Gyroscope error determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a gyroscope error determination method and device, electronic equipment and a storage medium. According to the technical scheme, at least one first gyroscope data set of the target gyroscope is determined, and the gyroscope triaxial error derivative corresponding to each first gyroscope data set is determined through the predetermined target error relation and the at least one first gyroscope data set; after at least one first gyroscope data set is obtained, the triaxial error derivative of the gyroscope can be quickly determined, so that the error of the gyroscope can be quickly adjusted; according to the gyroscope three-axis error derivative, the gyroscope error corresponding to each first gyroscope data group is reversely deduced, so that the accurate determination of the gyroscope error is realized; through combined use of the steps, the error of the gyroscope under multiple physical fields can be accurately determined, and meanwhile, the complexity of the determination process is reduced.

Description

Technical Field

[0001] This invention relates to the field of gyroscope calibration technology, and in particular to a method, apparatus, electronic device, and storage medium for determining gyroscope errors. Background Technology

[0002] Gyroscopes are core components in inertial navigation and motion control, and are widely used in many fields.

[0003] The accuracy of gyroscopes limits their use and development. Currently, methods to improve gyroscope accuracy all involve establishing gyroscope error models to compensate for measurement errors. However, during the manufacturing and use of gyroscopes, numerous factors such as material anisotropy, structural geometric deformation, and uncertainties in load parameters make the error analysis of models that include physical field influences overly complex. This makes it difficult to accurately determine gyroscope errors while simultaneously reducing model complexity. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and storage medium for determining gyroscope errors, thereby addressing the problem that gyroscope error analysis is too complex and it is difficult to accurately determine gyroscope errors, while reducing the complexity of the analysis process.

[0005] According to one aspect of the present invention, a method for determining gyroscope error is provided, the method comprising:

[0006] Determine at least one first gyroscope data set for the target gyroscope. The first gyroscope data set includes the gyroscope's three-axis angular velocity, gyroscope's three-axis acceleration, the time delay angular velocity corresponding to each axis angular velocity in the gyroscope's three-axis angular velocity, and at least one gyroscope physical field data. The gyroscope physical field is the physical field applied to the gyroscope.

[0007] Based on the predetermined target error relationship, the derivative of the three-axis error of the gyroscope corresponding to each first gyroscope data group is determined. The target error relationship is obtained by increasing the dimension of the three-axis error of the first gyroscope to obtain the three-axis error of the second gyroscope. The derivative of the three-axis error of the second gyroscope is determined based on the three-axis error of the second gyroscope and is determined based on the nonlinear dynamics sparse identification algorithm. The target error relationship corresponds one-to-one with the target gyroscope.

[0008] The gyroscope error corresponding to each first gyroscope data group is determined based on the derivative of the three-axis error of the gyroscope corresponding to each first gyroscope data group.

[0009] According to another aspect of the present invention, a gyroscope error determination device is provided, the device comprising:

[0010] The data determination module is used to determine at least one first gyroscope data set of the target gyroscope. The gyroscope data set includes the gyroscope's three-axis angular velocity, gyroscope's three-axis acceleration, the time delay angular velocity corresponding to each axis angular velocity in the gyroscope's three-axis angular velocity, and at least one gyroscope physical field data. The gyroscope physical field is the physical field applied to the gyroscope.

[0011] The error derivative determination module is used to determine the three-axis error derivative of each first gyroscope data group based on a predetermined target error relationship and at least one first gyroscope data group. The target error relationship is obtained by increasing the dimension of the first gyroscope three-axis error to obtain the second gyroscope three-axis error, and the second gyroscope three-axis error derivative is determined based on the second gyroscope three-axis error. The target error relationship is determined one-to-one with the target gyroscope.

[0012] The error determination module is used to determine the gyroscope error corresponding to each first gyroscope data group based on the gyroscope three-axis error derivative corresponding to each first gyroscope data group.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory that is communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor, such that the at least one processor is able to perform the gyroscope error determination method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to execute and implement the gyroscope error determination method of any embodiment of the present invention.

[0018] The technical solution of this invention determines at least one first gyroscope data set for a target gyroscope and, through a pre-determined target error relationship, determines the three-axis error derivative of each first gyroscope data set. This allows for rapid determination of the three-axis error derivative of the gyroscope after obtaining at least one first gyroscope data set, thereby enabling rapid adjustment of the gyroscope error. Furthermore, the gyroscope error corresponding to each first gyroscope data set is inferred from the three-axis error derivative, thus achieving accurate determination of the gyroscope error. By combining these steps, accurate determination of gyroscope error under multi-physics conditions can be achieved while reducing the complexity of the determination process.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a gyroscope error determination method provided in Embodiment 1 of the present invention;

[0022] Figure 2 This is a flowchart of another gyroscope error determination method provided in Embodiment 2 of the present invention;

[0023] Figure 3 This is a schematic diagram of a gyroscope error determination device according to Embodiment 3 of the present invention;

[0024] Figure 4 This is a schematic diagram of the structure of an electronic device that implements the gyroscope error determination method of the present invention. Detailed Implementation

[0025] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0027] Example 1

[0028] Figure 1 This is a flowchart of a gyroscope error determination method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where high gyroscope accuracy is required. The method can be executed by a gyroscope error determination device, which can be implemented in hardware and / or software. This gyroscope error determination device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0029] S110. Determine at least one first gyroscope data set for the target gyroscope. The gyroscope data set includes the gyroscope's three-axis angular velocity, gyroscope's three-axis acceleration, the time delay angular velocity corresponding to each axis angular velocity in the gyroscope's three-axis angular velocity, and at least one gyroscope physical field data. The gyroscope physical field is the physical field applied to the gyroscope.

[0030] The first gyroscope data set can be a set of gyroscope data from the target gyroscope at various acquisition moments within the current time period. A gyroscope data set includes the gyroscope's three-axis angular velocities, three-axis accelerations, time-delayed angular velocities corresponding to each axis of the three-axis angular velocities, and at least one gyroscope physical field data set. The three-axis angular velocities can be the rotational speeds measured by the gyroscope along three mutually perpendicular axes (usually called the X-axis, Y-axis, and Z-axis). The X-axis angular velocity represents the speed of rotation of the object around the X-axis; the Y-axis angular velocity represents the speed of rotation of the object around the Y-axis; and the Z-axis angular velocity represents the speed of rotation of the object around the Z-axis. The three-axis accelerations can be the accelerations of the object in three mutually perpendicular directions (usually called the X-axis, Y-axis, and Z-axis) measured by a three-axis accelerometer. The time-delayed angular velocity can be the angular velocity measured or calculated with a time delay. The gyroscope physical field data can be the data of the physical fields applied to the gyroscope, including temperature and magnetic fields.

[0031] As a core component in inertial navigation and motion control, gyroscopes are widely used in military, industrial, and other fields. However, with the increasing diversity and complexity of working scenarios, gyroscope accuracy has become a major factor restricting its use.

[0032] Currently, methods to improve gyroscope accuracy all involve establishing gyroscope error models to compensate for measurement errors. However, because gyroscopes are affected by physical fields, the calculated measurement errors are often inaccurate when these fields are ignored. Therefore, it is necessary to consider the physical fields acting on the gyroscope when determining its error.

[0033] Therefore, before determining the error of the target gyroscope, it is necessary to acquire at least one set of first gyroscope data for the target gyroscope, ensuring that each set contains at least one gyroscope physical field data point. When considering the influence of only one physical field on the target gyroscope, only one set of gyroscope physical field data can be used; when considering the influence of multiple physical fields, multiple sets of gyroscope physical field data can be used simultaneously. Since in practice, gyroscope operation is often influenced by multiple physical fields, the more physical fields considered, the more accurate the determination of the target gyroscope's error will be.

[0034] S120. Based on the predetermined target error relationship, the derivative of the three-axis error of the gyroscope corresponding to each first gyroscope data group is determined through at least one first gyroscope data group. The target error relationship is obtained by increasing the dimension of the first gyroscope three-axis error to obtain the second gyroscope three-axis error, and the derivative of the second gyroscope three-axis error is determined based on the second gyroscope three-axis error. The target error relationship is determined one-to-one with the target gyroscope.

[0035] The first gyroscope three-axis error can be derived from the target gyroscope's three-axis errors occurring over a historical period. The derivative of the gyroscope three-axis error can be the derivative of the error on each of the three axes of the gyroscope corresponding to the first gyroscope data set in the target gyroscope. The second gyroscope three-axis error can be obtained by increasing the dimensionality of the first gyroscope's three-axis error. The nonlinear dynamics sparsity identification algorithm can be a method for identifying nonlinear dynamic system models from observation data.

[0036] By analyzing the first gyroscope three-axis error of the target gyroscope in historical periods and performing dimensionality-upgrading, the data characteristics in the first gyroscope three-axis error can be better determined, thereby obtaining the second gyroscope three-axis error. Then, the derivative of the second gyroscope three-axis error is determined through the second gyroscope three-axis error. Based on the nonlinear dynamics sparse identification algorithm, the data relationship between the derivative of the second gyroscope three-axis error and each data in the target gyroscope data set in historical periods is determined, thus obtaining the target error relationship.

[0037] Subsequently, after obtaining at least one set of first gyroscope data, the three-axis error derivatives of the corresponding gyroscopes for each set of first gyroscope data can be determined based on the target error relationship.

[0038] By using a predetermined target error relationship and at least one set of first gyroscope data, the derivative of the three-axis error of the gyroscope corresponding to each set of first gyroscope data is determined. This enables the rapid determination of the derivative of the three-axis error of the gyroscope after obtaining at least one set of first gyroscope data, thereby achieving rapid adjustment of the gyroscope error.

[0039] Optionally, the target error relationship is determined by the target gyroscope model.

[0040] Since determining the target error relationship may require a large amount of data to calculate, the fast computing power of neural network models can be used to improve computational efficiency, thereby enabling the rapid determination of the target error relationship and obtaining the target gyroscope model after training.

[0041] Accordingly, the process of constructing the target gyroscope model may include steps A1-A6:

[0042] Step A1: Determine the sample data of the target gyroscope. The sample data includes at least one second gyroscope data set of the target gyroscope and the three-axis error of the first gyroscope corresponding to each second gyroscope data set.

[0043] Step A2: Divide the sample data of the target gyroscope according to the X-axis, Y-axis and Z-axis of the target gyroscope to obtain single-axis sample data. The single-axis sample data includes the single-axis acceleration of the target gyroscope, at least one single-axis physical field data of the target gyroscope and the single-axis error of the first target gyroscope.

[0044] Step A3: Based on the single-axis sample data, determine the single-axis sample data derivative array. The single-axis sample data derivative array includes a first-order single-axis sample data derivative array and a second-order single-axis sample data derivative array. The first-order single-axis sample data derivative array includes the first-order target gyroscope single-axis acceleration derivative, the first first-order target gyroscope single-axis error derivative, and the first-order target gyroscope single-axis physical field derivative corresponding to each target gyroscope single-axis physical field data. The second-order single-axis sample data derivative array includes the second-order target gyroscope single-axis acceleration derivative and the second-order target gyroscope single-axis physical field derivative corresponding to each target gyroscope single-axis physical field data.

[0045] Step A4: Based on the pre-built kernel function encoder, the single-axis error of the first gyroscope of the target is increased in dimensionality to obtain the single-axis error of the second gyroscope of the target.

[0046] Step A5: Based on the nonlinear dynamics sparse identification algorithm, determine the relationship between the single-axis error of the second gyroscope of the target and the single-axis error of the first gyroscope of the target, the single-axis acceleration of the target gyroscope, and the initial target error of the single-axis sample data derivative array.

[0047] Step A6: Generate the target gyroscope model based on the initial target error relationship.

[0048] The sample data can be data obtained from actual testing of the target gyroscope in a historical period, including at least one set of second gyroscope data for the target gyroscope and the three-axis error of the first gyroscope corresponding to each set of second gyroscope data.

[0049] Since different gyroscopes may be produced in the same batch, it is still impossible to guarantee that the gyroscopes in that batch are completely identical. Therefore, each gyroscope needs to have a corresponding target gyroscope model determined separately.

[0050] Similarly, when training the target gyroscope model, it must also be trained using data from the corresponding gyroscope. Therefore, it is necessary to obtain sample data from the target gyroscope.

[0051] Since axis misalignment has a relatively small impact on the gyroscope compared to external physical fields, the three axes of the gyroscope can be separated to reduce the computational load. To achieve this, the sample data of the target gyroscope can be divided according to the X-axis, Y-axis, and Z-axis of the target gyroscope to obtain the corresponding single-axis sample data.

[0052] The processing logic for single-axis sample data on the X, Y, and Z axes is the same; therefore, this application will only use the X-axis as an example for explanation. Subsequent single-axis sample data will also be X-axis single-axis sample data.

[0053] The angular velocity error along the ox axis in the single-axis sample data is defined as the single-axis error of the target's first gyroscope, denoted as X. The derivative of this single-axis error is then calculated to obtain the first-order derivative of the target gyroscope's single-axis error, denoted as X. Determine the single-axis acceleration *a* of the target gyroscope and calculate its derivative, including the first-order derivative of the single-axis acceleration of the target gyroscope.

[0054] For ease of understanding, the physical fields of the target gyroscope will be set as temperature and magnetic fields. However, this is only for illustrative purposes. When considering other physical fields, the processing method is the same as that for temperature and magnetic fields.

[0055] Determine the temperature data T and magnetic field data H of the target gyroscope in the sample data, and calculate the first-order single-axis physical field derivative of the target gyroscope corresponding to the single-axis physical field data of each target gyroscope, that is... and Then, by taking the second derivative based on the first derivative, we obtain the second derivative. as well as

[0056] The nonlinear dynamics sparsity identification algorithm can identify nonlinear dynamic systems from observation data. Using this algorithm, the single-axis error of the target's first gyroscope can be established. The relationship between X, a, and the data in the derivative array of the first-order single-axis sample data, and the data in the derivative array of the second-order single-axis sample data. However, due to... Due to the high degree of coupling with other data, in order to reduce the modeling difficulty of nonlinear dynamic sparse identification algorithms, it is necessary to construct a kernel function encoder through a pre-constructed kernel function to upgrade the data dimensionality.

[0057] Based on a pre-built kernel function encoder, the single-axis error X of the first gyroscope of the target is increased in dimensionality to obtain the single-axis error Z of the second gyroscope of the target. Then, based on a nonlinear dynamics sparse recognition algorithm, a [further details are needed]. With data Z, a, The initial target error relationship between the data and other physical field data is constructed. The dynamic model, where Θ is a function library, This is the actual coefficient matrix. Once the model is built, it will... Predicted value Transformed by the decoder It can achieve the following: The prediction.

[0058] Based on the initial target error relationship, the target gyroscope model is finally generated.

[0059] By determining the sample data of the target gyroscope and dividing it according to the X, Y, and Z axes, single-axis sample data is obtained. This reduces the amount of data processing while minimizing the error in the final result, thus enabling rapid verification of the target gyroscope model. Furthermore, by determining the single-axis sample data derivative based on the single-axis sample data, and using a pre-built kernel encoder, the single-axis error of the first target gyroscope is increased in dimensionality to obtain the single-axis error of the second target gyroscope. Based on a nonlinear dynamics sparse identification algorithm, the initial target error relationship between the single-axis error of the second target gyroscope and the single-axis error of the first target gyroscope, the single-axis acceleration of the target gyroscope, and the single-axis sample data derivative is determined. Based on this initial target error relationship, the target gyroscope model is generated, enabling rapid convergence of the target gyroscope model and improving its generation efficiency.

[0060] In one alternative approach, after generating the target gyroscope model based on the data mapping relationship, steps B1-B4 are also included:

[0061] Step B1: Determine the training data for the target gyroscope. The training data includes at least one third gyroscope data set for the target gyroscope and the three-axis error of the third gyroscope corresponding to each third gyroscope data set.

[0062] Step B2: Based on the kernel function encoder, perform data dimensionality upscaling on the three-axis errors of the third gyroscope corresponding to each third gyroscope data group to obtain the three-axis errors of the fourth training gyroscope.

[0063] Step B3: Based on the three-axis error of the fourth training gyroscope and the training data, adjust the initial target error relationship to obtain the data mapping relationship.

[0064] Step B4: Adjust the target gyroscope model based on the target error relationship.

[0065] To ensure the accuracy of the calculation results of the target gyroscope model, it is necessary to adjust the parameters of the target gyroscope model using the training data of the target gyroscope, thereby improving the accuracy of the calculation results.

[0066] In this regard, the coefficient of determination R 2 The calculation formula reflects the regression fit of the model, R0 2 The closer the value is to 1, the more accurate the model and the better its performance.

[0067] Among them, the coefficient of determination R 2 The formula for determining it is:

[0068]

[0069] c represents the three-axis error of the gyroscope calculated from the target gyroscope model, d represents the third three-axis error of the gyroscope in the training data of the target gyroscope, and N represents the number of three-axis errors of the gyroscope. This represents the average three-axis error of the third gyroscope in the training data of N target gyroscopes.

[0070] Meanwhile, the mean squared error (MSE) is used to reflect the deviation between the predicted data and the actual data; the smaller the value, the better the prediction effect.

[0071] The formula for determining the mean squared error (MSE) is:

[0072]

[0073] Its R 2 A value close to 1 and a small mean square error (MSE) indicate a better target gyroscope model.

[0074] In addition, the loss function of the target gyroscope model training process also needs to be considered.

[0075] The expression for the loss function is:

[0076]

[0077] In the formula, Z is the data obtained from X through encoding and decoding processes, and Z is obtained by increasing the dimensionality of data X. The derivative of Z, For dynamic model, This is the actual coefficient matrix; α, β, and γ are all preset weights.

[0078] During training, the training data of the target gyroscope is used, and based on the kernel function encoder, the three-axis error of the third gyroscope corresponding to each third gyroscope data group is upgraded to obtain the three-axis error of the fourth training gyroscope. Based on the three-axis error of the fourth training gyroscope and the training data, the initial target error relationship is adjusted to update the target gyroscope model, thereby improving the accuracy of the target gyroscope model calculation results.

[0079] In one alternative approach, sample data from the target gyroscope is determined, including steps C1-C3:

[0080] Step C1: Obtain the initial sample data of the target gyroscope. The initial sample data includes abnormal initial sample data and non-abnormal initial sample data. Abnormal initial sample data is the initial sample data that can increase the error rate of the target gyroscope model calculation results, while non-abnormal initial sample data is the initial sample data that cannot increase the error rate of the target gyroscope model calculation results.

[0081] Step C2: If the amount of abnormal initial sample data is greater than the preset amount of data, then reacquire the initial sample data of the target gyroscope until the amount of abnormal initial sample data is less than the preset amount of data.

[0082] Step C3: If the amount of abnormal initial sample data is less than the preset amount of data, then adjust the abnormal initial sample data according to the non-abnormal initial sample data to obtain the sample data of the target gyroscope.

[0083] When acquiring sample data from the target gyroscope, some data may be lost or abnormal. After acquiring the initial sample data from the target gyroscope, the data in the initial sample data will be divided into abnormal initial sample data and non-abnormal initial sample data. If the amount of abnormal initial sample data is greater than the preset amount of data, it means that the initial sample data is no longer usable. At this time, the initial sample data of the target gyroscope will be acquired again.

[0084] If the amount of abnormal initial sample data is less than the preset amount of data, it indicates that the abnormal initial sample data in the initial sample data can be removed or adjusted. At this time, the abnormal initial sample data can be adjusted based on the non-abnormal initial sample data to obtain the sample data of the target gyroscope.

[0085] In addition to the amount of data, the ratio between abnormal initial sample data and non-abnormal initial sample data can be used to determine whether to adjust the initial sample data or to reacquire the initial sample data.

[0086] In one alternative approach, after determining the sample data of the target gyroscope, the method further includes steps D1-D3:

[0087] Step D1: Based on the Discrete Fourier Transform, determine the three-axis error spectrum of the first gyroscope.

[0088] Step D2: Filter the triaxial error spectrum to determine the target triaxial error spectrum. The frequency of the target triaxial error spectrum is lower than the preset frequency, and the amplitude is greater than the preset amplitude.

[0089] Step D3: Determine the target three-axis error of the target gyroscope based on the target three-axis error spectrum.

[0090] After obtaining the three-axis error of the first gyroscope, since some of the errors in the three-axis error of the first gyroscope may be random errors or have a small impact on the overall system, the three-axis error of the first gyroscope can be screened to reduce the analysis of random errors or parts with a small impact on the overall system, thereby reducing the amount of data to be analyzed.

[0091] This can be addressed by using Discrete Fourier Transform to convert the determined three-axis error of the first gyroscope into a three-axis error spectrum. The portion of the three-axis error spectrum with a frequency lower than a preset frequency and an amplitude greater than a preset amplitude is retained, and the remaining portion is used as the target three-axis error of the target gyroscope. In subsequent operations, the target three-axis error is used instead of the three-axis error of the first gyroscope for processing.

[0092] Optionally, after determining the sample data of the target gyroscope, the following may also be included:

[0093] The sample data is normalized to obtain normalized sample data.

[0094] Since there are differences in the order of magnitude among the data points in the sample data of the target gyroscope, the sample data can be normalized to reduce the impact of the order of magnitude difference and avoid the problem caused by excessive differences in order of magnitude.

[0095] S130. Determine the gyroscope error corresponding to each first gyroscope data group based on the derivative of the three-axis error of the gyroscope corresponding to each first gyroscope data group.

[0096] After obtaining the derivatives of the three-axis errors of the gyroscope corresponding to each first gyroscope data set, the gyroscope errors corresponding to each first gyroscope data set can be deduced from the derivatives of the three-axis errors, thereby achieving accurate determination of the gyroscope errors.

[0097] According to the technical solution of the present invention, by determining at least one first gyroscope data set for the target gyroscope, and through a pre-determined target error relationship, the three-axis error derivative of the gyroscope corresponding to each first gyroscope data set is determined. This enables rapid determination of the three-axis error derivative of the gyroscope after obtaining at least one first gyroscope data set, thereby achieving rapid adjustment of the gyroscope error. Furthermore, the gyroscope error corresponding to each first gyroscope data set is deduced from the three-axis error derivative, thus achieving accurate determination of the gyroscope error. Through the combined use of the above steps, accurate determination of the gyroscope error under multi-physics conditions can be achieved while reducing the complexity of the determination process.

[0098] Example 2

[0099] Figure 2 This invention provides a flowchart of another method for determining gyroscope errors. This embodiment further optimizes the process described in the previous embodiments, which involves determining the three-axis error derivatives of each first gyroscope data group based on a predetermined target error relationship. This embodiment can be combined with various optional solutions from one or more of the above embodiments. Figure 2 As shown, the gyroscope error determination method of this embodiment may include the following steps:

[0100] S210. Determine at least one first gyroscope data set for the target gyroscope. The gyroscope data set includes the gyroscope's three-axis angular velocity, gyroscope's three-axis acceleration, the time delay angular velocity corresponding to each axis angular velocity in the gyroscope's three-axis angular velocity, and at least one gyroscope physical field data. The gyroscope physical field is the physical field applied to the gyroscope.

[0101] S220. Based on the predetermined target error relationship, the derivative of the three-axis error of the gyroscope corresponding to each first gyroscope data group is determined through at least one first gyroscope data group. The target error relationship is obtained by increasing the dimension of the first gyroscope three-axis error to obtain the second gyroscope three-axis error, and the derivative of the second gyroscope three-axis error is determined based on the second gyroscope three-axis error. The target error relationship is determined one-to-one with the target gyroscope.

[0102] S230. Based on the Euler integral method, the derivatives of the three-axis errors of the gyroscope corresponding to each first gyroscope data group are processed to obtain the predicted gyroscope error.

[0103] Euler's integral method is a numerical method for solving initial value problems of ordinary differential equations.

[0104] After obtaining the three-axis error derivatives of the gyroscope corresponding to each first gyroscope data set, the Euler integral method can be used to integrate the three-axis error derivatives of the gyroscope, thereby obtaining the possible gyroscope error of the target gyroscope within a future preset time period.

[0105] According to the technical solution of the present invention, the derivative of the three-axis error of the gyroscope corresponding to each first gyroscope data group is processed by the Euler integral method to obtain the predicted gyroscope error, thereby realizing the prediction of the three-axis error of the gyroscope in the future period of time, thereby avoiding damage to the equipment using the gyroscope due to untimely adjustment when adjusting the target gyroscope in the future.

[0106] Example 3

[0107] Figure 3 This invention provides a structural block diagram of a gyroscope error determination device, applicable to situations requiring high gyroscope accuracy. This gyroscope error determination device can be implemented in hardware and / or software and can be configured in an electronic device with data processing capabilities. Figure 3 As shown, the gyroscope error determination device of this embodiment may include: a data determination module 310, an error derivative determination module 320, and an error determination module 330. Wherein:

[0108] The data determination module 310 determines at least one first gyroscope data group of the target gyroscope. The gyroscope data group includes the three-axis angular velocity of the gyroscope, the three-axis acceleration of the gyroscope, the time delay angular velocity corresponding to each axis angular velocity of the three-axis gyroscope, and at least one gyroscope physical field data. The gyroscope physical field is the physical field applied to the gyroscope.

[0109] The error derivative determination module 320 is used to determine the three-axis error derivative of each first gyroscope data group according to a predetermined target error relationship and through at least one first gyroscope data group. The target error relationship is obtained by increasing the dimension of the first gyroscope three-axis error to obtain the second gyroscope three-axis error, and the second gyroscope three-axis error derivative is determined according to the second gyroscope three-axis error. The target error relationship is determined based on a nonlinear dynamics sparse identification algorithm. The target error relationship corresponds one-to-one with the target gyroscope.

[0110] The error determination module 330 is used to determine the gyroscope error corresponding to each first gyroscope data group based on the gyroscope three-axis error derivative corresponding to each first gyroscope data group.

[0111] Based on the above embodiments, optionally, the error derivative determination module 320 includes:

[0112] The target error relationship is determined by the target gyroscope model;

[0113] Accordingly, the process of constructing the target gyroscope model includes:

[0114] Determine the sample data of the target gyroscope. The sample data includes at least one set of data from a second gyroscope and the three-axis error of the first gyroscope corresponding to each set of data from the second gyroscope.

[0115] The sample data of the target gyroscope is divided according to the X-axis, Y-axis and Z-axis of the target gyroscope to obtain single-axis sample data. The single-axis sample data includes the single-axis acceleration of the target gyroscope, at least one single-axis physical field data of the target gyroscope and the single-axis error of the first target gyroscope.

[0116] Based on the single-axis sample data, the single-axis sample data derivative array is determined. The single-axis sample data derivative array includes a first-order single-axis sample data derivative array and a second-order single-axis sample data derivative array. The first-order single-axis sample data derivative array includes the first-order target gyroscope single-axis acceleration derivative, the first first-order target gyroscope single-axis error derivative, and the first-order target gyroscope single-axis physical field derivative corresponding to each target gyroscope single-axis physical field data. The second-order single-axis sample data derivative array includes the second-order target gyroscope single-axis acceleration derivative and the second-order target gyroscope single-axis physical field derivative corresponding to each target gyroscope single-axis physical field data.

[0117] Based on a pre-built kernel function encoder, the single-axis error of the first gyroscope of the target is increased in dimensionality to obtain the single-axis error of the second gyroscope of the target.

[0118] Based on a nonlinear dynamic sparse identification algorithm, the relationship between the single-axis error of the second gyroscope of the target and the single-axis error of the first gyroscope of the target, the single-axis acceleration of the target gyroscope, and the initial target error of the single-axis sample data derivative array is determined.

[0119] Based on the initial target error relationship, a target gyroscope model is generated.

[0120] Based on the above embodiments, optionally, after generating the target gyroscope model according to the data mapping relationship, the method further includes:

[0121] Determine the training data for the target gyroscope. The training data includes at least one set of third gyroscope data for the target gyroscope and the three-axis error of the third gyroscope corresponding to each set of third gyroscope data.

[0122] Based on the kernel function encoder, the three-axis error of the third gyroscope corresponding to each third gyroscope data group is upgraded to obtain the three-axis error of the fourth training gyroscope.

[0123] Based on the three-axis error of the fourth training gyroscope and the training data, the initial target error relationship is adjusted to obtain the data mapping relationship;

[0124] The target gyroscope model is adjusted based on the target error relationship.

[0125] Based on the above embodiments, optionally, determining the sample data of the target gyroscope includes:

[0126] Acquire the initial sample data of the target gyroscope. The initial sample data includes abnormal initial sample data and non-abnormal initial sample data. Abnormal initial sample data is the initial sample data that can increase the error rate of the calculation result of the target gyroscope model, while non-abnormal initial sample data is the initial sample data that cannot increase the error rate of the calculation result of the target gyroscope model.

[0127] If the amount of abnormal initial sample data is greater than the preset amount of data, the initial sample data of the target gyroscope will be reacquired until the amount of abnormal initial sample data is less than the preset amount of data.

[0128] If the amount of abnormal initial sample data is less than the preset amount of data, the abnormal initial sample data is adjusted based on the non-abnormal initial sample data to obtain the sample data of the target gyroscope.

[0129] Based on the above embodiments, optionally, after determining the sample data of the target gyroscope, the method further includes:

[0130] Based on the discrete Fourier transform, the three-axis error spectrum of the first gyroscope is determined.

[0131] The triaxial error spectrum is filtered to determine the target triaxial error spectrum, which has a frequency lower than the preset frequency and an amplitude greater than the preset amplitude.

[0132] The target three-axis error of the target gyroscope is determined based on the target three-axis error spectrum.

[0133] Accordingly, the single-axis sample data includes the target gyroscope's single-axis acceleration, at least one target gyroscope's single-axis physical field data, and the target gyroscope's single-axis error, including:

[0134] The single-axis sample data includes the target gyroscope's single-axis acceleration, at least one target gyroscope's single-axis physical field data, and the target single-axis error.

[0135] Based on the above embodiments, optionally, after determining the sample data of the target gyroscope, the method further includes:

[0136] The sample data is normalized to obtain normalized sample data.

[0137] Based on the above embodiments, optionally, the error derivative determination module 320 includes:

[0138] Based on the Euler integral method, the derivatives of the three-axis errors of the gyroscope corresponding to each first gyroscope data group are processed to obtain the predicted gyroscope error.

[0139] The gyroscope error determination device provided in the embodiments of the present invention can execute the gyroscope error determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method.

[0140] Example 4

[0141] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0142] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0143] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0144] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the gyroscope error determination method.

[0145] In some embodiments, the gyroscope error determination method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the gyroscope error determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the gyroscope error determination method by any other suitable means (e.g., by means of firmware).

[0146] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0149] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0150] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0151] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0152] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0153] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining gyroscope error, characterized in that, include: Determine at least one first gyroscope data set for the target gyroscope. The gyroscope data set includes the gyroscope's three-axis angular velocity, gyroscope's three-axis acceleration, the time delay angular velocity corresponding to each axis angular velocity in the gyroscope's three-axis angular velocity, and at least one gyroscope physical field data. The gyroscope physical field is the physical field applied to the gyroscope. Based on a predetermined target error relationship, the derivative of the three-axis error of each first gyroscope data group is determined through at least one first gyroscope data group. The target error relationship is obtained by increasing the dimension of the first gyroscope three-axis error to obtain the second gyroscope three-axis error, and the derivative of the second gyroscope three-axis error is determined based on the second gyroscope three-axis error. The target error relationship is determined one-to-one with the target gyroscope. The gyroscope error corresponding to each first gyroscope data group is determined based on the derivative of the three-axis error of the gyroscope corresponding to each first gyroscope data group.

2. The method according to claim 1, characterized in that, The target error relationship is determined by the target gyroscope model; Accordingly, the construction process of the target gyroscope model includes: The sample data of the target gyroscope is determined, and the sample data includes at least one second gyroscope data set of the target gyroscope and the first gyroscope three-axis error corresponding to each second gyroscope data set; The sample data of the target gyroscope is divided according to the X-axis, Y-axis and Z-axis of the target gyroscope to obtain single-axis sample data. The single-axis sample data includes the single-axis acceleration of the target gyroscope, at least one single-axis physical field data of the target gyroscope and the single-axis error of the first target gyroscope. Based on the single-axis sample data, a single-axis sample data derivative array is determined. The single-axis sample data derivative array includes a first-order single-axis sample data derivative array and a second-order single-axis sample data derivative array. The first-order single-axis sample data derivative array includes the first-order target gyroscope single-axis acceleration derivative, the first first-order target gyroscope single-axis error derivative, and the first-order target gyroscope single-axis physical field derivative corresponding to each target gyroscope single-axis physical field data. The second-order single-axis sample data derivative array includes the second-order target gyroscope single-axis acceleration derivative and the second-order target gyroscope single-axis physical field derivative corresponding to each target gyroscope single-axis physical field data. Based on a pre-built kernel function encoder, the single-axis error of the first gyroscope of the target is increased in dimensionality to obtain the single-axis error of the second gyroscope of the target. Based on a nonlinear dynamics sparse identification algorithm, the relationship between the single-axis error of the second gyroscope of the target and the single-axis error of the first gyroscope of the target, the single-axis acceleration of the target gyroscope, and the initial target error of the single-axis sample data derivative array is determined. The target gyroscope model is generated based on the initial target error relationship.

3. The method according to claim 2, characterized in that, After generating the target gyroscope model based on the data mapping relationship, the process further includes: The training data for the target gyroscope is determined, and the training data includes at least one third gyroscope data set for the target gyroscope and the three-axis error of the third gyroscope corresponding to each third gyroscope data set; Based on the kernel function encoder, the data dimension upscaling of the third gyroscope three-axis error corresponding to each third gyroscope data group is performed to obtain the fourth training gyroscope three-axis error. Based on the three-axis error of the fourth training gyroscope and the training data, the initial target error relationship is adjusted to obtain the data mapping relationship; The target gyroscope model is adjusted based on the target error relationship.

4. The method according to claim 2, characterized in that, Determining the sample data of the target gyroscope includes: The initial sample data of the target gyroscope is obtained. The initial sample data includes abnormal initial sample data and non-abnormal initial sample data. The abnormal initial sample data is the initial sample data that can increase the error rate of the calculation result of the target gyroscope model, and the non-abnormal initial sample data is the initial sample data that cannot increase the error rate of the calculation result of the target gyroscope model. If the amount of abnormal initial sample data is greater than the preset amount of data, then the initial sample data of the target gyroscope is reacquired until the amount of abnormal initial sample data is less than the preset amount of data. If the amount of abnormal initial sample data is less than the preset amount of data, then the abnormal initial sample data is adjusted according to the non-abnormal initial sample data to obtain the sample data of the target gyroscope.

5. The method according to claim 2, characterized in that, After determining the sample data of the target gyroscope, the process also includes: Based on the discrete Fourier transform, the three-axis error spectrum of the first gyroscope is determined. The triaxial error spectrum is filtered to determine the target triaxial error spectrum, wherein the frequency of the target triaxial error spectrum is lower than the preset frequency and the amplitude is greater than the preset amplitude; The target three-axis error of the target gyroscope is determined based on the target three-axis error spectrum. Accordingly, the single-axis sample data includes the target gyroscope's single-axis acceleration, at least one target gyroscope's single-axis physical field data, and the target gyroscope's single-axis error, including: The single-axis sample data includes the target gyroscope's single-axis acceleration, at least one target gyroscope's single-axis physical field data, and the target single-axis error.

6. The method according to claim 2, characterized in that, After determining the sample data of the target gyroscope, the process also includes: The sample data is normalized to obtain normalized sample data.

7. The method according to claim 1, characterized in that, After determining the gyroscope error corresponding to each of the first gyroscope data sets, the method further includes: Based on the Euler integral method, the derivatives of the three-axis errors of the gyroscopes corresponding to each of the first gyroscope data groups are processed to obtain the predicted gyroscope errors.

8. A gyroscope error determination device, characterized in that, include: The data determination module is used to determine at least one first gyroscope data set of the target gyroscope. The gyroscope data set includes the gyroscope's three-axis angular velocity, gyroscope's three-axis acceleration, the time delay angular velocity corresponding to each axis angular velocity of the gyroscope's three-axis angular velocity, and at least one gyroscope physical field data. The gyroscope physical field is the physical field applied to the gyroscope. The error derivative determination module is used to determine the three-axis error derivative of each first gyroscope data group according to a predetermined target error relationship. The target error relationship is obtained by increasing the dimension of the three-axis error of the first gyroscope to obtain the three-axis error of the second gyroscope, and the three-axis error derivative of the second gyroscope is determined according to the three-axis error of the second gyroscope. The target error relationship is determined one-to-one with the target gyroscope. The error determination module is used to determine the gyroscope error corresponding to each of the first gyroscope data groups based on the gyroscope three-axis error derivatives corresponding to each of the first gyroscope data groups.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the gyroscope error determination method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the gyroscope error determination method according to any one of claims 1-7.

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