynomial fitting and extension on the discontinuity boundary lines to obtain prediction boundary data for the dip profile to be predicted; constructing a detailed 3D model of the coal mining face based on the prediction boundary data of the dip profile to be predicted; meshing the detailed 3D model of the coal mining face, obtaining the ash content value of each mesh using spatial interpolation, and calculating the average ash content of each mesh; obtaining the number of meshes corresponding to the coal seam and rock strata in the preset mining area, and predicting the coal production based on the corresponding number of meshes. This application enables production prediction to be directly correlated with the spatial occurrence morphology of coal and rock, improving the accuracy and reliability of the prediction.

Description

Technical Field

[0001] This application relates to the field of coal production prediction technology, and in particular to a coal production prediction method based on fine three-dimensional modeling of coal mining faces. Background Technology

[0002] The formulation of coal enterprises' "production, beneficiation, sales, transportation, and trade" plans heavily relies on accurate production forecasts of coal mining faces over a future period. Traditional production forecasting methods often estimate production by comparing the average thickness of the coal seam in the area to be mined with parameters such as coal and gangue density and recovery rate. However, these methods fail to fully utilize the complete boundary data of the exposed dip profile behind the coal mining face, relying instead on overall averaging. This approach cannot accurately characterize the spatial heterogeneity of coal and rock occurrence, resulting in coarse production forecasts. Furthermore, some existing production forecasting methods rely on indirect data from the industrial chain, such as electricity consumption, to build forecasting models. They lack core production data from the coal enterprise's production site, such as coal-rock boundary data and ash distribution data. This not only makes them unsuitable for refined forecasting of specific coal mining faces but also introduces significant forecasting biases. These factors negatively impact the accuracy of production forecasts, making it difficult to meet the decision-making needs of coal enterprises for scientifically formulating production and sales plans. Summary of the Invention

[0003] To address the aforementioned technical problems, this application provides a coal production prediction method based on fine three-dimensional modeling of coal mining faces, which at least partially solves the problems existing in the prior art.

[0004] In a first aspect of this application, a method for predicting coal production based on fine three-dimensional modeling of a coal mining face is provided, the method comprising the following steps:

[0005] Collect boundary data of the exposed dip profile behind the strike of the coal mining face; the boundary data includes coal seam top boundary data, coal seam bottom boundary data, rock strata top boundary data, and rock strata bottom boundary data.

[0006] The boundary lines of discontinuities are extracted from the boundary data; where the boundary lines of discontinuities are the boundary contour lines in three-dimensional space between the coal-rock interface or between different rock strata in the coal mining face.

[0007] Polynomial fitting and extension are performed on the boundary lines of the discontinuous surfaces to obtain the predicted boundary data of the dip profile to be predicted.

[0008] Based on the predicted boundary data of the dip profile to be predicted, a detailed 3D model of the coal mining face is constructed; the detailed 3D model of the coal mining face adopts active modeling using boundary topology driving and hierarchical independent growth.

[0009] The fine three-dimensional model of the coal mining face is meshed, and the ash content of each mesh is obtained by spatial interpolation and the average ash content of each mesh is calculated.

[0010] Obtain the number of grids corresponding to the coal seams and rock strata in the preset mining area, and predict the coal production based on the corresponding number of grids.

[0011] This application has at least the following beneficial effects:

[0012] This application first collects complete boundary data of the exposed dip profile behind the strike of the coal mining face, making full use of the actual geological data as a basis to avoid prediction bias caused by neglecting measured data in traditional methods. Then, it extracts the discontinuity boundary lines by reversing the relationships between preceding, current, and subsequent nodes, accurately capturing the spatial contours of the coal-rock interface and the interfaces between different rock strata, providing reliable feature basis for subsequent boundary prediction. Subsequently, it performs polynomial fitting and extension on the discontinuity boundary lines, combining the division of regions and intervals, and deploys sampling points at fixed intervals, calculating elevations through spatial interpolation, so that the boundary data of the dip profile to be predicted can naturally extend in accordance with the geometric laws of the exposed boundaries. Based on this predicted boundary data, a refined 3D model of the coal mining face is constructed. The refined 3D model of the coal mining face adopts boundary topology-driven and layered independent growth for active modeling. The complete boundary lines of the dip profile to be predicted between the coal seam and rock strata are then used. The model is transformed into an independent boundary topology network, with boundary line nodes as topology control points. Adjacency relationships, elevation associations, and dip constraints are assigned to these nodes. Starting from the latest actual profile, each coal and rock layer is driven to grow independently along its strike, solving the problem of morphological distortion in traditional modeling. Next, the 3D model is meshed, and the ash content of each mesh is obtained through spatial interpolation using the ash content values ​​of sampling points. The average ash content is then calculated using mesh volume as a weight, accurately reflecting the spatial distribution differences of ash content and replacing the coarse processing of the traditional overall averaging method. Finally, the mining volume is calculated by statistically analyzing the number of coal and rock layer meshes in the preset mining area. Combined with parameters such as coal and rock bulk density and recovery rate, the mixed coal production is estimated. Then, based on the washing and beneficiation scheme and yield, the commercial coal production is calculated, enabling production prediction to be directly linked to the spatial occurrence morphology of coal and rock. This gradually improves the precision and reliability of the prediction, providing strong support for coal enterprises' "production, beneficiation, sales, transportation, and trade" planning. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 A flowchart of a coal production prediction method based on fine 3D modeling of a coal mining face, provided in an embodiment of this application;

[0015] Figure 2This is a cross-sectional view of the coal seam in the coal mining face provided in the embodiments of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0018] It should be noted that the following description covers various aspects of embodiments within the scope of the appended claims. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0019] Please refer to Figure 1 and Figure 2 As shown, embodiments of this application provide a method for predicting coal production based on detailed 3D modeling of a coal mining face. The method includes the following steps:

[0020] S100 collects boundary data of the exposed dip profile behind the strike of the coal mining face; the boundary data includes coal seam top boundary data, coal seam bottom boundary data, strata top boundary data, and strata bottom boundary data.

[0021] Specifically, the coal mining face is the area where coal mining operations are directly carried out; it is the core location for underground coal production in a coal mine and is typically a long, narrow space. In one embodiment, Figure 2 This is a cross-sectional view of the coal seam in the coal mining face of this application, and a schematic cross-sectional view of the rock strata. Figure 2 Similar. The direction behind the coal face is the opposite direction of the coal face (i.e., the mined area). The dip profile is the vertical profile of the coal seam or rock strata cut along the dip direction (the horizontal direction perpendicular to the extension of the coal seam); the exposed dip profile is the dip profile that has been exposed through actual mining.

[0022] The coal seam top boundary data represents the spatial location of the interface between the top of the coal seam and the overlying strata (roof strata), existing in the form of a three-dimensional coordinate node sequence. Each coordinate node marks the specific location of the top of the coal seam, and when strung together, they form the complete boundary trajectory of the coal seam top. The coal seam bottom boundary data represents the spatial location of the interface between the bottom of the coal seam and the underlying strata (floor strata), also existing in the form of a three-dimensional coordinate node sequence. Together with the coal seam top boundary data, it defines the thickness and spatial distribution range of the coal seam. The rock stratum top boundary data represents the spatial location of the interface between the top of a rock stratum (such as roof strata or interbedded rock strata) and the overlying medium (coal seam or other rock strata), existing in the form of a three-dimensional coordinate node sequence. The rock stratum bottom boundary data represents the spatial location of the interface between the bottom of a rock stratum and the underlying medium (coal seam or other rock strata), existing in the form of a three-dimensional coordinate node sequence. Together with the rock stratum top boundary data, it defines the thickness and spatial distribution of that rock stratum.

[0023] S200, extract the discontinuity boundary line based on the boundary data; where the discontinuity boundary line is the boundary trajectory line of the coal-rock interface in the coal mining face.

[0024] Specifically, step S200 also includes:

[0025] S210, for the boundary data of each revealed dip profile, sort them according to the dip direction, and mark the node type according to the reverse relationship between the directed line segments of the preceding node, the current node and the subsequent node; wherein, the node type includes discontinuous points and continuous points.

[0026] Here, the sequence node is determined to be a discontinuous or continuous point according to the following steps: if a reversal occurs, the sequence node is determined to be a discontinuous point; otherwise, it is determined to be a continuous point. Whether a reversal occurs is determined based on the angle between directed line segments. If the angle is within a preset angle range, it is determined to be a reversal. The reversal direction is determined based on the angle relationship between the directed line segment and the forward-looking vector. The reversal direction includes positive and negative directions.

[0027] Specifically, in one example, the preset angle range can be 120°-240°. When determining the reversal direction, firstly, a forward-biased vector is preset, and then the angles α and β between the first directed line segment formed by the preceding node and the current node, the second directed line segment formed by the current node and the subsequent node, and the forward-biased vector are calculated. If α < 90° and β > 90°, or α > 90° and β < 90°, the reversal direction is recorded as negative; otherwise, it is recorded as positive. The forward-biased vector is a reference direction vector along the dip of the coal mining face, using the positive y-axis direction of the mining area's independent coordinate system.

[0028] S220: Using the boundary discontinuity point of the latest revealed dip profile as the reference discontinuity point, the first discontinuity point on the boundary of each revealed dip profile in the same reverse direction as the reference discontinuity point is traced sequentially along the strike backward.

[0029] S230, connect the tracked discontinuities in sequence to form discontinuity surface boundary lines, and delete the tracked discontinuities on each boundary to obtain several discontinuity surface boundary lines.

[0030] In one embodiment, such as Figure 2 As shown, 31 (the boundary discontinuity point) is selected as the reference discontinuity point. The first discontinuity point (21 and 11) on the boundary of each exposed dip section with the same reverse direction as the reference discontinuity point is traced backward along the strike to obtain the discontinuity surface boundary line 6. After deleting points 31, 21, and 11, the boundary discontinuity point of the latest exposed dip section is 32. The first discontinuity point (22 and 12) on the boundary of each exposed dip section with the same reverse direction as the reference discontinuity point is traced backward along the strike to obtain the discontinuity surface boundary line 7. This process is repeated to obtain all discontinuity surface boundary lines.

[0031] It is worth noting that all the starting and ending points of the inclined section boundary are also discontinuities, participating in the discontinuity boundary line.

[0032] S300 performs polynomial fitting and extension on the boundary line of the discontinuous surface to obtain the predicted boundary data of the dip profile to be predicted.

[0033] Specifically, step S300 also includes:

[0034] S310, the extracted discontinuity boundary line is fitted with a cubic polynomial curve and extended forward along the strike to the advance distance corresponding to the preset mining area.

[0035] Here, a cubic polynomial curve is used to fit each extracted discontinuous interface boundary line. During the fitting process, the goodness of fit R² must be greater than or equal to a preset goodness of fit threshold. In one embodiment, the preset goodness of fit threshold can be 0.95. If R² is less than the preset goodness of fit threshold, the boundary line is segmented according to the direction of travel (each segment has ≥10 nodes), and the segments are fitted and then stitched together to ensure that the fitted curve can accurately reflect the actual shape of the boundary line.

[0036] The fitted curve is extended along the forward direction (in the direction of the unmined area) to the advance distance corresponding to the preset mining area. The advance distance corresponding to the preset mining area is the distance that the coal face can advance within a certain period of time in the preset mining area, which is determined by the mining technology (such as fully mechanized mining or blasting mining) and the production plan (such as monthly or quarterly mining plans).

[0037] It should be noted that cubic polynomial curve fitting can fit the natural curvature of the coal-rock interface well; the goodness of fit R² is an indicator that measures the degree of fit between the fitted curve and the actual node. The closer R² is to 1, the better the fitting effect.

[0038] S320 combines two adjacent discontinuous surface boundary lines in the dip direction to form a segmented region, calculates the intersection points of the boundary curve and the dip profile to be predicted, and the pairwise intersection points constitute the segmented interval; wherein, the boundary curve is a continuous three-dimensional curve extending to the preset mining area after the discontinuous surface boundary line is fitted with a cubic polynomial curve.

[0039] S330: Sampling points are set up at preset intervals in each segmented interval, and the elevation of the sampling points is calculated by Kriging interpolation to obtain the prediction boundary data of the dip profile to be predicted.

[0040] Here, two adjacent discontinuous interface boundary lines are combined in pairs to form a strip-shaped closed segmented area, clarifying the location of the dip profile to be predicted. In one embodiment, the dip profile to be predicted is set at intervals of 10-20m (to 5m in areas with drastic changes in the coal-rock interface). The dip profile to be predicted is a plane perpendicular to the strike direction, and its equation is x=x0 (x0 is the coordinate value of the profile in the strike direction). The intersection points of each fitted extended boundary curve and the dip profile to be predicted are calculated. The intersection points on the same dip profile are sorted according to the dip direction. Each pair of adjacent intersection points constitutes a segmented interval. In one embodiment, sampling points are evenly distributed at intervals of 0.5-1m within each segmented interval. For thin coal seams (thickness < 1.3m), the interval is 0.5m, and for thick coal seams (thickness > 3m), the interval is 1m. This ensures that the sampling point density can reflect the subtle changes in the boundary line within the interval. The elevation of each sampling point is calculated using the Kriging interpolation method. This method is based on the correlation of spatial data. The elevation of the boundary nodes of the exposed profile within the segmented area and the elevation of the sampling points in adjacent segmented areas are known data. By fitting a spherical variogram (to determine parameters such as range, sill value, and nugget value), the elevation values ​​of unknown sampling points are estimated. All sampling points are sorted according to the dip direction and connected to form the top boundary line of the coal seam, the bottom boundary line of the coal seam, the top boundary line of the rock strata, and the bottom boundary line of the rock strata to be predicted, thus completing the prediction of the coal and rock dip profile ahead of the strike.

[0041] In this embodiment, the reasonable sampling point interval ensures the precision of the boundary line, and the application of Kriging interpolation improves the accuracy of elevation prediction. Compared with traditional average thickness estimation, it can accurately capture the local undulations of the coal-rock interface and provide high-precision profile data for subsequent 3D modeling.

[0042] It should be noted that, following steps S100 to S300, the predicted boundary data of the predicted dip profiles of the coal seam and rock strata in the coal mining face are obtained respectively, so as to obtain the complete boundary lines of the coal seam and rock strata in the coal mining face.

[0043] S400: Based on the predicted boundary data of the dip profile to be predicted, a fine three-dimensional model of the coal mining face is constructed; the fine three-dimensional model of the coal mining face adopts boundary topology driving and layered independent growth for active modeling.

[0044] Specifically, the detailed 3D model of the coal mining face includes a detailed 3D model of the coal seam and a detailed 3D model of the rock strata.

[0045] Step S400 includes:

[0046] S410, Based on the predicted boundary data of the dip profile to be predicted, construct the coal seam boundary topology network and the rock strata boundary topology network respectively; the coal seam boundary topology network and the rock strata boundary topology network use the predicted boundary line composed of the predicted boundary data of the corresponding predicted dip profile as the growth reference surface, and use the predicted boundary data of the corresponding predicted dip profile as the topology control points.

[0047] Here, complete boundary lines of the predicted dip profiles of coal seams and strata are collected. Each boundary line contains two sub-boundaries, one at the top and one at the bottom, and each sub-boundary is composed of ordered three-dimensional coordinate nodes. Then, all predicted dip profiles are sorted from the strike backward to the strike forward (from mined to unmined), and the predicted dip profile closest to the mined area is marked as the initial reference profile.

[0048] Using the top and bottom boundary lines of each predicted dip profile of the coal seam and strata as two growth reference surfaces, nodes on each boundary line are defined as topological control points, and each topological control point is assigned a unique ID. On the same boundary line, the preceding node ID and subsequent node ID of each control point are recorded (e.g., node 11's preceding node ID is 10, and its subsequent node ID is 12). Within the same predicted dip profile, the vertical correlation between the top boundary control point and the corresponding control point on the bottom boundary is recorded (by calculating the dip direction distance between the two points; if the distance is ≤ a preset distance threshold, they are considered corresponding control points). Furthermore, elevation correlation (elevation difference between adjacent control points with the same ID on the predicted dip profile) and dip direction constraints are added to each topological control point (based on the mining area coordinate system, the dip vector is set as the positive y-axis, and the deviation of the control point's movement direction from this vector must not be greater than or less than a preset angle).

[0049] S420 uses the topological network of coal seam boundary lines or rock strata boundary lines closest to the predicted dip profile of the mined area as the starting point for growth, and grows continuously in the forward direction of strike to obtain fine three-dimensional models of coal seam and rock strata respectively.

[0050] Specifically, step S420 also includes:

[0051] S421, taking the coal seam boundary line topology network or stratum boundary line topology network closest to the predicted dip profile of the mined area as the starting point of growth, splits it according to the layout spacing of the dip profile to be predicted to obtain the growth step.

[0052] S422, based on the growth step, generates an intermediate transition surface after each growth step until it covers all the dip profiles to be predicted, so as to obtain a fine three-dimensional model of the coal seam and a fine three-dimensional model of the strata respectively.

[0053] Here, step S422 includes:

[0054] S4221, obtain the matching relationship between each current growth node and the corresponding growth node of the next adjacent predicted dip profile; wherein, the matching relationship is obtained by calculating the spatial vector based on the preset ID between the corresponding growth nodes of two adjacent predicted dip profiles; the preset ID includes rock layer type information, boundary line type information, predicted dip profile sequence information and growth node number; the growth node is the predicted boundary data of the dip profile to be predicted.

[0055] S4222, based on the growth step size and matching relationship, calculate the displacement of each growth node corresponding to the growth spacing.

[0056] S423, grow according to the displacement of each growth node corresponding to the growth spacing to generate an intermediate transition surface.

[0057] Here, the topology network of the top or bottom boundary of the coal seam or stratum closest to the predicted dip profile of the mined area is selected as the starting point, and all topological control points of this predicted dip profile are the initial growth nodes. Along the strike direction, the growth axis is set as the positive x-axis based on the mining area coordinate system, and the growth trajectory is perpendicular to the dip profile. The growth is divided according to the spacing of the predicted dip profile, generating an intermediate transition surface after each growth step. Using the starting topology network as a reference, the initial growth nodes are mapped one-to-one with the corresponding topological control points of the next adjacent predicted dip profile: through preset ID matching (for example: the starting coal seam-top-3-11 corresponds to the next profile's coal seam-top-4-11, meaning that growth nodes with the same stratum type and boundary line type information, adjacent predicted dip profile sequence information, and the same growth node number are called corresponding growth nodes), the spatial vector (Δx, Δy, Δz) between the two points is calculated. The vector is split according to the growth step size to obtain the node displacement of each growth step (for example: if the total vector Δx = 20m and the step size is 5m, then each step Δx = 5m, and Δy and Δz are split proportionally).

[0058] Based on the displacement after splitting, the initial growth node at the starting end is driven to move forward step by step along the direction. With each step, the node position is adjusted based on the adjacency relationship of adjacent nodes to ensure that the distance between nodes is always consistent with the node spacing of the original boundary line (deviation ≤ preset deviation threshold). The specific adjustment steps are as follows:

[0059] The initial position of the initial growth node is determined based on the coordinates of the corresponding node in the boundary line topology network of the latest exposed dip profile. The initial growth node is then moved forward along the strike to a preset intermediate transition position according to the displacement amount after splitting. The preceding and subsequent adjacent node information recorded in the topology network for this initial growth node is retrieved to clarify the adjacency relationship between nodes. The actual distance between the initial growth node and its adjacent nodes after movement is calculated and compared with the standard distance between corresponding nodes in the original boundary line. If there is a deviation between the actual distance and the standard distance, the spatial position of the initial growth node is fine-tuned according to the constraint logic of the adjacency relationship, while simultaneously adjusting the positions of adjacent nodes (ensuring that the adjacency relationship is not broken). The distance detection and position fine-tuning operations are repeated until the actual distance between the initial growth node and its adjacent nodes meets the standard distance requirement, completing this step of the movement and position adjustment. After all initial growth nodes complete this step of movement and position adjustment in sequence according to the above process, the next growth cycle begins, continuously achieving continuous growth of the top or bottom surface.

[0060] It should be noted that during the growth process, if there is a morphological abrupt change in the boundary line of adjacent predicted dip profiles, a smooth transition is achieved through an intermediate transition surface: in the two growth steps before and after the abrupt change node, the Δz value of the displacement is gradually adjusted to avoid the appearance of a broken line protrusion on the surface.

[0061] Specifically, during the growth process, the boundary line morphology of adjacent predicted dip profiles is monitored in real time to identify abrupt change nodes with elevation or trend change characteristics, and the location of the abrupt change nodes in the topology network and their associated nodes are determined. The two growth cycles corresponding to the abrupt change nodes are determined, and these two growth cycles are set as smooth transition stages, with the starting and ending growth steps of the transition stage defined. The trend of the boundary line morphology before and after the abrupt change nodes is analyzed to obtain the extension direction of the boundary line before the abrupt change, the extension direction of the boundary line after the abrupt change, and the morphological differences between the two. Based on the morphological differences, the total Δz adjustment amount to be adjusted in the transition stage is calculated, and the total adjustment amount is evenly divided according to the growth step sequence to determine the gradual adjustment range of Δz in each growth step. When performing the first step of transition growth, the Δz value of the displacement of this step is corrected according to the initial adjustment range after the division, driving the abrupt change nodes and associated nodes to grow along the corrected trajectory, so that the surface initially transitions to the post-abrupt morphology. When performing the second step of transition growth, the Δz value is further corrected according to the subsequent adjustment range after the division to further conform to the morphology of the post-abrupt boundary line, completing the gradual connection from the pre-abrupt to the post-abrupt morphology.

[0062] After growth is complete, the surface continuity of the transition area is checked to ensure that the transition surface has no zigzag protrusions and that it is naturally connected with the surface morphology of the non-transition areas before and after, which is consistent with the smooth characteristics of the geological body.

[0063] When growing to the next predicted dip profile, verify the position of the grown nodes against the coordinates of the topological control points of that profile. If the deviation is large, reverse the node displacement of all intermediate transition surfaces to ensure that the grown surface completely conforms to the boundary line of the profile. Repeat the above process until all predicted dip profiles are covered, forming a complete three-dimensional entity of the coal seam or strata.

[0064] The modeling scheme in this embodiment adopts an active modeling mode driven by boundary topology and with independent growth in layers. It transforms the complete boundary line of the predicted dip profile of the coal seam and rock strata into an independent boundary topology network. The boundary line nodes are used as topology control points, and they are assigned adjacency relationships, elevation associations and dip constraints. Then, the latest actual exposed profile is used as the starting point to drive the coal and rock to grow independently forward along the strike. During the growth process, it only relies on its own topology network and does not cross-call data from other geological bodies.

[0065] S500 meshes the fine three-dimensional model of the coal mining face, obtains the ash content of each mesh through spatial interpolation, and calculates the average ash content of each mesh.

[0066] Specifically, step S500 also includes:

[0067] S510 divides the fine three-dimensional model of the coal mining face into a hexahedral grid of preset size and extracts the coordinates of the center point of each grid.

[0068] As an example: the preset size can be 1m×1m×1m.

[0069] S520 uses inverse distance weighted interpolation to calculate the gray content of each grid center point.

[0070] Here, during the interpolation process, several nearest sampling points around the grid center point (sampling points set on the trend profile to be predicted, used to collect gray data) are selected as the interpolation data source. The weight of the sampling points is inversely proportional to the distance (the closer the distance, the greater the weight).

[0071] It should be noted that inverse distance weighted interpolation is an interpolation algorithm based on the distance decay effect. The core idea is that the closer a known point is to the unknown point, the greater its influence on the attribute value of the unknown point.

[0072] Ash content refers to the mineral content in coal and is a key indicator affecting coal quality and washing efficiency.

[0073] The inverse distance weighted interpolation method is simple to calculate and has reliable accuracy. It can quickly transform discrete sampling point gray data into continuous grid gray data. Compared with the traditional overall averaging method, it can accurately reflect the spatial distribution differences of gray.

[0074] S530 calculates the overall average ash content of coal seams and rock strata by using the volume of each grid as the weight and weighted averaging.

[0075] Here, the ash content variation coefficient (ash content standard deviation ÷ average ash content) is required to be less than or equal to the preset variation coefficient threshold. For example, the preset variation coefficient threshold can be 0.15. If it exceeds the preset variation coefficient threshold, additional sampling points need to be added and re-interpolated (such as adding sampling points in ash content abrupt change areas).

[0076] S600: Obtain the number of grids corresponding to the coal seam and rock strata in the preset mining area, and predict the coal production based on the corresponding number of grids.

[0077] Specifically, step S600 also includes:

[0078] S610 calculates the mining volume based on the number of grids corresponding to the coal seam and rock strata in the preset mining area.

[0079] The corresponding mining volume is calculated based on the number of grids and the preset grid size.

[0080] S620, the mixed coal yield is calculated based on the mining volume.

[0081] In one embodiment, the blended coal production rate is calculated as follows: (coal seam mining volume × coal seam bulk density × coal seam recovery rate + rock strata mining volume × gangue bulk density × rock strata blending rate) × (1 - average ash content / 100). It should be noted that "(1 - average ash content / 100)" is the effective coal production coefficient after deducting the mineral content (ash) in the coal.

[0082] S630 calculates coal production based on the mixed coal output, coking coal washing scheme and yield.

[0083] Here, based on the pre-set coking coal washing scheme (such as heavy media coal preparation process), the yield parameters corresponding to different ash content ranges are defined as follows: clean coal yield 60%-70%, middlings yield 20%-30%, and coal slime yield 5%-10%. Clean coal output = mixed coal output × clean coal yield; middlings output = mixed coal output × middlings yield; coal slime output = mixed coal output × coal slime yield. The final "coal production" is the sum of the outputs of the above three types of commercial coal.

[0084] Furthermore, although the steps of the method in this application are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0085] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0086] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for predicting coal production based on fine three-dimensional modeling of coal mining faces, characterized in that, The method includes: Collect boundary data of the exposed dip profile behind the strike of the coal mining face; the boundary data includes coal seam top boundary data, coal seam bottom boundary data, rock strata top boundary data, and rock strata bottom boundary data. The boundary lines of discontinuities are extracted from the boundary data; where the boundary lines of discontinuities are the boundary contour lines in three-dimensional space between the coal-rock interface or between different rock strata in the coal mining face. Polynomial fitting and extension are performed on the boundary lines of the discontinuous surfaces to obtain the predicted boundary data of the dip profile to be predicted. Based on the predicted boundary data of the dip profile to be predicted, a detailed 3D model of the coal mining face is constructed; the detailed 3D model of the coal mining face adopts active modeling using boundary topology driving and hierarchical independent growth. The fine three-dimensional model of the coal mining face is meshed, and the ash content of each mesh is obtained by spatial interpolation and the average ash content of each mesh is calculated. Obtain the number of grids corresponding to the coal seams and rock strata in the preset mining area, and predict the coal production based on the corresponding number of grids.

2. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 1, characterized in that, The step of extracting the boundary lines of discontinuous surfaces based on boundary data includes: For the boundary data of each revealed dip profile, sort them according to the dip direction, and label the node type according to the reverse relationship between the directed line segments of the preceding node, the current node and the subsequent node; wherein, the node type includes discontinuous points and continuous points. Using the boundary discontinuity point of the latest revealed dip profile as the reference discontinuity point, the first discontinuity point on the boundary of each revealed dip profile with the same reverse direction as the reference discontinuity point is traced sequentially along the strike backward. Connect the traced discontinuities sequentially to form the boundary lines of the discontinuous surface, and delete the traced discontinuities on each boundary to obtain several boundary lines of the discontinuous surface.

3. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 2, characterized in that, When an ordinal node is determined to be a discontinuous or continuous point according to the following steps: If a reversal occurs, the current node will be determined as a discontinuous point; otherwise, it will be determined as a continuous point. Whether a reversal occurs is determined by the angle between the directed line segments. If the angle is within a preset range, a reversal is determined. The direction of reversal is determined by the angle between the directed line segment and the forward vector. The direction of reversal includes positive and negative directions.

4. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 1, characterized in that, The process of performing polynomial fitting and extension on the discontinuous surface boundary lines to obtain the predicted boundary data of the dip profile to be predicted includes: The extracted discontinuity boundary line is fitted with a cubic polynomial curve and extended forward along the strike to the advance distance corresponding to the preset mining area. The boundary lines of two adjacent discontinuous surfaces dip in the direction of change are combined to form a segmented region. The intersection points of the boundary curve and the dip profile to be predicted are calculated, and the pairwise intersection points constitute the segmented interval. The boundary curve is a continuous three-dimensional curve that extends from the boundary line of the discontinuous surface to the preset mining area after being fitted with a cubic polynomial curve. Sampling points are set up at preset intervals within each segmented interval, and the elevation of the sampling points is calculated by Kriging interpolation to obtain the prediction boundary data of the dip profile to be predicted.

5. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 1, characterized in that, The detailed 3D model of the coal mining face includes a detailed 3D model of the coal seam and a detailed 3D model of the rock strata.

6. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 5, characterized in that, Based on the prediction boundary data of the dip profile to be predicted, a detailed three-dimensional model of the coal mining face is constructed, including: Based on the predicted boundary data of the dip profile to be predicted, coal seam boundary topology network and rock stratum boundary topology network are constructed respectively. The predicted boundary line composed of the predicted boundary data of the corresponding predicted dip profile is used as the growth reference surface for the coal seam boundary topology network and the predicted boundary data of the corresponding predicted dip profile is used as the topology control point. Using the topological network of coal seam boundary lines or rock strata boundary lines closest to the predicted dip profile of the mined area as the starting point for growth, continuous growth is carried out in the forward direction of strike to obtain fine three-dimensional models of coal seam and rock strata, respectively.

7. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 6, characterized in that, Starting with the topological network of coal seam boundaries or strata boundaries closest to the predicted dip profile of the mined area, the model is continuously grown along the strike-forward direction to obtain detailed 3D models of the coal seam and strata, respectively, including: The growth start point is the coal seam boundary line topology network or rock stratum boundary line topology network that is closest to the predicted dip profile of the mined area. The network is then divided according to the layout spacing of the dip profile to be predicted to obtain the growth step. Based on the growth step, an intermediate transition surface is generated after each growth step until all the dip profiles to be predicted are covered, so as to obtain a fine three-dimensional model of the coal seam and a fine three-dimensional model of the strata respectively.

8. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 7, characterized in that, Based on the growth step size, an intermediate transition surface is generated after each growth step, including: Obtain the matching relationship between each current growth node and the corresponding growth node of the next adjacent predicted dip profile; wherein, the matching relationship is obtained by calculating the spatial vector based on the preset ID between the corresponding growth nodes of two adjacent predicted dip profiles; the preset ID includes rock layer type information, boundary line type information, predicted dip profile sequence information and growth node number; the growth node is the predicted boundary data of the dip profile to be predicted; Based on the growth step size and matching relationship, the displacement of each growth node corresponding to the growth spacing is calculated. Growth is performed based on the displacement of each growth node corresponding to the growth spacing to generate an intermediate transition surface.

9. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 1, characterized in that, The process of meshing a fine three-dimensional model of the coal mining face, obtaining the ash content value of each mesh using spatial interpolation, and calculating the average ash content of each mesh includes: The fine three-dimensional model of the coal mining face is divided into a hexahedral grid of preset size, and the coordinates of the center point of each grid are extracted. The inverse distance weighted interpolation method is used to calculate the gray content value of each grid center point; The overall average ash content of the coal seam and rock strata is calculated by weighted averaging, using the volume of each grid as the weight.

10. The coal production prediction method based on fine three-dimensional modeling of the coal mining face according to claim 1, characterized in that, Obtain the number of grids corresponding to the coal seams and rock strata in the preset mining area, and predict the coal production based on the corresponding number of grids, including: The mining volume is calculated based on the number of grids corresponding to the coal seam and rock strata in the preset mining area. The yield of mixed coal is calculated based on the mining volume. The coal production is calculated based on the mixed coal output, the coking coal washing and beneficiation scheme and yield.

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