A three-dimensional trajectory positioning method for a drilling device based on remote control

Abstract

The present invention discloses a three-dimensional trajectory positioning method for a drilling device based on remote control, which relates to the field of geological engineering technology and includes the following steps: obtaining target point cloud data of the drilling area through a laser scanner or a geological radar; preprocessing the obtained point cloud data and calculating the normal vector of each measurement point using the least squares method; calculating the local curvature tensor of each measurement point based on the normal vector; adopting a non-Euclidean geometric model for trajectory optimization, constructing an objective function and introducing curvature constraints; after obtaining the optimized trajectory data, establishing a three-dimensional finite element model to simulate the movement process of the drilling device in different geological layers; monitoring the three-dimensional trajectory of the drilling device in real time through a remote control platform, and automatically sending a correction instruction if it is detected that the trajectory deviates from the predetermined path. The present invention can effectively cope with the dynamic changes in the underground environment, improve the automation and intelligence level of the drilling operation, and ensure the accuracy and stability of the drilling trajectory.

Description

Technical Field

[0001] The present invention relates to the technical field of geological engineering, and particularly to a three-dimensional trajectory positioning method for a drilling device based on remote control. Background Art

[0002] In the underground environment, especially in drilling operations in fields such as tunnels and mines, existing trajectory positioning methods mostly rely on traditional two-dimensional or three-dimensional modeling techniques based on Euclidean geometry. These methods predict the drilling path through simplified geometric models and usually achieve good results in environments with relatively simple and stable geological conditions. However, in the face of highly complex and irregular underground geology, traditional trajectory optimization techniques face significant limitations and cannot effectively handle non-linear and non-regular geometric features in underground structures.

[0003] Existing technologies usually perform path planning through models based on Euclidean geometry. These models cannot accurately reflect the complexity in the underground environment, such as formation bending, rock fractures, or the influence of water flow. These factors often cause deviations in the drilling path. Due to the lack of the ability to respond to geological changes in real time, existing trajectory optimization methods cannot cope with the dynamic changes in the underground environment. Therefore, traditional methods often cannot guarantee the accuracy and stability of the drilling path and are difficult to correct the drilling trajectory in real time, resulting in low operation efficiency and even possible equipment damage or operation failure.

[0004] In addition, traditional trajectory optimization methods are generally based on static path planning and lack an effective combination with the real-time status feedback of the drilling device. The drilling device is dynamically affected by the underground environment during operation. Therefore, static path planning methods often cannot handle geological mutations and unexpected changes in actual situations. The trajectory of the device may deviate from the predetermined path, resulting in a decrease in operation accuracy and even affecting the overall operation progress and safety.

[0005] Therefore, a three-dimensional trajectory positioning method for a drilling device based on remote control is proposed. Summary of the Invention

[0006] The purpose of the present invention is to solve the problem that existing trajectory positioning methods cannot effectively handle non-linear and non-regular geometric features, resulting in deviations in the drilling path, and to propose a three-dimensional trajectory positioning method for a drilling device based on remote control.

[0007] To achieve the above purpose, the present invention adopts the following technical solutions:

[0008] A three-dimensional trajectory positioning method for a drilling device based on remote control, comprising the following steps:

[0009] Step 1: Obtain target point cloud data in the drilling area through a laser scanner or ground penetrating radar. The point cloud data contains the three-dimensional coordinate information of each measurement point, and the point cloud data covers all or part of the spatial area of the drilling path.

[0010] Step 2: Preprocess the obtained point cloud data, and use the least squares method to calculate the normal vector within the neighborhood of each measurement point. where the normal vector represents the local surface direction of this measurement point. The calculation of the normal vector is performed by fitting the point cloud within a certain radius around each measurement point to determine a neighborhood point set. The neighborhood point set consists of all points whose distance from the measurement point does not exceed this radius r. Based on the least squares method, solve the plane equation of this neighborhood point set to obtain the local normal vector of this point.

[0011] Step 3: Based on the result of the normal vector calculation, calculate the local curvature tensor of each measurement point through the Laplace operator and second-order partial derivatives. where the local curvature tensor represents the curvature of this measurement point and its neighborhood. The calculation process of the local curvature tensor includes solving the higher-order partial derivatives of the neighborhood of the measurement point and calculating the principal curvatures and ;

[0012] Step 4: Use a non-Euclidean geometric model for trajectory optimization. Based on Riemannian geometry theory, the optimization process through the processing of path curvature includes: constructing an objective function for path optimization according to the local curvature tensor : where, is the dynamic geological factor, reflecting the physical properties of the geological layer where the i-th measurement point is located; and are weight coefficients, respectively adjusting the relative influence of curvature and geological factors on path optimization; is the influence coefficient of the geological factor on path optimization; use the objective function to adjust the drilling path through an optimization algorithm and optimize it along the trajectory with the minimum curvature; through iterative optimization, ensure the accuracy and stability of the trajectory.

[0013] Step 5: After obtaining the optimized trajectory data, establish a three-dimensional finite element model, and simulate the movement process of the drilling equipment in different geological layers through the finite element method, simulate the mechanical reactions that the drilling equipment may encounter during geological changes, and analyze the interaction between the equipment and the underground medium; through the simulation results, further evaluate the stability of the drilling trajectory; feedback the finite element simulation results to the drilling equipment, adjust the trajectory in real time, and further improve the accuracy of the drilling operation.

[0014] Step 6: Through the remote control platform, the three-dimensional trajectory of the drilling equipment is monitored in real time. If it is detected that the trajectory deviates from the predetermined path, a correction instruction is sent to the drilling equipment through the remote control system to automatically adjust the movement direction of the drilling equipment, thereby ensuring the accuracy and stability of the drilling trajectory.

[0015] Preferably, the calculation process of the normal vector includes: performing plane fitting on the point set within a certain radius around each measurement point, and using the least squares method to solve the plane equation where a, b, and c are the components of the plane normal vector, d is the constant term, and the normal vector is obtained The normal vector is used to describe the local surface direction of this measurement point.

[0016] Preferably, the calculation process of the local curvature tensor includes: calculating the second-order partial derivatives of the neighborhood points of each measurement point to solve the local curvature tensor and obtaining the principal curvatures through eigenvalue decomposition and whose curvature information reflects the degree of curvature of this point in different directions.

[0017] Preferably, the trajectory optimization is based on the non-Euclidean geometry model and Riemannian geometry theory described in Step 4, and uses the shortest curvature path optimization algorithm to adjust the curvature of the drilling path and make the path as smooth as possible to adapt to complex underground geological layers and minimize the vibration and error of the drilling equipment.

[0018] Preferably, the trajectory optimization process is realized through the curvature constraint optimization algorithm, and the specific steps are as follows: Based on the point cloud data obtained by the laser scanner or ground penetrating radar, use the straight line fitting method to generate the preliminary drilling path; calculate the local curvature of each point on the path, adjust the path through the optimization algorithm to minimize the curvature between adjacent points; correct the path through the iterative algorithm to minimize the degree of curvature, and adjust the path in real time according to the geological conditions to obtain the optimal path.

[0019] Preferably, the measurement point data is repeatedly collected by the laser scanner or ground penetrating radar, and data filtering is performed through the point cloud processing algorithm to remove outliers and noise data. The specific steps are as follows: Use the laser scanner or ground penetrating radar to scan multiple times to ensure comprehensive data; apply the RANSAC algorithm to remove abnormal data, use DBSCAN clustering to identify and delete error points; smooth the data through weighted averaging and interpolate to fill in the missing areas to ensure uniform data distribution.

[0020] Preferably, in step five, the finite element simulation uses software such as ABAQUS or ANSYS to perform dynamic modeling on the drilling equipment. Combining with the physical properties of the underground medium, it simulates the mechanical response during the drilling process and optimizes the trajectory path according to the simulation results. The specific steps are as follows: Use ABAQUS or ANSYS for three-dimensional modeling and ensure the calculation accuracy through mesh generation; simulate the interaction between the drilling equipment and the underground medium, and calculate the resistance, vibration, and dynamic load of the equipment; adjust the drilling path according to the simulation results to avoid the equipment entering high-pressure or soft geological layers and ensure stability and accuracy.

[0021] Preferably, the drilling equipment further includes an intelligent feedback module. The intelligent feedback module is linked with the sensor network of the drilling equipment, can monitor the operating state of the drilling equipment in real time, and issue an alarm or make fine adjustments when the trajectory deviates.

[0022] Preferably, the three-dimensional trajectory positioning method combines B-spline surface or NURBS surface model to model the geological data of the underground layer and is used to construct the geometric reference of the drilling path to improve the accuracy of the trajectory.

[0023] The present invention has the following beneficial effects:

[0024] In the present invention, by combining technologies such as laser scanning, ground penetrating radar, non-Euclidean geometric model, and finite element analysis, the positioning accuracy of the drilling equipment in complex underground environments is significantly improved. The application of laser scanning and ground penetrating radar makes the obtained three-dimensional point cloud data more comprehensive and accurate, and can effectively reflect the complexity of the underground geological structure. By calculating the normal vector and local curvature tensor through the least squares method, the local surface characteristics of the measurement points can be accurately described, providing a reliable data basis for subsequent trajectory optimization. In addition, the introduction of the non-Euclidean geometric model makes the path optimization process more flexible, can adapt to the drilling requirements under different geological conditions, and reduces the vibration and error of the drilling equipment.

[0025] In the present invention, through remote control technology, the automation and intelligence level of the drilling operation are improved. This method can not only detect the trajectory deviation of the drilling equipment in real time, but also automatically generate correction instructions to ensure that the equipment always operates along the predetermined path. This dynamic adjustment mechanism significantly improves the safety and efficiency of the drilling operation, and reduces the risk of equipment damage and operation failure caused by trajectory deviation. At the same time, by evaluating the stability of the drilling path through finite element analysis, the accuracy of the drilling operation is further optimized to ensure the stable operation of the equipment under different geological conditions. Overall, the present invention provides an efficient and reliable solution for the drilling operation and has a wide range of application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1A three-dimensional trajectory positioning method for a drilling device based on remote control proposed by the present invention. Detailed implementation manners

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] As Figure 1 shown, a three-dimensional trajectory positioning method for a drilling device based on remote control proposed by the present invention.

[0029] Step 1: Obtain measurement point data: This step aims to obtain three-dimensional point cloud data of the drilling area through a laser scanner (LiDAR) or a ground penetrating radar (GPR) to provide accurate measurement point coordinate information. This data will serve as the basis for subsequent calculation of the drilling trajectory. The specific process is as follows:

[0030] 1. Equipment selection and layout: Laser scanner (LiDAR): The laser scanner measures the distance of the target object by emitting laser beams and receiving the reflected laser signals, thereby obtaining the three-dimensional coordinates of the measurement points. Ground penetrating radar (GPR): The ground penetrating radar emits high-frequency electromagnetic waves and receives the wave signals reflected in different underground media to detect underground structures and their physical properties. Compared with the laser scanner, GPR is particularly suitable for obtaining underground geological layer information, especially when there are irregular underground media or obstacles, it can provide additional measurement data. By scanning the underground structure, detailed information about the geological layers below and around the drilling path can be obtained.

[0031] 2. Measurement area selection and coverage: Before measurement, it is necessary to determine the specific range of the area where the drilling path is located and its surrounding areas. The measurement area should cover all or part of the space of the drilling path to ensure sufficient and comprehensive data. The measurement data needs to include the spatial information of the geological layers around the drill hole to provide accurate reference for subsequent trajectory calculation. Considering the complexity of the drilling path, the measurement equipment needs to be arranged from multiple angles to ensure that the point cloud data of the entire drilling area can be comprehensively covered. The equipment should be arranged at appropriate positions to maximize the coverage of the measurement area. Especially in the case of high geological complexity or obstacles, it is necessary to ensure the integrity and accuracy of the data.

[0032] 3. Data Acquisition: Laser Scanning Data Acquisition: The measurement area is scanned comprehensively by a laser scanner to obtain the point cloud data of the target area. The laser scanner emits laser beams rapidly and receives the returned reflected signals, calculating the distance between each measurement point and the scanner. Through this process, the spatial position (i.e., X, Y, Z coordinates) of each measurement point is accurately recorded. Ground Penetrating Radar Data Acquisition: In the drilling area, a ground penetrating radar device emits high-frequency electromagnetic waves and receives the reflected waves from underground substances. Based on the propagation time of the reflected signals, the ground penetrating radar can calculate the depth and shape of different underground layers and structures. This process can generate two-dimensional or three-dimensional images of the underground structure, providing information on the geological distribution and obstacles below the drilling path. By collecting data multiple times, comprehensive underground data can be obtained at different depths and directions, further improving the prediction of the drilling path.

[0033] 4. Point Cloud Data Processing and Storage: Data Processing: The acquired raw point cloud data usually contains noise and outliers. Especially in complex environments, the sensor may be affected by external factors. To ensure the accuracy of the data, preprocessing of the point cloud data is required. Remove the incorrect or inaccurate data points that appear during the measurement process. Data Storage: The processed point cloud data will be stored in a standard format (such as PLY, XYZ, LAS, etc.) and provided as input for subsequent steps. These data not only contain the three-dimensional coordinates (X, Y, Z) of the measurement points but may also include other relevant parameters for each point (such as reflection intensity, point cloud density, sampling angle, etc.), which help to further optimize the accuracy of trajectory calculation and path planning.

[0034] Step Two: Normal Vector Calculation: In this step, based on the acquired point cloud data, the neighborhood of each measurement point is processed to calculate the local normal vector of the measurement point. The normal vector can effectively describe the local surface direction of the location where the measurement point is located, thus providing an important reference for subsequent local curvature calculation, trajectory optimization, and stability analysis of the drilling path. The specific steps are as follows:

[0035] 1. First, it is necessary to determine the neighborhood range of each measurement point. For each measurement point , a suitable radius r is set to determine its neighborhood. The set of points within the neighborhood consists of all points whose distance from the measurement point does not exceed this radius r. The size and distribution of the neighborhood point set will affect the calculation accuracy of the normal vector. Therefore, the selection of the radius r needs to be adjusted according to the actual environment and the density of the point cloud data to ensure that the neighborhood point set has sufficient representativeness.

[0036] After obtaining the neighborhood point set, these points are preprocessed to remove potential noise points or outliers. For example, distance thresholds are used to remove outliers, or statistical methods are used to remove points deviating from the main trend to ensure that the remaining point set can truly reflect the geometric features of the local surface as much as possible.

[0037] 2. Calculate the local normal vector of the measurement point, which requires fitting a plane to the selected neighborhood point set. Specifically, the least squares method is used to fit a plane model, which is used to represent the local surface of the neighborhood point set. The process of least squares fitting is as follows:

[0038] Assume the measurement point has a neighborhood point set of , where each point has known three-dimensional coordinates , where the subscript j represents the index from 1 to n.

[0039] Represent this plane in the form of a plane equation: , where a, b, and c are the components of the plane normal vector, and d is the constant term.

[0040] The goal of the least squares method is to minimize the distance between the fitted plane and the neighborhood point set by adjusting the values of a, b, c, and d. Specifically, the least squares method will minimize the sum of the perpendicular distances from each point to the fitted plane:

[0041]

[0042] Through this optimization process, the best fitting parameters of the plane equation can be obtained, that is, the components of the plane normal vector. This normal vector represents the local surface direction at the location of the measurement point .

[0043] 3. The calculated normal vector is the local normal vector of this measurement point, which describes the orientation of the surface in the neighborhood of this point. During the process of point cloud data processing, situations where the normal vector directions are inconsistent or reversed may be encountered. For example, in different parts of the point cloud, the direction of the normal vector may be opposite to the expected geological structure direction, resulting in inconsistent calculation results.

[0044] To ensure the correctness of the normal vector, calibration is carried out. This includes: adjusting the direction of the normal vector by comparing it with known geological structures or other measurement data. Using the direction constraints of the surface normal vector, for example, assuming that the local surface where the measurement point is located should face the movement direction of the drilling equipment, so as to adjust the direction of the normal vector to match the actual situation.

[0045] This step reduces the curvature of the preliminary path through the optimization of the initially generated trajectory and curvature, improves the accuracy of the 3D trajectory, and avoids excessive deviation of the path.

[0046] Step 3: Local curvature tensor calculation: In this step, based on the results of the normal vector calculation in the previous step, the local curvature tensor of each measurement point is calculated through the Laplace operator and second-order partial derivatives. This local curvature tensor describes the curvature of the measurement point and its neighborhood. The local curvature tensor contains the principal curvatures and , which represent the maximum and minimum curvatures at the measurement point, respectively. These two principal curvatures reflect the degree of curvature of the local geological structure in two orthogonal directions.

[0047] 1. To extract curvature information from the point cloud data, it is first necessary to operate on the local area of the measurement point through the Laplace operator. Calculate the curvature characteristics of a function at a certain point. In the point cloud data, the Laplace operator can reveal the degree of curvature of the local surface.

[0048] The specific steps are as follows: Smooth the neighborhood of each measurement point to remove the influence of noise and ensure the accuracy of the data. By interpolating the points in the neighborhood, a mathematical model of the neighborhood points is constructed, usually using interpolation methods such as polynomials or B-splines. Calculate the second-order derivative of each point in this neighborhood to obtain the local curvature information of the point. The role of the Laplace operator in this method is to extract the geometric characteristics of the local surface as a mathematical expression and further provide the data support required for curvature calculation.

[0049] 2. The calculation of the local curvature tensor depends on the second-order partial derivatives of the neighborhood of the measurement point. Specifically, first, it is necessary to perform high-order differential calculations on the point cloud data around the measurement point to obtain relevant information on the surface curvature. The calculation process is as follows: Second-order partial derivative calculation: At each measurement point , calculate the second-order partial derivatives of this point in two orthogonal directions. That is, calculate respectively, where z represents the height value of the measurement point, and x and y are the plane coordinate axes. Through these partial derivatives, the second-order change rate of the surface at this point is obtained. Construct the local curvature tensor: Based on the above partial derivatives, construct the local curvature tensor , and this tensor can be expressed as: This local curvature tensor contains the second-order curvature characteristics of the surface where the measurement point is located. Through eigenvalue decomposition, the principal curvatures and can be obtained.

[0050] 3. By performing eigenvalue decomposition on the local curvature tensor, the principal curvatures are obtained. and , and these two values respectively represent the maximum and minimum curvatures of the local geological layer in two orthogonal directions. The process of calculating the principal curvatures is as follows: Perform eigenvalue decomposition on the local curvature tensor to obtain its eigenvalues . These two eigenvalues respectively correspond to the curvatures of the local surface in two orthogonal directions. If the principal curvature is large, it indicates a high degree of bending in the area of that point, and the drilling path may need to be adjusted in this area to avoid path deviation or excessive load on the equipment.

[0051] This step helps reduce path bending, thereby improving the accuracy of the drilling path in complex underground environments, avoiding unnecessary trajectory distortion, and ensuring a smoother and more stable three-dimensional trajectory.

[0052] Step Four: Non-Euclidean Geometry Optimization: In this step, a non-Euclidean geometry model is adopted for optimizing the drilling path. By introducing Riemannian geometry theory, in three-dimensional space, based on curvature constraints, the path is adjusted through an optimization algorithm to ensure that the drilling path travels along the shortest curvature path.

[0053] 1. During the path optimization process, dynamic geological factors and local curvature information are introduced to construct an optimization objective function. The purpose of the objective function is to optimize the smoothness of the path by minimizing the bending degree of the drilling path, while considering the path adaptability under different geological conditions. The objective function is defined as follows: , where is the dynamic geological factor, reflecting the physical properties of the geological layer where the i-th measurement point is located; and are weight coefficients, respectively adjusting the relative influence of curvature and geological factors on path optimization; is the influence coefficient of the geological factor on path optimization;

[0054] 2. During the path optimization process, the bending degree of the drilling path is restricted to avoid excessive path bending. Curvature constraint conditions are set to ensure that the local curvature of the path does not exceed the set maximum value , and the specific form is:

[0055]

[0056] 3. The local curvature information obtained from Step Three is used as the input for path optimization. The local curvature values and of each measurement point will be the main basis in path optimization. These data help determine the best bending direction of the drilling path at each point.

[0057] Path adjustment algorithm: Based on local curvature information , combined with dynamic geological factors , guiding the path optimization process. Through numerical optimization methods (such as gradient descent method, Newton's method, etc.), the drilling path is adjusted. In each iteration, the algorithm adjusts the path according to the curvature distribution of the current path, making the curvature of the path gradually decrease and avoiding excessive bending in unsuitable geological areas. The specific path adjustment formula is as follows:

[0058]

[0059] where is the coordinate of the path at the t-th step, is the learning rate, controlling the step size of each path adjustment, is the gradient of the objective function at the current path point, indicating the direction of path adjustment at the current position. Through this formula, the dynamic geological factors change in real time with the progress of the drilling process, such as the hardness, humidity, pressure, etc. of the geological layer. The optimization algorithm updates the path in each iteration and gradually converges to the optimal path. The optimization process will stop when one of the following conditions is met:

[0060] The objective function converges;

[0061] The amplitude of path adjustment is less than the set threshold;

[0062] The maximum number of iterations is reached.

[0063] In this step, the drilling path is optimized through a non-Euclidean geometric model. Combining Riemannian geometry theory, through the combination of curvature constraint and objective function optimization, the curvature of the drilling path is minimized to the greatest extent. Through the optimization algorithm based on local curvature information, the drilling path is adjusted to adapt to different geological layer structures, ensuring the shortest curvature travel of the path, and ultimately improving the drilling accuracy and operation stability.

[0064] Step Five: Finite element analysis and 3D modeling: In this step, through finite element analysis (FEA), the drilling path is further simulated and optimized to ensure the stability and accuracy of the drilling equipment under different geological conditions. By combining the dynamic characteristics of the drilling equipment and the physical properties of the underground geological medium, a 3D finite element model is established to simulate the interaction between the movement trajectory of the drilling equipment and the underground geological layers. The core objective of this process is to analyze and optimize the stability of the drilling path, thereby improving the efficiency and safety of the drilling operation.

[0065] 1. Finite Element Analysis (FEA) is a numerical calculation method widely used in solving structural and physical problems. Finite Element Analysis is used to simulate the interaction between drilling equipment and underground geological layers, and optimize the drilling path by analyzing the behavior of the equipment in the underground environment. This includes: First, through the data from the actual drilling operation site, obtain various physical parameters of the underground geological layers (such as density, elastic modulus, Poisson's ratio, etc.) and the dynamic characteristics of the drilling equipment (such as mass, moment of inertia, vibration characteristics, etc.). These parameters constitute the basic input of the finite element model. The specific construction steps are as follows: Modeling of the geological medium: The modeling of the geological layer depends on the geological data obtained from on-site drilling, including physical properties such as the thickness, hardness, shear modulus, density of the strata. Modeling of the drilling equipment: The dynamic characteristics of the drilling equipment include its structural stiffness, vibration frequency, motion mode, etc. Construction of the contact model: The interaction between the drilling equipment and the underground geological layer is described by the contact model.

[0066] Process of finite element analysis: Through the finite element model for dynamic simulation, simulate the actual movement process of the drilling equipment in the underground medium. The main task of finite element analysis is to calculate the interaction forces between the drilling equipment and the geological medium, including cutting force, friction force, pressure distribution, etc. The main steps involved in the analysis process are as follows: First, set the boundary conditions in the simulation process to ensure that the force and motion states of the drilling equipment in actual operation can be accurately simulated. The setting of boundary conditions includes: Initial state of the equipment: including the position, velocity, acceleration, etc. of the equipment. Boundary conditions of the geological medium: Set the physical constraint conditions of the underground geological layer, especially the influence of physical parameters such as the elastic modulus and shear modulus of the medium on the drilling path. Setting of contact forces: The contact force model between the drilling equipment and the geological medium includes friction force, normal force and cutting force.

[0067] In the dynamic simulation, the finite element model will consider the dynamic response of the drilling equipment when it contacts the geological medium, including the movement trajectory, vibration response, etc. of the drilling equipment. By solving the mechanical equations, the force conditions of the drilling equipment at different stages can be obtained, and further analyze the stability of the drilling path. By solving the dynamic variables such as displacement, velocity, acceleration, etc. of the equipment at each moment, analyze the motion state of the drilling equipment. Dynamic analysis can reveal possible vibration and instability phenomena during the drilling process, such as path deviation or equipment damage. Mechanical analysis conducts force analysis on each point on the drilling path, calculates the force distribution of the drilling equipment under different geological conditions, especially the cutting force and friction force when contacting the geological layer. The drilling path can be optimized to ensure that the equipment maintains good stability in the underground environment.

[0068] Based on the results of finite element analysis, evaluate the stability and accuracy of the drilling path. The specific evaluation indicators include: Path stability: Analyze the deviation and error of the drilling path, evaluate the movement stability of the equipment under different geological conditions, and ensure that the path does not exhibit sharp bends or instability. Vibration response: By analyzing the vibration mode of the drilling equipment, evaluate whether there is resonance or excessive vibration, which may affect the drilling accuracy. Force distribution: Analyze the force distribution during the contact process with different geological layers, especially the change in cutting force during drilling, in order to determine whether the drilling equipment can cut smoothly and maintain accuracy.

[0069] 3. The results obtained through finite element analysis will provide a guiding basis for the optimization of subsequent steps. Modify the drilling path according to the analysis results to ensure that the equipment can remain stable under different geological conditions and optimize the drilling trajectory. Specific feedback and correction measures include: Path correction: Based on the stability evaluation of finite element analysis, correct the drilling path to avoid excessive bending or instability of the equipment in the underground environment. Dynamic characteristic adjustment: If excessive vibration or resonance is found in the drilling equipment during finite element analysis, the stability of the equipment can be improved by adjusting its dynamic characteristics, such as increasing damping and changing the motion mode. Contact force adjustment: During the analysis process, if uneven contact force distribution between the drilling equipment and the geological medium is found, which may lead to excessive wear of the equipment or path deviation, further optimize the contact force model and adjust the motion mode of the drilling equipment.

[0070] This step ensures that during the drilling process, the path always remains on the predetermined trajectory through a real-time path correction mechanism, improving the positioning accuracy, avoiding path deviation or unstable equipment operation, and further enhancing the real-time response ability and accuracy of the trajectory.

[0071] Step Six: Remote Control and Trajectory Correction: In this step, use a remote control platform to monitor the movement trajectory of the drilling equipment in real time to ensure that the equipment can accurately drill according to the predetermined path in the underground environment. Detect the real-time position and trajectory of the drilling equipment through the remote control system. If the equipment is found to deviate from the predetermined trajectory, the system can automatically issue a correction instruction to adjust the movement direction and position of the equipment to ensure the accuracy and stability of the drilling operation. This process not only improves the accuracy of the drilling operation but also significantly enhances the automation and intelligent level of the operation.

[0072] 1. Positioning sensors: Use high-precision Global Positioning System (GPS) or Inertial Measurement Unit (IMU) sensors to track the position of the drilling equipment in real time. Tilt sensors and attitude detection: Include the tilt angle, rotation angle, etc. of the drill bit. Real-time data transmission: Transmit the data collected by the sensors to the remote control platform in real time through wireless communication technologies (such as 4G / 5G, Wi-Fi or dedicated communication links).

[0073] 2. Trajectory deviation detection is mainly carried out in the following ways: Comparison of the predetermined trajectory and the actual trajectory: The predetermined trajectory is the ideal path set according to geological data and operation plans before the drilling operation. The remote monitoring system receives the actual trajectory data of the equipment in real time and compares it with the predetermined trajectory. If the deviation of the equipment exceeds the preset threshold (such as deviation angle, position deviation), it is considered that the trajectory has deviated. Trajectory error calculation: The system calculates the magnitude of the trajectory error according to the motion state and actual path of the equipment. The error value may be a distance error or an angle error, and the system will judge whether trajectory correction is required according to the magnitude of the error. Multi-sensor fusion: By fusing data from different sensors (such as GPS, IMU, tilt sensors, etc.), the accuracy of trajectory detection is improved. The fusion algorithm of multi-sensors can effectively reduce the error of a single sensor and provide more stable and accurate trajectory data.

[0074] When the trajectory of the drilling equipment is detected to deviate from the predetermined path, the remote control platform will automatically generate correction instructions. These correction instructions will be sent to the drilling equipment to guide the equipment to adjust the movement direction.

[0075] Trajectory correction algorithm: According to the type of trajectory deviation (such as position deviation, angle deviation, etc.), the system calculates the correction amount and automatically adjusts the movement path of the drilling equipment. The correction process includes adjusting the direction, speed and feed rate of the drill bit to ensure that the equipment returns to the predetermined trajectory. Motion control system: The correction instructions are sent to the motion control system of the drilling equipment through wireless communication, and the control system adjusts the motion parameters of the equipment according to the instructions. These parameters include the forward direction, steering angle, drilling speed, etc. of the equipment to ensure that the drilling equipment can accurately correct the trajectory. Dynamic correction feedback mechanism: During the execution of the correction instructions, the system continuously monitors the position change of the equipment to ensure real-time feedback and adjustment of the correction process. If the trajectory deviation is not completely eliminated after the first correction, the system will continue to perform dynamic adjustment until the equipment completely returns to the predetermined trajectory.

[0076] 3. After the trajectory correction operation is completed, it is necessary to evaluate the correction effect to ensure that the drilling equipment has returned to the predetermined trajectory and the operation accuracy has been restored. The evaluation methods include: Precision comparison: Compare the corrected trajectory with the predetermined trajectory to evaluate the degree of deviation of the equipment from the trajectory and the accuracy restoration after correction. Correction response time: Evaluate the speed of the system's response to the correction instruction to ensure that the correction process is fast and timely without affecting the overall drilling operation progress. Operation efficiency: Evaluate the impact of the correction operation on the drilling operation efficiency to ensure the continuity and stability of the drilling operation during the correction process.

[0077] Step six realizes precise control and real-time adjustment of the drilling equipment through the remote control and trajectory correction mechanism. When the equipment deviates from the predetermined trajectory, the remote monitoring platform automatically issues a correction instruction to adjust the movement direction and position of the equipment to ensure the accuracy and stability of the drilling operation.

[0078] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional trajectory positioning method for drilling equipment based on remote control, characterized in that: The following steps are involved: Step 1: Acquire target point cloud data in the drilling area by using a laser scanner or geological radar, wherein the point cloud data includes the three-dimensional coordinate information of each measuring point, and the point cloud data covers all or part of the spatial area of ​​the drilling path; Step 2: Preprocess the acquired point cloud data and use the least squares method to calculate the normal vector in the neighborhood of each measurement point. , where the normal vector Represents the local surface direction of the measurement point. The normal vector calculation is performed by fitting the point cloud within a certain radius around each measurement point to determine a neighborhood point set. The neighborhood point set is composed of the distance measurement point The plane equation of the neighborhood point set is solved based on the least squares method to obtain the local normal vector of the point. Step 3: Based on the result of normal vector calculation, the local curvature tensor of each measurement point is calculated by Laplace operator and second-order partial derivatives. , where the local curvature tensor represents the curvature of the measurement point and its neighborhood. The calculation process includes solving high-order partial derivatives in the neighborhood of the measurement point and calculating the principal curvature and ; Step 4: Use non-Euclidean geometry model to optimize the trajectory. Based on Riemann geometry theory, the optimization process of processing the path curvature includes: According to the local curvature tensor Construct the objective function of path optimization: ,in, is a dynamic geological factor, reflecting the physical characteristics of the geological layer where the i-th measurement point is located; and are weight coefficients, which adjust the relative influence of curvature and geological factors on path optimization respectively; is the influence coefficient of geological factors on path optimization; using the objective function, the drilling path is adjusted through the optimization algorithm and optimized along the trajectory with the minimum curvature; through iterative optimization, the accuracy and stability of the trajectory are guaranteed; Step 5: After obtaining the optimized trajectory data, a three-dimensional finite element model is established. The movement process of the drilling equipment in different geological layers is simulated by the finite element method, the mechanical response that the drilling equipment may encounter during geological changes is simulated, and the interaction between the equipment and the underground medium is analyzed. The stability of the drilling trajectory is further evaluated through the simulation results. The finite element simulation results are fed back to the drilling equipment to adjust the trajectory in real time to further improve the accuracy of the drilling operation. Step 6: The three-dimensional trajectory of the drilling equipment is monitored in real time through the remote control platform. If it is detected that the trajectory deviates from the predetermined path, a correction instruction is sent to the drilling equipment through the remote control system to automatically adjust the movement direction of the drilling equipment, thereby ensuring the accuracy and stability of the drilling trajectory.

2. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The calculation process of the normal vector includes: performing plane fitting on a set of points with a certain radius around each measuring point, and using the least squares method to solve the plane equation during the fitting process. , where a, b, c are the components of the plane normal vector, d is a constant term, and the normal vector is obtained , the normal vector Describes the local surface orientation at this measurement point.

3. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The local curvature tensor The calculation process includes: at each measurement point, the second-order partial derivatives of the neighboring points of the point are calculated to solve the local curvature tensor , and the principal curvature is obtained by eigenvalue decomposition and , and its curvature information reflects the curvature of the point in different directions.

4. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The trajectory optimization is based on the non-Euclidean geometry model and Riemann geometry theory described in step 4, and adopts the shortest curvature path optimization algorithm to adjust the curvature of the drilling path and make the path as smooth as possible to adapt to the complex underground geological layers and minimize the vibration and error of the drilling equipment.

5. The method for three-dimensional trajectory positioning of drilling equipment based on remote control according to claim 1, characterized in that: The trajectory optimization process is implemented through a curvature constrained optimization algorithm, and the specific steps are as follows: based on the point cloud data obtained by a laser scanner or a geological radar, a preliminary drilling path is generated using a straight line fitting method; the local curvature of each point on the path is calculated, and the path is adjusted through an optimization algorithm to minimize the curvature between adjacent points; the path is corrected through an iterative algorithm to minimize the curvature, and the path is adjusted in real time according to geological conditions to obtain the optimal path.

6. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The measurement point data is repeatedly collected by laser scanner or geological radar, and the data is filtered by point cloud processing algorithm to remove outliers and noise data. The specific steps are as follows: use laser scanner or geological radar to scan multiple times to ensure comprehensive data; apply RANSAC algorithm to remove abnormal data, use DBSCAN clustering to identify and delete error points; smooth data by weighted average, and fill in missing areas by interpolation to ensure uniform data distribution.

7. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The finite element simulation in step five uses ABAQUS or ANSYS software to perform dynamic modeling of the drilling equipment, combines the physical properties of the underground medium, simulates the mechanical response during the drilling process, and optimizes the trajectory path based on the simulation results. The specific steps are as follows: use ABAQUS or ANSYS for three-dimensional modeling, and ensure calculation accuracy through meshing; simulate the interaction between the drilling equipment and the underground medium, and calculate the resistance, vibration and dynamic load of the equipment; adjust the drilling path according to the simulation results to prevent the equipment from entering high-pressure or soft geological layers, and ensure stability and accuracy.

8. The method for three-dimensional trajectory positioning of drilling equipment based on remote control according to claim 1, characterized in that: The drilling equipment further includes an intelligent feedback module, which is linked to the sensor network of the drilling equipment and can monitor the operating status of the drilling equipment in real time, and issue an alarm or make fine adjustments when the trajectory deviates.

9. A three-dimensional trajectory positioning method for drilling equipment based on remote control according to claim 1, characterized in that: The three-dimensional trajectory positioning method combines B-spline surface or NURBS surface model to model underground geological data, and is used to construct a geometric reference of the drilling path to improve the accuracy of the trajectory.

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