An active damping method and system based on adaptive adjustment of drill pipe load

Abstract

The present application belongs to the field of drilling technology, and provides an active damping method and system based on adaptive adjustment of drill pipe load to effectively reduce drill pipe vibration, by converting real-time feeding oil pressure signals into real-time feeding thrust and converting real-time rotating oil pressure signals into real-time rotating torque; the original vibration signals are filtered by singular value subspace decomposition to obtain real vibration signals; and according to preset feeding thrust initial value and preset rotating torque initial value, adaptive comparison calculation is carried out by using a neural network adaptive PID control algorithm to obtain a vibration adjustment amount, and active damping is carried out according to the vibration adjustment amount, the system includes six closed-loop control modules of preset parameter input, signal acquisition, signal filtering, control algorithm, control output and execution, precise damping control is realized, the drill bit bounce phenomenon is eliminated, the drilling deviation error is reduced, the stick-slip vibration is inhibited, and the drill pipe jam failure rate is greatly reduced.

Description

Technical Field

[0001] This invention belongs to the field of drilling technology, specifically relating to an active vibration reduction method and system based on adaptive adjustment of drill pipe load. Background Technology

[0002] The drilling rig is the main drilling device of the anchor bolt drilling rig, used to drill anchor holes in coal mine roadways. Powered by the drive system, the drill rod inside the rig rotates and breaks through the surrounding rock in the coal roadway, drilling an anchor hole approximately 1.2 meters long. In coal mine roadways, the hardness of the surrounding rock varies considerably, especially when encountering semi-coal-rock roadways or faults, where the hardness increases significantly. Under these conditions, the drill rod, during high-speed rotation and rock breaking, is subjected to axial thrust and periodic alternating cutting loads, generating complex vibrations including lateral, longitudinal, and torsional vibrations. These vibrations not only cause the drill bit to bounce but also lead to drill rod stick-slip, severely affecting drilling quality and efficiency.

[0003] Therefore, how to effectively reduce drill pipe vibration and improve borehole quality and drilling efficiency is an urgent problem to be solved in the field of drilling technology. Summary of the Invention

[0004] In order to solve at least one of the above-mentioned technical problems in the prior art, the present invention provides an active vibration reduction method and system based on adaptive adjustment of drill pipe load.

[0005] According to the first aspect, an active vibration reduction method based on adaptive adjustment of drill pipe load includes the following steps: The system acquires the real-time feed hydraulic pressure signal, real-time rotary hydraulic pressure signal, and original vibration signal of the target drill pipe, and converts the real-time feed hydraulic pressure signal into real-time feed thrust and the real-time rotary hydraulic pressure signal into real-time rotary torque. The original vibration signal was filtered using the singular value subspace decomposition method to extract the real vibration signal of the target drill rod after noise reduction. Based on the preset initial values ​​of feed thrust and rotational torque, as well as the real-time feed thrust, real-time rotational torque, and the actual vibration signal, an adaptive comparison calculation is performed using a neural network adaptive PID control algorithm to obtain the vibration adjustment amount, and active vibration reduction is performed based on the vibration adjustment amount.

[0006] Preferably, the singular value subspace decomposition method filters the original vibration signal, including: Construct the Hankei matrix based on the preset data sampling points; Singular value decomposition is performed on the Hankei matrix to obtain a first square matrix, a second square matrix, and a first matrix. The first matrix is ​​initialized to zero based on a preset threshold to obtain the second matrix; The reconstructed matrix is ​​obtained by reconstructing the matrix based on the first square matrix, the second square matrix, and the second matrix, and the corresponding elements in the reconstructed matrix are weighted and averaged to obtain the true vibration signal of the target drill pipe after denoising.

[0007] Preferably, an adaptive comparison calculation is performed using a neural network adaptive PID control algorithm to obtain the vibration adjustment amount, including: Using the basic equations of vibration dynamics and based on the actual vibration signal, the generalized displacement of the target drill pipe vibration is obtained, and the force load matrix of the target drill pipe is obtained based on the real-time feed thrust and real-time rotational torque. The vibration adjustment amount of the target drill pipe force load matrix is ​​dynamically adjusted based on the change of the generalized displacement of the target drill pipe vibration.

[0008] Preferably, the fundamental equation of vibration dynamics is expressed as follows: In the formula, The target drill pipe mass matrix; The generalized acceleration matrix for the target drill pipe vibration; The target drill pipe damping matrix; The generalized velocity matrix for the target drill pipe vibration; Target drill pipe stiffness matrix; The generalized displacement matrix for the target drill pipe vibration; The target drill pipe force load matrix.

[0009] Preferably, the target drill pipe force load matrix is: In the formula, The axial feed thrust of the target drill pipe; The target drill pipe axial feed resistance; The target is the weight of the drill pipe; The target drill pipe circumferential rotational resistance torque; The target drill pipe circumferential rotation torque.

[0010] Preferably, the neural network adaptive PID control algorithm includes: Obtain the initial error value of the target drill pipe force load; The error value is transformed into a first parameter in product form, a second parameter that varies with time, and a third parameter in sum form. The first parameter, the second parameter, and the third parameter are then used as the proportional part, the derivative part, and the integral part of the neural network adaptive PID control algorithm, respectively, and equivalent transformations, weighted summations, and factorizations are performed to obtain the general error. Based on the general error and the activation function in the neural network, the fourth parameter is obtained, and the output value of the target drill pipe force load is continuously accumulated and weighted to obtain the fifth parameter. The adaptive parameters of the PID control algorithm are updated by summing the fourth and fifth parameters.

[0011] According to a second aspect, an active vibration reduction system based on adaptive adjustment of drill pipe load is capable of performing an active vibration reduction method based on adaptive adjustment of drill pipe load as described in the first aspect and any preferred embodiment, comprising: Preset parameter input module: used to input the initial value of the preset feed thrust and the initial value of the preset rotational torque of the target drill pipe; Signal acquisition module: used to acquire signals from the feed hydraulic pressure sensor, rotary hydraulic pressure sensor and vibration sensor through multiple channels to obtain real-time feed hydraulic pressure signal, real-time rotary hydraulic pressure signal and raw vibration signal; Signal filtering module: used to denoise the original vibration signal using the singular value subspace decomposition method to obtain the real vibration signal; Control algorithm module: used to integrate neural network adaptive PID control algorithm, obtain vibration adjustment amount based on real-time feed thrust, real-time rotational torque and the real vibration signal, and generate corresponding control commands; Control output module: converts control commands into standardized commands that the execution module can recognize; Execution module: Adjusts the target drill pipe feed thrust and rotation torque according to instructions to achieve active vibration reduction.

[0012] Preferably, the control output module receives data via the CAN bus protocol and converts it into a PWM signal to drive the execution module.

[0013] Preferably, the execution module is a hydraulic drive unit that directly controls the pressure of the drill pipe's feed oil circuit and rotation oil circuit.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention uses the singular value subspace decomposition method to accurately extract the real vibration signal of the target drill rod, which greatly improves the signal-to-noise ratio compared with related technologies and solves the control inaccuracy problem caused by high-frequency noise interference. At the same time, it combines the vibration dynamics equation to construct a physical model, which provides theoretical support for vibration reduction control and realizes precise vibration reduction control. Furthermore, the neural network adaptive PID algorithm integrates nonlinear mapping capability (neural network) and dynamic adjustment capability (PID), which can achieve rapid response in different construction environments, improve the universality of the invention, and avoid overshoot or oscillation caused by manual parameter adjustment. The constructed active vibration reduction system based on drill rod load adaptive adjustment is a closed-loop control architecture, which can adjust the force load matrix in real time, eliminate drill bit bounce phenomenon, reduce borehole deviation error, and suppress stick-slip vibration, which greatly reduces the drill rod jamming failure rate. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic flowchart of the active vibration reduction method based on adaptive adjustment of drill pipe load provided in an embodiment of the present invention; Figure 2 This is a diagram of the overall architecture of the active vibration reduction system based on adaptive adjustment of drill pipe load provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the singular value subspace decomposition filtering process provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the principle of the neural network adaptive PID control algorithm provided in this embodiment of the invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be noted that the structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the conditions under which the present invention can be implemented. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportional relationships, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should fall within the scope of the technical content disclosed in the present invention. It should be noted that in this specification, relational terms such as "first" and "second" are only used to distinguish one entity from several other entities, and do not necessarily require or imply any actual relationship or order between these entities.

[0019] like Figure 2 The diagram shows the overall architecture of an active vibration reduction system that adaptively adjusts based on drill pipe load, including: Preset parameter input module: used to input the initial value of the preset feed thrust and the initial value of the preset rotational torque of the target drill pipe; Signal acquisition module: used to acquire signals from the feed hydraulic pressure sensor, rotary hydraulic pressure sensor and vibration sensor through multiple channels to obtain real-time feed hydraulic pressure signal, real-time rotary hydraulic pressure signal and raw vibration signal; Signal filtering module: used to denoise the original vibration signal using the singular value subspace decomposition method to obtain the real vibration signal; Control algorithm module: used to integrate neural network adaptive PID control algorithm, obtain vibration adjustment amount based on real-time feed thrust, real-time rotational torque and the real vibration signal, and generate corresponding control commands; Control output module: converts control commands into standardized commands that the execution module can recognize; Execution module: Adjusts the target drill pipe feed thrust and rotation torque according to instructions to achieve active vibration reduction.

[0020] In this embodiment, based on the load characteristics of the drill pipe in the anchor drilling rig, a feed hydraulic pressure sensor is installed in the feed hydraulic pressure circuit of the target drill pipe to collect real-time feed hydraulic pressure signals, and a rotation hydraulic pressure sensor is installed in the rotation hydraulic pressure circuit of the target drill pipe to collect real-time rotation hydraulic pressure signals. A vibration sensor (an acceleration vibration sensor) is installed at the root of the target drill pipe. The signal acquisition module acquires the original vibration signals of the target drill pipe in different directions under load. The original vibration signals are then filtered by a signal filtering module using singular value subspace decomposition to remove noise interference. Simultaneously, the signal acquisition module acquires the real-time feed thrust signal (characterized by real-time feed hydraulic pressure) and real-time rotation hydraulic pressure signal of the target drill pipe under load. The torque signal (characterized by real-time rotary hydraulic pressure) is used to input the initial values ​​of the target drill pipe's preset feed thrust and preset rotational torque through the preset parameter input module. These initial values ​​are then compared adaptively with the real-time feed thrust, real-time rotational torque acquired by the acquisition module, and the actual vibration signal processed by the signal filtering module. The control algorithm module employs a neural network adaptive PID control algorithm. The calculation results from the control algorithm module are output through the control output module and transmitted to the execution module, ultimately adjusting the feed thrust and rotational torque of the target drill pipe to actively reduce its vibration.

[0021] In this embodiment, the preset parameter input module inputs a preset initial value of feed thrust and a preset initial value of rotational torque based on the rock hardness and rock strata conditions in the construction environment.

[0022] In this embodiment, the signal filtering module adopts the singular value subspace decomposition filtering method. By utilizing the distribution characteristics of the signal in the singular value space, the useful vibration signal components are separated from the complex noise background, effectively removing high-frequency noise interference and restoring the true vibration signal that can truly reflect the essential characteristics of drill pipe vibration.

[0023] In this embodiment, the control algorithm module integrates a neural network adaptive PID control algorithm. The neural network has a strong nonlinear mapping capability and automatically matches the relationship between drill pipe vibration and load under different working conditions. At the same time, the adaptive PID algorithm dynamically adjusts the control parameters based on the neural network calculation results and real-time error feedback to achieve precise and rapid adjustment of the target drill pipe feed and rotation signals.

[0024] In this embodiment, the control output module acts as a "bridge" between the control algorithm module and the execution module. It is responsible for standardizing and normalizing the control commands generated by complex calculations, converting them into a command format that the execution module can recognize and execute, ensuring the accurate and stable transmission of control commands, so as to drive the execution module to adjust the feed and rotation of the drill pipe in real time.

[0025] In this embodiment, the execution module directly acts on the physical drive unit of the target drill pipe, and adjusts the feed and rotation of the target drill pipe according to the control command received from the control output module.

[0026] In this embodiment, the data calculated by the control algorithm module is transmitted to the control output module via the CAN bus protocol. Based on the control output module, the data calculated by the control algorithm module is converted into a PWM signal using a multi-protocol conversion program, which is a signal that the execution module can recognize. The converted data is then transmitted to the execution module.

[0027] like Figure 1 The diagram shows a flowchart of an active vibration reduction method based on adaptive adjustment of drill pipe load, which includes the following steps: S1: Acquire the real-time feed hydraulic pressure signal, real-time rotary hydraulic pressure signal and original vibration signal of the target drill pipe, and convert the real-time feed hydraulic pressure signal into real-time feed thrust and the real-time rotary hydraulic pressure signal into real-time rotary torque; S2: The original vibration signal is filtered using the singular value subspace decomposition method to extract the real vibration signal of the target drill rod after noise reduction; S3: Based on the preset initial value of feed thrust and preset initial value of rotational torque, as well as the real-time feed thrust, real-time rotational torque and the real vibration signal, an adaptive comparison calculation is performed using a neural network adaptive PID control algorithm to obtain the vibration adjustment amount, and active vibration reduction is performed based on the vibration adjustment amount.

[0028] In this embodiment, the signal acquisition module acquires the real-time feed oil pressure signal, real-time rotary oil pressure signal, and original vibration signal from the feed oil pressure sensor, rotary oil pressure sensor, and vibration sensor, respectively. The real vibration signal is filtered by the signal filtering module, while the real-time feed oil pressure signal and real-time rotary oil pressure signal are not filtered by the signal filtering module because they are less affected by interference from other signals.

[0029] Optionally, the singular value subspace decomposition method is used to filter the original vibration signal, including: constructing a Hankei matrix based on preset data sampling points; performing singular value decomposition on the Hankei matrix to obtain a first square matrix, a second square matrix, and a first matrix; setting the elements of the first matrix to zero according to a preset threshold to obtain a second matrix; reconstructing the matrix based on the first square matrix, the second square matrix, and the second matrix, and then weighting the corresponding elements in the reconstructed matrix to obtain the denoised true vibration signal of the target drill rod.

[0030] In this embodiment, as Figure 3 The diagram shows the singular value subspace decomposition filtering process. Starting with the construction of the Hankel matrix from the original vibration signal, and ending with the reconstruction of the filtered signal, including details such as determining the number of sampling points, singular value decomposition, and selecting the truncation threshold, the implementation principle of this filtering technique can be clearly understood.

[0031] In this embodiment, the process of transforming a noisy vibration signal into a denoised vibration signal involves five steps. The first step is to construct the Hankei matrix A. Based on the actual acquisition model of the original vibration signal obtained by the signal acquisition module, the number of sampling points is set to 128, and the Hankei matrix is ​​constructed as follows: in The first step is to obtain the actual sampled vibration signal; the second step is singular value decomposition: based on the periodic decomposition of the actual sampled signal, the order of matrix A is set to 64. The corresponding Hankei matrix A is then 65×64. Singular value decomposition is performed on matrix A. The decomposed matrix can be represented by the following formula: The formula is as follows: U is a 65×65 first square matrix; S is a 65×64 first matrix; V is a 64×64 second square matrix; the third step is to set the smaller elements in the first matrix S to zero: the elements on the diagonal of the first matrix S are the singular values ​​of matrix A, and the remaining elements are 0; the fourth step is to use the formula... To reconstruct the matrix and obtain matrix B: First, based on a preset threshold T, set all elements in the first matrix S that are less than T to zero, thus obtaining the second matrix. The first square matrix U and the second matrix The second square matrix V is multiplied to obtain matrix B; the fifth step is to sum the weighted average of the corresponding elements in matrix B: the weighted average of the corresponding elements in matrix B is used to obtain the true vibration signal of the target drill rod after denoising.

[0032] Optionally, an adaptive comparison calculation is performed using a neural network adaptive PID control algorithm to obtain the vibration adjustment amount, including: using the basic equations of vibration dynamics and based on the real vibration signal to obtain the generalized displacement of the target drill pipe vibration, and simultaneously obtaining the target drill pipe force load matrix based on the real-time feed thrust and real-time rotation torque; and dynamically adjusting the vibration adjustment amount of the target drill pipe force load matrix according to the change of the generalized displacement of the target drill pipe vibration.

[0033] In this embodiment, the active vibration reduction principle of the target drill pipe is based on the fundamental equations of vibration dynamics: In the formula, The target drill pipe mass matrix; The generalized acceleration matrix for the target drill pipe vibration; The target drill pipe damping matrix; The generalized velocity matrix for the target drill pipe vibration; The target drill pipe stiffness matrix; The generalized displacement matrix for the target drill pipe vibration; The target drill pipe force load matrix.

[0034] In this embodiment, under the known physical conditions of the drill pipe, the target drill pipe mass matrix is... Target drill pipe damping matrix and target drill pipe stiffness matrix Given the conditions, construct the generalized displacement matrix of the drill pipe vibration using the vibration signals of the target drill pipe obtained by the vibration sensor. The real-time feed hydraulic pressure signal and real-time rotary hydraulic pressure signal of the target drill pipe are obtained by the feed hydraulic pressure sensor and the rotary hydraulic pressure sensor, and the force load matrix of the target drill pipe is constructed. Based on the generalized displacement matrix of the target drill pipe vibration Adjusting the target drill pipe force load matrix based on the changing characteristics The output of the target drill pipe is reduced, thereby reducing the vibration of the target drill pipe and achieving the purpose of active vibration reduction of the target drill pipe.

[0035] In this embodiment, the preset initial values ​​of the feed thrust and the preset initial values ​​of the rotational torque of the target drill pipe are input through the preset parameter input module. These values ​​are then compared with the real-time feed thrust, real-time rotational torque obtained by the acquisition module, and the real vibration signal processed by the signal filtering module through the control algorithm module. The adaptive calculation employs a neural network adaptive PID control algorithm.

[0036] Optionally, the neural network adaptive PID control algorithm includes: obtaining the initial error value of the target drill pipe force load; converting the error value into a first parameter in product form, a second parameter that varies with time, and a third parameter in sum form, and performing equivalent transformations, factorization, and weighted summation on the proportional, integral, and derivative parts of the neural network adaptive PID control algorithm to obtain a general error; obtaining a fourth parameter based on the general error and the activation function in the neural network, and simultaneously accumulating and weighting the output value of the target drill pipe force load to obtain a fifth parameter; and updating the adaptive parameters of the PID control algorithm based on the summation of the fourth and fifth parameters.

[0037] Originally a private school, such as Figure 4 The diagram illustrates the architecture of the neural network, the input and output node settings, and the data interaction process with the adaptive PID controller. This enables effective control of the target drill pipe vibration. The initial error value e, reflecting the drill pipe force load, is mapped to x. This is achieved through the product of the first parameter AB (A and B are the poles of x after Laplace transform and factorization), the third parameter A+B, and the time-varying second parameter. The general error E is obtained by performing equivalent transformations, weighted summations, and factorizations on the proportional, derivative, and integral parts of the neural network adaptive PID control algorithm, respectively. Multiplying the general error E by the result of the activation function in the neural network yields the fourth parameter. The fifth parameter is obtained by continuously accumulating and weighting the output values ​​of drill pipe force load. , the fourth parameter and the fifth parameter Summation and update adaptive parameters In practical applications, the output is not adjusted by setting proportional, integral, or derivative coefficients during the learning and training process; instead, a single adaptive parameter is used. adjust.

[0038] This application provides an active vibration reduction method and system for anchor bolt drilling rigs based on adaptive adjustment of drill rod load. It solves the problem in existing technologies where, during anchor bolt drilling, the drill rod is subjected to axial thrust and periodic alternating cutting loads during high-speed rock breaking, resulting in complex vibrations including lateral, longitudinal, and torsional vibrations. By collecting the original vibration signal, real-time feed thrust signal, and real-time rotational torque signal of the drill rod, and using a neural network adaptive PID control algorithm for calculation and processing, the feed and rotation parameters of the drill rod are adjusted to achieve active vibration reduction, reducing drill bit bounce and stick-slip phenomena.

[0039] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. An active vibration reduction method based on adaptive adjustment of drill pipe load, characterized in that, The steps include the following: The system acquires the real-time feed hydraulic pressure signal, real-time rotary hydraulic pressure signal, and original vibration signal of the target drill pipe, and converts the real-time feed hydraulic pressure signal into real-time feed thrust and the real-time rotary hydraulic pressure signal into real-time rotary torque. The original vibration signal was filtered using the singular value subspace decomposition method to extract the real vibration signal of the target drill rod after noise reduction. Based on the preset initial value of feed thrust and preset initial value of rotational torque, as well as the real-time feed thrust, real-time rotational torque and the real vibration signal, an adaptive comparison calculation is performed using a neural network adaptive PID control algorithm to obtain the vibration adjustment amount, and active vibration reduction is performed based on the vibration adjustment amount. The adaptive comparison calculation using a neural network adaptive PID control algorithm to obtain the vibration adjustment amount includes: Using the basic equations of vibration dynamics and based on the actual vibration signal, the generalized displacement of the target drill pipe vibration is obtained, and the force load matrix of the target drill pipe is obtained based on the real-time feed thrust and real-time rotational torque. The vibration adjustment amount of the target drill pipe force load matrix is ​​dynamically adjusted based on the change of the generalized displacement of the target drill pipe vibration. The fundamental equation of vibration dynamics is expressed as follows: In the formula, The target drill pipe mass matrix; The generalized acceleration matrix for the target drill pipe vibration; The target drill pipe damping matrix; The generalized velocity matrix for the target drill pipe vibration; The target drill pipe stiffness matrix; The generalized displacement matrix for the target drill pipe vibration; The target drill pipe force load matrix; The target drill pipe force load matrix is: In the formula, The axial feed thrust of the target drill pipe; The target drill pipe axial feed resistance; The target is the weight of the drill pipe; The target drill pipe circumferential rotational resistance torque; The target drill pipe circumferential rotation torque.

2. The active vibration reduction method based on adaptive adjustment of drill pipe load according to claim 1, characterized in that, The singular value subspace decomposition method filters the original vibration signal, including: Construct the Hankei matrix based on the preset data sampling points; Singular value decomposition is performed on the Hankei matrix to obtain a first square matrix, a second square matrix, and a first matrix. The first matrix is ​​initialized to zero based on a preset threshold to obtain the second matrix; The reconstructed matrix is ​​obtained by reconstructing the matrix based on the first square matrix, the second square matrix, and the second matrix, and the corresponding elements in the reconstructed matrix are weighted and averaged to obtain the true vibration signal of the target drill pipe after denoising.

3. The active vibration reduction method based on adaptive adjustment of drill pipe load according to claim 1, characterized in that, The neural network adaptive PID control algorithm includes: Obtain the initial error value of the target drill pipe force load; The error value is transformed into a product of a first parameter, a time-varying second parameter, and a sum of a third parameter. The first parameter, the second parameter, and the third parameter are then used as the proportional part, the derivative part, and the integral part of the neural network adaptive PID control algorithm, respectively, and then subjected to equivalent transformations, weighted summations, and factorizations to obtain the general error. Based on the general error and the activation function in the neural network, the fourth parameter is obtained, and the output value of the target drill pipe force load is continuously accumulated and weighted to obtain the fifth parameter. The adaptive parameters of the PID control algorithm are updated by summing the fourth and fifth parameters.

4. An active vibration reduction system based on adaptive adjustment of drill pipe load, characterized in that, An active vibration reduction method based on adaptive adjustment of drill pipe load, capable of performing any one of claims 1-3, comprises: Preset parameter input module: used to input the initial value of the preset feed thrust and the initial value of the preset rotation torque of the target drill pipe; Signal acquisition module: used to acquire signals from the feed hydraulic pressure sensor, rotary hydraulic pressure sensor and vibration sensor through multiple channels to obtain real-time feed hydraulic pressure signal, real-time rotary hydraulic pressure signal and raw vibration signal; Signal filtering module: used to denoise the original vibration signal using the singular value subspace decomposition method to obtain the real vibration signal; Control algorithm module: used to integrate neural network adaptive PID control algorithm, obtain vibration adjustment amount based on real-time feed thrust, real-time rotational torque and the real vibration signal, and generate corresponding control commands; Control output module: converts control commands into standardized commands that the execution module can recognize; Execution module: Adjusts the target drill pipe feed thrust and rotation torque according to instructions to achieve active vibration reduction.

5. The active vibration reduction system based on adaptive adjustment of drill pipe load according to claim 4, characterized in that, The control output module receives data via the CAN bus protocol and converts it into a PWM signal to drive the execution module.

6. The active vibration reduction system based on adaptive adjustment of drill pipe load according to claim 4, characterized in that, The execution module is a hydraulic drive unit that directly controls the pressure of the drill pipe's feed oil circuit and rotation oil circuit.

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