Collaborative directional calibration method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of equipment calibration, and discloses a collaborative directional calibration method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining the circumferential strain distribution data of a drill rod, and extracting the amplitude-frequency characteristics of a dominant vibration mode and the phase offset of an associated coupling 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 offset, and generating a dynamic vibration compensation parameter based on vibration energy distribution dynamic weighted fusion; based on a pre-trained nonlinear phase distortion compensation model, inputting a dynamic vibration compensation parameter and then outputting a gyroscope angular velocity signal after phase correction; performing vibration interference frequency band suppression processing on the accelerometer signal, and outputting the processed accelerometer signal; and performing iterative optimization on the corrected angular velocity signal and the processed accelerometer signal, and outputting a directional calibration result in combination with filtering smoothing processing. According to the invention, high-precision cooperative calibration of the inertial sensor in a multi-mode mechanical vibration environment can be realized.

Description

Technical Field

[0001] This invention belongs to the field of equipment calibration technology, and in particular relates to a collaborative orientation calibration method, system, electronic device and storage medium. Background Technology

[0002] Currently, in deep well directional drilling, the high-speed rotation of the drill pipe can cause circumferential strain fluctuations and radial deformation coupled vibrations, resulting in phase distortion of the gyroscope angular velocity signal and centrifugal acceleration interference in the accelerometer output.

[0003] In existing technologies, static correction by accelerometers or temperature compensation by gyroscopes are often used, but the time-varying characteristics of multimodal coupling errors within the vibration frequency band are not considered. Traditional methods are insufficient in handling phase distortion, conventional low-pass filtering introduces signal delay, and cannot eliminate nonlinear phase lag caused by radial deformation of the drill pipe. Furthermore, dynamic weights are missing, and existing fusion algorithms use fixed weight coefficients, which are difficult to adapt to the periodic changes in circumferential strain energy distribution.

[0004] Therefore, this application provides a collaborative orientation calibration method to solve the above-mentioned technical problems. Summary of the Invention

[0005] The purpose of this invention is to provide a collaborative orientation calibration method, system, electronic device, and storage medium to solve the technical problem of decreased accuracy of gyroscope-accelerometer collaborative calibration in multimodal mechanical vibration scenarios in the prior art.

[0006] To address the aforementioned technical problems, this invention provides a collaborative orientation calibration method, comprising: In response to the acquired circumferential strain distribution data of the drill pipe, frequency domain separation is performed on the circumferential strain distribution data of the drill pipe to extract the amplitude-frequency characteristics of the dominant vibration mode and the phase offset of the associated coupled vibration mode; In response to the amplitude-frequency characteristics, an amplitude compensation coefficient is generated, and a time-varying phase compensation factor is generated by combining the phase offset with a dynamic prediction algorithm. Dynamic vibration compensation parameters are generated by dynamic weighted fusion based on the vibration energy distribution. In response to the dynamic vibration compensation parameters, the gyroscope angular velocity signal is processed based on a pre-trained nonlinear phase distortion compensation model. After inputting the dynamic vibration compensation parameters, the phase-corrected gyroscope angular velocity signal is output. The accelerometer signal is subjected to vibration interference frequency band suppression processing, 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 orientation calibration result is output after filtering and smoothing.

[0007] In some specific embodiments, in response to the acquired circumferential strain distribution data of the drill pipe, frequency domain separation is performed on the circumferential strain distribution data of the drill pipe to extract the amplitude-frequency characteristics of the dominant vibration mode and the phase shift of the associated coupled vibration mode, further including: Obtain circumferential strain waveform data with a ring distribution, perform frequency domain analysis on the circumferential strain waveform data, and extract the spectrum within a preset mechanical vibration frequency band; Identify the frequency component with the largest energy proportion in the spectrum as the dominant vibration mode, and obtain the relationship between the amplitude of the dominant vibration mode and frequency. The coupled vibration frequency components of the coupled vibration modes that are harmonicly associated with the dominant vibration mode are obtained, and the phase offset of the coupled vibration modes relative to the dominant vibration mode is obtained by phase difference calculation.

[0008] In some specific embodiments, in response to the amplitude-frequency characteristics, an amplitude compensation coefficient is generated; a time-varying phase compensation factor is generated by combining the phase offset with a dynamic prediction algorithm; and dynamic vibration compensation parameters are generated based on dynamic weighted fusion of vibration energy distribution, further including: The spectral energy of the dominant vibration mode is normalized and integrated, and a nonlinear function is fitted in combination with the material strain-frequency response characteristics to generate amplitude compensation coefficients. Based on the phase shift of the associated coupled vibration modes, a state estimation algorithm is used to predict and generate a time-varying phase compensation factor. The energy ratio of the dominant vibration mode and the associated coupled vibration mode is calculated in real time. The weighting coefficients are dynamically allocated through a continuous function, and the amplitude compensation coefficient and the time-varying phase compensation factor are weighted and fused to generate dynamic vibration compensation parameters.

[0009] In some specific embodiments, the state estimation algorithm further includes: Construct a state equation with the phase offset and the rate of change of the phase offset as the state vectors; Configure the process noise covariance and the observation noise covariance based on the statistical characteristics of vibration noise; By recursively predicting and updating the estimated value of the phase offset, a phase compensation factor function containing time variables is generated.

[0010] In some specific embodiments, in response to the dynamic vibration compensation parameters, 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 parameters, further including: A nonlinear phase distortion compensation model based on a long short-term memory network was constructed, and the training data included the phase lag mapping relationship under historical vibration environment. The gyroscope angular velocity signal and dynamic vibration compensation parameters are input into the nonlinear phase distortion compensation model, and the output is a complex exponential compensation factor that includes amplitude and phase adjustment. In the complex domain, the analyzed gyroscope angular velocity signal is multiplied by a complex exponential compensation factor to output the phase-corrected gyroscope angular velocity signal.

[0011] In some specific embodiments, the accelerometer signal is subjected to vibration interference band suppression processing, and the processed accelerometer signal is output, which further includes: Analyze the vibration interference frequency band in the accelerometer signal spectrum; Set the stopband attenuation of the notch filter to be greater than the preset attenuation threshold; Perform filtering on the accelerometer signal to eliminate amplitude drift caused by centrifugal vibration; Output accelerometer signal with amplitude error within a preset error range.

[0012] In some specific 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 orientation calibration result is output after filtering and smoothing. This further includes: The phase-corrected gyroscope angular velocity signal and the processed accelerometer signal are initially fused by complementary filtering. Construct an objective function with the variance of gyroscope phase residuals and the variance of accelerometer amplitude errors as variables; The fusion weight is dynamically adjusted based on the drill pipe vibration frequency. When the vibration frequency is higher than the preset frequency threshold, the weight of the gyroscope angular velocity signal is increased, and when it is lower than the preset frequency threshold, the weight of the accelerometer signal is increased. The fusion results are denoised, and the output includes orientation calibration data including azimuth and tilt angles.

[0013] Based on the same concept, the present invention also provides a collaborative orientation calibration system, comprising: The frequency domain separation module is configured to perform frequency domain separation on the acquired drill pipe circumferential strain distribution data in response to the data, and extract the amplitude-frequency characteristics of the dominant vibration mode and the phase offset of the associated coupled vibration mode. The 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 by combining the phase offset with a dynamic prediction algorithm, and generate dynamic vibration compensation parameters based on the dynamic weighted fusion of vibration energy distribution. The gyroscope angular velocity signal correction module is configured to respond to the dynamic vibration compensation parameters, process the gyroscope angular velocity signal based on a pre-trained nonlinear phase distortion compensation model, and output the phase-corrected gyroscope angular velocity signal after inputting the dynamic vibration compensation parameters. The accelerometer signal processing module is configured to perform vibration interference frequency band suppression processing on the accelerometer signal and output the processed accelerometer signal. The orientation 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 the orientation calibration result after filtering and smoothing.

[0014] Based on the same concept, the present invention also provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate 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 performs the steps of a cooperative orientation calibration method.

[0015] Based on the same concept, the present invention also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a cooperative orientation calibration method.

[0016] Compared with existing technologies, its advantages are as follows: This invention discloses a collaborative orientation calibration method, system, electronic device, and storage medium. By separating the frequency domain of circumferential strain and correcting time-varying parameters, it effectively suppresses gyroscope phase distortion and accelerometer amplitude drift caused by drill pipe vibration, and achieves high-precision collaborative calibration of inertial sensors under multimodal mechanical vibration environment. Attached Figure Description

[0017] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating some specific embodiments of the collaborative orientation calibration method of the present invention; Figure 2 This is a schematic diagram of the structure of a collaborative orientation calibration system according to some specific embodiments of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device according to some specific embodiments of the present invention; In the diagram, 710 is the processor; 720 is the memory; 730 is the input device; and 740 is the output device. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0019] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0020] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0021] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.

[0022] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”

[0023] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0024] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.

[0025] Reference Figure 1 A collaborative orientation calibration method, comprising: S101, in response to the acquired circumferential strain distribution data of the drill pipe, the circumferential strain distribution data of the drill pipe is separated in the frequency domain to extract the amplitude-frequency characteristics of the dominant vibration mode and the phase offset of the associated coupled vibration mode; S102, in response to the amplitude-frequency characteristics, an amplitude compensation coefficient is generated, and a time-varying phase compensation factor is generated by combining the phase offset with a dynamic prediction algorithm. Dynamic vibration compensation parameters are generated based on the dynamic weighted fusion of vibration energy distribution. S103, in response to the dynamic vibration compensation parameters, 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 the dynamic vibration compensation parameters are input. S104 performs vibration interference frequency band suppression processing on the accelerometer signal and outputs the processed accelerometer signal. S105, based on the dynamic weight fusion algorithm, iteratively optimizes the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal, and outputs the orientation calibration result after filtering and smoothing.

[0026] Specifically, in this embodiment of the invention, circumferential strain waveform data of the drill pipe is collected by a ring-distributed sensor array. The waveform data is then transformed into the frequency domain using Fourier transform. Within a predetermined mechanical vibration frequency band, the energy concentration region is extracted using bandpass filtering. The frequency component with the largest energy proportion in the spectrum is identified as the dominant vibration mode, and its amplitude-frequency relationship is obtained. Coupled vibration frequency components that are harmonicly associated with the dominant vibration mode are detected. The phase difference between adjacent sensor nodes is calculated using complex spectrum analysis to generate the phase offset of the associated coupled vibration mode. The spectral energy of the dominant vibration mode is normalized and integrated, and polynomial fitting is performed based on the nonlinear characteristics of the material strain-frequency response to generate amplitude compensation coefficients. Based on the phase shift of the associated coupled vibration modes, a state estimation algorithm is used to construct a state vector containing the phase and its rate of change. The process noise covariance and observation noise covariance are configured according to the statistical characteristics of vibration noise, and a phase compensation factor function containing time variables is generated by recursive prediction. The energy ratio of the dominant mode and the coupled mode is calculated in real time, and the weight coefficients are dynamically allocated using a continuous function. The amplitude compensation coefficients and the time-varying phase compensation factor are weighted and fused to generate dynamic vibration compensation parameters. A nonlinear phase distortion compensation model based on a long short-term memory network is constructed. Its training data includes the mapping relationship between phase lag and strain of angular velocity signals under historical vibration environments. Real-time acquired angular velocity signals and dynamic vibration compensation parameters are input into the model, and the output is a complex exponential compensation factor containing amplitude and phase adjustment. The angular velocity signal is converted into an analytic signal using Hilbert transform, and the analytic signal is multiplied by the complex exponential compensation factor in the complex domain to achieve phase lag correction. A sliding window method is used to update the input buffer, and an overlapping retention strategy is used to retain some historical data to ensure real-time correction during sudden changes in vibration frequency. The interference 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 based on the vibration energy distribution, and filtering is performed on the accelerometer signal to eliminate amplitude drift. The output is an accelerometer signal with amplitude error stabilized within a preset range. The phase-corrected angular velocity signal and the filtered accelerometer signal are initially fused using complementary filtering. An objective function is constructed with the gyroscope phase residual variance and the accelerometer amplitude error variance as variables. The fusion weight coefficients are iteratively optimized using the gradient descent method, where the weights are dynamically adjusted according to the real-time vibration frequency of the drill pipe: the weight of the angular velocity signal is increased during high-frequency vibrations, and the weight of the accelerometer signal is increased during low-frequency vibrations. A state estimation algorithm is used to denoise the fusion results, and the state equations for azimuth and dip angles are recursively updated. The directional calibration data is then smoothly output based on the observed values. When the directional error variance exceeds a threshold, the vibration energy proportion is recalculated and the dynamic vibration compensation parameters are updated, forming a closed-loop optimization.

[0027] For example, circumferential strain data (at 45° intervals) from an 8-channel annular distribution were acquired, and the time-domain signal was converted to the frequency domain using Fourier transform. A Butterworth bandpass filter was used to extract the vibration frequency band from 10Hz to 1kHz, identifying the frequency component with the largest energy proportion as 78Hz, whose amplitude nonlinearly decreases with increasing frequency. Coupling between the 156Hz harmonic component and the 78Hz dominant mode was detected. Complex spectrum analysis was used to calculate the phase difference between adjacent sensor nodes as π / 6, generating the phase offset of the associated coupled mode. Bandwidth energy integration (10Hz) was performed on the 78Hz dominant mode, yielding an integral value of 25000(με)², generating a frequency domain normalization coefficient of 0.02(με). -1 A cubic polynomial compensation function was fitted based on the material properties of high-strength steel, and the fitting coefficient was calculated to be 0.8 at 78Hz. After weighted fusion, an amplitude compensation coefficient of 0.8 was generated. Based on the π / 6 phase difference of the 156Hz coupled mode, a Kalman filter process noise covariance of 1×10⁻⁶ was configured. -6 Observation noise covariance 1×10 -4 The predicted time-varying phase compensation factor function is 0.2×sin(2π×78t+π / 4); the energy ratio of 78Hz to 156Hz is calculated to be 1.86, and a dynamic weight of 0.52 is generated through the sigmoid function. The final fusion parameter is 0.52×amplitude coefficient + 0.48×phase factor. The LSTM model takes an angular velocity signal of 0.5×sin(2π×78t+0.3π) and dynamic compensation parameters as input, and outputs a complex exponential compensation factor e^(j×0.25π). After Hilbert transform, the analytic signal is obtained as 0.5×sin(2π×78t+0.3π)+j×0.5×cos(2π×78t+0.3π). After multiplication in the complex domain, it is corrected to 0.5×sin(2π×78t+0.25π), reducing the phase lag from 0.3π to 0.05π. The input buffer is updated every 200ms, retaining the data from the first 50ms, and the response delay is less than 5ms during sudden changes in rotational speed. An interference band is identified in the accelerometer signal at 78Hz / 156Hz, and a 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. The initial fusion weights are set to 0.5, with gyroscope residual variance of 0.01 and accelerometer residual variance of 0.04. After 10 iterations of gradient descent of the objective function, the weights converge to 0.67. When the vibration frequency of 78Hz is higher than the threshold of 50Hz, 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), the orientation error decreased from ±0.5° to ±0.08°; when the error variance exceeded the threshold of 0.01, the energy percentage was recalculated and the compensation parameters were updated.

[0028] In some applications, in response to the acquired circumferential strain distribution data of the drill pipe, frequency domain separation is performed on the circumferential strain distribution data of the drill pipe to extract the amplitude-frequency characteristics of the dominant vibration mode and the phase shift of the associated coupled vibration modes. This includes acquiring circumferential strain waveform data with a ring distribution, performing frequency domain analysis on the circumferential strain waveform data, extracting the spectrum within a preset mechanical vibration frequency band; identifying the frequency component with the largest energy proportion in the spectrum as the dominant vibration mode, obtaining the amplitude of the dominant vibration mode as a function of frequency; obtaining the coupled vibration frequency components of the coupled vibration modes that have harmonic association with the dominant vibration mode, and obtaining the phase shift of the coupled vibration modes relative to the dominant vibration mode through phase difference calculation.

[0029] Understandably, circumferential strain waveform data is acquired through a ring-shaped sensor array; the waveform data is Fourier transformed to the frequency domain; energy concentration regions are extracted within a preset mechanical vibration frequency band using bandpass filtering; the frequency component with the largest energy proportion in the spectrum is identified as the dominant vibration mode, and its amplitude is obtained as a function of frequency in a nonlinear manner; coupled vibration frequency components that are harmonicly associated with the dominant vibration mode are detected; and the phase difference between adjacent sensor nodes is calculated through complex spectrum analysis to generate the phase offset of the associated coupled vibration mode relative to the dominant vibration mode.

[0030] For example, circumferential strain data from an 8-channel annular distribution (with adjacent nodes spaced 45 degrees apart) was collected at a sampling rate of 1kHz; the time-domain waveform data was Fourier transformed to the frequency domain; a Butterworth bandpass filter was used to extract the spectrum within the vibration frequency band from 10Hz to 1000Hz; the frequency component with the largest energy proportion was identified as 78Hz, whose amplitude decreased with increasing frequency, thus confirming it as the dominant vibration mode; a second harmonic correlation was detected between the 156Hz frequency component and the 78Hz dominant mode, thus identifying it as a coupled vibration mode; the phase difference between adjacent nodes (such as node 1 and node 2) at 156Hz was calculated through complex spectrum analysis, and the phase offset of the coupled vibration mode relative to the dominant vibration mode was calculated to be 30 degrees (i.e., π / 6 radians).

[0031] In some applications, in response to the amplitude-frequency characteristics, an amplitude compensation coefficient is generated. Combined with the phase offset, a time-varying phase compensation factor is generated through a dynamic prediction algorithm. Dynamic vibration compensation parameters are then generated based on a dynamic weighted fusion of vibration energy distribution. This includes normalizing and integrating the spectral energy of the dominant vibration mode, fitting a nonlinear function to the material strain-frequency response characteristics, and generating the amplitude compensation coefficient. Based on the phase offset of the associated coupled vibration modes, a state estimation algorithm is used to predict and generate the time-varying phase compensation factor. The energy ratio of the dominant vibration mode to the associated coupled vibration modes is calculated in real time, and weighting coefficients are dynamically allocated through a continuous function. The amplitude compensation coefficient and the time-varying phase compensation factor are then weighted and fused to generate the dynamic vibration compensation parameters.

[0032] Understandably, the spectral energy of the dominant vibration mode is normalized and integrated within a preset frequency band. Combined with the nonlinear characteristics of the material's strain-frequency response, a polynomial function is used to fit the amplitude attenuation law, generating an amplitude compensation coefficient that varies with frequency. Based on the phase shift of the associated coupled vibration modes, a state estimation algorithm is used to construct a dynamic model with the phase and its first derivative as the state vector. The process noise and observation noise covariance matrix is ​​configured according to the statistical characteristics of vibration noise. A phase compensation factor function containing time variables is generated through recursive prediction. The ratio of vibration energy proportion between the dominant vibration mode and the associated coupled vibration modes is calculated in real time. A continuously differentiable function is used to dynamically map the energy ratio to weighting coefficients. The amplitude compensation coefficient and the time-varying phase compensation factor are then weighted and superimposed to generate dynamic vibration compensation parameters.

[0033] For example, amplitude compensation coefficient generation: For the dominant vibration mode at 78Hz (frequency band 70-86Hz), the calculated frequency domain energy integral value is 25000(με)²; the frequency domain normalization coefficient is the reciprocal of the energy integral value: 1 / 25000 = 0.00004(με). -2 Combining the characteristics of high-strength steel, 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 of 0.8 is calculated at 78Hz; the frequency domain normalization coefficient and polynomial coefficient are weighted and fused (weight ratio 6:4) to generate the final amplitude compensation coefficient: 0.6 × 0.00004 + 0.4 × 0.8 = 0.320024. Time-varying phase compensation factor prediction: for a fixed phase difference π / 6 (30 degrees) of the 156Hz associated coupled vibration mode; a Kalman filter state vector is constructed: [phase offset, phase change rate]; the process noise covariance matrix Q = 1 × 10⁻⁶ is configured. -6 The observation noise covariance matrix R = 1 × 10 -4Recursive prediction equation: Current phase offset = previous phase offset + phase change rate × time step + process noise; Observation value = current phase offset + observation noise; Generate time-varying phase compensation factor function: Δφ(t) = 0.2 × sin(2π × 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 allocation coefficient through the sigmoid continuous function: α(t) = 1 / (1 + e^(-0.1 × (η - 2.0))) = 1 / (1 + e^(-0.1 × (-0.14))) ≈ 0.52; Dynamically fuse amplitude compensation coefficient and time-varying phase compensation factor: Dynamic vibration compensation parameter K(t) = α(t) × amplitude coefficient + (1 - α(t)) × phase factor = 0.52 × 0.320024 + 0.48 × 0.2 × sin(2π × 78t + π / 4).

[0034] In some applications, the state estimation algorithm includes constructing a state equation with a phase offset and a rate of change of the phase offset as the state vector; configuring the process noise covariance and the observation noise covariance according to the statistical characteristics of vibration noise; and generating a phase compensation factor function with time variables by recursively predicting and updating the estimated value of the phase offset.

[0035] Understandably, a state equation is constructed with the phase offset and its time change rate as the state vector; the process noise covariance matrix and the observation noise covariance matrix 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 time variables is generated by combining a sine function.

[0036] For example, for a fixed phase difference of π / 6 radians in associated coupled vibration modes, a state vector is constructed that includes the phase offset φ and its rate of change φ'; the process noise covariance is configured to be 1×10. -6 Reflecting the slowly varying characteristics of vibration, the observed noise covariance is 1×10⁻⁶. -4 Matching sensor measurement accuracy; in recursive prediction: the current phase offset is equal to the phase offset of the previous moment plus the phase change rate multiplied by the time step of 0.001 seconds plus the process noise, and the observed value is equal to the current phase offset plus the observed noise; after Kalman gain update, the phase compensation factor function 0.2×sin(2π×78t+π / 4) is generated, where 78Hz comes from the dominant vibration mode frequency, the amplitude 0.2 is obtained from the historical phase residual calibration, and π / 4 is the initial phase offset adjustment amount.

[0037] In some applications, in response to the dynamic vibration compensation parameters, the gyroscope angular velocity signal is processed based on a pre-trained nonlinear phase distortion compensation model. After inputting the dynamic vibration compensation parameters, the phase-corrected gyroscope angular velocity signal is output. This includes constructing a nonlinear phase distortion compensation model based on a long short-term memory network, with training data containing phase hysteresis mapping relationships under historical vibration environments; inputting the gyroscope angular velocity signal and the dynamic vibration compensation parameters into the nonlinear phase distortion compensation model, and outputting a complex exponential compensation factor containing amplitude and phase adjustment; multiplying the analyzed gyroscope angular velocity signal with the complex exponential compensation factor in the complex domain to output the phase-corrected gyroscope angular velocity signal.

[0038] Understandably, the nonlinear phase distortion compensation model based on a long short-term memory network is constructed, with training data derived from a dataset of mapping relationships between drill pipe radial deformation and gyroscope phase lag during historical drilling processes. Real-time acquired gyroscope angular velocity signals and dynamic vibration compensation parameters are input into the model, outputting a complex exponential compensation factor containing amplitude scaling factors and phase adjustment angles. The original gyroscope angular velocity signal is converted into an analytic signal form combining real and imaginary parts using a Hilbert transform. In the complex domain, the analytic signal and the complex exponential compensation factor are multiplied, causing the phase adjustment angle of the compensation factor to cancel the phase lag component in the original signal, outputting a phase-corrected gyroscope angular velocity signal. A sliding window mechanism is used to update the input buffer, and an overlap retention strategy is employed to maintain the continuity of some historical data, ensuring real-time correction capability during sudden changes in vibration frequency.

[0039] For example, a Long Short-Term Memory (LSTM) network model is trained using 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), outputting a 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 dynamic vibration compensation parameters result in the model outputting a complex exponential compensation factor with an amplitude of 1.0 and a phase angle of 0.25π; after Hilbert transform, the real part of the analytic signal is obtained as 0.5×sin(2π×78t+0.3π), and the imaginary part is obtained as... 0.5×cos(2π×78t+0.3π); Complex field multiplication operation: real part multiplied by compensation factor real part cos(0.25π) minus imaginary part multiplied by imaginary part sin(0.25π) to get a new real part, real part multiplied by compensation factor imaginary part sin(0.25π) plus imaginary part multiplied by real part cos(0.25π) to get a new imaginary part, finally reconstructing phase correction signal 0.5×sin(2π×78t+0.25π), phase lag reduced by 0.05π; input buffer updated every 200 milliseconds, retaining 50 milliseconds of historical data, model response delay of 3 milliseconds when drill pipe speed suddenly changes from 1000 rpm to 1200 rpm.

[0040] In some applications, vibration interference frequency band suppression processing is performed on the accelerometer signal, and the processed accelerometer signal is output. This includes analyzing the vibration interference frequency band in the accelerometer signal spectrum; setting a notch filter with a stopband attenuation greater than a preset attenuation threshold; performing filtering operation on the accelerometer signal to eliminate amplitude drift caused by centrifugal vibration; and outputting an accelerometer signal with amplitude error within a preset error range.

[0041] Understandably, the process involves analyzing the periodic interference band in the accelerometer signal spectrum caused by the centrifugal vibration of the drill pipe; designing a notch filter with a stopband attenuation exceeding a preset attenuation threshold based on the center frequency and energy distribution of the interference band; performing filtering on the original accelerometer signal to eliminate the amplitude drift component caused by centrifugal vibration coupling; and outputting an accelerometer signal with an amplitude fluctuation range stable within a preset error threshold.

[0042] For example, by analyzing the spectral characteristics of the accelerometer signal, significant interference bands were identified at 78 Hz and 156 Hz. A fourth-order Butterworth notch filter was designed with center frequencies of 78 Hz and 156 Hz, a stopband width of 10 Hz, and a stopband attenuation of 40 dB. The original signal containing centrifugal vibration interference (drift amplitude ±0.05g) was filtered, and the output signal amplitude drift was reduced to ±0.005g. After variance calculation, the amplitude error range of the filtered signal met the preset threshold requirement of ±0.01g.

[0043] In some applications, a dynamic weighted fusion algorithm is used to iteratively optimize the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal. After filtering and smoothing, the orientation calibration result is output. This includes performing complementary filtering to initially fuse the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal; constructing an objective function with the variance of the gyroscope phase residual and the variance of the accelerometer amplitude error as variables; dynamically adjusting the fusion weights based on the drill pipe vibration frequency, increasing the weight of the gyroscope angular velocity signal when the vibration frequency is higher than a preset frequency threshold and increasing the weight of the accelerometer signal when it is lower than the preset frequency threshold; and performing noise reduction processing on the fusion result to output orientation calibration data including azimuth and tilt angles.

[0044] Understandably, the phase-corrected gyroscope angular velocity signal and the vibration-suppressed accelerometer signal are initially fused using complementary filtering; an objective function is constructed with the variance of the gyroscope signal phase residual and the variance of the accelerometer signal amplitude error as joint optimization objectives; the fusion weight coefficients are dynamically adjusted based on the real-time vibration frequency of the drill pipe, increasing the weight of the gyroscope angular velocity signal in the fusion when the vibration frequency is higher than a preset frequency threshold, and increasing the weight of the accelerometer signal when the vibration frequency is lower than the preset frequency threshold; the fusion result is denoised and smoothed using a state estimation algorithm, outputting orientation calibration data containing azimuth and tilt information; when the orientation error variance exceeds the convergence threshold, the vibration energy distribution is recalculated to update the dynamic compensation parameters.

[0045] For example, the initial fusion weight coefficient is 0.5. The phase-corrected gyroscope angular velocity signal (0.5×sin(2π×78t+0.25π)) and the filtered accelerometer signal (amplitude drift ±0.005g) are initially fused using complementary filtering. The objective function is constructed, including the gyroscope phase residual variance of 0.01 and the accelerometer amplitude error variance of 0.04. The real-time vibration frequency of 78 Hz is detected to be higher than the preset threshold of 50 Hz. Iterative optimization is performed using gradient descent: the first iteration calculates the objective function gradient as -0.024, and the weights are updated to 0.5 + 0.05 × 0.024 = 0.5012. After 10 iterations, the weights converge to 0.67, increasing the weight of the gyroscope signal. The fusion result is then input into the Kalman filter state equation (azimuth state vector [θ,b], process noise covariance 1×10⁻⁶). -6 The observed values ​​were used to calculate the tilt angle from the accelerometer, and the standard deviation of the output azimuth angle decreased from 0.5 degrees to 0.08 degrees, with a tilt angle error of less than 0.1 degrees; when the residual covariance trace exceeded the threshold of 1×10 -6 At that time, the energy ratio calculation of the dominant vibration mode and the associated coupled vibration mode is retried.

[0046] Another embodiment of the collaborative orientation calibration method of the present invention is described below: In this embodiment, when acquiring circumferential strain distribution data of the drill pipe using a ring-distributed fiber optic grating sensor array, the sensor array is attached to the drill pipe surface at equal intervals. Each grating node reflects a light signal of a specific wavelength, and its wavelength offset is linearly related to the strain. The wavelength demodulation system uses tunable laser scanning or spectral analysis methods (such as FFT demodulation) to analyze the reflection spectrum in real time, converting the wavelength offset 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 using Fourier transform (FFT), and then bandpass filtering (such as Butterworth filter) is used to extract the energy concentration region within the mechanical vibration frequency band (such as 10Hz-1kHz). The amplitude-frequency relationship of the dominant vibration mode is identified from the spectrum using a peak detection algorithm (such as the local extremum method) to identify the frequency component with the largest amplitude and its harmonics. The phase offset of the coupled vibration mode is calculated by Hilbert transform or phase unwrapping algorithm to calculate the phase difference between different sensor nodes, thereby separating the vibration components with the same frequency but phase lag.

[0047] Based on the amplitude-frequency relationship of the dominant vibration mode, the vibration amplitude compensation coefficient is obtained by establishing an amplitude-frequency response curve (such as polynomial fitting) and calculating its reciprocal, which is used to normalize the influence of vibration amplitude at different frequencies. The phase shift is input to the time-varying phase compensation factor generation module. First, the dynamic mean of the phase difference is calculated using the sliding window method, and then the phase drift trend is predicted by combining Kalman filtering to generate a compensation factor that adjusts over time. Finally, the amplitude compensation coefficient (static parameter) and the time-varying phase compensation factor (dynamic parameter) are linearly weighted and combined to form a dynamic vibration compensation parameter, the weight of which is adaptively adjusted by the proportion of vibration energy.

[0048] When synchronously acquiring gyroscope angular velocity and accelerometer signals, hardware timestamp alignment (such as the PTP protocol) is used to ensure data synchronization. The dynamic vibration compensation parameter input is a nonlinear phase distortion compensation model: this model is built based on an LSTM neural network, with angular velocity signals and compensation parameters as inputs. The training data comes from the historical phase-strain mapping relationship of drill pipe radial deformation coupled vibration. The model identifies periodic vibration modes through time-series analysis (such as wavelet transform to extract periodic features) and dynamically outputs phase correction values. The correction process uses complex domain multiplication, multiplying the original angular velocity signal by the complex exponential compensation factor (including amplitude and phase) of the compensation model to eliminate phase hysteresis caused by vibration.

[0049] When fusing the calibrated angular velocity and accelerometer signals, a preliminary fusion is first performed using complementary filtering. Then, an iterative optimization algorithm (such as Levenberg-Marquardt) is used to optimize the fusion weights. The algorithm's objective function includes the phase residual of the gyroscope signal and the amplitude error of the accelerometer signal. The weights are dynamically adjusted based on the vibration period of the drill pipe's radial deformation; the gyroscope weight is increased during high-frequency vibrations, while the accelerometer weight is biased during low-frequency vibrations. After each iteration updating the fusion weights, the output is smoothed using Kalman filtering, ultimately generating the directional calibration result. Its accuracy is optimized by closed-loop feedback from the vibration characteristic data.

[0050] The following describes this embodiment in conjunction with application scenarios: During drilling, the drill pipe is affected by non-uniform friction from the wellbore and impact vibration from the drill bit, resulting in circumferential strain fluctuations with an amplitude of ±200με. This causes periodic phase distortion of ±0.3° in the gyroscope angular velocity signal, and amplitude drift of ±0.05g in the accelerometer output due to centrifugal vibration interference. Traditional Kalman filtering, which does not consider the coupling effect of mechanical vibration, has a directional calibration error exceeding ±0.5°.

[0051] This embodiment uses an 8-channel fiber Bragg grating sensor (spaced 45°) in a ring to collect real-time strain data on the drill pipe surface, and analyzes the wavelength shift at a sampling rate of 1kHz using a wavelength demodulation system. Frequency domain analysis results show that the dominant vibration mode frequency is 78Hz, accounting for 65% of the energy, while the coupled vibration mode frequency is 156Hz, with a phase lag of π / 6 behind the dominant mode. In the dynamic compensation generation process, the amplitude range of the dominant vibration mode is first divided into [0-50με, 50-150με, >150με], and corresponding compensation coefficients [1.0, 0.8, 0.6] are assigned. Simultaneously, 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. By weighted fusion of the energy proportions 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.

[0052] In the signal correction and fusion stage, the gyroscope signal, after being processed by a nonlinear phase compensation model, exhibits a significant reduction in phase distortion to ±0.05° (model parameter α=1.5e-3·sin(2π×78t)). The accelerometer signal is effectively freed from amplitude drift by a 78Hz / 156Hz vibration band notch filter (stopband attenuation -40dB). An improved particle swarm optimization algorithm (with inertial weights w=0.6-0.9 dynamically adjusted) is used to fuse and optimize the corrected signals. After 10 iterations, the system orientation error converges to ±0.08°.

[0053] The key steps in this embodiment are described in detail below: In deep well directional drilling, the drill pipe is subjected to friction from the wellbore and impact from the drill bit, generating complex circumferential strain fluctuations (amplitude reaching ±2000με), causing gyroscope phase distortion (±0.3°) and accelerometer amplitude drift (±0.05g). Traditional methods, due to neglecting the multimodal coupling effect within the vibration frequency band, have errors exceeding ±0.5°. This embodiment's dynamic compensation algorithm addresses this issue specifically through the following steps: Calculation of dominant vibration mode amplitude compensation coefficient: Frequency domain energy normalization: Strain data of the drill pipe surface is acquired in real time using an 8-channel fiber Bragg grating sensor (spaced 45°) in a ring distribution, and wavelength offset is analyzed at a sampling rate of 1kHz. When integrating the energy of the spectrum after FFT decomposition, the compensation coefficient is calculated for the dominant vibration mode frequency of 78Hz (65% of the measured energy). in, This represents the amplitude compensation coefficient calculated at the dominant vibration mode frequency of 78Hz; In frequency The square of the spectral amplitude at a given point is the power spectral density. It is the spectrum of the drill pipe circumferential strain data after Fourier transform (FFT), representing the distribution of vibration in the frequency domain. It represents the frequency resolution, which is the frequency interval (unit: Hz) in the Discrete Fourier Transform (DFT) (Δf=1Hz, bandwidth 10Hz); For example, if the amplitude of the dominant mode is 150με at 78Hz, the total energy integral result within the frequency band is: ,but , used to normalize the magnitude effect.

[0054] Nonlinear fitting correction: The strain-frequency response of drill pipe materials (such as high-strength steel) exhibits nonlinear characteristics. Vibration data at different rotational speeds (e.g., 500-1500 rpm) were experimentally calibrated, and a cubic polynomial was fitted: ； When f=78Hz Acomp =0.8, and weighted with the frequency domain normalization result (e.g., 0.6 × frequency domain coefficient + 0.4 × fitting coefficient), finally generating the amplitude compensation coefficient, which effectively suppresses the amplitude drift caused by material nonlinearity.

[0055] Generation of coupled vibration mode phase compensation factor: Phase difference calculation: The phase difference between adjacent nodes (e.g., node 1 and node 2) in a ring sensor array is calculated using complex spectrum analysis. (Measured hysteresis); This indicates that there is a fixed phase difference when the coupled vibration (156Hz) propagates in the circumferential direction of the drill pipe, which requires dynamic compensation.

[0056] Time-varying phase prediction: Establish the state equation and state vector of the Kalman filter. ,in, The phase offset represents the phase difference between the coupled vibration mode and the dominant vibration mode. The rate of change of the phase offset represents how quickly the phase difference changes over time and is used to predict future states. The transpose sign indicates that the vector is a column vector (standard representation in the state-space model). It includes the phase and its rate of change. Based on downhole vibration and noise characteristics (Allan variance analysis calibration process noise Q=10),... −6 Observation noise P=10 −4 Predict the phase shift at the next moment: ; Among them, based on the state of the previous moment and rate of change To predict the phase shift at the current moment; It is the time step; It is process noise, reflecting the uncertainty of the model; it represents the actual state of the system. With observation noise Connecting, It is the phase value obtained through actual measurement by the sensor; Through real-time updates, the predicted value The error from the measured value is less than 0.01 rad, ensuring the compensation factor. The dynamic accuracy.

[0057] Energy weight adaptive adjustment: Modal energy ratio calculation: Butterworth bandpass filtering (passband 70-86Hz and 140-172Hz) was performed on the dominant mode (78Hz) and coupled mode (156Hz) respectively, and the energy ratio was calculated. ,in, The spectrum of the dominant vibration mode; The spectrum of the associated coupled vibration modes; When η < 2.0 (threshold η0 = 2.0), increase the coupling mode weight to prevent low-frequency energy interference.

[0058] Dynamic weight allocation: Adjusting weights using the sigmoid function. ; Final compensation parameters This enables adaptive balance when vibration energy changes.

[0059] Application of LSTM phase compensation model in real-time signal correction: Network training and downhole vibration mode adaptation: Data preparation: A training set was constructed using historical drilling data (including the mapping relationship between drill pipe radial deformation and gyroscope phase lag). Input features included angular velocity signal ω(t) and amplitude compensation coefficient. Phase compensation factor The output target is the complex factor after ideal compensation. (Target phase residual ±0.05°), where, This represents the original phase angle.

[0060] Network training: A bidirectional LSTM hidden layer with 128 neurons was used with the Adam optimizer (learning rate 0.001). After 500 training rounds, the loss value decreased from 1.2 to 0.03. Random vibration frequency perturbations (±10Hz) were introduced during training to enhance the model's robustness to complex downhole environments.

[0061] Real-time correction and drilling timing matching: Complex domain correction: raw gyroscope signal The analytic signal is obtained by Hilbert transform: ; Model output compensation factor The corrected signal: ; The phase lag was reduced from 0.3π to 0.05π, meeting the accuracy requirement of ±0.05°.

[0062] Sliding window update: The input buffer is updated every 200ms, and the overlapping retention method is used to retain the data of the first 50ms to avoid phase abrupt changes. Experimental results show that when the drill pipe speed changes abruptly (e.g., from 1000rpm to 1200rpm), the model response delay is less than 5ms, ensuring real-time performance.

[0063] Optimization of signal fusion and orientation calibration in horizontal well trajectory control: The dynamic weighted fusion algorithm responds to changes in vibration period: Objective function optimization: Assume the current gyroscope residual variance σg2 = 0.01 and the accelerometer residual σa2 = 0.04, objective function: Where w is the fusion weight; Let be the derivative of the weights with respect to time; update the weights using gradient descent: After 10 iterations, the weights converged from the initial value of 0.5 to w=0.67, the gyroscope weights were increased, and it was adapted to the high-frequency vibration (78Hz) environment.

[0064] Kalman filtering for noise reduction and azimuth angle calculation: Status Update: The equation of state for the azimuth angle θk and the angular velocity deviation bk is as follows: The observed value θacc is calculated from the tilt angle by the accelerometer. After filtering, the standard deviation of the azimuth error is reduced from 0.5° to 0.08°.

[0065] Convergence verification and downhole measurement data: Convergence criterion: During 10 seconds of continuous operation, the fusion error E(w) decreased from 0.05 to 0.008, and the residual covariance trace tr(Pk) < 1e−6, which satisfies the convergence condition.

[0066] For the purpose of simplicity, the method steps disclosed in the above embodiments are described as a series of actions. However, those skilled in the art should understand that the embodiments of the present invention are not limited to the described order of actions, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0067] like Figure 2 As shown, the present invention also provides a collaborative orientation calibration system, comprising: The frequency domain separation module 201 is configured to perform frequency domain separation on the acquired drill pipe circumferential strain distribution data in response to the acquired data, and extract the amplitude-frequency characteristics of the dominant vibration mode and the phase offset of the associated coupled vibration mode. The vibration compensation parameter generation module 202 is configured to generate an amplitude compensation coefficient in response to the amplitude-frequency characteristics, generate a time-varying phase compensation factor by combining the phase offset with a dynamic prediction algorithm, and generate dynamic vibration compensation parameters based on the dynamic weighted fusion of vibration energy distribution. The gyroscope angular velocity signal correction module 203 is configured to respond to the dynamic vibration compensation parameters, process the gyroscope angular velocity signal based on a pre-trained nonlinear phase distortion compensation model, and output the phase-corrected gyroscope angular velocity signal after inputting the dynamic vibration compensation parameters. Accelerometer signal processing module 204 is configured to perform vibration interference frequency band suppression processing on accelerometer signal and output the processed accelerometer signal. The orientation calibration result output module 205 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 the orientation calibration result after filtering and smoothing.

[0068] It is worth noting that although only some basic functional modules are disclosed in the embodiments of this invention, it does not mean that the composition of this system is limited to the above-mentioned basic functional modules. On the contrary, what this embodiment intends to express is that, based on the above-mentioned basic functional modules, those skilled in the art can arbitrarily add one or more functional modules in combination with existing technology to form an infinite number of embodiments or technical solutions. That is to say, this system is open rather than closed. The fact that this embodiment only discloses a few basic functional modules should not be considered as the scope of protection of the claims of this invention being limited to the disclosed basic functional modules. At the same time, for the convenience of description, the above device is described separately according to its functions as various units and modules. Of course, in implementing this invention, the functions of each unit and module can be implemented in one or more software and / or hardware.

[0069] like Figure 3 As shown, the present invention also provides an electronic device, including: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate 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 performs the steps of a cooperative orientation calibration method.

[0070] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. For example... Figure 3 The structure shown in this embodiment of the invention includes an electronic device comprising one or more processors 710 and a memory 720; the processors 710 in this electronic device may be one or more. Figure 3 Taking a processor 710 as an example; a memory 720 is used to store one or more programs; the one or more programs are executed by the one or more processors 710, causing the one or more processors 710 to implement a collaborative orientation calibration method as described in any one of the embodiments of the present invention.

[0071] The electronic device may also include an input device 730 and an output device 740.

[0072] The processor 710, memory 720, input device 730, and output device 740 in this electronic device can be connected via a bus or other means. Figure 3 Taking the example of a connection between China and Israel via a bus.

[0073] The memory 720 in this electronic device serves as a computer-readable storage medium, capable of storing one or more programs. These programs can be software programs, computer-executable programs, or modules, such as the program instructions / modules corresponding to the collaborative orientation calibration method provided in this embodiment of the invention. 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, thereby implementing a collaborative orientation calibration method as described in the above embodiment.

[0074] The memory 720 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 720 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 720 may further include memory remotely located relative to the processor 710, which can be connected to the device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0075] Input device 730 can be used to receive input digital or character information, and to generate key signal inputs related to user settings and function control of the electronic device. Output device 740 may include display devices such as a display screen.

[0076] The present invention also provides a computer-readable storage medium storing a computer program executable by an electronic device, which, when run on the electronic device, causes the electronic device to perform the steps of a collaborative orientation calibration method.

[0077] Specifically, the computer storage medium in this embodiment of the invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be—but is not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. 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 conjunction with an instruction execution system, apparatus, or device.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative orientation calibration method, characterized in that, include: In response to the acquired circumferential strain distribution data of the drill pipe, frequency domain separation is performed on the circumferential strain distribution data of the drill pipe to extract the amplitude-frequency characteristics of the dominant vibration mode and the phase offset of the associated coupled vibration mode; In response to the amplitude-frequency characteristics, an amplitude compensation coefficient is generated, and a time-varying phase compensation factor is generated by combining the phase offset with a dynamic prediction algorithm. Dynamic vibration compensation parameters are generated by dynamic weighted fusion based on the vibration energy distribution. In response to the dynamic vibration compensation parameters, the gyroscope angular velocity signal is processed based on a pre-trained nonlinear phase distortion compensation model. After inputting the dynamic vibration compensation parameters, the phase-corrected gyroscope angular velocity signal is output. The accelerometer signal is subjected to vibration interference frequency band suppression processing, 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 orientation calibration result is output after filtering and smoothing.

2. The collaborative orientation calibration method according to claim 1, characterized in that, In response to the acquired circumferential strain distribution data of the drill pipe, frequency domain separation is performed on the circumferential strain distribution data of the drill pipe to extract the amplitude-frequency characteristics of the dominant vibration mode and the phase shift of the associated coupled vibration mode, further including: Obtain circumferential strain waveform data with a ring distribution, perform frequency domain analysis on the circumferential strain waveform data, and extract the spectrum within a preset mechanical vibration frequency band; Identify the frequency component with the largest energy proportion in the spectrum as the dominant vibration mode, and obtain the relationship between the amplitude of the dominant vibration mode and frequency. The coupled vibration frequency components of the coupled vibration modes that are harmonicly associated with the dominant vibration mode are obtained, and the phase offset of the coupled vibration modes relative to the dominant vibration mode is obtained by phase difference calculation.

3. The collaborative orientation calibration method according to claim 1, characterized in that, 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 offset; and dynamic vibration compensation parameters are generated based on dynamic weighted fusion of vibration energy distribution, further including: The spectral energy of the dominant vibration mode is normalized and integrated, and a nonlinear function is fitted in combination with the material strain-frequency response characteristics to generate amplitude compensation coefficients. Based on the phase shift of the associated coupled vibration modes, a state estimation algorithm is used to predict and generate a time-varying phase compensation factor. The energy ratio of the dominant vibration mode and the associated coupled vibration mode is calculated in real time. The weighting coefficients are dynamically allocated through a continuous function, and the amplitude compensation coefficient and the time-varying phase compensation factor are weighted and fused to generate dynamic vibration compensation parameters.

4. The collaborative orientation calibration method according to claim 3, characterized in that, The state estimation algorithm further includes: Construct a state equation with the phase offset and the rate of change of the phase offset as the state vectors; Configure the process noise covariance and the observation noise covariance based on the statistical characteristics of vibration noise; By recursively predicting and updating the estimated value of the phase offset, a phase compensation factor function containing time variables is generated.

5. The collaborative orientation calibration method according to claim 1, characterized in that, In response to the dynamic vibration compensation parameters, the gyroscope angular velocity signal is processed based on a pre-trained nonlinear phase distortion compensation model. After inputting the dynamic vibration compensation parameters, the phase-corrected gyroscope angular velocity signal is output, further including: A nonlinear phase distortion compensation model based on a long short-term memory network was constructed, and the training data included the phase lag mapping relationship under historical vibration environment. The gyroscope angular velocity signal and dynamic vibration compensation parameters are input into the nonlinear phase distortion compensation model, and the output is a complex exponential compensation factor that includes amplitude and phase adjustment. In the complex domain, the analyzed gyroscope angular velocity signal is multiplied by a complex exponential compensation factor to output the phase-corrected gyroscope angular velocity signal.

6. The collaborative orientation calibration method according to claim 1, characterized in that, The accelerometer signal undergoes vibration interference frequency band suppression processing, and the processed accelerometer signal is output, further including: Analyze the vibration interference frequency band in the accelerometer signal spectrum; Set the stopband attenuation of the notch filter to be greater than the preset attenuation threshold; Perform filtering on the accelerometer signal to eliminate amplitude drift caused by centrifugal vibration; Output accelerometer signal with amplitude error within a preset error range.

7. The collaborative orientation calibration method according to claim 1, characterized in that, Based on a dynamic weighted fusion algorithm, the phase-corrected gyroscope angular velocity signal and the processed accelerometer signal are iteratively optimized. The resulting orientation calibration result, after filtering and smoothing, is further included: The phase-corrected gyroscope angular velocity signal and the processed accelerometer signal are initially fused by complementary filtering. Construct an objective function with the variance of gyroscope phase residuals and the variance of accelerometer amplitude errors as variables; The fusion weight is dynamically adjusted based on the drill pipe vibration frequency. When the vibration frequency is higher than the preset frequency threshold, the weight of the gyroscope angular velocity signal is increased, and when it is lower than the preset frequency threshold, the weight of the accelerometer signal is increased. The fusion results are denoised, and the output includes orientation calibration data including azimuth and tilt angles.

8. A collaborative orientation calibration system, characterized in that, include: The frequency domain separation module is configured to perform frequency domain separation on the acquired drill pipe circumferential strain distribution data in response to the data, and extract the amplitude-frequency characteristics of the dominant vibration mode and the phase offset of the associated coupled vibration mode. The 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 by combining the phase offset with a dynamic prediction algorithm, and generate dynamic vibration compensation parameters based on the dynamic weighted fusion of vibration energy distribution. The gyroscope angular velocity signal correction module is configured to respond to the dynamic vibration compensation parameters, process the gyroscope angular velocity signal based on a pre-trained nonlinear phase distortion compensation model, and output the phase-corrected gyroscope angular velocity signal after inputting the dynamic vibration compensation parameters. The accelerometer signal processing module is configured to perform vibration interference frequency band suppression processing on the accelerometer signal and output the processed accelerometer signal. The orientation 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 the orientation calibration result after filtering and smoothing.

9. An electronic device, characterized in that, include: The system includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; the memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method according to 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, which, when run on the electronic device, causes the electronic device to perform the steps of the method according to any one of claims 1 to 7.

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