A method for dynamic positioning of drill bit of horizontal drilling rig

Abstract

The invention discloses a method for dynamic positioning of a drill bit of a horizontal drilling rig based on a mobile magnetic beacon, and proposes a dynamic positioning operation mode of a horizontal drilling rig based on a mobile magnetic beacon, which is different from the current mode of using a handheld guiding device. The mode is divided into two parts, namely, a ground magnetic beacon and a drilling magnetic sensor. The coordinates of the drill bit are positioned by the real-time position of the ground mobile magnetic beacon and the measurement data of the drilling magnetic sensor; a model is built for the electromagnetic characteristics of the mobile magnetic beacon; a model is built for the measurement data processing of the drilling magnetic sensor; and then an adaptive extended Kalman filter is used to perform slow-speed dynamic positioning of the underground drill bit according to the electromagnetic characteristics of the ground magnetic beacon. The invention can further optimize the guiding operation scene of the horizontal drilling rig, so that the drill bit can be remotely positioned even in areas with complex terrain.

Description

Technical Field

[0001] The invention relates to a horizontal drilling rig drill bit dynamic positioning method based on a mobile magnetic beacon, belonging to the technical field of horizontal drilling rig guidance systems. Background Art

[0002] Horizontal directional drilling is a construction method that uses underground navigation and positioning equipment to adjust and control the drilling trajectory of underground drilling rigs. This technology was first proposed by the United States in the early 1970s and has since developed rapidly. The core of this technology is to track the position of the drill bit in real time. The real-time positioning of the drill bit has always been the difficulty and focus of horizontal directional drilling technology research. At present, the mainstream foreign guidance and positioning system uses handheld radar to scan on the ground, but this method has many restrictions on construction sites, a small scope of application, low positioning efficiency, and cumbersome operation. The research on horizontal directional drilling technology in China started late, and there is no positioning and guidance system in the engineering sense. Most of them use foreign products. At the same time, the operation of horizontal drilling rigs is still very dependent on manual labor, and various operating technicians are required to operate, guide, and position the horizontal drilling rig. In the current construction method, a manual handheld guide is required to find the vertical position of the drill bit on the ground, which will cause inconvenience to the construction when encountering areas that cannot be reached by manual labor during the construction process. Therefore, it is necessary to develop a new drill bit detection and guidance technology. Summary of the invention

[0003] Purpose of the invention: In view of the shortcomings of the existing guidance and positioning technology development, the present invention provides a method for dynamic positioning of a horizontal drilling rig drill bit based on a mobile magnetic beacon. The main content of the method is: a dynamic positioning operation mode of a horizontal drilling rig based on a mobile magnetic beacon is proposed, which is different from the current use of a handheld guidance device and is different from the current use of a handheld guidance device. The mode is divided into two parts, namely a ground magnetic beacon and a drilling magnetic sensor. The coordinates of the drill bit are located by the real-time position of the ground mobile magnetic beacon and the measurement data of the drilling magnetic sensor; a model is built for the electromagnetic characteristics of the mobile magnetic beacon; a model is built for the measurement data processing of the drilling magnetic sensor; and then an adaptive extended Kalman filter is used to perform slow-speed dynamic positioning of the underground drill bit according to the electromagnetic characteristics of the ground magnetic beacon. The present invention can further optimize the guidance operation scene of the horizontal drilling rig, so that the drill bit can be remotely positioned even in areas with complex terrain. Technical solution: A method for dynamic positioning of a horizontal drilling rig drill bit, the method comprising the following steps:

[0004] Step 1: Dynamic positioning mode of horizontal drilling rig drill head based on mobile magnetic beacon;

[0005] Step 2: Modeling the electromagnetic characteristics of the mobile magnetic beacon;

[0006] Step 3: Modeling of while-drilling magnetic sensor measurement data processing;

[0007] Step 4: Drill bit slow speed dynamic positioning method based on adaptive extended Kalman filter.

[0008] As an improvement of the present invention, in step 1, the positioning mode uses a mobile ground magnetic beacon to continuously move in a rectangular trajectory and continuously emit low-frequency magnetic signals. The magnetic beacon is also equipped with a high-precision positioning device, which can measure the real-time position of the magnetic beacon in real time. The underground drill bit is equipped with a drilling magnetic sensor to measure the magnetic signal emitted by the magnetic beacon and perform data preprocessing, and transmit the processed data back to the ground for joint positioning and solution with the real-time position measurement value of the magnetic beacon.

[0009] As an improvement of the present invention, in step 2, the uniaxial solenoid installed on the mobile magnetic beacon is used as a magnetic field signal emission source to generate a low-frequency magnetic field as a positioning information source, and the drilling magnetic sensor is positioned by measuring and analyzing the magnetic field signal emitted by the uniaxial solenoid. The uniaxial solenoid is analogized to a circular current-carrying loop, and the magnetic field can be analyzed using a magnetic dipole model. According to the Biot-Savart law, the magnetic field of the magnetic dipole can be expressed as:

[0010]

[0011] Where r represents the distance vector from the magnetic beacon to the magnetic dipole, and m represents the magnetic moment vector. The magnetic permeability in the environment is μ0, the current amplitude in the solenoid is I, the current signal frequency is ω, the number of coil turns is N, the cross-sectional area of ​​the coil is S, and when the magnetic moment direction of the magnetic dipole coincides with the z-axis of the coordinate system, the magnetic induction vector of any point P(r,φ,θ) in space can be expressed as:

[0012]

[0013] Among them, B x , B y , B z represents the components of the magnetic field vector at point P on the three coordinate axes, r represents the distance from point P to the origin O, φ represents the pitch angle, θ represents the azimuth angle, and M(t)=NIS sin(ωt) is the magnetic moment of the solenoid.

[0014] In order to facilitate position calculation, formula (2) is converted to the Cartesian coordinate system, and the magnetic induction vector can be expressed as:

[0015]

[0016] As an improvement of the present invention, in step 3, the vector coordinate values ​​in formula (3) are all calculated based on the magnetic beacon coordinate system. However, in the actual measurement process, the vector coordinates output by the magnetic sensor are based on the sensor coordinate system. When there is a deviation between the two coordinate systems, the measured values ​​will be inaccurate. The magnetic field vector B'(t) measured in the magnetic sensor coordinate system and the theoretical magnetic field vector B(t) in the magnetic beacon coordinate system need to be converted using a rotation matrix. The conversion process is as follows:

[0017]

[0018] in Assume that the magnetic sensor coordinate system is the magnetic beacon coordinate system rotated by an angle of α around the x-axis, then by an angle of β around the new coordinate system y-axis, and then by an angle of γ around the new coordinate system z-axis. Then the rotation matrix It is expressed as:

[0019]

[0020] Although the magnetic field vector data measured by the sensor is converted to the magnetic beacon coordinate system by formula (5), the Euler angle needs to be measured by the attitude sensor. While increasing the cost, the accuracy of the converted magnetic field will also be affected by the measurement error of the attitude sensor. However, the relationship between the magnetic field vector and the sensor position that is not affected by the sensor attitude can be obtained by calculating the second norm of the measured magnetic field vector. The derivation process is as follows:

[0021]

[0022] The magnetic moment and magnetic field measurement values ​​in formula (6) are the instantaneous magnetic field and instantaneous magnetic moment at a certain moment, which are difficult to use in practical engineering applications. The received magnetic signal data amplitude can be solved by fast Fourier transform (FFT) of data in a relatively short period of time. The magnetic moment can be used as a priori parameter through data calibration before measurement. Therefore, formula (6) can be further converted into the following form:

[0023]

[0024] Among them, B0 is the result of calculating the second norm after FFT transformation of the three-axis measurement data of the magnetic sensor, and M0 is the prior magnetic beacon magnetic moment data. Because the magnetic moment is a priori data and can be used as a constant, we can use This expression can be further transformed into a more concise form:

[0025]

[0026] However, the coordinates in formula (8) are in a coordinate system with the magnetic beacon as the origin. When using a mobile magnetic beacon for positioning, the coordinates (x, y, z) of the drill bit in a standard coordinate system need to be located. The positioning data of the mobile magnetic beacon itself is also relative to the position of the standard coordinate system (x', y', z'). Therefore, formula (8) needs to be expressed in the following form, and Substitute:

[0027]

[0028] The drilling magnetic sensor on the drill bit can obtain magnetic field data that is independent of its own posture through the above steps, and this data can be jointly positioned and solved with the real-time position of the magnetic beacon.

[0029] As an improvement of the present invention, in step 4, an extended Kalman filter can be used to perform static positioning or slow positioning of the drill bit coordinates. The extended Kalman filter (Extended Kalman Filter, EKF) is a nonlinear version of the Kalman filter (KF) and is used to process systems where the linear assumption does not hold. The EKF approximates the nonlinear system by performing a first-order Taylor expansion (i.e., linearization) of the nonlinear function at the current estimation point at each time step, thereby allowing the use of an update step similar to a linear Kalman filter. The linearization steps of the state transfer equation and the measurement equation are as follows:

[0030]

[0031] Among them, f(x t ,u t ) is the state transfer equation, is the measurement equation, x t is the state vector of the system, u t is the control vector. In this method, there is no control vector. In this method, the state vector of the system is defined as x t =[x,y,z,v x ,v y ,v z ] T , that is, the real-time position of the drill bit and the speed of the drill bit in all directions. The state transfer equation is as follows:

[0032]

[0033] Where I is a unit vector, T s is the sampling time. The measurement equation is formula (9). From formula (10), we can see that each update step of the extended Kalman filter requires knowing the Jacobian matrix of the measurement equation with respect to the state matrix, that is, in:

[0034]

[0035]

[0036] The Adaptive Extended Kalman Filter (AEKF) is an improved version of the Extended Kalman Filter (EKF) and is used to handle the state estimation problem of nonlinear systems. AEKF can adaptively adjust the estimates of process noise and observation noise according to the actual observation data, thereby improving the filter's adaptability to changes in system noise characteristics. Adaptive noise adjustment usually relies on the statistical characteristics of the residuals or the analysis of the residual sequence, and uses the size of the residuals to dynamically adjust the measurement noise covariance matrix R and the process noise covariance matrix Q; for example, if the size of the residuals is continuously larger than expected, it may mean that R is underestimated; if the prediction error of the system state increases, it may mean that Q is underestimated; the adjustment method is as follows:

[0037]

[0038] R new =αR+β(rr T ) (16)

[0039]

[0040] Where H is the measurement matrix, is the predicted value of the state vector, α, β, γ and δ are adjustment coefficients that need to be selected and adjusted according to the actual application scenario. T is the outer product of the residuals, reflecting the uncertainty of the current observation; It reflects the uncertainty or error of the state estimation.

[0041] During the positioning process, the mobile magnetic beacon continuously uses the solenoid to emit magnetic field signals and collects the real-time position of the magnetic beacon at the same time. The underground drill bit continuously collects the magnetic field signals emitted by the magnetic beacon and inputs them into the algorithm. During the algorithm update process, the real-time position of the magnetic beacon is used to calculate the Jacobian matrix, the drill bit collects the magnetic field signal as a measurement vector, and the algorithm outputs the positioning position of the drill bit.

[0042] Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0043] 1. In view of the inconvenience of current drill bit positioning, the present invention proposes a positioning mode based on a mobile magnetic beacon. This positioning mode uses a mobile ground magnetic beacon to continuously emit low-frequency magnetic signals. The magnetic beacon is also equipped with a high-precision positioning device, which can measure the real-time position of the magnetic beacon in real time. The underground drill bit is equipped with a drilling magnetic sensor to measure the magnetic signal emitted by the magnetic beacon and perform data preprocessing, and transmit the processed data back to the ground for joint positioning and solution with the real-time position measurement value of the magnetic beacon.

[0044] 2. Aiming at the problem of dynamic positioning method of drill bit of horizontal drilling rig, the present invention proposes a dynamic positioning method of drill bit based on adaptive extended Kalman filter. By deriving the electromagnetic characteristics of magnetic beacon, the processing of drill bit measurement while drilling, and the adaptive positioning model, static positioning or slow dynamic positioning of drill bit can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of the measurement mode of the embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent forms of modifications to the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0047] A method for dynamic positioning of a drill bit of a horizontal drilling rig comprises the following steps:

[0048] Step 1: Dynamic positioning mode of horizontal drilling rig drill head based on mobile magnetic beacon;

[0049] Step 2: Modeling the electromagnetic characteristics of the mobile magnetic beacon;

[0050] Step 3: Modeling of while-drilling magnetic sensor measurement data processing;

[0051] Step 4: Drill bit slow speed dynamic positioning method based on adaptive extended Kalman filter.

[0052] like Figure 1 As shown in the figure, the dynamic positioning mode of the horizontal drilling rig drill bit based on the mobile magnetic beacon uses a mobile ground magnetic beacon to continuously move in a rectangular trajectory and continuously emit low-frequency magnetic signals. The magnetic beacon is also equipped with a high-precision positioning device, which can measure the real-time position of the magnetic beacon in real time. The underground drill bit is equipped with a drilling magnetic sensor to measure the magnetic signal emitted by the magnetic beacon and perform data preprocessing, and transmit the processed data back to the ground for joint positioning and solution with the real-time position measurement value of the magnetic beacon. The specific implementation method is as follows:

[0053] (1) Modeling of electromagnetic characteristics of mobile magnetic beacons

[0054] The uniaxial solenoid installed on the mobile magnetic beacon is used as a magnetic field signal transmitter to generate a low-frequency magnetic field as a positioning information source. The magnetic sensor locates by measuring and analyzing the magnetic field signal emitted by the uniaxial solenoid. If the uniaxial solenoid is analogous to a circular current-carrying loop, the magnetic dipole model can be used to analyze the magnetic field. According to the Biot-Savart law, the magnetic field of the magnetic dipole can be expressed as:

[0055]

[0056] Where r represents the distance vector from the magnetic beacon to the magnetic dipole, and m represents the magnetic moment vector. The magnetic permeability in the environment is μ0, the current amplitude in the solenoid is I, the current signal frequency is ω, the number of coil turns is N, the cross-sectional area of ​​the coil is S, and when the magnetic moment direction of the magnetic dipole coincides with the z-axis of the coordinate system, the magnetic induction vector of any point P(r,φ,θ) in space can be expressed as:

[0057]

[0058] Among them, B x , B y , B z represents the components of the magnetic field vector at point P on the three coordinate axes, r represents the distance from point P to the origin O, φ represents the pitch angle, θ represents the azimuth angle, and M(t)=NIS sin(ωt) is the magnetic moment of the solenoid.

[0059] In order to facilitate position calculation, formula (2) is converted to the Cartesian coordinate system, and the magnetic induction vector can be expressed as:

[0060]

[0061] (2) Modeling of while-drilling magnetic sensor measurement data processing

[0062] The vector coordinate values ​​in formula (3) are all calculated based on the magnetic beacon coordinate system. However, in the actual measurement process, the vector coordinates output by the magnetic sensor are based on the sensor coordinate system. When there is a deviation between the two coordinate systems, the measured values ​​will be inaccurate. The magnetic field vector B'(t) measured in the sensor coordinate system and the theoretical magnetic field vector B(t) in the magnetic beacon coordinate system need to be converted using a rotation matrix. The conversion process is as follows:

[0063]

[0064] in Assume that the sensor coordinate system is the rotation matrix, which is the magnetic beacon coordinate system rotated around the x-axis by an angle of α, then around the y-axis of the new coordinate system by an angle of β, and then around the z-axis of the new coordinate system by an angle of γ. Then the rotation matrix It is expressed as:

[0065]

[0066] Although the magnetic field vector data measured by the sensor is converted to the magnetic beacon coordinate system by formula (5), the Euler angle needs to be measured by the attitude sensor. While increasing the cost, the accuracy of the converted magnetic field will also be affected by the measurement error of the attitude sensor. However, the relationship between the magnetic field vector and the sensor position that is not affected by the sensor attitude can be obtained by calculating the second norm of the measured magnetic field vector. The derivation process is as follows:

[0067]

[0068] The magnetic moment and magnetic field measurement values ​​in formula (6) are the instantaneous magnetic field and instantaneous magnetic moment at a certain moment, which are difficult to use in practical engineering applications. The received magnetic signal data amplitude can be solved by fast Fourier transform (FFT) of data in a relatively short period of time. The magnetic moment can be used as a priori parameter through data calibration before measurement. Therefore, formula (6) can be further converted into the following form:

[0069]

[0070] Among them, B0 is the result of calculating the second norm after FFT transformation of the three-axis measurement data of the magnetic sensor, and M0 is the prior magnetic beacon magnetic moment data. Because the magnetic moment is a priori data and can be used as a constant, we can use This expression can be further transformed into a more concise form:

[0071]

[0072] However, the coordinates in formula (8) are in a coordinate system with the magnetic beacon as the origin. When using a mobile magnetic beacon for positioning, the coordinates (x, y, z) of the drill bit in a standard coordinate system need to be located. The positioning data of the mobile magnetic beacon itself is also relative to the position of the standard coordinate system (x', y', z'). Therefore, formula (8) needs to be expressed in the following form, and Substitute:

[0073]

[0074] The drilling magnetic sensor on the drill bit can obtain magnetic field data that is independent of its own posture through the above steps, and this data can be jointly positioned and solved with the real-time position of the magnetic beacon.

[0075] (3) Drill bit slow speed dynamic positioning method based on adaptive extended Kalman filter

[0076] The extended Kalman filter can be used to perform static or slow positioning of the drill bit coordinates. The extended Kalman filter (EKF) is a nonlinear version of the Kalman filter (KF) used to handle systems where the linear assumption does not hold. The EKF approximates the nonlinear system by performing a first-order Taylor expansion (i.e., linearization) of the nonlinear function at the current estimation point at each time step, allowing the use of an update step similar to that of a linear Kalman filter. The linearization steps of the state transfer equation and the measurement equation are as follows:

[0077]

[0078] Among them, f(x t ,u t ) is the state transfer equation, is the measurement equation, x t is the state vector of the system, u t is the control vector. In this method, there is no control vector. In this method, the state matrix of the system is defined as x t =[x,y,z,v x ,v y ,v z ] T , that is, the real-time position of the drill bit and the speed of the drill bit in all directions. The state transfer equation is as follows:

[0079]

[0080] Where I is a unit vector, T s is the sampling time. The measurement equation is formula (9). From formula (10), we can see that each update step of the extended Kalman filter requires knowing the Jacobian matrix of the measurement equation with respect to the state matrix, that is, in:

[0081]

[0082] The Adaptive Extended Kalman Filter (AEKF) is an improved version of the Extended Kalman Filter (EKF) and is used to handle the state estimation problem of nonlinear systems. AEKF can adaptively adjust the estimates of process noise and observation noise according to the actual observation data, thereby improving the filter's adaptability to changes in system noise characteristics; adaptive noise adjustment usually relies on the statistical characteristics of the residuals or the analysis of the residual sequence, and uses the size of the residuals to dynamically adjust the measurement noise covariance matrix R and the process noise covariance matrix Q. For example, if the size of the residuals is continuously larger than expected, it may mean that R is underestimated; if the prediction error of the system state increases, it may mean that Q is underestimated. The adjustment method is as follows:

[0083]

[0084] R new =αR+β(rr T ) (16)

[0085]

[0086] Where H is the measurement matrix, is the predicted value of the state vector, α, β, γ and δ are adjustment coefficients that need to be selected and adjusted according to the actual application scenario. T is the outer product of the residuals, reflecting the uncertainty of the current observation; It reflects the uncertainty or error of the state estimation.

[0087] During the positioning process, the mobile magnetic beacon continuously uses the solenoid to emit magnetic field signals, while collecting the real-time position of the magnetic beacon. The underground drill bit continuously collects the magnetic field signals emitted by the magnetic beacon and inputs them into the algorithm. During the algorithm update process, the real-time position of the magnetic beacon is used to calculate the Jacobian matrix, the drill bit collects the magnetic field signal as the measurement vector, and the algorithm outputs the positioning position of the drill bit.

Claims

1. A method for dynamic positioning of a drill bit of a horizontal drilling rig, characterized in that: The method comprises the following steps: Step 1: Dynamic positioning mode of horizontal drilling rig drill head based on mobile magnetic beacon; Step 2: Modeling the electromagnetic characteristics of the mobile magnetic beacon; Step 3: Modeling of while-drilling magnetic sensor measurement data processing; Step 4: Drill bit slow speed dynamic positioning method based on adaptive extended Kalman filter; In step 2, the uniaxial solenoid installed on the mobile magnetic beacon is used as a magnetic field signal emission source to generate a low-frequency magnetic field as a positioning information source, and the drilling magnetic sensor is positioned by measuring and analyzing the magnetic field signal emitted by the uniaxial solenoid; the uniaxial solenoid is analogized to a circular current-carrying loop, and the magnetic field is analyzed using a magnetic dipole model. According to the Biot-Savart law, the magnetic field of the magnetic dipole is expressed as: Where r represents the distance vector from the magnetic beacon to the magnetic dipole, m represents the magnetic moment vector, the magnetic permeability in the environment is μ0, the frequency of the current signal in the solenoid is ω, and when the magnetic moment direction of the magnetic dipole coincides with the z-axis of the coordinate system, the magnetic induction vector of any point P(r,φ,θ) in space is expressed as: Among them, B x , B y , B z represents the components of the magnetic field vector at point P on the three coordinate axes, r represents the distance from point P to the origin O, φ represents the pitch angle, θ represents the azimuth angle, and M(t)=NISsin(ωt) is the magnetic moment of the solenoid; In order to facilitate position calculation, formula (2) is converted to the Cartesian coordinate system, and the magnetic induction vector is expressed as: In step 3, the vector coordinate values ​​in formula (3) are all calculated based on the magnetic beacon coordinate system. In the actual measurement process, the vector coordinates output by the magnetic sensor are based on the sensor coordinate system. The magnetic field vector B′(t) measured in the magnetic sensor coordinate system and the theoretical magnetic field vector B(t) in the magnetic beacon coordinate system need to be converted using a rotation matrix. The conversion process is as follows: in Assume that the magnetic sensor coordinate system is the rotation matrix, and the magnetic beacon coordinate system is rotated by an angle of α around the x-axis, then by an angle of β around the y-axis of the new coordinate system, and then by an angle of γ around the z-axis of the new coordinate system; then the rotation matrix It is expressed as: Although the magnetic field vector data measured by the sensor is converted to the magnetic beacon coordinate system by formula (5), the relationship between the magnetic field vector and the sensor position that is not affected by the sensor posture is obtained by calculating the second norm of the measured magnetic field vector. The derivation process is as follows: The magnetic moment and magnetic field measurement values ​​in formula (6) are the instantaneous magnetic field and instantaneous magnetic moment at a certain moment, which are difficult to use in actual engineering applications. The received magnetic signal data amplitude is solved by fast Fourier transform of data in a relatively short period of time. The magnetic moment is used as a priori parameter through data calibration before measurement; formula (6) is further converted into the following form: Among them, B0 is the result of calculating the second norm after FFT transformation of the three-axis measurement data of the magnetic sensor, and M0 is the prior magnetic beacon magnetic moment data; in formula (7), let This expression is further transformed into: The coordinates in formula (8) are in the coordinate system with the magnetic beacon as the origin. When using a mobile magnetic beacon for positioning, it is necessary to locate the coordinates (x, y, z) of the drill bit in a standard coordinate system. The positioning data of the mobile magnetic beacon itself is also the position (x', y', z') relative to the standard coordinate system; therefore, formula (8) needs to be expressed in the following form, and Substitute: The drilling magnetic sensor on the drill bit is processed through the above steps to obtain magnetic field data that is independent of its own posture. This data is used for joint positioning and solution with the real-time position of the magnetic beacon. In step 4, the extended Kalman filter is used to perform static positioning or slow positioning of the drill bit coordinates, and the linearization steps of the state transfer equation and the measurement equation are as follows: Among them, f(x t ,u t ) is the state transfer equation, is the measurement equation, x t is the state vector of the system, u t is the control vector. In this method, there is no control vector. Define the state matrix of the system as x t =[x,y,z,v x ,v y ,v z ] T , that is, the real-time position of the drill bit and the speed of the drill bit in all directions. The state transfer equation is as follows: Where I is a unit vector, T s is the sampling time; from formula (10), it can be seen that each update step of the extended Kalman filter requires knowing the Jacobian matrix of the measurement equation with respect to the state matrix; The adaptive extended Kalman filter AEKF is used to deal with the state estimation problem of nonlinear systems. AEKF can adaptively adjust the estimation of process noise and observation noise according to the actual observation data. The adaptive noise adjustment usually depends on the statistical characteristics of the residual or the analysis of the residual sequence, and uses the size of the residual to dynamically adjust the measurement noise covariance matrix R and the process noise covariance matrix Q; the adjustment method is as follows: R new =αR+β(rr T ) (16) Where H is the measurement matrix, is the predicted value of the state vector, α, β, γ and δ are adjustment coefficients that need to be selected and adjusted according to the actual application scenario, rr T is the outer product of the residuals, reflecting the uncertainty of the current observation; It reflects the uncertainty or error of the state estimation.

2. The method for dynamic positioning of a drill bit for a horizontal drilling rig according to claim 1, characterized in that: The positioning mode uses a ground mobile magnetic beacon to continuously move in a rectangular trajectory. The mobile magnetic beacon continuously uses a solenoid to emit a magnetic field signal. The magnetic beacon is also equipped with a positioning device to collect the real-time position of the magnetic beacon. The underground drill bit is equipped with a drilling magnetic sensor to measure the magnetic signal emitted by the magnetic beacon and perform data preprocessing, and the processed data is transmitted back to the ground for joint positioning solution with the real-time position measurement value of the magnetic beacon.

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