A cooperative orientation calibration method and system, electronic device and storage medium

Abstract

The application relates to the technical field of equipment calibration, and discloses a cooperative directional calibration method, a cooperative directional calibration system, electronic equipment and a storage medium, which comprise the following steps: acquiring the circumferential strain distribution data of a drill rod, extracting the amplitude-frequency characteristics of a dominant vibration mode and the phase shift amount of a coupled vibration mode; generating an amplitude compensation coefficient according to the amplitude-frequency characteristics, generating a time-varying phase compensation factor in combination with the phase shift amount, and generating a dynamic vibration compensation parameter based on vibration energy distribution dynamic weighting fusion; based on a pre-trained nonlinear phase distortion compensation model, inputting the dynamic vibration compensation parameter, and outputting a phase-corrected gyroscope angular velocity signal; performing vibration interference frequency band suppression processing on an accelerometer signal, and outputting the processed accelerometer signal; performing iterative optimization on the corrected angular velocity signal and the processed accelerometer signal, and outputting a directional calibration result after filter smoothing processing. The application can realize high-precision cooperative calibration of inertial sensors in a multi-modal mechanical vibration environment.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of equipment calibration, and particularly relates to a cooperative directional calibration method and system, an electronic device and a storage medium. BACKGROUND

[0002] At present, in deep well directional drilling, high-speed rotation of a drill pipe can cause circumferential strain fluctuation and radial deformation coupled vibration, resulting in phase distortion of a gyroscope angular velocity signal and centrifugal acceleration interference contained in an accelerometer output.

[0003] In the prior art, static correction of an accelerometer or temperature compensation of a gyroscope is usually adopted, but the time-varying characteristics of multi-modal coupled errors in a vibration frequency band are not considered, the traditional method is insufficient in phase distortion processing, conventional low-pass filtering can introduce signal delay, and cannot eliminate nonlinear phase lag caused by radial deformation of the drill pipe, and dynamic weight loss, the existing fusion algorithm adopts fixed weight coefficients, and it is difficult to adapt to periodic changes in circumferential strain energy distribution.

[0004] Therefore, the application provides a cooperative directional calibration method to solve the above technical problems. SUMMARY

[0005] The application aims to provide a cooperative directional calibration method, system, electronic device and storage medium, to solve the technical problem of decreased gyroscope-accelerometer cooperative calibration accuracy in a multi-modal mechanical vibration scene in the prior art.

[0006] To solve the above technical problems, the application provides a cooperative directional calibration method, comprising:

[0007] In response to the obtained drill pipe circumferential strain distribution data, the drill pipe circumferential strain distribution data is subjected to frequency domain separation, the amplitude-frequency characteristics of a dominant vibration mode and the phase shift of an associated coupled vibration mode are extracted;

[0008] In response to the amplitude-frequency characteristics, an amplitude compensation coefficient is generated, a time-varying phase compensation factor is generated through a dynamic prediction algorithm in combination with the phase shift, and a dynamic vibration compensation parameter is generated based on dynamic weighting fusion of vibration energy distribution;

[0009] In response to the dynamic vibration compensation parameter, a gyroscope angular velocity signal is processed based on a pre-trained nonlinear phase distortion compensation model, and a phase-corrected gyroscope angular velocity signal is output after the dynamic vibration compensation parameter is input;

[0010] The accelerometer signal is subjected to vibration interference frequency band suppression processing, and the processed accelerometer signal is output;

[0011] Based on the dynamic weight fusion algorithm, the phase-corrected gyro angular velocity signal and the processed accelerometer signal are iteratively optimized, and the directional calibration result is output after filtering and smoothing.

[0012] In some embodiments, in response to the acquired drill pipe circumferential strain distribution data, the drill pipe circumferential strain distribution data is subjected to frequency domain separation, the amplitude-frequency characteristics of the dominant vibration mode and the phase shift of the associated coupled vibration mode are extracted, and further comprising:

[0013] Obtaining the circumferential strain waveform data of the annular distribution, performing frequency domain analysis on the circumferential strain waveform data, and extracting the frequency spectrum in the preset mechanical vibration frequency band;

[0014] Identify the frequency component with the largest energy proportion in the frequency spectrum as the dominant vibration mode, and obtain the amplitude-frequency relationship of the dominant vibration mode;

[0015] Obtaining the coupling vibration frequency component of the coupled vibration mode which has harmonic correlation with the dominant vibration mode, and obtaining the phase shift of the coupled vibration mode relative to the dominant vibration mode through phase difference calculation.

[0016] In some embodiments, in response to the amplitude-frequency characteristics, an amplitude compensation coefficient is generated, a time-varying phase compensation factor is generated through a dynamic prediction algorithm combined with the phase shift, and a dynamic vibration compensation parameter is generated based on vibration energy distribution dynamic weighting fusion, further comprising:

[0017] Normalizing the spectral energy of the dominant vibration mode, fitting a nonlinear function combined with the material strain frequency response characteristics to generate an amplitude compensation coefficient;

[0018] Based on the phase shift of the associated coupled vibration mode, a state estimation algorithm is used to predict a time-varying phase compensation factor;

[0019] Real-time calculation of the energy proportion ratio of the dominant vibration mode and the associated coupled vibration mode, dynamic allocation of weight coefficients through a continuous function, and weighted fusion of amplitude compensation coefficient and time-varying phase compensation factor to generate a dynamic vibration compensation parameter.

[0020] In some embodiments, the state estimation algorithm further comprises:

[0021] Constructing a state equation with phase shift and phase shift rate as state vectors;

[0022] According to the vibration noise statistical characteristics, the process noise covariance and the observation noise covariance are configured;

[0023] The estimated value of the phase shift is updated through recursive prediction to generate a phase compensation factor function containing time variable.

[0024] In some embodiments, in response to the dynamic vibration compensation parameter, the gyroscope angular velocity signal is processed based on a pre-trained nonlinear phase distortion compensation model, and the phase-corrected gyroscope angular velocity signal is output after inputting the dynamic vibration compensation parameter, further comprising:

[0025] A nonlinear phase distortion compensation model based on a long short-term memory network is constructed, and the training data contains the phase lag mapping relationship in the historical vibration environment;

[0026] The gyroscope angular velocity signal and the dynamic vibration compensation parameter are input into the nonlinear phase distortion compensation model, and a complex exponential compensation factor containing amplitude and phase adjustment amount is output;

[0027] The analyzed gyroscope angular velocity signal is multiplied by the complex exponential compensation factor in the complex domain, and the phase-corrected gyroscope angular velocity signal is output.

[0028] In some embodiments, the accelerometer signal is subjected to vibration interference frequency band suppression processing, and the processed accelerometer signal is output, further comprising:

[0029] The vibration interference frequency band in the accelerometer signal spectrum is analyzed;

[0030] A notch filter with a stopband attenuation greater than a preset attenuation threshold is set;

[0031] The filter operation is performed on the accelerometer signal to eliminate the amplitude drift caused by centrifugal vibration;

[0032] The accelerometer signal with an amplitude error within a preset error range is output.

[0033] In some embodiments, based on a dynamic weight fusion algorithm, the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal are iteratively optimized, and the directional calibration result is output after filtering and smoothing processing, further comprising:

[0034] The phase-corrected gyroscope angular velocity signal and the processed accelerometer signal are complementarily filtered and preliminarily fused;

[0035] A target function is constructed with the gyroscope phase residual variance and the accelerometer amplitude error variance as variables;

[0036] The fusion weight is dynamically adjusted based on the drill pipe vibration frequency, the gyroscope angular velocity signal weight is increased when the vibration frequency is higher than a preset frequency threshold, and the accelerometer signal weight is increased when the vibration frequency is lower than the preset frequency threshold;

[0037] The fusion result is subjected to noise reduction processing, and the directional calibration data including the azimuth angle and the inclination angle is output.

[0038] Based on the same concept, the application also provides a cooperative directional calibration system, comprising:

[0039] A frequency domain separation module is configured to perform frequency domain separation on the acquired drill pipe circumferential strain distribution data, extract the amplitude-frequency characteristics of the dominant vibration mode and the phase shift of the associated coupled vibration mode in response to the drill pipe circumferential strain distribution data;

[0040] A vibration compensation parameter generation module is configured to generate an amplitude compensation coefficient in response to the amplitude-frequency characteristics, generate a time-varying phase compensation factor through a dynamic prediction algorithm in combination with the phase shift, and generate a dynamic vibration compensation parameter based on vibration energy distribution dynamic weighted fusion;

[0041] A gyroscope angular velocity signal correction module is configured to process the gyroscope angular velocity signal based on a pre-trained nonlinear phase distortion compensation model in response to the dynamic vibration compensation parameter, and output the phase-corrected gyroscope angular velocity signal after inputting the dynamic vibration compensation parameter;

[0042] An accelerometer signal processing module is configured to perform vibration interference frequency band suppression processing on the accelerometer signal, and output the processed accelerometer signal;

[0043] A directional calibration result output module is configured to perform iterative optimization on the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal based on a dynamic weight fusion algorithm, and output a directional calibration result after filter smoothing processing.

[0044] Based on the same concept, the application also provides an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of a cooperative directional calibration method.

[0045] Based on the same concept, the application also provides a computer readable storage medium storing a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of a cooperative directional calibration method.

[0046] Compared with the prior art, the application has the beneficial effects that:

[0047] The application discloses a cooperative directional calibration method and system, an electronic device and a storage medium, which effectively suppresses the gyroscope phase distortion and accelerometer amplitude drift caused by drill pipe vibration through circumferential strain frequency domain separation and time-varying parameter correction, and realizes high-precision cooperative calibration of inertial sensors in a multi-modal mechanical vibration environment. BRIEF DESCRIPTION OF DRAWINGS

[0048] Other features, objects, and advantages of the application will become more apparent from the following detailed description when read in connection with the following accompanying drawings:

[0049] Figure 1 is a flow diagram of a cooperative directional calibration method according to some embodiments of the application;

[0050] Figure 2 is a structural diagram of a cooperative directional calibration system according to some embodiments of the application;

[0051] Figure 3 is a structural diagram of an electronic device according to some embodiments of the application;

[0052] In the drawings, 710 is a processor; 720 is a memory; 730 is an input device; and 740 is an output device. DETAILED DESCRIPTION

[0053] In order to make the purposes, technical solutions and advantages of the application clearer, the following will further describe the application with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the application, but not all of the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the application.

[0054] The terms used in the embodiments of the application are only for the purpose of describing particular embodiments and are not intended to limit the application. The singular forms "a", "an" and "the" used in the embodiments of the application and the appended claims are intended to include plural forms, unless the context clearly indicates otherwise. "Plural" generally includes at least two.

[0055] It should be understood that the term "and / or" used herein is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0056] It should be understood that although the terms first, second, third, etc. can be used in the embodiments of the application to describe, these descriptions should not be limited to these terms. These terms are only used to distinguish the description. For example, without departing from the scope of the embodiments of the application, the first can also be called the second, and similarly, the second can also be called the first.

[0057] Depending on the context, the word "if" as used herein can be interpreted to mean "when" or "while" or "in response to determining" or "in response to detecting." Similarly, the phrase "if it is determined" or "if [a stated condition or event] is detected" can be interpreted to mean "when it is determined" or "in response to determining" or "when [a stated condition or event] is detected" or "in response to detecting [a stated condition or event]."

[0058] It is also important to note that the terms "comprises", "comprising", or other variations thereof are intended to cover a non-exclusive inclusion, such that a product or method that comprises a list of elements does not include only those elements but can also include other elements not expressly listed or inherent to such product or method. An element proceeded by "comprises... a" does not, without more constraints, exclude the presence of additional identical elements in the product or method that comprises the element.

[0059] It is particularly important to note that the symbols and / or numbers present in the description, if not marked in the description of the figures, are not figure references.

[0060] With reference to Figure 1 A cooperative directional calibration method comprises:

[0061] S101, in response to the acquired drill pipe circumferential strain distribution data, performing frequency domain separation on the drill pipe circumferential strain distribution data, extracting amplitude-frequency characteristics of a dominant vibration mode and phase shift amounts of associated coupled vibration modes;

[0062] S102, in response to the amplitude-frequency characteristics, generating an amplitude compensation coefficient, generating a time-varying phase compensation factor through a dynamic prediction algorithm in combination with the phase shift amounts, and generating a dynamic vibration compensation parameter based on vibration energy distribution dynamic weighted fusion;

[0063] S103, in response to the dynamic vibration compensation parameter, processing a gyroscope angular velocity signal based on a pre-trained nonlinear phase distortion compensation model, and outputting a phase-corrected gyroscope angular velocity signal after inputting the dynamic vibration compensation parameter;

[0064] S104, performing vibration interference frequency band suppression processing on an accelerometer signal, and outputting a processed accelerometer signal;

[0065] S105, based on a dynamic weight fusion algorithm, performing iterative optimization on the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal, and outputting a directional calibration result after combination with filter smoothing processing.

[0066] Specifically, in the embodiments of the present application, the circumferential strain waveform data of the drill pipe is collected by the annularly distributed sensor array, the waveform data is converted to the frequency domain by Fourier transform, and the energy concentration area is extracted in the predetermined mechanical vibration frequency band by band-pass filtering; the frequency component with the largest energy proportion in the frequency spectrum is identified as the dominant vibration mode, and the amplitude change relationship with the frequency is obtained; the coupling vibration frequency component associated with the harmonic of the dominant vibration mode is detected, the phase difference of adjacent sensor nodes is calculated by complex spectrum analysis, and the phase offset of the associated coupling vibration mode is generated. The spectral energy of the dominant vibration mode is normalized and integrated, and the amplitude compensation coefficient is generated by polynomial fitting combined with the nonlinear characteristics of the material strain-frequency response; based on the phase offset of the associated coupling vibration mode, a state estimation algorithm is used to construct a state vector containing the phase and its change rate, the process noise covariance and the observation noise covariance are configured according to the statistical characteristics of the vibration noise, and the phase compensation factor function containing the time variable is generated by recursive prediction; the energy proportion ratio of the dominant mode and the coupling mode is calculated in real time, the weight coefficient is dynamically allocated using the continuous function, and the dynamic vibration compensation parameter is generated by weighted fusion of the amplitude compensation coefficient and the time-varying phase compensation factor. A nonlinear phase distortion compensation model based on long short-term memory network is constructed, and the training data of the model includes the angular velocity signal phase lag and strain mapping relationship in the historical vibration environment; the real-time acquired angular velocity signal and the dynamic vibration compensation parameter are input into the model, and the complex exponential compensation factor containing the amplitude and phase adjustment amount is output; the angular velocity signal is converted into an analytic signal by Hilbert transform, and the analytic signal is multiplied by the complex exponential compensation factor in the complex domain to realize the phase lag correction; the sliding window method is used to update the input buffer, and part of the historical data is retained by the overlap retention strategy to ensure the real-time correction when the vibration frequency suddenly changes. The interference frequency band caused by centrifugal vibration in the accelerometer signal spectrum is analyzed, and a notch filter with a stopband attenuation greater than a preset threshold is designed; the stopband center frequency and bandwidth are determined according to the vibration energy distribution, and the filter operation is performed on the accelerometer signal to eliminate the amplitude drift; and the accelerometer signal with the amplitude error stabilized in the preset range is output. The phase-corrected angular velocity signal and the filtered accelerometer signal are complementarily filtered and preliminarily fused; a target function with the gyro phase residual variance and the accelerometer amplitude error variance as variables is constructed, and the fusion weight coefficient is iteratively optimized by the gradient descent method, wherein the weight is dynamically adjusted according to the real-time vibration frequency of the drill pipe: the angular velocity signal weight is increased when the vibration is high frequency, and the accelerometer signal weight is increased when the vibration is low frequency; the state estimation algorithm is used to denoise the fusion result, and the azimuth and inclination state equations are recursively updated to output the directional calibration data smoothly; when the directional error variance exceeds the threshold, the vibration energy proportion is recalculated and the dynamic vibration compensation parameter is updated, forming a closed-loop optimization.

[0067] For example, 8 channels of circumferential strain data with ring distribution 8 are collected (interval 45°), and the time domain signal is converted to the frequency domain by Fourier transform; the Butterworth band-pass filter is used to extract the vibration frequency band of 10 Hz to 1 kHz, and the frequency component with the largest energy ratio is identified as 78 Hz, and its amplitude shows a nonlinear decay with the increase of frequency; the 156 Hz harmonic component is detected to be coupled with the 78 Hz dominant mode, and the phase difference between adjacent sensor nodes is calculated to be π / 6 by complex spectrum analysis, and the phase offset of the associated coupled mode is generated. The integral value of the 78 Hz dominant mode is 25000(με)², and the frequency domain normalization coefficient is 0.02(με) -1 ; combined with the characteristics of high-strength steel material, a cubic polynomial compensation function is fitted, and the fitting coefficient is calculated to be 0.8 at 78 Hz, and the amplitude compensation coefficient is generated after weighted fusion 0.8; based on the π / 6 phase difference of the 156 Hz coupled mode, the Kalman filter process noise covariance is configured to be 1×10 -6 , the observation noise covariance is 1×10 -4 , and the prediction time-varying phase compensation factor function is 0.2×sin(2π×78t+π / 4); the energy ratio of 78 Hz to 156 Hz is calculated to be 1.86, and the dynamic weight is generated by the sigmoid function 0.52, and the final fusion parameter is 0.52×amplitude coefficient+0.48×phase factor. The LSTM model inputs the angular velocity signal 0.5×sin(2π×78t+0.3π) and the dynamic compensation parameter, and outputs the complex exponential compensation factor e^(j×0.25π); the analytic signal is obtained by Hilbert transform 0.5×sin(2π×78t+0.3π)+j×0.5×cos(2π×78t+0.3π), and the phase lag is corrected to 0.5×sin(2π×78t+0.25π) after multiplication in the complex domain. The phase lag is reduced from 0.3π to 0.05π; the input buffer is updated every 200ms, and the previous 50ms data is retained, and the response delay is less than 5ms when the speed suddenly changes. The interference frequency band of the accelerometer signal at 78 Hz / 156 Hz is identified, and the notch filter with a stopband attenuation of-40dB is designed; after filtering the signal containing ±0.05g drift, the amplitude error is reduced to ±0.005g. Initialize the fusion weight to 0.5, set the gyroscope residual variance to 0.01 and the accelerometer residual variance to 0.04; after 10 times of gradient descent iteration of the target function, the weight converges to 0.67; when the vibration frequency 78 Hz is higher than the threshold 50 Hz, the angular velocity weight is increased to 0.67; the Kalman filter updates the azimuth state equation (angular velocity deviation 1×10 -6 rad / s), and the directional error is reduced from ±0.5° to ±0.08°; when the error variance exceeds the threshold value 0.01, the energy ratio is recalculated and the compensation parameter is updated.

[0068] In some applications, in response to the acquired drill pipe circumferential strain distribution data, the drill pipe circumferential strain distribution data is subjected to frequency domain separation, the amplitude-frequency characteristics of the dominant vibration mode and the phase shift of the associated coupled vibration mode are extracted, including acquiring the circumferential strain waveform data of the annular distribution, the frequency domain analysis is performed on the circumferential strain waveform data, and the frequency spectrum in the preset mechanical vibration frequency band is extracted; the frequency component with the largest energy ratio in the frequency spectrum is identified as the dominant vibration mode, and the amplitude of the dominant vibration mode with the frequency is obtained; the coupling vibration frequency component of the coupled vibration mode associated with the dominant vibration mode is obtained, and the phase shift of the coupled vibration mode relative to the dominant vibration mode is obtained by phase difference calculation.

[0069] It can be understood that the circumferential strain waveform data is collected by the annularly distributed sensor array; the waveform data is subjected to Fourier transform to convert to the frequency domain; the energy concentration area is extracted in the preset mechanical vibration frequency band by band-pass filtering; the frequency component with the largest energy ratio in the frequency spectrum is identified as the dominant vibration mode, and the nonlinear change relationship of its amplitude with the frequency is obtained; the coupling vibration frequency component associated with the dominant vibration mode is detected; the phase difference between adjacent sensor nodes is calculated by complex spectrum analysis to generate the phase shift of the associated coupled vibration mode relative to the dominant vibration mode.

[0070] For example, 8-channel circumferential strain data of annular distribution is collected (adjacent nodes are spaced 45 degrees), the sampling rate is 1 kHz; the time domain waveform data is subjected to Fourier transform to convert to the frequency domain; the frequency spectrum in the vibration frequency band of 10 Hz to 1000 Hz is extracted by using a Butterworth band-pass filter; the frequency component with the largest energy ratio is identified as 78 Hz, and the amplitude decreases with the increase of the frequency, which is confirmed as the dominant vibration mode; it is detected that the frequency component of 156 Hz is associated with the dominant mode of 78 Hz in the second harmonic, which is determined as the associated coupled vibration mode; the phase difference of adjacent nodes (such as node 1 and node 2) at 156 Hz is calculated by complex spectrum analysis, and the phase shift of the coupled vibration mode relative to the dominant vibration mode is obtained as 30 degrees (i.e. π / 6 radian).

[0071] In some applications, in response to the amplitude-frequency characteristics, an amplitude compensation coefficient is generated, a time-varying phase compensation factor is generated by a dynamic prediction algorithm in combination with the phase offset, and a dynamic vibration compensation parameter is dynamically weighted and fused based on the vibration energy distribution, including normalizing the spectral energy of the dominant vibration mode, fitting a nonlinear function in combination with the material strain-frequency response characteristics to generate the amplitude compensation coefficient; based on the phase offset of the associated coupled vibration mode, a state estimation algorithm is used to predict the generation of the time-varying phase compensation factor; the energy ratio of the dominant vibration mode to the associated coupled vibration mode is calculated in real time, and the weight coefficient is dynamically allocated through a continuous function to weight and fuse the amplitude compensation coefficient and the time-varying phase compensation factor to generate the dynamic vibration compensation parameter.

[0072] It can be understood that the spectral energy of the dominant vibration mode is normalized in the preset frequency band, the amplitude attenuation law is fitted by a polynomial function in combination with the nonlinear characteristics of the material strain-frequency response, and the amplitude compensation coefficient varying with frequency is generated; based on the phase offset of the associated coupled vibration mode, a state estimation algorithm is used to construct a dynamic model with phase and its first derivative as state vectors, process noise and observation noise covariance matrices are configured according to the statistical characteristics of vibration noise, and a phase compensation factor function containing time variable is generated by recursive prediction; the vibration energy ratio of the dominant vibration mode to the associated coupled vibration mode is calculated in real time, and the energy ratio is dynamically mapped to the weight coefficient using a continuous differentiable function, and the amplitude compensation coefficient and the time-varying phase compensation factor are weighted and superimposed according to the weight to generate the dynamic vibration compensation parameter.

[0073] For example, the amplitude compensation coefficient is generated: for the 78Hz dominant vibration mode (frequency band 70-86Hz), the frequency domain energy integral value is calculated as 25000(με)²; the frequency domain normalization coefficient is the inverse of the energy integral value: 1 / 25000=0.00004(με) -2 ; in combination with the characteristics of high-strength steel materials, a cubic polynomial function of strain-frequency response is fitted: amplitude compensation coefficient=1.2-0.005×frequency+0.000011×frequency²-0.000000008×frequency³; the polynomial output value at 78Hz is calculated as 0.8; the final amplitude compensation coefficient is generated by weighted fusion of the frequency domain normalization coefficient and the polynomial coefficient (weight ratio 6:4): 0.6×0.00004+0.4×0.8=0.320024. Time-varying phase compensation factor prediction: the fixed phase difference of the 156Hz associated coupled vibration mode is π / 6 (30 degrees); the Kalman filter state vector is constructed: [phase offset, phase change rate]; the process noise covariance matrix Q=1×10 -6 , the observation noise covariance matrix R=1×10 -4; recursive prediction equation: current phase offset = previous phase offset + phase rate of change x time step + process noise; observation = current phase offset + observation noise; generate time-varying phase compensation factor function: Δφ(t) = 0.2 x sin(2π x 78t + π / 4). Dynamic vibration compensation parameter fusion: calculate the energy ratio of the 78Hz dominant mode and the 156Hz coupled mode: energy ratio η = dominant mode energy / coupled mode energy = 65% / 35% = 1.86; calculate the weight distribution coefficient through the sigmoid continuous function: α(t) = 1 / (1 + e^(-0.1 x (η - 2.0))) = 1 / (1 + e^(-0.1 x (-0.14))) ≈ 0.52; dynamically fuse the amplitude compensation coefficient and the time-varying phase compensation factor: dynamic vibration compensation parameter K(t) = α(t) x amplitude coefficient + (1 - α(t)) x phase factor = 0.52 x 0.320024 + 0.48 x 0.2 x sin(2π x 78t + π / 4).

[0074] In some applications, the state estimation algorithm includes constructing a state equation with phase offset and phase offset rate of change as state vectors; configuring process noise covariance and observation noise covariance according to vibration noise statistical characteristics; updating the estimated value of the phase offset through recursive prediction to generate a phase compensation factor function containing a time variable.

[0075] It can be understood that a state equation is constructed with the phase offset and its time rate of change as state vectors; process noise covariance matrices and observation noise covariance matrices are configured according to the statistical distribution characteristics of downhole vibration noise; the estimated value of the phase offset is updated through a recursive prediction mechanism, and a phase compensation factor function containing a time variable is generated in combination with a sine function.

[0076] For example, for a fixed phase difference π / 6 radians of the associated coupled vibration mode, a state vector containing phase offset φ and its rate of change φ' is constructed; process noise covariance 1 x 10 -6 Reflecting the vibration slow change characteristics, the observation noise covariance is 1 x 10 -4 Matching the sensor measurement accuracy; in recursive prediction: the current phase offset is equal to the previous phase offset plus the phase rate of change multiplied by the time step 0.001 seconds plus the process noise, and the observation value is equal to the current phase offset superimposed with the observation noise; after updating through the Kalman gain, a phase compensation factor function 0.2 x sin(2π x 78t + π / 4) is generated, where 78Hz is derived from the dominant vibration mode frequency, the amplitude 0.2 is obtained by historical phase residual calibration, and π / 4 is the initial phase offset adjustment amount.

[0077] In some applications, in response to the dynamic vibration compensation parameter, the gyroscope angular velocity signal is processed based on a pre-trained nonlinear phase distortion compensation model, and the phase-corrected gyroscope angular velocity signal is output after inputting the dynamic vibration compensation parameter, including constructing a nonlinear phase distortion compensation model based on a long short-term memory network, and the training data contains a phase lag mapping relationship in a historical vibration environment; the gyroscope angular velocity signal and the dynamic vibration compensation parameter are input into the nonlinear phase distortion compensation model, and a complex exponential compensation factor containing amplitude and phase adjustment is output; the analyzed gyroscope angular velocity signal and the complex exponential compensation factor are multiplied in the complex number field, and the phase-corrected gyroscope angular velocity signal is output.

[0078] It can be understood that the nonlinear phase distortion compensation model based on the long short-term memory network is constructed, and the training data is derived from the mapping relationship dataset of the radial deformation of the drill pipe and the phase lag of the gyroscope in the historical drilling process; the real-time acquired gyroscope angular velocity signal and the dynamic vibration compensation parameter are input into the model, and a complex exponential compensation factor containing an amplitude scaling factor and a phase adjustment angle is output; the original gyroscope angular velocity signal is converted into an analytical signal form combined with real and imaginary parts through Hilbert transform; multiplication operation is performed on the analytical signal and the complex exponential compensation factor in the complex number field, so that the phase adjustment angle of the compensation factor offsets the phase lag component in the original signal, and the phase-corrected gyroscope angular velocity signal is output; the input buffer is updated by using a sliding window mechanism, and the continuity of part of the historical data is maintained by using an overlap retention strategy, so as to ensure the real-time correction capability when the vibration frequency suddenly changes.

[0079] For example, the long short-term memory network model is trained by historical drilling data (input angular velocity signal 0.5×sin(2π×78t+0.3π), amplitude compensation coefficient 0.32, phase compensation factor 0.2×sin(2π×78t+π / 4), output target complex exponential compensation factor e^(j×0.25π); in real-time processing, the input gyroscope signal 0.5×sin(2π×78t+0.3π) and the dynamic vibration compensation parameter are input, and the model outputs the complex exponential compensation factor with an amplitude of 1.0 and a phase angle of 0.25π; the analytical signal real part 0.5×sin(2π×78t+0.3π) and the imaginary part 0.5×cos(2π×78t+0.3π) are obtained through Hilbert transform; complex number field multiplication operation: the new real part is obtained by multiplying the real part by the real part of the compensation factor cos(0.25π) and subtracting the imaginary part multiplied by the imaginary part sin(0.25π), the new imaginary part is obtained by multiplying the real part by the imaginary part of the compensation factor sin(0.25π) and adding the imaginary part multiplied by the real part cos(0.25π), and the final phase-corrected signal 0.5×sin(2π×78t+0.25π) is reconstructed, and the phase lag is reduced by 0.05π; the input buffer is updated every 200 milliseconds, 50 milliseconds of historical data are retained, and the model response delay is 3 milliseconds when the drill pipe speed suddenly changes from 1000 revolutions per minute to 1200 revolutions per minute.

[0080] In some applications, the accelerometer signal is subjected to a vibration interference frequency band suppression process, and the processed accelerometer signal is output, including analyzing the vibration interference frequency band in the spectrum of the accelerometer signal; setting a notch filter with a stopband attenuation greater than a preset attenuation threshold; performing a filtering operation on the accelerometer signal to eliminate amplitude drift caused by centrifugal vibration; and outputting the accelerometer signal with an amplitude error within a preset error range.

[0081] It can be understood that the periodic interference frequency band caused by the centrifugal vibration of the drill pipe is analyzed in the spectrum of the accelerometer signal; based on the center frequency and energy distribution of the interference frequency band, a notch filter with a stopband attenuation exceeding a preset attenuation threshold is designed; a filtering operation is performed on the original accelerometer signal to eliminate the amplitude drift component caused by centrifugal vibration coupling; and the accelerometer signal with a stable amplitude fluctuation range within the preset error threshold is output.

[0082] For example, the spectrum characteristics of the accelerometer signal are analyzed to identify the existence of significant interference frequency bands at 78 Hz and 156 Hz; a fourth-order Butterworth notch filter is designed with center frequencies of 78 Hz and 156 Hz, a stopband width of 10 Hz, and a stopband attenuation of 40 decibels; the original signal containing centrifugal vibration interference (drift amplitude ±0.05g) is subjected to filtering processing, and the output signal amplitude drift is reduced to ±0.005g; through variance calculation, the amplitude error range of the filtered signal meets the preset threshold of ±0.01g.

[0083] In some applications, based on a dynamic weight fusion algorithm, the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal are iteratively optimized, and a directional calibration result is output after filtering and smoothing, including complementary filtering and preliminary fusion of the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal; constructing a target function with the gyroscope phase residual variance and the accelerometer amplitude error variance as variables; dynamically adjusting the fusion weight based on the drill pipe vibration frequency, increasing the gyroscope angular velocity signal weight when the vibration frequency is higher than a preset frequency threshold, and increasing the accelerometer signal weight when the vibration frequency is lower than the preset frequency threshold; noise reduction processing is performed on the fusion result, and directional calibration data including azimuth angle and inclination angle are output.

[0084] It can be understood that the gyro angular velocity signal after phase correction and the accelerometer signal after vibration suppression processing are complementarily filtered and preliminarily fused; a target function is constructed, taking the gyro signal phase residual variance and the accelerometer signal amplitude error variance as the joint optimization target; the fusion weight coefficient is dynamically adjusted based on the real-time vibration frequency of the drill pipe, when the vibration frequency is higher than the preset frequency threshold, the weight proportion of the gyro angular velocity signal in the fusion is increased, when the vibration frequency is lower than the preset frequency threshold, the weight proportion of the accelerometer signal is increased; the state estimation algorithm is used for noise reduction and smoothing processing on the fusion result, and directional calibration data containing azimuth angle and inclination angle information are output; when the directional error variance exceeds the convergence threshold, the vibration energy distribution recalculation is triggered to update the dynamic compensation parameters.

[0085] For example, the fusion weight coefficient is initialized to 0.5, the gyro angular velocity signal after phase correction 0.5xsin(2pi x 78t+0.25pi) and the filtered accelerometer signal (amplitude drift ±0.005g) are complementarily filtered and preliminarily fused; the target function is constructed to contain the gyro phase residual variance 0.01 and the accelerometer amplitude error variance 0.04; the real-time vibration frequency 78 Hz is higher than the preset threshold 50 Hz, and the gradient descent method is used for iterative optimization: the first iteration calculates the gradient of the target function-0.024, the weight is updated to 0.5+0.05x0.024=0.5012, and after 10 iterations, the weight converges to 0.67, and the weight of the gyro signal is increased; the fusion result is input into the Kalman filter state equation (azimuth angle state vector [theta,b], process noise covariance 1x10 -6 ), the observation value is calculated by the accelerometer, the azimuth angle standard deviation is reduced from 0.5 degrees to 0.08 degrees, and the inclination angle error is less than 0.1 degrees; when the residual covariance trace exceeds the threshold 1x10 -6 , the energy proportion calculation of the dominant vibration mode and the associated coupled vibration mode is triggered again.

[0086] Another embodiment of the cooperative directional calibration method of the application is described below:

[0087] This embodiment collects the circumferential strain distribution data of the drill pipe by the annularly distributed fiber Bragg grating sensor array. The sensor array is attached to the surface of the drill pipe in an equal interval manner. Each grating node reflects a specific wavelength of light signal, and the wavelength shift is linearly related to the strain. The wavelength demodulation system uses tunable laser scanning or spectral analysis method (such as FFT demodulation) to analyze the reflected spectrum in real time, and converts the wavelength shift into strain waveform data. Subsequently, the circumferential strain distribution data is separated in the frequency domain. First, the time domain strain signal is converted to the frequency domain by Fourier transform (FFT), and then the energy concentration area in the mechanical vibration frequency band (such as 10 Hz-1 kHz) is extracted by combining band-pass filtering (such as Butterworth filter). The amplitude-frequency relationship of the dominant vibration mode is identified by the peak detection algorithm (such as local extremum method) from the frequency spectrum to identify the frequency component with the maximum amplitude and its harmonics. The phase shift of the coupled vibration mode is calculated by Hilbert transform or phase unwrapping algorithm to separate the vibration components with the same frequency but phase lag.

[0088] According to the amplitude-frequency relationship of the dominant vibration mode, the vibration amplitude compensation coefficient is obtained by establishing the amplitude-frequency response curve (such as polynomial fitting) and calculating its reciprocal, which is used to normalize the vibration amplitude influence at different frequencies. The phase shift is input into the time-varying phase compensation factor generation module. First, the dynamic mean value of the phase difference is calculated by the sliding window method, and then the phase drift trend is predicted by combining Kalman filtering to generate a compensation factor that adjusts with time. Finally, the amplitude compensation coefficient (static parameter) and the time-varying phase compensation factor (dynamic parameter) are combined into a dynamic vibration compensation parameter by linear weighting, and the weight is adaptively adjusted by the vibration energy proportion.

[0089] When the gyro angular velocity signal and the accelerometer signal are acquired synchronously, the hardware timestamp alignment (such as PTP protocol) is used to ensure data synchronization. The dynamic vibration compensation parameter is input into the nonlinear phase distortion compensation model. The model is based on LSTM neural network, and the input is the angular velocity signal and the compensation parameter. The training data comes from the historical phase-strain mapping relationship of the drill pipe radial deformation coupled vibration. The model identifies the periodic vibration mode (such as wavelet transform to extract periodic features) through time series analysis and dynamically outputs the phase correction amount. The correction process uses complex domain multiplication to multiply the original angular velocity signal with the complex exponential compensation factor (including amplitude and phase) of the compensation model, which eliminates the phase lag caused by vibration.

[0090] When fusing the corrected angular velocity signal and the accelerometer signal, firstly, the complementary filtering is used for preliminary fusion, and then an iterative optimization algorithm (such as Levenberg-Marquardt) is used to optimize the fusion weight. The objective function of the algorithm contains the phase residual of the gyroscope signal and the amplitude error of the accelerometer signal, and the weight is dynamically adjusted according to the vibration period of the radial deformation of the drill pipe. The weight of the gyroscope is increased during high-frequency vibration, and the weight of the accelerometer is increased during low-frequency vibration. After each iteration, the fusion weight is updated, and the output is smoothed through Kalman filtering to generate the directional calibration result. The accuracy is optimized through the closed-loop feedback of the vibration characteristic data.

[0091] The embodiment will be described below in combination with an application scenario.

[0092] During the drilling process, the drill pipe is affected by the non-uniform friction of the well wall and the impact vibration of the drill bit, generating a circumferential strain fluctuation with an amplitude of ±200με, which causes the gyroscope angular velocity signal to have a periodic phase distortion of ±0.3°, and the accelerometer output is disturbed by the centrifugal vibration, generating an amplitude drift of ±0.05g. The traditional Kalman filtering does not consider the mechanical vibration coupling effect, and the directional calibration error exceeds ±0.5°.

[0093] In this embodiment, an annularly distributed 8-channel fiber grating sensor (spaced 45°) is used to collect the strain data of the drill pipe surface in real time, and a wavelength demodulation system is used to analyze the wavelength shift at a sampling rate of 1kHz. The frequency domain analysis result shows that the dominant vibration mode frequency is 78Hz, and the energy ratio is 65%, while the coupled vibration mode frequency is 156Hz, and the phase lag of the main mode is π / 6. In the dynamic compensation generation process, firstly, the amplitude interval of the dominant vibration mode is divided into [0-50με, 50-150με, >150με], and the corresponding compensation coefficients are [1.0, 0.8, 0.6], and at the same time, based on the phase shift of the 156Hz coupled mode, a time-varying phase compensation factor Δφ=0.2sin(2π×78t+π / 4) is generated. Through the energy ratio weighted fusion of the main mode 70% and the coupled mode 30%, the dynamic compensation parameter K=0.7×A(t)+0.3×Δφ(t) is finally generated, where A is the amplitude compensation coefficient, Δφ is the phase compensation factor, and t is the time variable.

[0094] In the signal correction and fusion link, after the gyroscope signal is processed through the nonlinear phase compensation model, the phase distortion is significantly reduced to ±0.05° (model parameter α=1.5e-3·sin(2π×78t)), and the accelerometer signal is effectively eliminated through the 78Hz / 156Hz vibration band-stop filter (stop-band attenuation -40dB). The improved particle swarm algorithm (inertia weight w=0.6-0.9 dynamically adjusted) is used to fuse and optimize the corrected signal. After 10 iterations, the system directional error converges to ±0.08°.

[0095] The key steps in this embodiment are described in detail as follows:

[0096] In deep directional drilling, the drill pipe is subjected to well wall friction and drill bit impact, resulting in complex circumferential strain fluctuations (amplitude up to ±2000με), causing gyroscope phase distortion (±0.3°) and accelerometer amplitude drift (±0.05g). The traditional method ignores the multi-modal coupling effect in the vibration frequency band, with an error of more than ±0.5°. The dynamic compensation algorithm of this embodiment solves the problem in the following steps:

[0097] Main vibration mode amplitude compensation coefficient calculation:

[0098] Frequency domain energy normalization: The drill pipe surface strain data is collected in real time by the 8-channel fiber Bragg grating sensor (interval 45°) in the ring distribution, and the wavelength shift is analyzed at a sampling rate of 1 kHz. When the energy integral is performed on the frequency spectrum after FFT decomposition, the compensation coefficient is calculated for the dominant vibration mode frequency 78 Hz (the measured energy accounts for 65%):

[0099] wherein, represents the amplitude compensation coefficient calculated at the dominant vibration mode frequency 78 Hz; is the square of the spectral amplitude at the frequency , i.e. the power spectral density. is the frequency spectrum of the drill pipe circumferential strain data after Fourier transform (FFT), indicating the distribution of vibration in the frequency domain. represents the frequency resolution, i.e. the frequency interval (unit: Hz) in the discrete Fourier transform (DFT) (Δf=1Hz, frequency band width 10Hz);

[0100] For example, if the amplitude of the main mode is 150με at 78Hz, and the total energy integral result in the frequency band is , then is used for normalization amplitude influence.

[0101] Nonlinear fitting correction:

[0102] The strain-frequency response of the drill pipe material (such as high-strength steel) has nonlinear characteristics. By calibrating the vibration data at different speeds (such as 500-1500rpm) through experiments, a cubic polynomial is fitted:

[0103] ；

[0104] When f=78Hz, A comp =0.8, and the frequency domain normalization result is weighted (such as 0.6x frequency domain coefficient+0.4x fitted coefficient), to generate the amplitude compensation coefficient, effectively suppressing the amplitude drift caused by material nonlinearity.

[0105] Coupling vibration modal phase compensation factor generation:

[0106] Phase difference solution: for the phase difference of adjacent nodes (such as node 1 and node 2) in the ring sensor array, the complex spectrum analysis is calculated:

[0107] (the measured lag amount);

[0108] It is shown that there is a fixed phase difference when the coupling vibration (156 Hz) propagates in the circumference of the drill pipe, which needs to be dynamically compensated.

[0109] Time-varying phase prediction: establish Kalman filter state equation, state vector , wherein, is the phase offset, which represents the phase difference of the coupling vibration modal relative to the dominant vibration modal; is the rate of change of the phase offset, which represents the speed of change of the phase difference with time, used to predict the future state, is the transpose symbol, indicating that the vector is a column vector (standard representation in state space model). Contains phase and its rate of change. According to the characteristics of downhole vibration noise (Allan variance analysis, process noise Q=10 −6 , observation noise P=10 −4 ), the phase offset at the next time is predicted:

[0110] ;

[0111] wherein, based on the state and the rate of change at the previous time, the phase offset at the current time is predicted; is the time step; is the process noise, reflecting the uncertainty of the model; the real state of the system is related to the observation noise , is the phase value actually measured by the sensor;

[0112] Through real-time updating, the error between the predicted value and the measured value is less than 0.01 rad, ensuring the dynamic accuracy of the compensation factor .

[0113] Energy weight adaptive adjustment:

[0114] Modal energy ratio calculation: Butterworth band-pass filtering (passband 70-86 Hz and 140-172 Hz) is performed on the main modal (78 Hz) and the coupling modal (156 Hz) respectively, and the energy ratio is calculated:

[0115] where, is the frequency spectrum of the dominant vibration mode; is the frequency spectrum of the associated coupled vibration mode;

[0116] When η < 2.0 (threshold η0=2.0), increase the coupling mode weight to prevent low-frequency energy interference.

[0117] Dynamic weight distribution: adjust the weight using the sigmoid function:

[0118] ;

[0119] Final compensation parameters , achieve adaptive balance when the vibration energy changes.

[0120] Application of LSTM phase compensation model in real-time signal correction:

[0121] Network training and downhole vibration mode adaptation:

[0122] Data preparation: use historical drilling data (including the mapping relationship between drill pipe radial deformation and gyroscope phase lag) to construct the training set. Input features include angular velocity signal ω(t), amplitude compensation coefficient , phase compensation factor , and the output target is the ideal compensated complex factor (target phase residual ±0.05°), where, represents the original phase angle.

[0123] Network training: 128 neurons in the bidirectional LSTM hidden layer, using the Adam optimizer (learning rate 0.001), after 500 rounds of training, the loss value decreases from 1.2 to 0.03. Random vibration frequency disturbance (±10Hz) is introduced during training to enhance the model's robustness to complex downhole environments.

[0124] Real-time correction and drilling time sequence matching:

[0125] Complex domain correction: the original gyroscope signal is transformed into an analytical signal by Hilbert transform:

[0126] ;

[0127] Model output compensation factor , corrected signal:

[0128] ;

[0129] The phase lag is reduced from 0.3π to 0.05π, meeting the accuracy requirement of ±0.05°.

[0130] Sliding window update: update the input buffer every 200 ms, and reserve the previous 50 ms data using the overlap reservation method to avoid phase mutation. The test shows that when the drill pipe speed suddenly changes (e.g. from 1000 rpm to 1200 rpm), the model response delay is less than 5 ms, ensuring real-time performance.

[0131] Signal fusion and directional calibration optimization in horizontal well trajectory control:

[0132] Dynamic weight fusion algorithm responds to vibration period changes:

[0133] Objective function optimization: assuming the current gyroscope residual variance σg2=0.01 and the accelerometer residual σa2=0.04, the objective function is:

[0134] where w is the fusion weight; is the derivative of the weight with respect to time; the weight is updated by gradient descent:

[0135] After 10 iterations, the weight converges from the initial value of 0.5 to w=0.67, and the gyroscope weight is increased to adapt to the high-frequency vibration (78 Hz) environment.

[0136] Kalman filter denoising and azimuth angle calculation:

[0137] State update:

[0138] State equation of azimuth angle θk and angular velocity deviation bk: The observation value θacc is calculated by the accelerometer to calculate the inclination, and after filtering, the azimuth angle error standard deviation is reduced from 0.5° to 0.08°.

[0139] Convergence verification and downhole measured data:

[0140] Convergence criterion: in 10 seconds of continuous operation, the fusion error E(w) is reduced from 0.05 to 0.008, and the residual covariance trace tr(Pk) is less than 1e−6, meeting the convergence condition.

[0141] For the method steps disclosed in the above embodiments, the method steps are described as a series of action combinations for the purpose of simple description, but those skilled in the art should know that the embodiments of the present application are not limited by the order of the described actions, because according to the embodiments of the present application, certain steps can be performed in other order or simultaneously. Secondly, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0142] As Figure 2 shown, the application also provides a cooperative directional calibration system, comprising:

[0143] a frequency domain separation module 201, configured to perform frequency domain separation on the acquired drill pipe circumferential strain distribution data, extract amplitude-frequency characteristics of a dominant vibration mode and phase shift amounts of associated coupled vibration modes in response to the drill pipe circumferential strain distribution data;

[0144] a vibration compensation parameter generation module 202, configured to generate an amplitude compensation coefficient in response to the amplitude-frequency characteristics, generate a time-varying phase compensation factor through a dynamic prediction algorithm in combination with the phase shift amounts, and generate dynamic vibration compensation parameters based on dynamic weighted fusion of vibration energy distribution;

[0145] a gyroscope angular velocity signal correction module 203, configured to process a gyroscope angular velocity signal based on a pre-trained nonlinear phase distortion compensation model in response to the dynamic vibration compensation parameters, and output a phase-corrected gyroscope angular velocity signal after inputting the dynamic vibration compensation parameters;

[0146] an accelerometer signal processing module 204, configured to perform vibration interference frequency band suppression processing on an accelerometer signal, and output a processed accelerometer signal;

[0147] a directional calibration result output module 205, configured to perform iterative optimization on the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal based on a dynamic weight fusion algorithm, and output a directional calibration result after filter smoothing processing.

[0148] It is worth noting that, although only some basic functional modules are disclosed in the embodiments of the application, it does not mean that the composition of the system is limited to only the above basic functional modules, on the contrary, the meaning expressed by the embodiments is: on the basis of the above basic functional modules, those skilled in the art can add one or more functional modules to form infinite embodiments or technical solutions in combination with existing technologies, that is, the system is open rather than closed, and the protection scope of the claims of the application cannot be limited to the disclosed basic functional modules. At the same time, in order to describe conveniently, the above device is described as various units and modules. Of course, the functions of the units and modules can be realized in the same software and / or hardware in the implementation of the application.

[0149] As Figure 3As shown, the present application also provides an electronic device, comprising: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete the communication among each other through the communication bus; the memory stores a computer program, when the computer program is executed by the processor, the processor executes the steps of a cooperative orientation calibration method.

[0150] Figure 3 is a structural schematic diagram of an electronic device provided by an embodiment of the present application. As shown in the structural diagram, Figure 3 the electronic device provided by the embodiment of the present application comprises: one or more processors 710 and a memory 720; the processor 710 in the electronic device can be one or more, Figure 3 In the embodiment, the processor 710 is taken as an example; the memory 720 is used for storing one or more programs; the one or more programs are executed by the one or more processors 710, so that the one or more processors 710 implement a cooperative orientation calibration method according to any one of the embodiments of the present application.

[0151] The electronic device can further comprise: an input device 730 and an output device 740.

[0152] The processor 710, the memory 720, the input device 730 and the output device 740 in the electronic device can be connected through a bus or other means, Figure 3 In the embodiment, the connection through the bus is taken as an example.

[0153] The memory 720 in the electronic device is a computer readable storage medium, which can be used to store one or more programs, and the programs can be software programs, computer executable programs and modules, such as program instructions / modules of a cooperative orientation calibration method provided by the embodiment of the present application. The processor 710 executes various functional applications and data processing of the electronic device by running the software programs, instructions and modules stored in the memory 720, that is, implements the cooperative orientation calibration method in the above method embodiment.

[0154] The memory 720 can include a program storage area and a data storage area, wherein the program storage area can store an operating system and at least one application program required by a function; the data storage area can store data created according to the use of the electronic device, etc. In addition, the memory 720 can include a high-speed random access memory, and can also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device or other non-volatile solid-state memory device. In some examples, the memory 720 can further include a memory remotely arranged relative to the processor 710, and these remote memories can be connected to the device through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0155] The input device 730 can be used to receive input digital or character information, and to generate key signal input related to user settings and function controls of the electronic device. The output device 740 can include a display device such as a display screen.

[0156] The present application also provides a computer readable storage medium storing a computer program executable by an electronic device, which when executed on the electronic device, causes the electronic device to perform the steps of a cooperative directional calibration method.

[0157] In particular, the computer storage medium of the embodiments of the present application can adopt any combination of one or more computer readable mediums. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, be but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus or device.

[0158] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement to part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of synergistic directional calibration, characterized by, The method comprises the following steps: In response to the obtained circumferential strain distribution data of the drill pipe, the circumferential strain distribution data of the drill pipe is separated in the frequency domain, the amplitude-frequency characteristics of the dominant vibration mode and the phase shift of the associated coupled vibration mode are extracted; In response to the amplitude-frequency characteristics, the amplitude compensation coefficient is generated, the time-varying phase compensation factor is generated by combining the phase shift through a dynamic prediction algorithm, and the dynamic vibration compensation parameter is generated based on the dynamic weighted fusion of the vibration energy distribution; In response to the dynamic vibration compensation parameter, the gyroscope angular velocity signal is processed based on the pre-trained nonlinear phase distortion compensation model, and the phase-corrected gyroscope angular velocity signal is output after the dynamic vibration compensation parameter is input; The accelerometer signal is processed for vibration interference frequency band suppression, and the processed accelerometer signal is output; Based on the dynamic weight fusion algorithm, the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal are iteratively optimized, and the directional calibration result is output after combining the filter smoothing processing.

2. The method of claim 1, wherein, In response to the obtained circumferential strain distribution data of the drill pipe, the circumferential strain distribution data of the drill pipe is separated in the frequency domain, the amplitude-frequency characteristics of the dominant vibration mode and the phase shift of the associated coupled vibration mode are extracted, and further comprising: Obtain the circumferential strain waveform data of the annular distribution, analyze the frequency domain of the circumferential strain waveform data, and extract the frequency spectrum in the preset mechanical vibration frequency band; Identify the frequency component with the largest energy proportion in the frequency spectrum as the dominant vibration mode, and obtain the relationship between the amplitude of the dominant vibration mode and the frequency; Obtain the coupling vibration frequency component of the coupled vibration mode which has harmonic correlation with the dominant vibration mode, and obtain the phase shift of the coupled vibration mode relative to the dominant vibration mode through phase difference calculation.

3. The method of claim 1, wherein, In response to the amplitude-frequency characteristics, the amplitude compensation coefficient is generated, the time-varying phase compensation factor is generated by combining the phase shift through a dynamic prediction algorithm, and the dynamic vibration compensation parameter is generated based on the dynamic weighted fusion of the vibration energy distribution, further comprising: Normalizing the spectral energy of the dominant vibration mode, fitting a nonlinear function based on the material strain frequency response characteristics, and generating an amplitude compensation coefficient; Based on the phase shift of the associated coupled vibration mode, a state estimation algorithm is used to predict the time-varying phase compensation factor; Real-time calculation of the energy proportion ratio of the dominant vibration mode and the associated coupled vibration mode, dynamic allocation of the weight coefficient through the continuous function, and weighted fusion of the amplitude compensation coefficient and the time-varying phase compensation factor to generate the dynamic vibration compensation parameter.

4. The method of claim 3, wherein, The state estimation algorithm further comprises: Constructing a state equation with phase shift and phase shift rate as state vectors; According to the vibration noise statistical characteristics, the process noise covariance and the observation noise covariance are configured; The estimated value of the phase shift is updated through recursive prediction to generate a phase compensation factor function containing time variable.

5. The method of claim 1, wherein, In response to the dynamic vibration compensation parameter, the gyroscope angular velocity signal is processed based on the pre-trained nonlinear phase distortion compensation model, and the phase-corrected gyroscope angular velocity signal is output after the dynamic vibration compensation parameter is input, further comprising: A nonlinear phase distortion compensation model based on a long short-term memory network is constructed, and training data contains a phase lag mapping relationship in a historical vibration environment; The gyro angular velocity signal and the dynamic vibration compensation parameter are input into the nonlinear phase distortion compensation model, and a complex exponential compensation factor containing an amplitude and a phase adjustment amount is output; In the complex domain, the analyzed gyro angular velocity signal is multiplied by the complex exponential compensation factor, and a phase-corrected gyro angular velocity signal is output.

6. The method of claim 1, wherein, The accelerometer signal is subjected to vibration interference frequency band suppression processing, and a processed accelerometer signal is output, further comprising: analyzing the vibration interference frequency band in the spectrum of the accelerometer signal; setting a notch filter with a stopband attenuation greater than a preset attenuation threshold; performing a filtering operation on the accelerometer signal to eliminate amplitude drift caused by centrifugal vibration; outputting the accelerometer signal with an amplitude error within a preset error range.

7. The method of claim 1, wherein, Based on a dynamic weight fusion algorithm, the phase-corrected gyro angular velocity signal and the processed accelerometer signal are iteratively optimized, and a directional calibration result is output after filtering and smoothing, further comprising: complementary filtering and preliminary fusion of the phase-corrected gyro angular velocity signal and the processed accelerometer signal; constructing a target function with gyro phase residual variance and accelerometer amplitude error variance as variables; based on the dynamic adjustment of the fusion weight of the drill pipe vibration frequency, increasing the gyro angular velocity signal weight when the vibration frequency is higher than the preset frequency threshold, and increasing the accelerometer signal weight when the vibration frequency is lower than the preset frequency threshold; noise reduction processing is performed on the fusion result, and directional calibration data including azimuth and inclination are output.

8. A synergistic directional calibration system, characterized by, It includes: a frequency domain separation module configured to respond to the acquired drill pipe circumferential strain distribution data, separate the drill pipe circumferential strain distribution data in the frequency domain, extract the amplitude-frequency characteristics of the dominant vibration mode and the phase offset of the associated coupled vibration mode; a vibration compensation parameter generation module configured to generate an amplitude compensation coefficient in response to the amplitude-frequency characteristics, generate a time-varying phase compensation factor through a dynamic prediction algorithm in combination with the phase offset, and generate dynamic vibration compensation parameters based on vibration energy distribution dynamic weighted fusion; a gyro angular velocity signal correction module configured to process gyro angular velocity signals based on a pre-trained nonlinear phase distortion compensation model in response to the dynamic vibration compensation parameters, and output phase-corrected gyro angular velocity signals after inputting the dynamic vibration compensation parameters; an accelerometer signal processing module configured to perform vibration interference frequency band suppression processing on the accelerometer signal and output a processed accelerometer signal; a directional calibration result output module configured to iteratively optimize the phase-corrected gyro angular velocity signal and the processed accelerometer signal based on a dynamic weight fusion algorithm, and output a directional calibration result after filtering and smoothing.

9. An electronic device, comprising: It includes: A processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, It stores a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of the method in any one of claims 1 to 7.

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