Filter optimization method and system for inertial measurement unit fiber optic gyroscope

Abstract

The application provides a filtering optimization method and system for an inertial measurement unit fiber-optic gyroscope. The three-dimensional vibration signal of the inertial measurement unit fiber-optic gyroscope is collected, and the vibration modal characteristic data is generated through short-time Fourier transform. The magnetic field strength of the gyroscope is dynamically adjusted according to the data, and the environmental vibration energy is converted into current data based on the adjusted magnetic field strength. The original angular velocity data output by the gyroscope is combined, a double-channel convolutional neural network is used to extract first and second noise features from the current data and the original angular velocity data respectively, and preliminary denoising data is output after fusion. The preliminary denoising data is subjected to variational modal decomposition, and the effective modal components are selected according to a preset kurtosis rule to obtain target angular velocity data, which is used to realize filtering optimization of the gyroscope. The technical scheme provided by the application improves the measurement accuracy and filtering optimization effect of the inertial measurement unit fiber-optic gyroscope.

Description

Technical Field

[0001] This application relates to the field of filtering optimization technology, and in particular to a filtering optimization method and system for an inertial measurement unit fiber optic gyroscope. Background Technology

[0002] In applications of fiber optic gyroscopes in inertial measurement units (IMUs), whether it's attitude control of drones, motion perception of autonomous vehicles, or orbit adjustment of spacecraft, all rely on high-precision angular velocity data output by the gyroscope. However, during operation, gyroscopes are susceptible to factors such as deformation of internal vibrating components, external electromagnetic interference, and changes in ambient temperature. This results in noise from multiple frequency bands being mixed into the raw measurement data. This noise reduces the accuracy of the data and consequently affects the reliability of subsequent control decisions. Therefore, it is urgent to eliminate noise interference through filtering optimization methods to improve the stability and accuracy of the data.

[0003] Currently, a common solution to the aforementioned noise problem is to use an adaptive filtering method based on fixed parameters. This method determines the filtering parameters based on initial statistical characteristics by pre-setting a noise model, and processes the raw data output by the gyroscope in real time, attempting to suppress noise by continuously adjusting the filtering intensity to obtain a cleaner measurement signal.

[0004] However, the existing solution has obvious drawbacks: its preset noise model is difficult to adapt to the complex and ever-changing actual environment. When the noise frequency band changes dynamically with factors such as vibration intensity and electromagnetic interference, the adaptive filtering with fixed parameters cannot adjust the suppression of noise in different frequency bands in time, resulting in some frequency bands of noise not being effectively filtered out, while the effective signal components may be excessively weakened, ultimately affecting the authenticity and integrity of the filtered data. Summary of the Invention

[0005] This application provides a filtering optimization method and system for fiber optic gyroscopes in inertial measurement units, which solves the problems of poor filtering optimization effect caused by insufficient model adaptability, untimely parameter adjustment, and unbalanced filtering effect in the prior art.

[0006] In a first aspect, embodiments of this application provide a filtering optimization method for an inertial measurement unit fiber optic gyroscope, including:

[0007] The three-dimensional vibration signal of the fiber optic gyroscope in the inertial measurement unit is acquired, and the three-dimensional vibration signal is subjected to short-time Fourier transform to generate vibration mode feature data.

[0008] The magnetic field strength of the fiber optic gyroscope in the inertial measurement unit is dynamically adjusted based on the vibration mode characteristic data. Based on the adjusted magnetic field strength, the environmental vibration energy of the fiber optic gyroscope in the inertial measurement unit is converted into current data.

[0009] Based on the current data, combined with the raw angular velocity data output by the fiber optic gyroscope of the inertial measurement unit, a first noise feature and a second noise feature are extracted from the current data and the raw angular velocity data respectively by using a dual-channel convolutional neural network. The first noise feature and the second noise feature are then fused to output preliminary noise reduction data.

[0010] Variational mode decomposition is performed on the preliminary noise reduction data. Effective mode components are selected from the preliminary noise reduction data after variational mode decomposition according to a preset kurtosis rule to obtain target angular velocity data. The target angular velocity data is used to optimize the filtering of the fiber optic gyroscope of the inertial measurement unit.

[0011] Optionally, based on the current data and combined with the raw angular velocity data output by the fiber optic gyroscope of the inertial measurement unit, a first noise feature and a second noise feature are extracted from the current data and the raw angular velocity data respectively using a dual-channel convolutional neural network. The first noise feature and the second noise feature are then fused to output preliminary noise reduction data, including:

[0012] The current data is fed into the first processing path of the dual-channel convolutional neural network, and the raw angular velocity data is fed into the second processing path of the dual-channel convolutional neural network. The first processing path and the second processing path run simultaneously.

[0013] Based on the first processing path, the current data is subjected to multiple feature extractions to obtain multiple first candidate features;

[0014] Based on the second processing path, multiple feature extractions are performed on the original angular velocity data to obtain multiple second candidate features;

[0015] Extract the first noise feature with the highest correlation to noise-related features from the plurality of first candidate features, and extract the second noise feature with the highest correlation to noise-related features from the plurality of second candidate features;

[0016] The first noise feature and the second noise feature are weighted according to their ability to represent noise and then merged to generate a comprehensive noise feature. Based on the comprehensive noise feature, a noise transfer function containing environmental parameters is constructed. Based on the noise transfer function, preliminary noise reduction data is generated to achieve dynamic suppression of multi-band noise.

[0017] Optionally, the step of assigning weights to the first noise feature and the second noise feature according to their ability to represent noise and merging them to generate a comprehensive noise feature, constructing a noise transfer function containing environmental parameters based on the comprehensive noise feature, and generating preliminary noise reduction data based on the noise transfer function to achieve dynamic noise suppression across multiple frequency bands includes:

[0018] Generate a first noise feature value corresponding to the first noise feature and a second noise feature value corresponding to the second noise feature. Based on the ability to represent noise, assign weights to the first noise feature value and the second noise feature value respectively.

[0019] The first noise feature value and the second noise feature value are calculated with their corresponding weights to obtain the corresponding comprehensive noise feature value. The comprehensive noise feature values ​​of all preset dimensions are combined to form a comprehensive noise feature.

[0020] The temperature and humidity parameters of the current environment are collected as environmental parameters. Based on the environmental parameters and the comprehensive noise characteristics, a noise transfer function containing noise suppression coefficients corresponding to different frequency bands is constructed.

[0021] Based on the noise transfer function, the corresponding noise suppression coefficients are applied to each frequency band to generate preliminary noise reduction data.

[0022] Optionally, the step of performing variational mode decomposition on the preliminary denoised data, and filtering effective mode components from the preliminary denoised data after variational mode decomposition according to a preset kurtosis rule to obtain target angular velocity data, wherein the target angular velocity data is used to achieve filtering optimization of the fiber optic gyroscope of the inertial measurement unit, includes:

[0023] Variational mode decomposition was performed on the initial denoised data to obtain multiple data segments;

[0024] Calculate the waveform distribution characteristic value for each of the data segments;

[0025] Each waveform distribution feature value is matched with a feature threshold range in a preset kurtosis rule, and data segments whose waveform distribution feature values ​​fall within the feature threshold range are selected as effective modal components.

[0026] Each of the effective modal components is combined to obtain the target angular velocity data.

[0027] Optionally, the step of dynamically adjusting the magnetic field strength of the fiber optic gyroscope in the inertial measurement unit based on the vibration mode characteristic data, and converting the environmental vibration energy of the fiber optic gyroscope into current data based on the adjusted magnetic field strength, includes:

[0028] The vibration modal feature data is divided according to a preset frequency band range, and vibration frequency data and corresponding amplitude data are extracted based on each frequency band after division.

[0029] Based on the corresponding amplitude data, calculate the power ratio of the vibration frequency data in the vibration modal characteristic data;

[0030] Based on the power ratio, the adjustment direction and value of the magnetic field strength are determined, and the magnetic field strength is adjusted based on the adjustment direction and the value.

[0031] Based on the adjusted magnetic field strength, the vibration sensing component inside the fiber optic gyroscope of the inertial measurement unit is controlled to deform. During the deformation process, it is converted into a corresponding initial voltage signal according to a predetermined conversion rule. Based on the initial voltage signal, current data is generated through the processing of the signal conversion component.

[0032] Optionally, determining the adjustment direction and value of the magnetic field strength based on the power ratio, and adjusting the magnetic field strength based on the adjustment direction and the value, includes:

[0033] For each frequency band, a corresponding upper limit and lower limit value are preset. Based on the magnitude of the vibration frequency data, the difference between the vibration frequency data and the upper limit value or the lower limit value is calculated to obtain the first deviation value of each frequency band.

[0034] Based on the power proportion of each frequency band, a corresponding weighting coefficient is assigned to each frequency band;

[0035] The first deviation value of each frequency band is calculated and multiplied by the weighting coefficient, and the result is accumulated to obtain the second deviation value. The adjustment direction is determined according to the positive or negative value of the second deviation value.

[0036] The adjustment value of the magnetic field strength is determined based on the product of the second deviation value and the preset adjustment coefficient.

[0037] Based on the adjustment direction and the adjustment value, the initial magnetic field strength value is calculated to obtain the adjusted magnetic field strength.

[0038] Optionally, the process of converting the signal into a corresponding initial voltage signal according to a predetermined conversion rule, and generating current data based on the initial voltage signal through processing by the signal conversion component, includes:

[0039] Based on the deformation, the change in magnetic flux within the vibration sensing component is determined, and the change in magnetic flux and the deformation amount satisfy a preset functional relationship.

[0040] Based on the change in magnetic flux, an induced electromotive force is generated in the coil;

[0041] The induced electromotive force is converted into an initial voltage signal by a rectifier according to a predetermined conversion rule. Based on the initial voltage signal, a filter is used to filter out high-frequency fluctuation components to obtain a stable target voltage signal.

[0042] The target voltage signal is converted into current data using a current conversion device.

[0043] Secondly, embodiments of this application provide a filtering optimization system for an inertial measurement unit fiber optic gyroscope, comprising:

[0044] The acquisition module is used to acquire the three-dimensional vibration signal of the fiber optic gyroscope of the inertial measurement unit, perform short-time Fourier transform on the three-dimensional vibration signal, and generate vibration mode feature data.

[0045] The adjustment module is used to dynamically adjust the magnetic field strength of the fiber optic gyroscope of the inertial measurement unit according to the vibration mode characteristic data, and convert the environmental vibration energy of the fiber optic gyroscope of the inertial measurement unit into current data based on the adjusted magnetic field strength.

[0046] The fusion module is used to extract a first noise feature and a second noise feature from the current data and the raw angular velocity data output by the fiber optic gyroscope of the inertial measurement unit, respectively, by using a dual-channel convolutional neural network, and to fuse the first noise feature and the second noise feature to output preliminary noise reduction data.

[0047] The optimization module is used to perform variational mode decomposition on the preliminary noise reduction data, and to filter effective mode components from the preliminary noise reduction data after variational mode decomposition according to a preset kurtosis rule to obtain target angular velocity data. The target angular velocity data is used to achieve filtering optimization of the fiber optic gyroscope of the inertial measurement unit.

[0048] Thirdly, embodiments of this application provide a computing device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are to be invoked and executed by the processing component to implement a filtering optimization method for an inertial measurement unit fiber optic gyroscope as described in any of the first aspects.

[0049] Fourthly, embodiments of this application provide a computer storage medium storing a computer program, which, when executed by a computer, implements a filtering optimization method for an inertial measurement unit fiber optic gyroscope as described in any of the first aspects.

[0050] This application provides a filtering optimization method for an inertial measurement unit (IMU) fiber optic gyroscope, comprising: acquiring three-dimensional vibration signals from the IMU fiber optic gyroscope; performing a short-time Fourier transform on the three-dimensional vibration signals to generate vibration mode feature data; dynamically adjusting the magnetic field strength of the IMU fiber optic gyroscope based on the vibration mode feature data; converting the environmental vibration energy of the IMU fiber optic gyroscope into current data based on the adjusted magnetic field strength; based on the current data, combined with the original angular velocity data output by the IMU fiber optic gyroscope, extracting a first noise feature and a second noise feature from the current data and the original angular velocity data respectively using a dual-channel convolutional neural network, fusing the first noise feature and the second noise feature to output preliminary noise reduction data; performing variational mode decomposition on the preliminary noise reduction data; and selecting effective mode components from the preliminary noise reduction data after variational mode decomposition according to a preset kurtosis rule to obtain target angular velocity data, which is used to achieve filtering optimization of the IMU fiber optic gyroscope.

[0051] This application has the following advantages: by acquiring the three-dimensional vibration signal from the fiber optic gyroscope of the inertial measurement unit and performing short-time Fourier transform to generate vibration mode feature data, the original vibration signal can be transformed into a feature form that can be used for subsequent processing; by adjusting the magnetic field strength according to the vibration mode feature data and converting the vibration energy into current data, the conversion of vibration energy into electrical signal can be realized; by extracting and fusing noise features from the current data and the original angular velocity data through a dual-channel convolutional neural network to output preliminary denoising data, noise features can be identified by combining multi-source data; by performing variational mode decomposition on the preliminary denoising data and filtering effective components to obtain target angular velocity data, the data can be further optimized to achieve filtering optimization.

[0052] Furthermore, this application feeds current data and raw angular velocity data into two parallel paths of a dual-channel convolutional neural network for parallel processing. After extracting candidate features, it selects the features with the highest correlation to noise, assigns weights according to their representational capabilities, and merges them to generate comprehensive noise features. It then constructs a transfer function containing frequency band noise suppression coefficients by combining environmental parameters and applies this function to generate preliminary noise reduction data. Therefore, this application can achieve collaborative processing of multi-source data, accurately capture noise features, and specifically suppress multi-frequency band noise, thereby improving the quality of preliminary noise reduction data.

[0053] These or other aspects of this application will become more apparent in the following description of the embodiments. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 A flowchart illustrating a filtering optimization method for an inertial measurement unit fiber optic gyroscope provided in an embodiment of this application;

[0056] Figure 2 A schematic diagram of a filtering optimization system for an inertial measurement unit fiber optic gyroscope provided in an embodiment of this application;

[0057] Figure 3 This is a schematic diagram of the structure of a computing device provided in an embodiment of this application. Detailed Implementation

[0058] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0059] In some of the processes described in the specification, claims, and accompanying drawings of this application, multiple operations appearing in a specific order are included. However, it should be clearly understood that these operations may not be executed in the order they appear herein, or may be executed in parallel. The operation numbers, such as 11, 12, etc., are merely used to distinguish different operations and do not themselves represent any execution order. Furthermore, these processes may include more or fewer operations, and these operations may be executed sequentially or in parallel. It should be noted that the descriptions such as "first," "second," etc., in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to different types.

[0060] The technical solutions of the embodiments of this application will be clearly and completely described 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 of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0061] To address the problems of poor filtering optimization caused by insufficient model adaptability, untimely parameter adjustment, and unbalanced filtering effects in existing technologies, this application provides a filtering optimization method for fiber optic gyroscopes in inertial measurement units. This method employs the following concept: by converting continuous vibration signals into feature data, effective extraction and analysis of vibration information are achieved, providing a foundation for subsequent processing; the magnetic field is dynamically adjusted based on vibration characteristics, and vibration energy is converted into current data, broadening the data sources for noise analysis; multi-source noise features are fused using a dual-channel convolutional neural network, initially reducing noise interference in the data; and data quality is further improved through variational mode decomposition and effective component screening. The final target angular velocity data can effectively optimize the gyroscope's filtering effect, improving measurement accuracy and reliability.

[0062] Figure 1 A flowchart illustrating a filtering optimization method for an inertial measurement unit fiber optic gyroscope provided in this application embodiment is shown below. Figure 1 As shown, the method includes:

[0063] S11. Acquire the three-dimensional vibration signal from the fiber optic gyroscope of the inertial measurement unit, perform a short-time Fourier transform on the three-dimensional vibration signal, and generate vibration mode characteristic data.

[0064] Among them, the three-dimensional vibration signal refers to the recorded data of the fiber optic gyroscope of the inertial measurement unit in the X, Y, and Z directions, including the intensity and variation of the vibration; the short-time Fourier transform is a method that divides the signal into multiple short time intervals and analyzes the frequency components contained in the signal in each time interval; the vibration modal characteristic data is obtained after the short-time Fourier transform and can reflect the vibration characteristics at different times and frequencies.

[0065] In this embodiment, firstly, vibration data of the fiber optic gyroscope in the inertial measurement unit (IMU) in the X, Y, and Z directions are collected via sensing components. For example, when the gyroscope vibrates during device operation, the magnitude of the vibration in each direction is recorded over time. Secondly, the collected three-dimensional vibration signal is divided into multiple segments of fixed short duration. A short-time Fourier transform is applied to each segment to obtain the intensity of different frequency components within each segment. Finally, the analysis results of all segments are integrated to form vibration modal characteristic data, which includes both time and frequency information.

[0066] S12. Dynamically adjust the magnetic field strength of the fiber optic gyroscope in the inertial measurement unit according to the vibration mode characteristic data, and convert the environmental vibration energy of the fiber optic gyroscope in the inertial measurement unit into current data based on the adjusted magnetic field strength.

[0067] Among them, vibration modal characteristic data are data that can reflect the characteristics of vibration in time and frequency; magnetic field strength refers to the strength of the magnetic field inside the fiber optic gyroscope of the inertial measurement unit; current data is a record that can reflect the changes in the strength of electrical signals, which is converted from vibration energy.

[0068] In this embodiment, firstly, the strength of the internal magnetic field of the fiber optic gyroscope in the inertial measurement unit is adjusted based on the vibration intensity and frequency changes reflected in the vibration modal characteristic data. Secondly, within the adjusted magnetic field, environmental vibrations experienced by the gyroscope cause internal components to move. This movement drives related components to cut the magnetic field, converting the vibration energy into an electrical signal. Finally, the generated electrical signal is converted into current data that reflects its intensity changes; the magnitude of this data varies with the vibration energy.

[0069] S13. Based on the current data and combined with the raw angular velocity data output by the fiber optic gyroscope of the inertial measurement unit, a first noise feature and a second noise feature are extracted from the current data and the raw angular velocity data respectively by using a dual-channel convolutional neural network. The first noise feature and the second noise feature are then fused to output preliminary noise reduction data.

[0070] Among them, the current data is an electrical signal record converted from vibration energy; the raw angular velocity data is directly measured by the fiber optic gyroscope of the inertial measurement unit, which reflects the original data of rotation speed; the dual-channel convolutional neural network is a computing model that can process two different data simultaneously; the first noise feature and the second noise feature are extracted from the current data and the raw angular velocity data respectively, which can reflect the characteristics of noise; the preliminary noise reduction data is obtained by fusing the two noise features, and the noise is reduced.

[0071] In this embodiment, firstly, the current data and the original angular velocity data are input into two processing parts of a dual-channel convolutional neural network, which operate simultaneously. Secondly, in the first processing path, the current data is analyzed multiple times to identify information that reflects noise characteristics, i.e., the first noise feature. In the second processing path, the original angular velocity data undergoes similar processing to obtain the second noise feature. Finally, the first and second noise features are combined and adjusted through calculation to reduce noise in the original data, resulting in preliminary denoised data.

[0072] S14. Perform variational mode decomposition on the preliminary noise reduction data, and select effective mode components from the preliminary noise reduction data after variational mode decomposition according to the preset kurtosis rule to obtain target angular velocity data. The target angular velocity data is used to achieve filtering optimization of the fiber optic gyroscope of the inertial measurement unit.

[0073] Among them, the preliminary noise reduction data is the data with reduced noise after preliminary processing; variational mode decomposition is a method to decompose data into multiple different parts, each with its own characteristics; the preset kurtosis rule is a standard for judging whether a data part is effective, based on the distribution of extreme values ​​in the data; the effective mode component refers to the data part that conforms to the preset kurtosis rule; the target angular velocity data is the final data obtained after screening and combination, which can be used to optimize the gyroscope filtering effect.

[0074] In this embodiment, firstly, the preliminary denoised data is divided into multiple distinct parts using variational mode decomposition, each reflecting different fluctuation characteristics. Secondly, each decomposed part is examined according to a preset kurtosis rule to determine its validity. Finally, all valid parts are combined to form the target angular velocity data.

[0075] For example, during the operation of the fiber optic gyroscope in the inertial measurement unit of device A, vibration data in three directions—horizontal left and right, horizontal front and back, and vertical up and down—is first collected. This data is divided into 0.5-second segments, and each segment undergoes a short-time Fourier transform to obtain vibration mode characteristic data that reflects the vibration at different frequencies within each time period. Then, the internal magnetic field strength of the gyroscope is dynamically adjusted based on this data; for example, the magnetic field is strengthened when the vibration frequency increases. At this time, the vibration energy during device operation is converted into electrical signals through the motion of internal components in the magnetic field, thereby generating corresponding current data. Next, the current data and the measured raw angular velocity data are input into two parts of the calculation model, respectively. The model extracts noise features caused by vibration from the current data and noise features caused by interference from the raw angular velocity data. Combining these two features, the raw data is processed to obtain preliminary noise reduction data. Finally, the preliminary noise reduction data is decomposed into five different parts. After checking according to preset rules, three parts are found to meet the requirements. These three parts are combined to obtain target angular velocity data that can be used to optimize the gyroscope's filtering effect.

[0076] By executing S11~S14, this embodiment of the application realizes the effective extraction and analysis of vibration information by converting continuous vibration signals into feature data, providing a foundation for subsequent processing; dynamically adjusting the magnetic field based on vibration features and converting vibration energy into current data broadens the data sources for noise analysis; using a dual-channel convolutional neural network to fuse multi-source noise features initially reduces noise interference in the data; through variational mode decomposition and effective component screening, the data quality is further improved, and the final target angular velocity data can effectively optimize the filtering effect of the gyroscope, improving measurement accuracy and reliability.

[0077] In one possible embodiment, S13, based on the current data and combined with the raw angular velocity data output by the fiber optic gyroscope of the inertial measurement unit, a first noise feature and a second noise feature are extracted from the current data and the raw angular velocity data respectively by employing a dual-channel convolutional neural network, and the first noise feature and the second noise feature are fused to output preliminary noise-reduced data, including:

[0078] Step 131: Input the current data into the first processing path of the dual-channel convolutional neural network, and input the raw angular velocity data into the second processing path of the dual-channel convolutional neural network. The first processing path and the second processing path run simultaneously.

[0079] Among them, the current data is an electrical signal record converted from the vibration energy of the fiber optic gyroscope in the inertial measurement unit, which can reflect the changes in vibration energy; the raw angular velocity data is directly measured by the gyroscope, which reflects the original information of rotation speed; the dual-channel convolutional neural network is a computing model that can process two different data simultaneously. It contains two independent processing parts, called the first processing path and the second processing path, respectively. The first processing path is used to process the current data, and the second processing path is used to process the raw angular velocity data. The two paths can run simultaneously to speed up the processing.

[0080] In this embodiment, current data is input into the first processing path of a dual-channel convolutional neural network, while raw angular velocity data is input into the second processing path of the network. The two paths start processing their respective data at the same time. For example, in the gyroscope data processing of device A, current data enters the first processing path, and raw angular velocity data enters the second processing path. The two paths start the analysis program simultaneously and perform data processing without affecting each other.

[0081] Step 132: Based on the first processing path, perform multiple feature extractions on the current data to obtain multiple first candidate features.

[0082] The first processing path is the part of the dual-channel convolutional neural network specifically used to process current data; multiple feature extractions refer to analyzing the current data in multiple different ways, with each analysis extracting information that reflects a certain aspect of the data; the first candidate feature is multiple pieces of information that reflect different characteristics of the current data after multiple feature extractions.

[0083] In this embodiment of the application, in the first processing path, the current data is first subjected to a first feature extraction, such as analyzing the fluctuation amplitude of the data over time to obtain a first candidate feature reflecting the magnitude of the fluctuation; then a second extraction is performed to analyze the frequency change of the fluctuation to obtain a first candidate feature reflecting the frequency characteristics; then a third extraction is performed to analyze the duration of the fluctuation to obtain a first candidate feature reflecting the duration. Through such multiple different analyses, multiple first candidate features are obtained. For example, the current data of device A is extracted three times to obtain three candidate features: fluctuation amplitude, frequency change, and duration.

[0084] Step 133: Based on the second processing path, perform multiple feature extractions on the original angular velocity data to obtain multiple second candidate features.

[0085] The second processing path is the part of the dual-channel convolutional neural network specifically used to process the raw angular velocity data; multiple feature extractions refer to analyzing the raw angular velocity data multiple times in different ways, with each analysis extracting information that reflects a certain aspect of the data; the second candidate features are multiple pieces of information that reflect different characteristics of the raw angular velocity data after multiple feature extractions.

[0086] In this embodiment of the application, in the second processing path, the original angular velocity data is first subjected to a first feature extraction, such as analyzing the numerical range of rotational speed in the data to obtain a second candidate feature reflecting the magnitude of the speed; then a second extraction is performed to analyze the difference in speed changes between adjacent time moments to obtain a second candidate feature reflecting the rate of speed change; then a third extraction is performed to analyze the stability of the speed over a long period of time to obtain a second candidate feature reflecting stability. Through such multiple different analyses, multiple second candidate features are obtained. For example, the original angular velocity data of device A is extracted three times to obtain three candidate features: speed magnitude, rate of change, and stability.

[0087] Step 134: Extract the first noise feature with the highest correlation to noise-related features from multiple first candidate features, and extract the second noise feature with the highest correlation to noise-related features from multiple second candidate features.

[0088] Among them, multiple first candidate features are multiple pieces of information extracted from current data that reflect its different characteristics; multiple second candidate features are multiple pieces of information extracted from raw angular velocity data that reflect its different characteristics; noise-related features refer to information that can reflect the characteristics of noise, such as irregular fluctuations, sudden numerical changes, etc.; correlation refers to the degree of similarity between candidate features and noise-related features; the first noise feature is the information with the highest correlation with noise-related features selected from multiple first candidate features; the second noise feature is the information with the highest correlation with noise-related features selected from multiple second candidate features.

[0089] In this embodiment, reference characteristics of noise-related features are first determined, such as irregular small fluctuations or sudden numerical jumps. Then, the similarity, i.e., correlation, between each first candidate feature and these reference characteristics is calculated. For example, the correlation of the three first candidate features is 0.7, 0.9, and 0.6, respectively. The feature with the highest correlation of 0.9 is selected as the first noise feature. At the same time, the correlation of each second candidate feature with the reference characteristics is calculated. For example, the correlation of the three second candidate features is 0.8, 0.6, and 0.95, respectively. The feature with the highest correlation of 0.95 is selected as the second noise feature.

[0090] Step 135: Assign weights to the first noise feature and the second noise feature according to their ability to represent noise, and merge them to generate a comprehensive noise feature. Based on the comprehensive noise feature, construct a noise transfer function containing environmental parameters. Based on the noise transfer function, generate preliminary noise reduction data to achieve dynamic suppression of multi-band noise.

[0091] Among them, the first noise feature and the second noise feature are the information with the highest correlation to noise selected from the current data and the raw angular velocity data, respectively; the characterization ability refers to the clarity of the feature's description of the noise characteristics; the weight is the importance value assigned to each feature according to the characterization ability; the comprehensive noise feature is the information that more comprehensively reflects the noise characteristics after merging the two noise features according to their weights; environmental parameters include information about the surrounding environment such as temperature and humidity; the noise transfer function is a calculation method that includes environmental parameters and noise suppression rules for different frequency bands; the preliminary noise reduction data is the data with reduced noise after processing; and multi-band noise dynamic suppression refers to targeted real-time suppression of noise at different frequencies.

[0092] In this embodiment, weights are assigned to the noise representation capabilities of the first and second noise features. For example, the first noise feature has a stronger representation capability and is assigned a weight of 0.6, while the second noise feature is assigned a weight of 0.4. The two features are multiplied by their corresponding weights and then added together to obtain the comprehensive noise feature. For example, if the value of the first noise feature is 25 and the value of the second noise feature is 20, the comprehensive noise feature is calculated as 25 × 0.6 + 20 × 0.4 = 15 + 8 = 23. Parameters such as temperature and humidity of the current environment are collected, and a noise transfer function is constructed in conjunction with the comprehensive noise feature. This function specifies the suppression method for noise at different frequencies. The original data is processed according to this function to generate preliminary noise reduction data, thereby achieving dynamic suppression of noise in different frequency bands.

[0093] For example, when device A is running, the current data generated by the gyroscope is input into the first processing path of the dual-channel calculation model, and the measured raw angular velocity data is input into the second processing path of the same model. Both paths start processing simultaneously. In the first processing path, feature extraction is performed on the current data three times, successively obtaining candidate features reflecting fluctuation amplitude, frequency change, and duration. The correlation degrees between these features and noise-related features are calculated to be 0.72, 0.89, and 0.65, respectively. The feature corresponding to 0.89 is selected as the first noise feature. In the second processing path, feature extraction is performed on the raw angular velocity data three times, successively obtaining candidate features reflecting velocity magnitude, rate of change, and stability. The correlation degrees between these features and noise-related features are calculated to be 0.85, 0.71, and 0.93, respectively. The feature corresponding to 0.93 is selected as the second noise feature. Based on the representational ability, the first and second noise features are assigned weights of 0.6 and 0.4 respectively. If the value of the first noise feature is 30 and the value of the second is 25, the comprehensive noise feature is calculated as 30×0.6+25×0.4=18+10=28. Combined with the ambient temperature of 26℃ and humidity of 55%, a noise transfer function is constructed. After processing the original data according to the function, preliminary noise reduction data is generated.

[0094] By executing steps 131 to 135, this embodiment of the application improves the efficiency of feature extraction by processing two data streams in parallel through dual channels; multiple feature extractions comprehensively capture the potential characteristics in the data, providing rich material for screening noise features; selecting the noise features with the highest correlation ensures the accuracy of noise information; by allocating weights according to representation capabilities and constructing a transfer function in combination with environmental parameters, targeted dynamic suppression of noise in different frequency bands is achieved, and the final generated preliminary noise reduction data effectively reduces noise interference, laying a good foundation for subsequent data optimization.

[0095] In one possible embodiment, step 135 involves assigning weights to the first noise feature and the second noise feature according to their ability to represent noise, merging them to generate a comprehensive noise feature, constructing a noise transfer function containing environmental parameters based on the comprehensive noise feature, and generating preliminary noise reduction data based on the noise transfer function to achieve dynamic suppression of multi-band noise, including:

[0096] a1. Generate a first noise feature value corresponding to the first noise feature and a second noise feature value corresponding to the second noise feature. Based on the ability to represent noise, assign weights to the first noise feature value and the second noise feature value respectively.

[0097] Among them, the first noise feature value is a numerical value representing the strength of the first noise feature, used to quantify the first noise feature; the second noise feature value is a numerical value representing the strength of the second noise feature, used to quantify the second noise feature; the ability to represent noise refers to the clarity with which the feature value describes the characteristics of the noise; the weight is a numerical value of importance assigned to the feature value according to the ability to represent it, and the stronger the ability to represent it, the greater the weight.

[0098] In this embodiment, the first noise feature and the second noise feature are first converted into corresponding first noise feature values ​​and second noise feature values. For example, 20 represents the fluctuation strength of the first noise feature, and 15 represents the abrupt change strength of the second noise feature. Next, weights are assigned based on the characterization ability. If the first feature value describes the noise more clearly, it is assigned a weight of 0.6, and the second feature value is assigned a weight of 0.4. For example, in device A, the first feature value better reflects the noise pattern, so it has a higher weight.

[0099] a2. Calculate the first noise feature value and the second noise feature value with their corresponding weights to obtain the corresponding comprehensive noise feature value. Combine the comprehensive noise feature values ​​of all preset dimensions to form a comprehensive noise feature.

[0100] Among them, the comprehensive noise feature value is the result of the product of the first noise feature value and its corresponding weight plus the product of the second noise feature value and its corresponding weight; the preset dimension is a pre-defined description of different aspects of noise (such as amplitude, frequency, duration, etc.); the comprehensive noise feature is the information that can comprehensively reflect the characteristics of noise formed by combining the comprehensive noise feature values ​​of all preset dimensions.

[0101] In this embodiment, the comprehensive noise feature value of a single preset dimension is first calculated. For example, the first feature value is 25 × 0.6 = 15, the second feature value is 20 × 0.4 = 8, and the sum of 23 is the comprehensive value of that dimension. The above calculation is then repeated for each preset dimension to obtain multiple comprehensive values. Finally, these comprehensive values ​​are combined to form a comprehensive noise feature, such as the combined amplitude, frequency, and duration dimensions in device A, to obtain comprehensive noise information.

[0102] a3. Collect the current ambient temperature and humidity parameters as environmental parameters. Based on the environmental parameters and comprehensive noise characteristics, construct a noise transfer function that includes noise suppression coefficients corresponding to different frequency bands.

[0103] Among them, environmental parameters are the current temperature and humidity data, reflecting the impact of the environment on noise; different frequency bands are noise categories divided according to frequency range, including low frequency 0-100Hz, mid frequency 100-1000Hz, and high frequency above 1000Hz; the noise suppression coefficient is the value corresponding to each frequency band, used to determine the degree of noise reduction in that frequency band. Each frequency band corresponds to a coefficient, and the larger the coefficient, the stronger the reduction; the noise transfer function is an expression describing the relationship between noise frequency, environmental parameters, comprehensive noise characteristics and suppression coefficient, and the expression is G(f,T,H,F)=k(f)×g(T,H,F), where G(f,T,H,F) is the noise transfer function, f is the noise frequency, T is the temperature, H is the humidity, F is the comprehensive noise characteristics, k(f) is the suppression coefficient corresponding to frequency f, and g(T,H,F) is a correction function based on environmental parameters and comprehensive noise characteristics.

[0104] In this embodiment, the ambient temperature and humidity are first collected, for example, the ambient temperature of device A is 25°C and the humidity is 60%. Next, combining the environmental parameters and overall noise characteristics, the suppression coefficients corresponding to low frequency, mid frequency, and high frequency are calculated, for example, 0.3 for low frequency, 0.5 for mid frequency, and 0.7 for high frequency. Finally, a noise transfer function containing the correspondence between these frequency bands and coefficients is constructed. The function can output the suppression coefficient for the corresponding frequency based on the input parameters.

[0105] a4. Based on the noise transfer function, apply the corresponding noise suppression coefficient to each frequency band to generate preliminary noise reduction data.

[0106] The noise transfer function is an expression that includes frequency bands and corresponding suppression coefficients; frequency bands are noise categories divided by frequency (low frequency, mid frequency, high frequency); the noise suppression coefficient is the value that reduces noise in the corresponding frequency band; the preliminary noise reduction data is the noise reduction data obtained after applying the suppression coefficient to the original data.

[0107] In this embodiment, the frequency bands of low-frequency, mid-frequency, and high-frequency noise in the original data are first determined based on the noise transfer function. Next, corresponding suppression coefficients are applied to each frequency band, such as a coefficient of 0.2 for low frequencies, 0.4 for mid frequencies, and 0.6 for high frequencies. Finally, the processed data from each frequency band is integrated to generate preliminary noise reduction data, such as the merged low-frequency, mid-frequency, and high-frequency data in device A, resulting in noise reduction.

[0108] For example, in device A, the first noise feature is first converted to 25, and the second noise feature is converted to 20. After evaluation, weights of 0.6 and 0.4 are assigned to them respectively. The comprehensive noise feature values ​​for each preset dimension are calculated: amplitude dimension: 25×0.6+20×0.4=15+8=23; frequency dimension: 18×0.6+12×0.4=10.8+4.8=15.6. These values ​​are combined to form the comprehensive noise feature. The ambient temperature is 26℃ and humidity is 55%. Based on the comprehensive noise feature, the suppression coefficients are calculated as follows: low frequency (0-100Hz): 0.2; mid frequency (100-1000Hz): 0.4; high frequency (above 1000Hz): 0.6. A noise transfer function is then constructed. Finally, corresponding coefficients are applied to the noise in each frequency band of the original data: low frequency is reduced by 20%, mid frequency by 40%, and high frequency by 60%. The merged data generates preliminary noise reduction data.

[0109] By executing a1~a4, this embodiment of the application achieves differentiated processing of noise features by quantifying and assigning weights to the noise features, allowing features that better reflect the characteristics of the noise to play a greater role; the comprehensive noise features formed by integrating multi-dimensional feature values ​​comprehensively capture multifaceted information about the noise; the noise transfer function constructed in conjunction with environmental parameters clarifies the relationship between different frequency bands and their corresponding suppression coefficients, achieving targeted suppression of noise in each frequency band; the final generated preliminary noise reduction data effectively reduces noise interference, laying the foundation for subsequent data optimization and improving the accuracy and reliability of the overall data processing.

[0110] In one possible embodiment, S14, variational mode decomposition is performed on the preliminary denoising data, and effective mode components are selected from the preliminary denoising data after variational mode decomposition according to a preset kurtosis rule to obtain target angular velocity data. The target angular velocity data is used to optimize the filtering of the fiber optic gyroscope of the inertial measurement unit, including:

[0111] Step 141: Perform variational mode decomposition on the initial denoised data to obtain multiple data segments.

[0112] Among them, the preliminary noise reduction data is the data whose noise has been reduced after preprocessing; variational mode decomposition is a method to split data into multiple parts, each of which has its own unique fluctuation characteristics; data segments are multiple independent data parts obtained through variational mode decomposition, each of which can reflect a certain type of fluctuation in the original data.

[0113] In this embodiment of the application, variational mode decomposition is used to process the initial noise reduction data. This method splits the data into multiple independent data segments according to the different fluctuation characteristics in the data. For example, in device A, after processing the initial noise reduction data, 5 data segments are obtained, each segment corresponding to different frequencies or amplitude fluctuations in the original data.

[0114] Step 142: Calculate the waveform distribution characteristic value of each data segment.

[0115] Among them, the data segment is a number of independent data parts obtained after variational mode decomposition; the waveform distribution characteristic value is a value used to describe the waveform distribution in the data segment, which can reflect whether the waveform is concentrated or dispersed.

[0116] In the embodiments of this application, for each data segment, the distribution state of its waveform is analyzed, and waveform distribution characteristic values ​​reflecting this distribution state are calculated. For example, in device A, the waveform distribution characteristic values ​​of the five decomposed data segments are calculated to be 2.3, 1.8, 3.5, 2.7 and 1.5 respectively. These values ​​reflect the degree of concentration or dispersion of the waveform of each segment.

[0117] Step 143: Match each waveform distribution feature value with the feature threshold range in the preset kurtosis rule, and select data segments whose waveform distribution feature values ​​are within the feature threshold range as effective modal components.

[0118] Among them, waveform distribution feature value is a numerical value describing the waveform distribution of a data segment; preset kurtosis rule is a pre-set standard used to judge whether a data segment is valid; feature threshold range is the range of valid waveform distribution feature values ​​specified in the preset kurtosis rule; valid modal component refers to a data segment whose waveform distribution feature value is within the feature threshold range, that is, the data part that meets the preset standard.

[0119] In this embodiment, the feature threshold range in the preset kurtosis rule is first determined, for example, this range is set to 1.6 to 3.0. Then, the waveform distribution feature value of each data segment is compared with this range. If the feature value is within this range, the corresponding segment is selected as an effective modal component. For example, in device A, the feature values ​​of the 5 data segments are 2.3, 1.8, 3.5, 2.7 and 1.5, respectively. Among them, 2.3, 1.8 and 2.7 are between 1.6 and 3.0, so these three segments are selected as effective modal components.

[0120] Step 144: Combine each effective modal component to obtain the target angular velocity data.

[0121] Among them, the effective modal components are data segments that meet the preset standards after being screened; the target angular velocity data is the final data obtained by combining all the effective modal components together, which is used to reflect the angular velocity of the fiber optic gyroscope of the inertial measurement unit.

[0122] In this embodiment of the application, all the selected effective modal components are collected and combined according to their temporal order or the correlation between fluctuations in the original data to form a complete data sequence, that is, the target angular velocity data. For example, in device A, the three selected effective modal components are combined according to their order in the preliminary noise reduction data to obtain the target data that can reflect the gyroscope angular velocity.

[0123] For example, in device A, variational mode decomposition is first performed on the preliminary noise reduction data to obtain 5 data segments. Then, each segment is analyzed and calculated to obtain their waveform distribution characteristic values ​​of 2.1, 1.9, 3.2, 2.5, and 1.6. Then, according to the feature threshold range of 1.7 to 3.1 in the preset kurtosis rule, these characteristic values ​​are compared one by one, and it is found that 2.1, 1.9, and 2.5 are within this range and are identified as effective modal components. Finally, these 3 effective modal components are combined according to their time order in the original data to obtain the target angular velocity data.

[0124] By executing steps 141 to 144, this embodiment of the application achieves refined analysis of the data by splitting the initial noise reduction data into multiple data segments, making the fluctuation characteristics of each segment clearer; calculating the waveform distribution characteristic value provides a quantitative basis for judging the validity of the data, making the screening process more objective; filtering out effective modal components according to preset rules ensures that the retained data portion meets the standard; combining the effective components into target angular velocity data further improves the reliability and accuracy of the data, providing high-quality results for the filtering optimization of the fiber optic gyroscope of the inertial measurement unit.

[0125] In one possible embodiment, S12, dynamically adjusting the magnetic field strength of the fiber optic gyroscope in the inertial measurement unit based on vibration mode characteristic data, and converting the environmental vibration energy of the fiber optic gyroscope into current data based on the adjusted magnetic field strength, including:

[0126] Step 121: Divide the vibration modal feature data into preset frequency bands, and extract the vibration frequency data and corresponding amplitude data based on each frequency band.

[0127] Among them, vibration modal characteristic data are data that can reflect the characteristics of vibration at different times and frequencies; preset frequency range is a number of pre-defined frequency intervals, such as low frequency 0-100Hz, mid frequency 100-1000Hz, and high frequency above 1000Hz, used to divide vibration frequencies; vibration frequency data is the specific frequency value of vibration in each frequency band; amplitude data is the intensity data of the corresponding vibration frequency.

[0128] In this embodiment, firstly, the vibration modal characteristic data is divided into different parts according to a preset frequency band range. For example, in device A, it is divided into three ranges: 0-100Hz, 100-1000Hz, and above 1000Hz. Secondly, within each divided frequency band, the vibration frequency values ​​and corresponding intensity data are extracted. For example, in the low-frequency band, a frequency of 50Hz and an amplitude of 0.8 are extracted, and in the mid-frequency band, a frequency of 500Hz and an amplitude of 1.2 are extracted.

[0129] Step 122: Based on the corresponding amplitude data, calculate the power ratio of vibration frequency data in vibration modal characteristic data.

[0130] Among them, amplitude data is a numerical value reflecting the strength of vibration frequency; vibration frequency data is the specific frequency value of each frequency band; power ratio refers to the proportion of vibration power of a certain frequency band in the total power of all frequency bands, and the power is calculated by square the amplitude data.

[0131] In this embodiment, firstly, the vibration power of each frequency band is calculated by squared the amplitude data of that frequency band. For example, if the amplitude of a frequency band is 1.2, its power is 1.2 multiplied by 1.2, which equals 1.44. Secondly, the power of all frequency bands is added together to obtain the total power. For example, if the powers of three frequency bands are 0.64, 1.44, and 0.36 respectively, the total power is 0.64 plus 1.44 plus 0.36, which equals 2.44. Finally, the power of each frequency band is divided by the total power to obtain the power percentage. For example, the percentage of the mid-frequency band is 1.44 divided by 2.44, which is approximately 0.59.

[0132] Step 123: Based on the power ratio, determine the adjustment direction and value of the magnetic field strength, and adjust the magnetic field strength based on the adjustment direction and value.

[0133] Among them, the power ratio is the proportion of vibration power in a certain frequency band to the total power; the direction of magnetic field strength adjustment refers to strengthening or weakening the magnetic field; the adjustment value is the specific magnitude of the change in magnetic field strength; and the magnetic field strength is the degree of strength of the magnetic field inside the gyroscope.

[0134] In this embodiment, firstly, a power percentage threshold is set, such as 50%. If the power percentage of a certain frequency band exceeds this threshold, it indicates that the vibration in that frequency band has a significant impact, and the magnetic field strength needs to be increased; conversely, it is weakened. For example, in device A, the mid-frequency power percentage is 59%, exceeding the threshold, so the magnetic field needs to be strengthened. Secondly, the adjustment value is determined based on the difference between the power percentage and the threshold. The larger the difference, the larger the adjustment. For example, a difference of 9% corresponds to an adjustment value of 0.2T, ultimately adjusting the original magnetic field strength of 1.0T to 1.2T.

[0135] Step 124: Based on the adjusted magnetic field strength, control the vibration sensing component inside the fiber optic gyroscope of the inertial measurement unit to generate deformation. During the deformation process, it is converted into the corresponding initial voltage signal according to the predetermined conversion rule. Based on the initial voltage signal, current data is generated through the processing of the signal conversion component.

[0136] Among them, the adjusted magnetic field strength is the strength of the magnetic field after adjustment; the vibration sensing component is the component inside the gyroscope that can change shape due to vibration; deformation is the change in the shape of the component; the initial voltage signal is the electrical signal generated during the deformation process; the signal conversion component is the device that converts the voltage signal into a current signal; and the current data is a record reflecting the strength of the electrical signal.

[0137] In this embodiment, firstly, the adjusted magnetic field strength causes a shape change in the vibration sensing component inside the gyroscope. The stronger the magnetic field, the more intense the vibration, and the greater the deformation. For example, after the magnetic field in device A is enhanced, the deformation amplitude of the component changes from 0.3mm to 0.5mm. Secondly, during the deformation process, an initial voltage signal is generated according to a predetermined rule (e.g., the greater the deformation, the higher the voltage). For example, a deformation of 0.5mm corresponds to a 2V voltage signal. Finally, the signal conversion component converts the initial voltage signal into current data, such as converting 2V voltage into 0.5A current, generating the corresponding current data.

[0138] For example, in device A, the vibration modal characteristic data is first divided into low frequency (0-100Hz), mid frequency (100-1000Hz), and high frequency (above 1000Hz), extracting the low frequency (50Hz, amplitude 0.8), mid frequency (500Hz, amplitude 1.2), and high frequency (2000Hz, amplitude 0.6). The power of each frequency band is calculated: low frequency is 0.8 x 0.8 = 0.64, mid frequency is 1.2 x 1.2 = 1.44, and high frequency is 0.6 x 0.6 = 0.36. The total power is 0.64 + 1.44 + 0.36 = 2.44. The mid-frequency power proportion is approximately 1.44 divided by 2.44, which is 0.59. Because this proportion exceeds the preset 50% threshold, the magnetic field strength is increased from 1.0T to 1.2T by 0.2T. The enhanced magnetic field causes the vibration sensing component to deform to 0.5mm, generating an initial voltage signal of 2V. After processing by the signal conversion component, it generates a current data of 0.5A.

[0139] By executing steps 121 to 124, this embodiment of the application can accurately locate vibrations at different frequencies by dividing vibration data according to preset frequency bands; calculating the power ratio can clarify the degree of influence of vibrations in each frequency band, providing a basis for magnetic field adjustment; adjusting the magnetic field strength according to the power ratio can specifically address the influence of major vibrations; converting deformation into current data enables effective capture of vibration energy, providing reliable data for subsequent analysis and helping to improve the stability and measurement accuracy of the gyroscope.

[0140] In one possible embodiment, step 123, determining the adjustment direction and value of the magnetic field strength based on the power ratio, and adjusting the magnetic field strength based on the adjustment direction and value, includes:

[0141] b1. Pre-set corresponding upper and lower limits for the vibration frequency data of each frequency band. Based on the magnitude of the vibration frequency data, calculate the difference between the vibration frequency data and the upper or lower limit to obtain the first deviation value of each frequency band.

[0142] Among them, frequency band is a vibration category divided according to frequency range, such as low frequency, medium frequency, and high frequency; vibration frequency data is the specific frequency value of vibration within each frequency band; upper limit and lower limit are the highest and lowest values ​​of the reasonable frequency range preset for each frequency band; the first deviation value is the difference between the vibration frequency data and the upper limit or lower limit. When the vibration frequency data is greater than the upper limit, the first deviation value is the vibration frequency data minus the upper limit. When it is less than the lower limit, it is the lower limit minus the vibration frequency data. If it is between the upper and lower limits, the first deviation value is 0.

[0143] In this embodiment, upper and lower limits are set for the vibration frequency data of each frequency band. For example, in device A, the upper limit for the low-frequency band (0-100Hz) is set to 90Hz and the lower limit to 10Hz, and the upper limit for the mid-frequency band (100-1000Hz) is set to 900Hz and the lower limit to 200Hz. The vibration frequency data of each frequency band is compared with the upper and lower limits to calculate the first deviation value. If the low-frequency band frequency is 100Hz (greater than the upper limit of 90Hz), the deviation value is 100-90=10; if the mid-frequency band frequency is 150Hz (less than the lower limit of 200Hz), the deviation value is 200-150=50; if the frequency is within the range, the deviation value is 0.

[0144] b2. Based on the power ratio of each frequency band, assign corresponding weighting coefficients to each frequency band.

[0145] Among them, the power ratio is the proportion of vibration power in a certain frequency band in the total power; the weighting coefficient is a value assigned to each frequency band based on the power ratio, indicating the importance. The larger the power ratio, the larger the weighting coefficient, which is used to reflect the degree of influence of the frequency band in the overall adjustment.

[0146] In this embodiment, the power percentage of each frequency band is determined. For example, in device A, the power percentages of low frequency, mid frequency, and high frequency are 10%, 70%, and 20%, respectively. Weighting coefficients are assigned according to the power percentage, with higher percentages having greater weights. For example, low frequency is assigned 0.1, mid frequency 0.7, and high frequency 0.2, so that frequency bands with greater influence are given more attention in subsequent calculations.

[0147] b3. Calculate the first deviation value of each frequency band and multiply it by the weighting coefficient, and then sum the results to obtain the second deviation value. Determine the adjustment direction based on the sign of the second deviation value.

[0148] Among them, the first deviation value is the difference between the vibration frequency data and the upper and lower limits; the weighting coefficient is a value that represents the importance of the frequency band; the second deviation value is the sum of the first deviation value of each frequency band multiplied by the corresponding weighting coefficient; the adjustment direction refers to strengthening or weakening the magnetic field. When the second deviation value is positive, the adjustment direction is strengthening, and when it is negative, it is weakening.

[0149] In this embodiment, the first deviation value of each frequency band is calculated as the product of the corresponding weighting coefficient. For example, in device A, the low-frequency deviation value is 10 × 0.1 = 1, the mid-frequency deviation value is 50 × 0.7 = 35, and the high-frequency deviation value is 0 × 0.2 = 0. The products are added together to obtain the second deviation value 1 + 35 + 0 = 36. Since the value is positive, the adjustment direction is determined to be enhancement.

[0150] b4. Based on the product of the second deviation value and the preset adjustment coefficient, determine the adjustment value of the magnetic field strength.

[0151] The second deviation value is the sum of the products of the deviation values ​​of each frequency band and the weights; the preset adjustment coefficient is a fixed value set in advance, used to convert the second deviation value into the magnetic field adjustment amplitude; the adjustment value is the specific magnitude of the change in magnetic field strength, which is obtained by multiplying the second deviation value by the preset adjustment coefficient.

[0152] In this embodiment of the application, a preset adjustment coefficient (e.g., 0.01T) is determined. For example, the preset coefficient in device A is 0.01T. The product of the second deviation value and the preset coefficient is calculated, i.e., 36 × 0.01T = 0.36T, resulting in an adjustment value of 0.36T.

[0153] b5. Based on the adjustment direction and adjustment value, the initial magnetic field strength value is calculated to obtain the adjusted magnetic field strength.

[0154] Among them, the adjustment direction is to strengthen or weaken the magnetic field; the adjustment value is the specific magnitude of the change in magnetic field strength; the initial magnetic field strength is the strength of the magnetic field before adjustment; the adjusted magnetic field strength is the result of the initial magnetic field strength calculated based on the adjustment direction and the adjustment value, which is the initial value plus the adjustment value when strengthening and the initial value minus the adjustment value when weakening.

[0155] In this embodiment, the initial magnetic field strength is specified (e.g., 1.0T), for example, the initial strength of device A is 1.0T. Based on the adjustment direction (enhancement) and the value (0.36T), the adjusted magnetic field strength is calculated to be 1.0T + 0.36T = 1.36T.

[0156] For example, in device A, the upper limit is set at 80Hz and the lower limit at 20Hz for the low-frequency band (0-100Hz), the upper limit at 800Hz and the lower limit at 300Hz for the mid-frequency band (100-1000Hz), and the upper limit at 1500Hz and the lower limit at 1200Hz for the high-frequency band (above 1000Hz). For a low-frequency vibration frequency of 90Hz (greater than the upper limit), the first deviation value is 90-80=10; for a mid-frequency frequency of 250Hz (less than the lower limit), the first deviation value is 300-250=50; and for a high-frequency frequency of 1300Hz (within the range), the deviation value is 0. Assigning weights of 0.1, 0.7, and 0.2 based on power percentages of 10%, 70%, and 20%, respectively, the calculated deviation values ​​are: low frequency 10×0.1=1, mid-frequency 50×0.7=35, and high-frequency 0×0.2=0. The total of 36 is the second deviation value (positive, indicating an increase in adjustment direction). The preset adjustment coefficient is 0.01T, and the adjustment value is 36 × 0.01 = 0.36T; the initial magnetic field strength is 1.0T, and the adjusted value is 1.0 + 0.36 = 1.36T.

[0157] By executing b1~b5, this embodiment of the application sets upper and lower limits to quantify the deviation between the vibration frequency and the reasonable range, and combines the power ratio to allocate weights, so that the frequency band with a large impact is given more attention in the adjustment; by calculating the deviation value to determine the direction and amplitude of the magnetic field adjustment, the magnetic field strength is precisely adjusted, so that the adjusted magnetic field is more adapted to the actual vibration situation, improves the equipment's adaptability to different vibrations, and provides better magnetic field conditions for subsequent energy conversion.

[0158] In one possible embodiment, step 124 involves controlling the vibration sensing component inside the fiber optic gyroscope of the inertial measurement unit to deform based on the adjusted magnetic field strength. During the deformation process, the deformation is converted into a corresponding initial voltage signal according to a predetermined conversion rule. Based on the initial voltage signal, current data is generated through processing by the signal conversion component, including:

[0159] c1. Based on deformation, determine the change in magnetic flux within the vibration sensing component. The change in magnetic flux and the deformation amount satisfy a preset functional relationship.

[0160] Among them, deformation is the change in shape of the vibration sensing component caused by vibration; deformation amount is the specific magnitude of deformation; change in magnetic flux is the change in the amount of magnetic field passing through the vibration sensing component; the preset functional relationship is an expression describing the relationship between the change in magnetic flux and deformation amount, which is usually a linear relationship, expressed as ΔΦ=k×x, where ΔΦ represents the change in magnetic flux, k is a proportionality coefficient, and x is the deformation amount. The larger the deformation amount, the larger the change in magnetic flux.

[0161] In this embodiment of the application, the deformation of the vibration sensing component is determined. For example, the deformation of device A is 0.5 mm. The change in magnetic flux is calculated according to the preset function relationship ΔΦ=k×x. If k=2, then ΔΦ=2×0.5=1, that is, the change in magnetic flux is 1 unit.

[0162] c2. Based on the change in magnetic flux, an induced electromotive force is generated in the coil.

[0163] Among them, the change in magnetic flux is the change in the amount of magnetic field passing through the component; the coil is a ring-shaped component made of wire; the induced electromotive force is the electrical signal generated in the coil when the magnetic flux changes, and its expression is E=N×ΔΦ / Δt, where E is the induced electromotive force, N is the number of turns of the coil, ΔΦ is the change in magnetic flux, and Δt is the time taken for the change. The more turns there are, the faster the magnetic flux changes, and the greater the induced electromotive force.

[0164] In this embodiment of the application, the induced electromotive force is calculated based on the change in magnetic flux, the number of coil turns, and the change time. For example, in device A, the number of coil turns N=100, the change in magnetic flux ΔΦ=1, and the change time Δt=0.1 seconds. According to E=100×1 / 0.1=1000, the induced electromotive force is 1000 units.

[0165] c3. The induced electromotive force is converted into an initial voltage signal by a rectifier according to a predetermined conversion rule. Based on the initial voltage signal, a filter is used to filter out high-frequency fluctuation components to obtain a stable target voltage signal.

[0166] The rectifier is a device that converts induced electromotive force into a stable voltage signal; the initial voltage signal is the preliminary electrical signal obtained after rectification; high frequency refers to a signal with a frequency higher than a preset value (such as 1000Hz); high frequency fluctuation component is the high frequency unstable part contained in the initial voltage signal; the filter is a device that removes the high frequency fluctuation component; and the target voltage signal is the stable voltage signal obtained after filtering.

[0167] In this embodiment of the application, the induced electromotive force is converted into an initial voltage signal by a rectifier. For example, the induced electromotive force of 1800 units in device A is rectified to obtain an initial voltage signal of 10V. This signal contains a high-frequency fluctuation component of 2000Hz. These components are then removed by a filter to obtain a stable target voltage signal of 9V.

[0168] c4. Convert the target voltage signal into current data using a current conversion device.

[0169] Among them, the current conversion device is a device that converts voltage signals into current signals; the target voltage signal is a filtered and stabilized voltage signal; and the current data is a record output by the current conversion device that reflects the magnitude of the current.

[0170] In this embodiment of the application, the target voltage signal is input into the current conversion device and converted according to the preset voltage and current correspondence. For example, the target voltage signal in device A is 11V, and the conversion relationship is 1V corresponds to 0.1A. The calculation is 11×0.1=1.1A, and the current data is 1.1A.

[0171] For example, in device A, the vibration sensing component generates a deformation of 0.3 mm due to vibration. According to the preset functional relationship ΔΦ=3×x, the change in magnetic flux is 3×0.3=0.9 units. This change generates an induced electromotive force in a 200-turn coil after 0.1 seconds, which, according to E=200×0.9 / 0.1=1800 units, is converted into a 12V initial voltage signal by a rectifier. This initial voltage signal contains a 2000Hz high-frequency fluctuation component. After processing by a filter, a stable 11V target voltage signal is obtained. This signal is then converted by a current converter according to the relationship of 1V corresponding to 0.2A, 11×0.2=2.2A, ultimately generating a current data of 2.2A.

[0172] By executing c1~c4, this embodiment of the application converts the deformation generated by vibration into a change in magnetic flux, and then further into an electrical signal, thus realizing the conversion of vibration energy into an electrical signal. After rectification and filtering, unstable components in the signal are removed, resulting in a stable voltage signal. Finally, the voltage signal is converted into current data, providing a stable and reliable basis for subsequent noise analysis. The entire process realizes the effective capture and conversion of vibration information.

[0173] Figure 2 This application provides a schematic diagram of the structure of a filtering optimization system for an inertial measurement unit fiber optic gyroscope, as shown in the embodiments of this application. Figure 2 As shown, the system includes:

[0174] The acquisition module 21 is used to acquire the three-dimensional vibration signal of the fiber optic gyroscope of the inertial measurement unit, perform short-time Fourier transform on the three-dimensional vibration signal, and generate vibration mode characteristic data.

[0175] The adjustment module 22 is used to dynamically adjust the magnetic field strength of the fiber optic gyroscope of the inertial measurement unit according to the vibration mode characteristic data, and convert the environmental vibration energy of the fiber optic gyroscope of the inertial measurement unit into current data based on the adjusted magnetic field strength.

[0176] The fusion module 23 is used to extract first noise features and second noise features from the current data and the raw angular velocity data output by the fiber optic gyroscope of the inertial measurement unit, respectively, by using a dual-channel convolutional neural network, and to fuse the first noise features and second noise features to output preliminary noise reduction data.

[0177] The optimization module 24 is used to perform variational mode decomposition on the preliminary noise reduction data, and to select effective mode components from the preliminary noise reduction data after variational mode decomposition according to the preset kurtosis rule to obtain target angular velocity data. The target angular velocity data is used to achieve filtering optimization of the fiber optic gyroscope of the inertial measurement unit.

[0178] Figure 2 The filtering optimization system of the inertial measurement unit fiber optic gyroscope can perform... Figure 1 The filtering optimization method for the fiber optic gyroscope of the inertial measurement unit described in the illustrated embodiment will not be repeated here. The specific operation methods of each module and unit in the filtering optimization system of the fiber optic gyroscope of the inertial measurement unit in the above embodiments have been described in detail in the embodiments related to this method, and will not be elaborated upon here.

[0179] In one possible design, Figure 2 The filtering optimization system for the fiber optic gyroscope in the inertial measurement unit of the embodiment shown can be implemented as a computing device, such as... Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32.

[0180] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are invoked and executed by the processing component 32.

[0181] The processing component 32 is used to perform the following process: acquiring three-dimensional vibration signals from the fiber optic gyroscope of the inertial measurement unit, performing short-time Fourier transform on the three-dimensional vibration signals to generate vibration mode feature data; dynamically adjusting the magnetic field strength of the fiber optic gyroscope based on the vibration mode feature data, and converting the environmental vibration energy of the fiber optic gyroscope into current data based on the adjusted magnetic field strength; based on the current data, combined with the original angular velocity data output by the fiber optic gyroscope, extracting the first noise feature and the second noise feature from the current data and the original angular velocity data respectively by using a dual-channel convolutional neural network, fusing the first noise feature and the second noise feature to output preliminary noise reduction data; performing variational mode decomposition on the preliminary noise reduction data, and filtering effective mode components from the preliminary noise reduction data after variational mode decomposition according to a preset kurtosis rule to obtain target angular velocity data, which is used to achieve filtering optimization of the fiber optic gyroscope of the inertial measurement unit.

[0182] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above-described method. Alternatively, the processing component may be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.

[0183] Storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Random Access Memory (RAM), Static Random-Access Memory (SRAM), Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0184] Of course, computing devices may also include other components, such as input / output interfaces, display components, communication components, etc.

[0185] Input / output interfaces provide interfaces between processing components and peripheral interface modules, which can be output devices, input devices, etc.

[0186] The communication components are configured to facilitate wired or wireless communication between computing devices and other devices.

[0187] The computing device can be a physical device or an elastic computing host provided by a cloud computing platform. In this case, the computing device can refer to a cloud server, and the aforementioned processing components, storage components, etc., can be basic server resources rented or purchased from the cloud computing platform.

[0188] This application also provides a computer storage medium storing a computer program, which, when executed by a computer, can perform the above-described functions. Figure 1 The filtering optimization method for the fiber optic gyroscope of the inertial measurement unit in the embodiment shown.

[0189] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0190] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0191] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0192] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application 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 of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A filtering optimization method for an inertial measurement unit fiber optic gyroscope, characterized by, The method comprises the following steps: Collecting a three-dimensional vibration signal of an inertial measurement unit fiber gyroscope, performing short-time Fourier transform on the three-dimensional vibration signal to generate vibration modal characteristic data; According to the vibration modal characteristic data, dynamically adjusting the magnetic field strength of the inertial measurement unit fiber gyroscope, and based on the adjusted magnetic field strength, converting the environmental vibration energy of the inertial measurement unit fiber gyroscope into current data; Based on the current data, combining the original angular velocity data output by the inertial measurement unit fiber gyroscope, and through the use of a double-channel convolutional neural network, extracting first noise features and second noise features from the current data and the original angular velocity data respectively, and fusing the first noise features and the second noise features to output preliminary noise reduction data; Performing variational modal decomposition on the preliminary noise reduction data, and according to a preset kurtosis rule, screening effective modal components from the variational modal decomposition of the preliminary noise reduction data to obtain target angular velocity data, which is used to realize the filtering optimization of the inertial measurement unit fiber gyroscope; The method of performing variational modal decomposition on the preliminary noise reduction data, and according to a preset kurtosis rule, screening effective modal components from the variational modal decomposition of the preliminary noise reduction data to obtain target angular velocity data, which is used to realize the filtering optimization of the inertial measurement unit fiber gyroscope, comprises: Performing variational modal decomposition on the preliminary noise reduction data to obtain a plurality of data segments; Calculating the waveform distribution characteristic value of each data segment; Matching each waveform distribution characteristic value with a characteristic threshold range in a preset kurtosis rule, and screening data segments with waveform distribution characteristic values within the characteristic threshold range as effective modal components; Combining each effective modal component to obtain target angular velocity data; The method of dynamically adjusting the magnetic field strength of the inertial measurement unit fiber gyroscope according to the vibration modal characteristic data, and based on the adjusted magnetic field strength, converting the environmental vibration energy of the inertial measurement unit fiber gyroscope into current data, comprises: Dividing the vibration modal characteristic data according to a preset frequency band range, and based on the divided frequency bands, extracting vibration frequency data and corresponding amplitude data; Based on the corresponding amplitude data, calculating the power proportion of the vibration frequency data in the vibration modal characteristic data; Based on the power proportion, determining the adjustment direction and value of the magnetic field strength, and based on the adjustment direction and the value, adjusting the magnetic field strength; Based on the adjusted magnetic field strength, controlling the vibration sensing components inside the inertial measurement unit fiber gyroscope to deform, and in the deformation process, converting into corresponding initial voltage signals according to a predetermined conversion rule, and based on the initial voltage signals, generating current data through the processing of a signal conversion component.

2. The method of claim 1, wherein, The current data is input into a first processing path in the dual-channel convolutional neural network, and the raw angular velocity data is input into a second processing path in the dual-channel convolutional neural network, and the first processing path and the second processing path run simultaneously. Based on the first processing path, the current data is subjected to multiple feature extractions to obtain multiple first candidate features. Based on the second processing path, the raw angular velocity data is subjected to multiple feature extractions to obtain multiple second candidate features. The first noise feature with the highest correlation degree to the noise-related feature is extracted from the multiple first candidate features, and the second noise feature with the highest correlation degree to the noise-related feature is extracted from the multiple second candidate features. The first noise feature and the second noise feature are assigned weights according to their noise representation capabilities and combined to generate a comprehensive noise feature, based on which an environmental parameter-containing noise transfer function is constructed, and based on the noise transfer function, preliminary noise reduction data is generated to achieve multi-band noise dynamic suppression. The first noise feature and the second noise feature are assigned weights according to their noise representation capabilities and combined to generate a comprehensive noise feature, based on which an environmental parameter-containing noise transfer function is constructed, and based on the noise transfer function, preliminary noise reduction data is generated to achieve multi-band noise dynamic suppression, including:

3. The method of claim 2, wherein, A first noise feature value corresponding to the first noise feature and a second noise feature value corresponding to the second noise feature are generated, and the first noise feature value and the second noise feature value are respectively assigned weights based on their noise representation capabilities. The first noise feature value and the second noise feature value are respectively calculated with the corresponding weights to obtain corresponding comprehensive noise feature values, and all the comprehensive noise feature values of the preset dimensions are combined to form a comprehensive noise feature. The temperature and humidity parameters of the current environment are collected as environmental parameters, and based on the environmental parameters and the comprehensive noise feature, a noise transfer function containing noise suppression coefficients corresponding to different frequency bands is constructed. Based on the noise transfer function, the corresponding noise suppression coefficients are applied to each frequency band to generate preliminary noise reduction data. The power ratio is used to determine the adjustment direction and value of the magnetic field strength, and the magnetic field strength is adjusted based on the adjustment direction and the value, including:

4. The method of claim 1, wherein, The upper limit value and the lower limit value corresponding to the vibration frequency data of each frequency band are pre-set, and the difference between the vibration frequency data and the upper limit value or the lower limit value is calculated based on the size of the vibration frequency data to obtain a first deviation value of each frequency band. Based on the power ratio of each frequency band, a corresponding weight coefficient is assigned to each frequency band. ​ The first deviation value of each frequency band is multiplied by the weight coefficient and accumulated to obtain a second deviation value, and the adjustment direction is determined according to the positive and negative of the second deviation value; Based on the product of the second deviation value and a preset adjustment coefficient, an adjustment value of the magnetic field strength is determined; Based on the adjustment direction and the adjustment value, an initial magnetic field strength value is operated to obtain an adjusted magnetic field strength.

5. The method of claim 1, wherein, The initial voltage signal is converted into the corresponding initial voltage signal according to the predetermined conversion rule, and the current data is generated based on the initial voltage signal through the processing of the signal conversion component, including: Based on the deformation, the change amount of the magnetic flux in the vibration sensing component is determined, and the change amount of the magnetic flux and the deformation amount of the deformation satisfy a preset function relationship; Based on the change amount of the magnetic flux, an induced electromotive force is generated in the coil; The induced electromotive force is converted into an initial voltage signal by the rectifier device according to the predetermined conversion rule, and the initial voltage signal is filtered by the filter device to remove high-frequency fluctuation components to obtain a stable target voltage signal; The target voltage signal is converted into current data by the current conversion device.

6. A filter optimization system for an inertial measurement unit fiber optic gyroscope, comprising: It includes: The acquisition module is used for collecting three-dimensional vibration signals of the inertial measurement unit fiber-optic gyroscope, performing short-time Fourier transform on the three-dimensional vibration signals, and generating vibration modal characteristic data; The adjustment module is used for dynamically adjusting the magnetic field strength of the inertial measurement unit fiber-optic gyroscope according to the vibration modal characteristic data, and converting the environmental vibration energy of the inertial measurement unit fiber-optic gyroscope into current data based on the adjusted magnetic field strength; The fusion module is used for combining the current data and the original angular velocity data output by the inertial measurement unit fiber-optic gyroscope, extracting first noise features and second noise features from the current data and the original angular velocity data respectively by using a double-channel convolutional neural network, and fusing the first noise features and the second noise features to output preliminary noise reduction data; The optimization module is used for performing variational modal decomposition on the preliminary noise reduction data, screening effective modal components from the variational modal decomposition preliminary noise reduction data according to a preset kurtosis rule, and obtaining target angular velocity data, which is used to realize filtering optimization of the inertial measurement unit fiber-optic gyroscope; The variational modal decomposition of the preliminary noise reduction data is performed, and the effective modal components are screened from the variational modal decomposition preliminary noise reduction data according to the preset kurtosis rule to obtain the target angular velocity data, which is used to realize the filtering optimization of the inertial measurement unit fiber-optic gyroscope, including: The preliminary noise reduction data is subjected to variational modal decomposition to obtain a plurality of data segments; The waveform distribution characteristic value of each data segment is calculated; Each of the waveform distribution characteristic values is matched with a characteristic threshold range in the preset kurtosis rule, and the data segments whose waveform distribution characteristic values are within the characteristic threshold range are screened as effective modal components; Each of the effective modal components is combined to obtain target angular velocity data; The method comprises the following steps: dividing the vibration modal characteristic data according to a preset frequency range, extracting vibration frequency data and corresponding amplitude data based on the divided frequency bands; calculating a power proportion of the vibration frequency data in the vibration modal characteristic data based on the corresponding amplitude data; determining an adjustment direction and a value of the magnetic field strength based on the power proportion, and adjusting the magnetic field strength based on the adjustment direction and the value; controlling a vibration sensing component inside the inertial measurement unit fiber-optic gyroscope to generate deformation based on the adjusted magnetic field strength, converting the deformation into corresponding initial voltage signals according to a predetermined conversion rule during the deformation process, and generating current data through processing of a signal conversion component based on the initial voltage signals. The method comprises the following steps: dividing the vibration modal characteristic data according to a preset frequency range, extracting vibration frequency data and corresponding amplitude data based on the divided frequency bands; calculating a power proportion of the vibration frequency data in the vibration modal characteristic data based on the corresponding amplitude data; determining an adjustment direction and a value of the magnetic field strength based on the power proportion, and adjusting the magnetic field strength based on the adjustment direction and the value; controlling a vibration sensing component inside the inertial measurement unit fiber-optic gyroscope to generate deformation based on the adjusted magnetic field strength, converting the deformation into corresponding initial voltage signals according to a predetermined conversion rule during the deformation process, and generating current data through processing of a signal conversion component based on the initial voltage signals. The method comprises the following steps: dividing the vibration modal characteristic data according to a preset frequency range, extracting vibration frequency data and corresponding amplitude data based on the divided frequency bands; calculating a power proportion of the vibration frequency data in the vibration modal characteristic data based on the corresponding amplitude data; determining an adjustment direction and a value of the magnetic field strength based on the power proportion, and adjusting the magnetic field strength based on the adjustment direction and the value; controlling a vibration sensing component inside the inertial measurement unit fiber-optic gyroscope to generate deformation based on the adjusted magnetic field strength, converting the deformation into corresponding initial voltage signals according to a predetermined conversion rule during the deformation process, and generating current data through processing of a signal conversion component based on the initial voltage signals. ​ ​ 7. A computing device, comprising: ​ 8. A computer storage medium, characterized in that, ​

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