Coal rock interface evolution system and method considering layering error of coal mining machine

Abstract

The invention discloses a coal rock interface evolution system and method considering layering errors of a coal mining machine, and relates to the technical field of intelligent mining of coal mines. The coal and rock block information determination module constructs a virtual coal seam model according to the coal seam initial data acquired by the data acquisition module, and determines multi-granularity virtual coal and rock block information; the model construction module is used for constructing a fully mechanized coal mining face equipment system model; the coal-rock interface determination module is used for performing cutting coupling and mapping conversion based on the virtual coal seam model and the fully mechanized coal mining face equipment system model to determine a coal-rock interface; and the adjustment and evolution module is used for performing cutting adjustment on the coal-rock interface according to the multi-granularity virtual coal-rock block information and a set coal-rock error grade to generate a new coal-rock interface so as to realize evolution of the coal-rock interface. The invention aims to improve the recognition precision of the coal rock interface.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent coal mining, and in particular to a coal-rock interface evolution system and method that takes into account coal mining machine layering errors. Background Art

[0002] The coal-rock interface, defined as the interface between a coal seam and surrounding rock, often exhibits a complex three-dimensional curved surface. Accurately identifying the coal-rock interface is crucial for optimizing mining processes, improving resource recovery, reducing energy consumption, and minimizing environmental impact. However, due to the complexity of geological conditions (such as faults and folds), noise interference from sensor data, and the limitations of human experience, traditional static modeling methods struggle to meet the demands of modern coal mining.

[0003] Currently known methods provide effective solutions for the construction and updating of coal-rock boundary surfaces during fully mechanized mining, but they still have some limitations.

[0004] (1) The construction of the coal-rock interface is based on the absolutely real coal-rock interface produced by the coal mining machine cutting, and the construction of the coal-rock interface under the influence of the coal mining machine sensor error is not considered.

[0005] (2) Although the virtual floor model performs well in refined modeling, it is limited to single boundary modeling and has weak real-time performance. It is difficult to achieve real-time interaction with the operating status of the coal mining machine to dynamically adjust the cutting parameters. It fails to fully consider the coal seam error during the coal mining machine cutting operation, which affects the actual application effect of the model.

[0006] (3) Virtual coal seam modeling mainly focuses on the construction of the coal-rock interface, while the properties of the coal seam itself and the independent modeling of the coal seam and the rock layer have not yet been achieved. At the same time, detailed modeling of different granularities has not been completed to meet the needs of different tasks.

[0007] (4) Although a preliminary updating mechanism for geological models has been established in known methods, data reliability is still insufficient to meet the needs of stable and efficient cutting of fully mechanized mining faces and high-precision navigation cutting maps. Summary of the Invention

[0008] The purpose of this application is to provide a coal-rock interface evolution system and method that takes into account the shearer layering error, which can improve the coal-rock interface recognition accuracy.

[0009] To achieve the above objectives, this application provides the following solutions:

[0010] In a first aspect, the present application provides a coal-rock interface evolution system that takes into account shearer layering errors, comprising: a data acquisition module, a coal-rock block information determination module, a model construction module, a coal-rock interface determination module, and an adjustment evolution module;

[0011] The coal-rock block information determination module is connected to the data acquisition module; the coal-rock interface determination module is connected to the coal-rock block information determination module and the model construction module respectively; the adjustment evolution module is connected to the coal-rock interface determination module and the coal-rock block information determination module respectively;

[0012] The data acquisition module is used to acquire initial coal seam data; the initial coal seam data includes: coal mine geological exploration data and preset coal seam data;

[0013] The coal rock block information determination module is used to construct a virtual coal seam model based on the initial coal seam data and determine the multi-granularity virtual coal rock block information; the virtual coal seam model is a physical model determined by using a spatial difference algorithm and a least squares projection method and generating a grid using a grid component in a set software scene;

[0014] The model construction module is used to construct a fully mechanized mining face equipment system model; the fully mechanized mining face equipment system model is a physical model that is collaboratively coupled and assembled based on a single equipment kinematic model, a multi-equipment collaborative kinematic model, and equipment geometry-physics-behavior-working condition modeling to perform collaborative operation; wherein, the single equipment kinematic model is a kinematic model determined by the DH parameter method to characterize motion characteristics; the multi-equipment collaborative kinematic model is a spatial kinematic model determined by inverse kinematics to analyze motion parameters to characterize walking trajectories and posture change relationships;

[0015] The coal-rock interface determination module is used to perform cutting coupling and mapping conversion based on the virtual coal seam model and the fully mechanized mining working face equipment system model to determine the coal-rock interface;

[0016] The adjustment evolution module is used to cut and adjust the coal-rock interface according to the multi-granularity virtual coal block information and the set coal-rock error level to generate a new coal-rock interface to achieve the evolution of the coal-rock interface.

[0017] In a second aspect, the present application provides a method for evolving a coal-rock interface taking into account a shearer stratification error. The method for evolving a coal-rock interface taking into account a shearer stratification error is implemented using a system for evolving a coal-rock interface taking into account a shearer stratification error. The method comprises:

[0018] Acquire initial coal seam data; the initial coal seam data includes: coal mine geological exploration data and preset coal seam data; the preset coal seam data includes: coal seam key nodes, coal seam strike angle, and coal seam undulation angle;

[0019] A virtual coal seam model is constructed based on the initial coal seam data, and information on virtual coal blocks with multiple granularities is determined; the virtual coal seam model is a physical model determined by generating a grid using a spatial difference algorithm and a least squares projection method, and by using grid components in a set software scene;

[0020] Construct a fully mechanized mining face equipment system model; the fully mechanized mining face equipment system model is a physical model that is collaboratively coupled and assembled based on a single-equipment kinematic model, a multi-equipment collaborative kinematic model, and equipment geometry-physics-behavior-working condition modeling to perform collaborative operation; wherein the single-equipment kinematic model is a kinematic model determined by the DH parameter method to characterize motion characteristics; the multi-equipment collaborative kinematic model is a spatial kinematic model determined by inverse kinematics to analyze motion parameters to characterize walking trajectories and posture change relationships;

[0021] Perform cutting coupling and mapping conversion based on the virtual coal seam model and the fully mechanized mining working face equipment system model to determine the coal-rock interface;

[0022] According to the multi-granularity virtual coal rock block information, the coal rock interface is cut and adjusted according to the set coal rock error level to generate a new coal rock interface to achieve the evolution of the coal rock interface.

[0023] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0024] The present application provides a coal-rock interface evolution system and method that takes into account coal mining machine layering errors. A coal block information determination module is used to construct a virtual coal seam model based on the initial coal seam data obtained by the data acquisition module, and to determine multi-granularity virtual coal block information. Because the virtual coal seam model is a physical model determined by using a spatial difference algorithm and a least squares projection method, and using the grid components in the set software scene to generate a grid, it can determine the coal-rock boundary surface and, in turn, the multi-granularity virtual coal block information. Through cutting coupling and mapping transformation, it can effectively simulate real fully mechanized mining conditions, thereby obtaining coal-rock interface data that is closer to reality. In addition, the present application can achieve accurate division of coal seam error levels in different regions to present coal seam distribution characteristics with regional characteristics. That is, by setting the coal rock error level, the coal-rock interface is cut and adjusted to generate a new coal-rock interface to achieve the evolution of the coal-rock interface. As a result, the present application can improve the accuracy of coal-rock interface recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a structural diagram of the coal-rock interface evolution system considering the shearer layering error;

[0026] Figure 2 A flowchart for the design of a coal-rock interface evolution method considering shearer stratification errors;

[0027] Figure 3 Construct a block diagram for the virtual coal seam model;

[0028] Figure 4 Construct a block diagram for the fully mechanized mining face equipment system model;

[0029] Figure 5 A flowchart for cutting, coupling and mapping conversion;

[0030] Figure 6 Flowchart of the coal-rock interface evolution method considering shearer stratification errors. DETAILED DESCRIPTION

[0031] In order to further improve the real-time performance and autonomous evolution capability of the cutting map, it is necessary to make full use of the cutting information of the coal mining machine to dynamically supplement and improve the map, so as to better meet the actual production needs.

[0032] To this end, this application proposes a method for dynamic modeling and real-time evolution of coal-rock boundary surfaces taking into account shearer stratification errors.

[0033] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0034] In an exemplary embodiment, Figure 1 As shown, a coal-rock interface evolution system that considers shearer layering errors is provided, comprising a data acquisition module, a coal-rock block information determination module, a model building module, a coal-rock interface determination module, and an adjustment evolution module. The coal-rock block information determination module, the model building module, the coal-rock interface determination module, and the adjustment evolution module can all be implemented using a processor, such as a CPU.

[0035] The coal-rock block information determination module is connected to the data acquisition module; the coal-rock interface determination module is connected to the coal-rock block information determination module and the model construction module respectively; the adjustment evolution module is connected to the coal-rock interface determination module and the coal-rock block information determination module respectively.

[0036] The data acquisition module is used to obtain initial coal seam data; initial coal seam data includes: coal mine geological exploration data and preset coal seam data. The data acquisition module can use a variety of sensors and Internet of Things devices.

[0037] The coal rock block information determination module is used to construct a virtual coal seam model based on the initial coal seam data and determine the multi-granularity virtual coal rock block information; the virtual coal seam model is a physical model determined by using the spatial difference algorithm and the least squares projection method, and using the grid components in the set software scene to generate a grid.

[0038] The model construction module is used to construct the equipment system model of the fully mechanized mining working face; the fully mechanized mining working face equipment system model is a physical model that is collaboratively coupled and assembled based on the single equipment kinematic model, the multi-equipment collaborative kinematic model, and the equipment geometry-physics-behavior-working condition modeling to perform collaborative operation; among them, the single equipment kinematic model is a kinematic model determined by the DH parameter method to characterize the motion characteristics; the multi-equipment collaborative kinematic model is a spatial kinematic model determined by the inverse kinematics analytical motion parameters to characterize the walking trajectory and posture change relationship.

[0039] The coal-rock interface determination module is used to perform cutting coupling and mapping conversion based on the virtual coal seam model and the fully mechanized mining working face equipment system model to determine the coal-rock interface.

[0040] The adjustment evolution module is used to cut and adjust the coal-rock interface according to the multi-granularity virtual coal block information and the set coal-rock error level, generate a new coal-rock interface, and realize the evolution of the coal-rock interface.

[0041] In one embodiment, the coal rock block information determination module specifically includes: a data processing submodule, a coal-rock interface data determination submodule, a surface information determination submodule, a coal seam NURBS surface model construction submodule, a virtual coal seam model determination submodule, and a multi-granularity virtual coal rock block information determination submodule.

[0042] The data processing submodule is used to perform data cleaning and anomaly elimination based on the initial coal seam data, and use the spatial difference algorithm to generate continuous geological point cloud data.

[0043] The coal-rock interface data determination submodule is connected to the data processing submodule. The coal-rock interface data determination submodule is used to perform three-dimensional reconstruction of the initial coal seam data based on the geological point cloud data using a spatial interpolation algorithm to obtain three-dimensional point cloud data, and perform proportional regional division, using the coal rock density, hardness and coal-rock ratio at the center point of the region as constraints to generate a discrete coal-rock data point set; and use a dynamic partitioning algorithm to divide the coal seam and rock layer to obtain coal-rock interface data.

[0044] The surface information determination submodule is connected to the coal-rock interface data determination submodule. The surface information determination submodule is used to map the three-dimensional point cloud data to the two-dimensional UV plane based on the coal-rock interface data using the least squares projection method and determine the node distribution to obtain surface information.

[0045] The coal seam NURBS surface model construction submodule is connected to the coal-rock interface data determination submodule and the surface information determination submodule. This submodule is used to construct the coal seam NURBS surface model. This model is determined by using the least squares method to minimize the fitting error between the 3D point cloud data and the surface information to adjust the weight factor, and then correcting the 3D point cloud data coordinates using the Levenberg-Marquardt iterative optimization algorithm.

[0046] The virtual coal seam model determination submodule is connected to the coal seam NURBS surface model construction submodule. The virtual coal seam model determination submodule is used to generate a mesh based on the coal seam NURBS surface model using the Mesh component in the Unity3d software scene to obtain a virtual coal seam model.

[0047] The multi-granularity virtual coal rock block information determination submodule is connected to the virtual coal seam model determination submodule. The multi-granularity virtual coal rock block information determination submodule is used to determine the coal seam space and coal rock data based on the virtual coal seam model, and perform spatial block division, as well as perform granularity transformation based on the coal rock blocks of the set granularity to determine the multi-granularity virtual coal rock block information.

[0048] The coal-rock interface determination module specifically includes:

[0049] The parameter acquisition submodule is used to obtain equipment dynamic parameters and geological characteristic parameters; equipment dynamic parameters include: six-degree-of-freedom posture data of the coal mining machine, traction speed and cutting motor current; geological characteristic parameters include: coal-rock mixture ratio, hardness coefficient and density.

[0050] The cutting current determination submodule is connected to the parameter acquisition submodule and is used to determine the dynamic cutting current of the coal mining machine based on the nonlinear mapping relationship equation, the equipment dynamic parameters and the geological characteristic parameters.

[0051] The coal-rock state information determination submodule is connected to the cutting current determination submodule and is used to determine the coal-rock state information based on the dynamic cutting current of the coal mining machine and the preset standard current.

[0052] The coal-rock interface determination submodule is connected to the coal-rock status information determination submodule and the coal-rock block information determination module respectively. It is used to perform mapping conversion of the cutting planning curve based on the coal-rock status information and the virtual coal seam model, and adopt the coal mining machine multi-layer sensing error transfer model to perform mapping conversion of the coal-rock interface to integrate and obtain the coal-rock interface.

[0053] The parameter acquisition submodule includes a sensing unit; the sensing unit uses an inertial measurement unit, a lidar, and an encoder to obtain the dynamic parameters of the equipment; among them, the six-degree-of-freedom posture data of the coal mining machine in the dynamic parameters of the equipment include: pitch angle, yaw angle, and roll angle.

[0054] In an exemplary embodiment, a method for coal-rock interface evolution considering shearer layering error is provided, which is implemented using a coal-rock interface evolution system considering shearer layering error. Figure 6 As shown, the method includes:

[0055] Step 100: Obtaining initial coal seam data. The initial coal seam data includes: coal mine geological exploration data and preset coal seam data; the preset coal seam data includes: coal seam key nodes, coal seam strike angle, and coal seam undulation angle.

[0056] Step 200: Construct a virtual coal seam model based on the initial coal seam data and determine the multi-granularity virtual coal rock information. The virtual coal seam model is a physical model determined by using a spatial difference algorithm and a least squares projection method, and by using the grid components in the set software scene to generate a grid.

[0057] Step 300: Construct a fully mechanized mining face equipment system model. The fully mechanized mining face equipment system model is a physical model based on the coordinated coupling assembly of a single-equipment kinematic model, a multi-equipment collaborative kinematic model, and equipment geometry-physics-behavior-operating condition modeling for coordinated operation. The single-equipment kinematic model is a kinematic model determined using the DH parameter method to characterize motion characteristics. The multi-equipment collaborative kinematic model is a spatial kinematic model determined using inverse kinematics to analyze motion parameters and characterize movement trajectories and posture change relationships.

[0058] Step 400: Perform cutting coupling and mapping conversion based on the virtual coal seam model and the fully mechanized mining working face equipment system model to determine the coal-rock interface.

[0059] Step 500: Based on the multi-granularity virtual coal rock block information and the set coal rock error level, the coal rock interface is cut and adjusted to generate a new coal rock interface to achieve the evolution of the coal rock interface.

[0060] In one embodiment, a virtual coal seam model is constructed based on the initial coal seam data, and information of multi-granularity virtual coal blocks is determined, specifically including:

[0061] Data cleaning and anomaly elimination are performed based on the initial coal seam data, and a spatial difference algorithm is used to generate continuous geological point cloud data.

[0062] Based on the geological point cloud data, a spatial interpolation algorithm is used to perform three-dimensional reconstruction of the initial coal seam data to obtain three-dimensional point cloud data, and proportional regional division is performed. The coal rock density, hardness and coal-rock ratio at the center point of the region are used as constraints to generate a discrete coal-rock data point set; and a dynamic partitioning algorithm is used to divide the coal seam and rock layer to obtain coal-rock interface data.

[0063] Based on the coal-rock interface data, the least squares projection method is used to map the three-dimensional point cloud data to a two-dimensional UV plane and determine the node distribution to obtain surface information.

[0064] A coal seam NURBS surface model is constructed; the coal seam NURBS surface model is determined by using the least squares method to minimize the fitting error between the three-dimensional point cloud data and the surface information to adjust the weight factor, and using the Levenberg-Marquardt iterative optimization algorithm to correct the coordinates of the three-dimensional point cloud data.

[0065] According to the coal seam NURBS surface model, the mesh component in the Unity3d software scene is used to generate the mesh to obtain the virtual coal seam model.

[0066] Based on the virtual coal seam model, the coal seam space and coal rock data are determined, and the space is divided into blocks. The granularity of the coal rock blocks with set granularity is transformed to determine the multi-granularity virtual coal rock block information.

[0067] In one embodiment, cutting coupling and mapping conversion are performed based on the virtual coal seam model and the fully mechanized mining working face equipment system model to determine the coal-rock interface, specifically including:

[0068] Obtain equipment dynamic parameters and geological characteristic parameters; equipment dynamic parameters include: six-degree-of-freedom posture data of the coal mining machine, traction speed and cutting motor current; geological characteristic parameters include: coal-rock mixture ratio, hardness coefficient and density.

[0069] Based on the nonlinear mapping relationship equation, the dynamic cutting current of the coal mining machine is determined according to the dynamic parameters of the equipment and the geological characteristic parameters.

[0070] The coal-rock state information is determined based on the dynamic cutting current of the coal shearer and the preset standard current. Based on the coal-rock state information, the cutting planning curve is mapped and transformed according to the virtual coal seam model, and the coal-rock interface is mapped and transformed using the coal shearer multi-layer sensing error transfer model to integrate the coal-rock interface.

[0071] The nonlinear mapping relationship equation is a mathematical equation determined based on a deep neural network to characterize the nonlinear mapping relationship between the equipment dynamic parameters and the geological characteristic parameters; the expression of the nonlinear mapping relationship equation is:

[0072] f(v1,α,β,γ,η,H,ρ)→I_std.

[0073] Among them, f is a deep neural network with learnable parameters; v1 is the traction speed; α is the pitch angle; γ is the yaw angle; β is the roll angle; η is the coal-rock mixture ratio; H is the hardness coefficient; ρ is the density; I_std is the cutting current.

[0074] The multi-layer sensing error transmission model of the coal shearer is a model that characterizes the coal shearer posture error based on the influence of each posture variable on the coal shearer on the overall posture of the coal shearer.

[0075] The expression corresponding to the coal seam NURBS surface model is:

[0076]

[0077] Among them, S(u,v) is the coordinate of the 3D point on the surface corresponding to the parameter (u,v); N i,p (u) is the p-order B-spline basis function in the u direction; N j,q (v) is the q-order B-spline basis function in the v direction; i is the row number; j is the column number; n is the maximum index of the control point grid in the v direction; m is the maximum index of the control point grid in the u direction; w i,j is the weight factor; P i,j For the control point.

[0078] like Figure 2 As shown, the method proposed in this application includes three models: 1) the initial virtual coal seam surface construction model, that is, the construction of a virtual coal seam model; 2) the construction and operation model of the virtual fully-mechanized mining equipment intelligent body, that is, the fully-mechanized mining working face equipment system model; 3) the virtual "coal seam + fully-mechanized mining equipment" multi-agent coupling cutting and advancing virtual-to-real mapping model, that is, cutting, coupling and mapping conversion are performed based on the virtual coal seam model and the fully-mechanized mining working face equipment system model. Among them, the initial virtual coal seam surface construction model and the construction and operation model of the virtual fully-mechanized mining equipment intelligent body construct the virtual coal seam and virtual equipment respectively, which are the basis of the virtual "coal seam + fully-mechanized mining equipment" multi-agent coupling cutting and advancing virtual-to-real mapping model. The following is a detailed description of the model and application method in this application.

[0079] 1. Initial virtual coal seam surface construction model (constructing virtual coal seam model), such as Figure 3 As shown:

[0080] Initial coal seam data was obtained to construct a NURBS surface model. This model was then imported into Unity3D through model conversion, where a virtual coal seam model was constructed using Mesh components. NURBS (Non-Uniform Rational B-Splines) can represent and construct complex curves and surfaces.

[0081] Step 101, data selection of virtual coal seam (initial coal seam data): Edit the UI interface in Unity3d and set two data sources: coal mine geological exploration data and key nodes of custom coal seams, coal seam strike angles, and coal seam undulation angles. Set up the Dropdown component in Unity3d to provide options for two data sources. Among them, geological exploration data is imported into local CSV, JSON, XLSX and other formats through Unity3d for high-precision modeling; the custom mode manually sets the key node coordinates, coal seam strike angles and undulation parameters to facilitate rapid prototyping.

[0082] Step 102, data extraction and geological point cloud data generation: Based on the initial coal seam data in step 101 (including coordinate axes X, Y, Z, and coal rock physical parameters and other information), use Python and other tools to clean the data, remove data points with abnormal height values, and perform spatial interpolation on the coal-rock boundary layer data to generate continuous geological point cloud data.

[0083] Step 103: Coal-rock interface data selection: Based on the geological point cloud data generated in step 102, a spatial interpolation algorithm is used to perform 3D reconstruction of the original coal seam data. Proportional regional division is performed, and physical properties such as coal-rock density, hardness, and coal-rock ratio at the regional center are used as constraints to generate a discrete coal-rock data point set. This data point set is then stored in a structured CSV database. For the coal-rock region, a dynamic partitioning algorithm is used to refine areas with significant physical property variation, demarcating the coal seam from the rock layer and obtaining coal-rock interface data.

[0084] Step 104, coal seam NURBS surface model construction: Based on the coal-rock interface data generated in step 103, the three-dimensional point cloud data is mapped to the two-dimensional UV plane through the least squares projection method, and the node distribution of NURBS is calculated. Based on the parameterization results, the control point network is inversely solved using the least squares method to minimize the fitting error between the three-dimensional point cloud data and the surface information. The fitting accuracy is improved by adjusting the control point weights (such as weight factors) and optimizing the node vectors. The Levenberg-Marquardt iterative optimization algorithm is used to correct the control point coordinates to ensure that the generated NURBS surface (surface information) meets the continuity requirements. The high-precision NURBS surface finally generated can be expressed as:

[0085]

[0086] Among them, S(u,v) is the coordinate of the 3D point on the surface corresponding to the parameter (u,v); N i,p (u) is the p-order B-spline basis function in the u direction; N j,q(v) is the q-order B-spline basis function in the v direction; i is the row number; j is the column number; n is the maximum index of the control point grid in the v direction; m is the maximum index of the control point grid in the u direction; w i,j is the weight factor; P i,j For the control point.

[0087] Step 105, importing the coal seam NURBS surface model: First, based on the high-precision NURBS surface (surface information) generated in step 104, perform fine division, divide the horizontal and vertical coordinates of the surface at equal intervals, obtain the two-dimensional plane values, and combine the height values ​​after the control point weights are adjusted to calculate the height information of the data points generated by the interpolation processing in step 103. Subsequently, these data are re-exported as a vertex CSV data set and imported into Unity3d. In Unity3d, the data of the CSV data set is read through C# programming, the vertex information is parsed and the triangle index of the Mesh mesh is set. In this process, the order of the triangle index needs to be clarified, which is divided into clockwise and counterclockwise to ensure the correct normal direction. Finally, the Mesh component in the Unity3d scene is used to generate a mesh to construct a complete virtual coal seam model.

[0088] Step 106: Generate multi-granularity virtual coal block information: Based on the structured CSV database from step 103, create coal blocks of equal size and minimum scale in Unity3D. This coal block is set up as a prefabricated object, and when the scene is running, the prefabricated object is replicated to form a complete coal block. The coal blocks are renamed based on their location, and the physical properties such as coal density, hardness, and coal proportion in the CSV database are matched according to the coal block location. A multi-granularity coal block model is established. Based on the above coal block sizes, coal blocks of different granularities with proportionally enlarged length, width, and height are established, such as coal blocks of multiples of 2, 4, and 6. Based on the physical property parameter data of all coal blocks contained in the enlarged coal block area, the enlarged data is recalculated, and the data is reduced in dimensionality using UMAP, while retaining the global and local features of the original data. A CSV database corresponding to the magnification factor is established to achieve multi-scale expression of coal blocks from macroscopic (meter level) to microscopic (decimeter level).

[0089] To sum up, the implementation process can be summarized as follows: 1) Select and import the initial data of the coal seam, process the data and generate geological point cloud data; 2) Based on the initial data of the coal seam, select the coal seam data and obtain the coal-rock interface data, and construct the coal seam NURBS surface; 3) Import the coal seam NURBS surface into Unity3d, and perform interpolation processing to convert it into a Mesh coal seam; 4) According to the coal seam space and coal rock data, block the space, generate virtual coal rock blocks and record the data; 5) Define coal rock blocks of different particle sizes, and transform the particle size of the coal rock blocks based on the real-time position of the coal mining machine.

[0090] 2. Construction and operation model of virtual fully mechanized mining equipment intelligent body (fully mechanized mining working face equipment system model). The specific implementation process is as follows: Figure 4 shown.

[0091] By establishing a three-level coordinate system for the virtual fully mechanized mining system, constructing a single-equipment kinematic model and a multi-equipment collaborative kinematic model, and constructing a multi-layer sensing error transmission model for the coal mining machine, the influence of the three-layer error on the position and posture of the coal mining machine is comprehensively considered, and an equipment geometry-physics-behavior-working condition model is constructed in Unity3d, ultimately realizing the coordinated advancement of the virtual system fully mechanized mining equipment.

[0092] Step 201, construction of a three-level coordinate system: In the virtual fully mechanized mining system, first, the starting point of the coal seam is used as the first-level coordinate system to provide a global reference benchmark for the entire system; the position of the middle slot of the first section of the scraper conveyor is set as the second-level coordinate system to describe the relative position and movement relationship of the equipment in the working face; a third-level coordinate system is set for each individual equipment, the shearer coordinate system is established at the center of the shearer body, and the coordinate systems of the scraper conveyor and the hydraulic support are respectively established at the center of the contact surface between their bases and the coal seam.

[0093] Step 202: Construct a kinematic model for a single piece of equipment: The complex kinematic characteristics of equipment such as shearers, hydraulic supports, and face conveyors are equated to industrial robot models. A third-level coordinate system, based on the method described in Step 201, is established for each piece of equipment. The Denavit-Hartenberg (DH) parameter method is introduced to clarify the geometric relationships and kinematic constraints of each joint, and forward kinematic equations are derived based on this. These equations are then analyzed to accurately calculate the pose information of each joint in different states, including position and posture, thereby fully describing the kinematic characteristics of the equipment.

[0094] Step 203, constructing a multi-equipment collaborative kinematic model: Obtain data on the midpoint of the initial coal-rock boundary surface CSV, and determine the coordinate position of the middle slot based on the width of the middle slot and the equation of the curve; equate the motion characteristics of the hydraulic support floating connection mechanism to an industrial robot model, and construct a spatial kinematic model. Use inverse kinematics to analyze the motion parameters of each structure based on the position of the scraper conveyor; based on the overall position of the scraper conveyor and the motion relationship of the coal shearer on the scraper conveyor, determine the walking trajectory of the coal shearer on the scraper conveyor and the change relationship of the coal shearer's position.

[0095] Step 204, construct a multi-layer sensor error transmission model for the coal mining machine: Divide the coal mining machine posture error into three layers according to the different degrees of influence of each posture variable on the coal mining machine's overall posture. The posture variables in the high-level error have a stronger impact on the overall posture of the coal mining machine, and will also affect the lower levels. The coal mining machine's fuselage position error is defined as the first level, the fuselage's posture error is the second level, and the coal mining machine rocker arm angle error is divided into the third level. Therefore, the coal mining machine drum full posture matrix N based on the multi-layer error can be expressed as:

[0096] N=[Δx Δy Δz Δα Δβ Δγ Δθ].

[0097] Among them, the variables Δx, Δy, and Δz represent the X, Y, and Z coordinate position errors of the coal mining machine body, respectively, which are the first level; the variables Δα, Δβ, and Δγ represent the errors corresponding to the pitch angle, roll angle, and yaw angle of the coal mining machine body, respectively; and the variable Δθ represents the angle error of the coal mining machine rocker arm height adjustment.

[0098] Step 205: Comprehensively consider the impact of the three-layer error on the drum position: When the shearer body posture changes, the changes in the X, Y, and Z coordinate positions of the body and the change in the body yaw angle have little impact on the rocker arm inclination angle. Only the impact of the changes in the body pitch angle and roll angle on the rocker arm inclination angle is considered:

[0099] (1) Consider the impact of the operational changes and measurement errors of the fuselage pitch angle on the rocker arm tilt angle: When the fuselage pitch angle changes counterclockwise or clockwise, the rocker arm tilt angle needs to be calculated with the fuselage pitch angle measurement value. When the fuselage pitch angle error changes clockwise, the fuselage pitch angle error is positive, and the rocker arm tilt angle error needs to be added to the rocker arm tilt angle error. Conversely, the rocker arm tilt angle error needs to be subtracted from the rocker arm tilt angle error:

[0100]

[0101] Among them, θ' is the rocker arm tilt angle measurement value, α is the pitch angle, θ″ αis the true value of the rocker arm inclination angle considering the pitch angle of the fuselage, Δθ' is the rocker arm inclination error measured by the inclination sensor, Δα is the error corresponding to the pitch angle of the coal mining machine fuselage, and Δθ″ α is the rocker arm tilt angle error considering the fuselage pitch angle error.

[0102] (2) Consider the impact of the fuselage roll angle measurement error on the rocker arm tilt angle: When the fuselage roll angle changes, the impact on the rocker arm tilt angle and error is as follows:

[0103]

[0104] Where β is the roll angle, θ″ β is the true value of the rocker arm inclination angle considering the pitch angle of the fuselage, Δβ is the error corresponding to the roll angle of the coal mining machine fuselage, and Δθ″ β is the rocker arm tilt angle error considering the roll angle error of the fuselage.

[0105] (3) Comprehensively consider the impact of the fuselage pitch angle and roll angle errors on the rocker arm tilt angle: Since the fuselage pitch angle and the rocker arm tilt angle measured by the tilt sensor are in the same plane, the impact of the fuselage pitch angle error can be considered on the basis of the fuselage roll angle error. Comprehensively considering the impact of the fuselage pitch angle and roll angle errors on the rocker arm tilt angle, the true value of the rocker arm tilt angle is:

[0106] θ r =arsin(cosβsinθ')+arcsin(cosΔβsinΔθ')+α+Δα.

[0107] Among them, θ r is the true value of the rocker arm inclination angle.

[0108] (4) Comprehensively consider the influence of the above three layers of errors on the roller position: the first layer of errors, the fuselage X, Y, and Z coordinate position errors, respectively affect the corresponding X, Y, and Z coordinate values ​​of the roller, the fuselage pitch angle affects the roller Y and Z coordinate values, the fuselage roll angle affects the roller X and Y coordinate values, and the fuselage yaw angle affects the roller X and Z coordinate values. Therefore, considering the above three layers of errors, the roller position based on the working surface coordinate system is:

[0109] T x +ΔT x +(cos(β+Δβ)cos(γ+Δγ))(c+d / 2)+cos(β+Δβ)sin(γ+Δγ)sin(arcsin(cosβsinθ')+arcsin(cosΔβ sinΔθ')+α+Δα)·l-sin(β+Δβ)(a / 2+cos(arcsin(cosβsinθ')+arcsin(cosΔβsinΔθ')+α+Δα)·l).

[0110] T y +ΔT y +(sin(α + Δα)sin(β + Δβ)cos(γ + Δγ) + cos(α + Δα)sin(γ + Δγ))(c + d / 2) + (sin(α + Δα)sin(β + Δβ)sin(γ + Δγ) - cos(α + Δα)cos(γ + Δγ))·sin(arcsin(cosβsinθ') + arcsin(cosΔβsinΔθ') + α + Δα)·l + sin(α + Δα)cos(β + Δβ)(a / 2 + cos(arcsin(cosβsinθ') + arcsin(cosΔβsinΔθ') + α + Δα)·l).

[0111] T z +ΔT z +(sin(α + Δα)sin(γ + Δγ) - cos(α + Δα)sin(β + Δβ)cos(γ + Δγ))(c + d / 2) - (cos(α + Δα)sin(β + Δβ)sin(γ + Δγ) + sin(α + Δα)cos(γ + Δγ))sin(arcsin(cosβsinθ') + arcsin(cosΔβsinΔθ') + α + Δα)·l - cos(α + Δα)cos(β + Δβ)(a / 2 + cos(arcsin(cosβsinθ') + arcsin(cosΔβsinΔθ') + α + Δα)·l).

[0112] Where, T x , T y , T z are respectively the three - axis coordinates of the shearer drum, ΔT x , ΔT y , ΔT z are respectively the changes in the three - axis coordinates of the shearer drum, Δγ is the yaw angle error, a, b, c, d, l are respectively the length of the shearer body, the thickness of the body, the thickness of the drum, the length of the rocker arm, and the rocker - arm inclination angle, θ' is the measured value of the rocker - arm inclination angle, and Δθ' is the error of the rocker - arm inclination angle measured by the inclination sensor.

[0113] Step 206, equipment geometry-physics-behavior-working condition modeling: In Unity3d, first import the 3D model of the equipment and establish an adjustable parameter system, including key parameters such as the length of the coal mining machine rocker arm and the inclination angle of the support top beam, so as to realize the function of changing the equipment model shape in real time through parameter adjustment. According to the simulation requirements, set up a multi-level refined model. The high-precision model retains the details of key mechanical components such as gears and bearings, while the low-precision model is simplified to a rigid body bounding box structure, retaining only basic motion functions. Build an equipment motion simulation system based on the Unity3d physics engine. By configuring physical parameters such as mass and resistance, and reasonably setting joint constraints and collision bodies, the authenticity of the motion simulation is ensured. C# scripting is used to achieve precise control of the equipment action sequence. At the same time, a visual parameter adjustment interface is developed to support dynamic adjustment of equipment action parameters and motion speed through code, realizing a flexible and controllable equipment motion simulation system.

[0114] Step 207: Collaborative advancement of fully mechanized mining equipment: In Unity3D, based on the single-equipment kinematic model from step 202, the multi-equipment collaborative kinematic model from step 203, and the equipment geometry-physics-behavior-operating condition modeling from step 206, collaborative coupling assembly is performed between equipment models. This complete fully mechanized mining face equipment system is constructed, achieving physical coupling between the equipment and the coal seam, and between each other, enabling complete coordinated operation of the equipment.

[0115] In summary, the process of implementing step 2 can be summarized as follows: 1) Analyze the kinematic model of a single piece of equipment and disassemble the modules of the equipment to obtain the component relationships and data of the single piece of equipment; 2) Develop collaborative kinematic models between each piece of equipment and between the equipment and the coal seam; 3) Model the geometry, physics, behavior, and working conditions of each piece of equipment in Unity3d, and achieve smooth and normal operation of the equipment by adjusting the corresponding parameters in the code; 4) Perform assembly and collaborative operation between virtual equipment.

[0116] 3. Virtual "coal seam + fully mechanized mining equipment" multi-agent coupling cutting and advancing virtual-real mapping model, the specific implementation process is as follows Figure 5 shown.

[0117] The virtual "coal seam + fully mechanized mining equipment" multi-agent coupled cutting and advancing virtual-to-real mapping model constructs a nonlinear mapping relationship by coupling the equipment's posture with the coal seam's roof and floor performance. Real-time data is used to determine the current coal-rock condition, and a mapping is established from the coal-rock interface to the cutting planning curve. The specific process for constructing this virtual "coal seam + fully mechanized mining equipment" multi-agent coupled cutting and advancing virtual-to-real mapping model is as follows:

[0118] Step 301, define the coal seam error level: define different levels of error, which can distinguish coal seams with different error levels caused by different detection methods. The error level of the coal seam that has been cut by the virtual working face is set to no error, that is, the coal seam after cutting is a transparent geological coal seam. The error level of the unmined coal seam that can be detected by the coal mining machine detection method is set to the centimeter level, that is, the coal seam in this area is a gray box geological model with a small error. The error level of the unmined coal seam that cannot be detected by the coal mining machine detection method is set to the decimeter level, that is, the coal seam is a black box geological model with a large error.

[0119] Step 302: Coupling behavior model between equipment posture and coal seam roof and floor posture performance: Using inertial measurement units (IMUs), lidars, encoders, and other sensor systems, the shearer's six-degree-of-freedom posture data (including pitch, yaw, and roll angles), traction speed, and cutting motor current are collected in real time. Geological characteristic parameters such as the coal-rock mixture ratio, hardness coefficient, and density at the coal seam cutting interface are also acquired simultaneously. A deep neural network model is constructed to characterize the nonlinear mapping relationship between coal and rock characteristics and equipment dynamic parameters:

[0120] f(v1,α,β,γ,η,H,ρ)→I_std.

[0121] Step 303: Coal Block Physical Properties and Equipment Cutting: Based on the sensor data collected in step 302, the standard current required for the shearer drum to properly cut the coal-rock interface in this position is determined. The current variation pattern for the shearer's upper and lower drums cutting the coal seam is then established. Based on physical properties such as coal block hardness, density, and coal-rock ratio, as well as the shearer's speed and position, and using the equations relating these coal and rock data to equipment position data, the shearer's dynamic cutting current is calculated. The calculated current is compared with the standard current to determine the current state of the coal and rock.

[0122] Step 304, mapping conversion of the coal-rock interface to the cutting planning curve: After the current cut is completed, the scraper conveyor and the hydraulic support cooperate to complete the pushing and moving actions. Based on the posture information of the scraper conveyor at the current cut and the cutting depth parameters of the coal mining machine, the target area of ​​the next cutting surface of the coal mining machine is determined. Combined with the coal-rock boundary surface model constructed in the above steps, the relevant data of the coal-rock boundary surface in the cutting area of ​​the cutter is extracted. The cutting path of the drum is calculated using the spline curve method based on the data points, and an initial cutting planning curve is generated. On this basis, the relationship between the speed adjustment of the coal mining machine and the rocker arm angle adjustment is comprehensively considered, and the cutting planning curve is optimized to ensure that the drum of the coal mining machine can accurately move along the planned path during operation, thereby achieving efficient and stable cutting operations.

[0123] Step 305: Mapping the cutting planning curve to the coal-rock interface: Based on the shearer's multi-layer sensor error transfer model from step 204, the shearer drum's real-time position is calculated. The shearer drum, even with sensor errors, performs cutting operations according to the cutting planning curve from step 304. During operation, the shearer obtains its own position data and data on the coal and rock blocks it cuts and collides with. Based on the nonlinear mapping relationship from step 302, the real-time current from step 303 is compared with the calculated standard current to determine the current coal and rock conditions of the top and bottom plates. Dynamic adjustments are made to the shearer's cutting position based on the coal and rock conditions, raising or lowering the rocker arm. During this process, the drum collides with coal and rock blocks, and the cut coal and rock blocks are transported away from the working face via the scraper conveyor, exposing uncut coal and rock blocks. The data for the uncut coal and rock blocks is updated to the coal and rock block CSV database. The data points in the CSV database are then processed, and the coordinate changes of adjacent data points are smoothed and optimized to improve data continuity. Then the new coal-rock interface is reconstructed and integrated with the coal-rock interface formed by previous cutting, and finally a complete coal-rock interface reflecting the overall cutting situation is generated.

[0124] Step 306: Autonomous Evolution of the Virtual Coal Seam Intelligent Entity: Based on the coal seam error level determined in step 301, the shearer detection range is set during the fully mechanized mining system. A smaller error level is used in the coal seam goaf, a medium error level is used within the sensor detection range in front of the shearer, and a larger error level is used in locations farther from the shearer and beyond the sensor's reach. Based on the multi-granularity virtual coal rock block information generated in step 106, during the initial phase of the fully mechanized mining scenario, all coal rock blocks within the scenario are of larger granularity, reflecting the macroscopic coal rock conditions. When the shearer begins operation, within the shearer's cutting range, the shearer automatically replaces coal rock blocks using code. Corresponding large-granularity coal rock blocks are parsed and replaced with smaller-granularity ones. The shearer then makes contact with these blocks, performing cutting adjustments as described in step 305, thereby generating a new coal-rock interface. As the coal seam is mined, a C# script is written to reset the coal seam error level, setting the error level based on the aforementioned cut count, thereby achieving autonomous evolution of the coal seam model.

[0125] The scheme mentioned in this application can be summarized as follows: 1) constructing a virtual coal seam model, and constructing coal and rock blocks in the corresponding coal and rock seam partitions to achieve matching of coal and rock block data with position data; 2) constructing a single equipment kinematic model, and constructing an equipment collaborative kinematic model, importing it into Unity3d, and realizing equipment collaborative operation; 3) generating a cutting planning curve according to the coal-rock interface, considering the multi-layer error of the coal mining machine, and performing cutting according to the cutting planning curve; 4) calculating the comparison between the standard current and the real-time current under the operating state of the coal mining machine to obtain the coal and rock condition of the current cutting state; 5) performing mutual mapping between the coal-rock interface and the cutting planning curve, generating a cutting planning curve and using the cutting planning curve to perform cutting operation; 6) performing autonomous evolution of the virtual coal seam.

[0126] This application uses the NURBS surface construction method to construct a virtual coal seam surface, and describes it using Mesh by importing it into Unity3d, thereby achieving the initial construction of the coal-rock boundary surface.

[0127] A virtual coal block model was constructed based on the physical properties of the coal seam data (such as coal density, hardness, and coal-rock ratio) and the coordinates of the data points. By scaling up the virtual coal block and processing the data using Unified Mapping (UMAP) dimensionality reduction technology, virtual coal blocks with various particle sizes were generated.

[0128] This application constructs a multi-layer sensing error transmission model for coal mining machines, and studies the cumulative impact of the step-by-step transmission mechanism from the basic sensing parameters of the machine body to the rocker arm inclination measurement error on the spatial positioning accuracy of the coal mining machine drum, so as to perform cutting according to the cutting planning curve, which can effectively simulate the real comprehensive mining conditions, thereby obtaining coal-rock interface information (coal-rock interface data) that is closer to reality.

[0129] This application constructs a coal seam model with multiple error levels, comprehensively considers the detection distance of the coal mining machine sensor, and dynamically adjusts the error level of the coal seam according to the real-time position of the coal mining machine, thereby achieving accurate division of the error levels of coal seams in different regions, and presenting coal seam distribution characteristics with regional characteristics.

[0130] As the cutting process progresses, this application integrates the real-time coal and rock conditions with the equipment posture data, and gradually converts the cutting line into the cutting surface; in addition, the geological model of the working face is gradually evolved automatically during mining and exploration, and the reliability of the limited transparent working face geological model can be gradually improved, providing a geological model for cutting pre-planning while gradually correcting the planning results.

[0131] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0132] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the system, method, and core concept of this application. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of this application. In summary, the contents of this specification should not be construed as limiting this application.

Claims

1. A coal-rock interface evolution system considering shearer layering errors, characterized by: include: Data acquisition module, coal-rock block information determination module, model building module, coal-rock interface determination module and adjustment evolution module; The coal-rock block information determination module is connected to the data acquisition module; the coal-rock interface determination module is connected to the coal-rock block information determination module and the model construction module respectively; the adjustment evolution module is connected to the coal-rock interface determination module and the coal-rock block information determination module respectively; The data acquisition module is used to acquire initial data of the coal seam; The coal seam initial data includes: coal mine geological exploration data and preset coal seam data; The coal rock block information determination module is used to construct a virtual coal seam model based on the initial coal seam data and determine the multi-granularity virtual coal rock block information; the virtual coal seam model is a physical model determined by using a spatial difference algorithm and a least squares projection method and generating a grid using a grid component in a set software scene; The model construction module is used to construct a fully mechanized mining face equipment system model; the fully mechanized mining face equipment system model is a physical model that is collaboratively coupled and assembled based on a single equipment kinematic model, a multi-equipment collaborative kinematic model, and equipment geometry-physics-behavior-working condition modeling to perform collaborative operation; wherein, the single equipment kinematic model is a kinematic model determined by the DH parameter method to characterize motion characteristics; the multi-equipment collaborative kinematic model is a spatial kinematic model determined by inverse kinematics to analyze motion parameters to characterize walking trajectories and posture change relationships; The coal-rock interface determination module is used to perform cutting coupling and mapping conversion based on the virtual coal seam model and the fully mechanized mining working face equipment system model to determine the coal-rock interface; The adjustment evolution module is used to cut and adjust the coal-rock interface according to the multi-granularity virtual coal block information and the set coal-rock error level to generate a new coal-rock interface to achieve the evolution of the coal-rock interface.

2. The coal-rock interface evolution system considering shearer layering error according to claim 1 is characterized in that: The coal and rock block information determination module specifically includes: A data processing submodule is used to perform data cleaning and anomaly elimination processing based on the initial coal seam data, and to generate continuous geological point cloud data using a spatial difference algorithm; a coal-rock interface data determination submodule, connected to the data processing submodule, for performing three-dimensional reconstruction of the initial coal seam data using a spatial interpolation algorithm based on the geological point cloud data to obtain three-dimensional point cloud data, and performing proportional regional division, using the coal-rock density, hardness, and coal-rock ratio at the regional center as constraints to generate a discrete coal-rock data point set; and, using a dynamic partitioning algorithm to divide the coal seam and rock stratum to obtain coal-rock interface data; a surface information determination submodule, connected to the coal-rock interface data determination submodule, for mapping the three-dimensional point cloud data to a two-dimensional UV plane using a least squares projection method based on the coal-rock interface data and determining node distribution to obtain surface information; a coal seam NURBS surface model construction submodule, connected to the coal-rock interface data determination submodule and the surface information determination submodule, respectively, for constructing a coal seam NURBS surface model; the coal seam NURBS surface model is determined by minimizing the fitting error between the three-dimensional point cloud data and the surface information using a least squares method to adjust a weight factor, and correcting the three-dimensional point cloud data coordinates using a Levenberg-Marquardt iterative optimization algorithm; A virtual coal seam model determination submodule is connected to the coal seam NURBS surface model construction submodule and is used to generate a mesh using the Mesh component in the Unity3d software scene according to the coal seam NURBS surface model to obtain a virtual coal seam model; The multi-granularity virtual coal rock block information determination submodule is connected to the virtual coal seam model determination submodule, and is used to determine the coal seam space and coal rock data based on the virtual coal seam model, and perform spatial block division, as well as perform granularity transformation based on the coal rock blocks of the set granularity to determine the multi-granularity virtual coal rock block information.

3. The coal-rock interface evolution system considering shearer layering error according to claim 1 is characterized in that: The coal-rock interface determination module specifically includes: Parameter acquisition submodule, used to obtain equipment dynamic parameters and geological characteristic parameters; a cutting current determination submodule, connected to the parameter acquisition submodule, for determining the dynamic cutting current of the coal mining machine based on a nonlinear mapping relationship equation, according to the equipment dynamic parameters and the geological characteristic parameters; a coal-rock state information determination submodule, connected to the cutting current determination submodule, for determining the coal-rock state information based on the dynamic cutting current of the coal mining machine and a preset standard current; The coal-rock interface determination submodule is connected to the coal-rock status information determination submodule and the coal-rock block information determination module respectively, and is used to perform mapping conversion of the cutting planning curve based on the coal-rock status information and the virtual coal seam model, and adopt the coal mining machine multi-layer sensing error transfer model to perform mapping conversion of the coal-rock interface to integrate and obtain the coal-rock interface.

4. The coal-rock interface evolution system considering shearer layering error according to claim 3 is characterized in that: The parameter acquisition submodule includes a sensing unit; The sensing unit uses an inertial measurement unit, a laser radar and an encoder to obtain the dynamic parameters of the equipment; wherein the six-degree-of-freedom posture data of the coal mining machine in the dynamic parameters of the equipment include: pitch angle, yaw angle and roll angle.

5. A coal-rock interface evolution method considering shearer layering error, characterized in that: The coal-rock interface evolution method considering coal shearer stratification error is implemented by using the coal-rock interface evolution system considering coal shearer stratification error according to any one of claims 1 to 4; the method comprises: Acquire initial coal seam data; the initial coal seam data includes: coal mine geological exploration data and preset coal seam data; the preset coal seam data includes: coal seam key nodes, coal seam strike angle, and coal seam undulation angle; A virtual coal seam model is constructed based on the initial coal seam data, and information on virtual coal blocks with multiple granularities is determined; the virtual coal seam model is a physical model determined by generating a grid using a spatial difference algorithm and a least squares projection method, and by using grid components in a set software scene; Construct a fully mechanized mining face equipment system model; the fully mechanized mining face equipment system model is a physical model that is collaboratively coupled and assembled based on a single-equipment kinematic model, a multi-equipment collaborative kinematic model, and equipment geometry-physics-behavior-working condition modeling to perform collaborative operation; wherein the single-equipment kinematic model is a kinematic model determined by the DH parameter method to characterize motion characteristics; the multi-equipment collaborative kinematic model is a spatial kinematic model determined by inverse kinematics to analyze motion parameters to characterize walking trajectories and posture change relationships; Perform cutting coupling and mapping conversion based on the virtual coal seam model and the fully mechanized mining working face equipment system model to determine the coal-rock interface; According to the multi-granularity virtual coal rock block information, the coal rock interface is cut and adjusted according to the set coal rock error level to generate a new coal rock interface to achieve the evolution of the coal rock interface.

6. The coal-rock interface evolution method considering shearer layering error according to claim 5, characterized in that: A virtual coal seam model is constructed based on the initial coal seam data, and information of virtual coal blocks with multiple granularities is determined, specifically including: Performing data cleaning and anomaly elimination processing based on the initial coal seam data, and using a spatial difference algorithm to generate continuous geological point cloud data; Based on the geological point cloud data, a spatial interpolation algorithm is used to perform three-dimensional reconstruction on the initial coal seam data to obtain three-dimensional point cloud data, and proportional regional division is performed. The coal rock density, hardness, and coal-rock ratio at the regional center point are used as constraints to generate a discrete coal-rock data point set; and a dynamic partitioning algorithm is used to divide the coal seam and rock layer to obtain coal-rock interface data. Based on the coal-rock interface data, the least squares projection method is used to map the three-dimensional point cloud data to a two-dimensional UV plane and determine the node distribution to obtain surface information; Constructing a coal seam NURBS surface model; the coal seam NURBS surface model is determined by minimizing the fitting error between the three-dimensional point cloud data and the surface information using a least squares method to adjust a weight factor, and correcting the coordinates of the three-dimensional point cloud data using a Levenberg-Marquardt iterative optimization algorithm; According to the coal seam NURBS surface model, a mesh is generated using the Mesh component in the Unity3d software scene to obtain a virtual coal seam model; The coal seam space and coal rock data are determined based on the virtual coal seam model, and the space is divided into blocks. The coal rock blocks with set granularity are transformed to determine the multi-granularity virtual coal rock block information.

7. The coal-rock interface evolution method considering shearer layering error according to claim 5, characterized in that: Based on the virtual coal seam model and the fully mechanized mining working face equipment system model, cutting coupling and mapping conversion are performed to determine the coal-rock interface, specifically including: Acquire equipment dynamic parameters and geological characteristic parameters; the equipment dynamic parameters include: six-degree-of-freedom position data of the coal mining machine, traction speed, and cutting motor current; the geological characteristic parameters include: coal-rock mixture ratio, hardness coefficient, and density; Determining the dynamic cutting current of the coal mining machine based on the nonlinear mapping relationship equation and the equipment dynamic parameters and the geological characteristic parameters; Determining coal rock status information based on the dynamic cutting current of the coal mining machine and a preset standard current; Based on the coal-rock state information, the cutting planning curve is mapped and transformed according to the virtual coal seam model, and the coal-rock interface is mapped and transformed using a shearer multi-layer sensing error transfer model to integrate and obtain the coal-rock interface.

8. The coal-rock interface evolution method considering shearer layering error according to claim 7, characterized in that: The nonlinear mapping relationship equation is a mathematical equation determined based on a deep neural network and used to characterize the nonlinear mapping relationship between the equipment dynamic parameters and the geological characteristic parameters; The expression of the nonlinear mapping relationship equation is: f(v1,α,β,γ,η,H,ρ)→I_std; Among them, f is a deep neural network with learnable parameters; v1 is the traction speed; α is the pitch angle; γ is the yaw angle; β is the roll angle; η is the coal-rock mixture ratio; H is the hardness coefficient; ρ is the density; I_std is the cutting current.

9. The coal-rock interface evolution method considering shearer layering error according to claim 7, characterized in that: The multi-layer sensing error transmission model for the coal shearer is a model for characterizing the coal shearer posture error determined based on the influence of each posture variable on the coal shearer on the overall posture of the coal shearer.

10. The coal-rock interface evolution method considering shearer layering error according to claim 6, characterized in that: The expression corresponding to the coal seam NURBS surface model is: Among them, S(u,v) is the coordinate of the 3D point on the surface corresponding to the parameter (u,v); N i,p (u) is the p-order B-spline basis function in the u direction; N j,q (v) is the q-order B-spline basis function in the v direction; i is the row number; j is the column number; n is the maximum index of the control point grid in the v direction; m is the maximum index of the control point grid in the u direction; w i,j is the weight factor; P i,j For the control point.

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