Dynamic filtering method, device and equipment suitable for fiber optic gyroscope and storage medium

Abstract

The application discloses a dynamic filtering method and device suitable for an optical fiber gyroscope, equipment and a storage medium, and is used for processing output data of the optical fiber gyroscope. The method comprises the following steps: acquiring sample data output by the optical fiber gyroscope for a period of time; establishing a dynamic filtering algorithm model provided with a temperature trigger threshold value, a trigger variable and a threshold variable which evolve based on sample data variation; setting preset parameters of the filtering model according to the sample data; inputting the preset parameters and the sample data into the filtering model, performing iterative calculation and filtering, and obtaining filtered optical fiber gyroscope output data. The application mainly depends on historical data of the optical fiber gyroscope and preset judgment threshold values, has high flexibility, has no requirement for noise in the whole gyroscope system, can adjust a temperature suppression coefficient, can realize filtering performance in a full-temperature range, and has a wide application range.

Description

Technical Field

[0001] This application relates to the field of fiber optic gyroscope data processing technology, and more specifically, to a dynamic filtering method, apparatus, device, and storage medium suitable for fiber optic gyroscopes. Background Technology

[0002] In inertial navigation systems, the gyroscope has always been the core inertial device. Among them, the fiber optic gyroscope, as a classic type of gyroscope, has received widespread attention due to its high reliability and high precision. Especially since the beginning of the 21st century, fiber optic gyroscopes have achieved significant breakthroughs in loop winding technology, signal processing, and software algorithms, gradually realizing miniaturization, high precision, and mass production. The noise in the entire gyroscope system can be roughly divided into optical path noise, circuit noise, and power supply noise. The presence of these noises causes the gyroscope's output to fluctuate around its zero position, thus affecting the gyroscope's performance. Therefore, extracting the true output of the gyroscope from the noise is an important aspect.

[0003] Currently, Kalman filtering is widely used in fiber optic gyroscope data filtering algorithms due to its excellent filtering capabilities and broad application models. Kalman filtering is an algorithm that uses the state equations of a linear system and observed data to optimally estimate the system state. It makes two assumptions: the dynamic model of the system is known, and the noise is assumed to be Gaussian white noise. Although most noise in nature is white noise, when it is affected by environmental factors and its statistical characteristics are altered, meaning it is no longer white noise, Kalman filtering loses its effectiveness. This is why Kalman filtering fails in some situations. Therefore, this invention addresses the shortcomings of current mainstream fiber optic gyroscope data filtering algorithms and proposes a more effective filtering method and filter, which has significant engineering implications. Summary of the Invention

[0004] To address at least one deficiency or improvement requirement in existing technologies, this invention provides a dynamic filtering method suitable for fiber optic gyroscopes, used for processing output data from fiber optic gyroscopes, characterized by comprising:

[0005] S1. Obtain sample data output by the fiber optic gyroscope over a period of time;

[0006] S2. Establish a dynamic filtering algorithm model with a temperature trigger threshold and trigger and threshold variables that evolve based on changes in sample data;

[0007] S3. Input the sample data into the filtering model, perform iterative calculations and filtering to obtain the filtered fiber optic gyroscope output data;

[0008] The sample data includes at least one of the following: the gyroscope output data before filtering, the zero position of the fiber optic gyroscope, the temperature information of the environment, and the preset parameters of the filtering model.

[0009] Furthermore, the preset parameters include at least one of the initial value of the trigger variable, the initial value of the threshold variable, the filtering parameters, and the temperature suppression parameters.

[0010] Furthermore, the dynamic filtering algorithm model is as follows:

[0011]

[0012] in, y ( k ) is a spinning top k The original output at any given moment. y ( sl The output is the filtered result. It is the first The next data update serves as the trigger variable. It is the first k Threshold variable at time, Here are the filter parameters, and Δ represents the current temperature. p This is the temperature suppression coefficient. This is the reference temperature.

[0013] Furthermore, the preset parameters and the gyroscope output data before filtering are input into the filtering model for iterative calculation, including the following steps:

[0014] S301. Input the preset parameters and the gyroscope output data before filtering into the filtering model to calculate the threshold variable and the trigger variable before updating, and construct the first judgment condition based on the filtering model;

[0015] S302. Substitute the pre-update threshold variable and the pre-update trigger variable into the first judgment condition to obtain a first judgment result; the first judgment result includes satisfying and not satisfying.

[0016] S303. When the first judgment result is not satisfied, the iteration factor is updated to obtain the updated iteration factor, and the updated iteration factor is substituted into the filtering model again to calculate and obtain the updated threshold variable;

[0017] S304. If the first judgment result is satisfied, then update the trigger variable and obtain the filtered gyroscope output data.

[0018] 5. The dynamic filtering method for fiber optic gyroscopes as described in claim 4, further comprising the step of:

[0019] S305. Substitute the previous iteration factor into the second judgment condition to obtain the second judgment result; the second judgment result includes satisfying and not satisfying.

[0020] S306. When the second judgment result is satisfied, the iteration factor before the update is updated to obtain the iteration factor after the update, and the above steps S301 to S305 are repeated.

[0021] S307. If the second judgment result is not satisfied, then output the filtered gyroscope output data.

[0022] Furthermore, the first judgment condition is:

[0023]

[0024] in, y ( k ) is a spinning top k The original output at any given moment. y ( sl The output is the filtered result. It is the first The next data update serves as the trigger variable. It is the first k Threshold variable at time, Here are the filter parameters, and Δ represents the current temperature. p This is the temperature suppression coefficient. This is the reference temperature.

[0025] Furthermore, the second judgment condition is: ,in For time, as an iteration factor, the calculation is performed. This is the moment when the fiber optic gyroscope sample data obtained in step S1 is at its maximum.

[0026] According to a second aspect of the present invention, a dynamic filtering device suitable for fiber optic gyroscopes is also provided, characterized in that a data acquisition module is used to acquire sample data output by the fiber optic gyroscope over a period of time.

[0027] A filtering model generation module is used to establish a dynamic filtering algorithm model with a temperature trigger threshold and trigger and threshold variables that evolve based on changes in gyroscope output data.

[0028] The filtering module is used to acquire the preset parameters and the sample data, perform iterative calculations and filtering, and generate filtered fiber optic gyroscope output data.

[0029] According to a third aspect of the invention, a filtering device suitable for fiber optic gyroscopes is also provided, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program that, when executed by the processing unit, causes the processing unit to perform the steps of any of the methods described above.

[0030] According to a fourth aspect of the invention, a storage medium is also provided that stores a computer program executable by an access authentication device, which, when run on the access authentication device, causes the access authentication device to perform the steps of any of the methods described above.

[0031] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0032] (1) This filtering method can effectively filter out noise and improve the zero-bias stability of fiber optic gyroscopes. Compared with traditional filtering methods, such as Kalman filtering, this method can design different filtering parameters to achieve the desired filtering effect, which is more flexible. On the other hand, it incorporates the influence of temperature into the filtering algorithm, making it more suitable for full-temperature gyroscope tests. It can adjust the temperature suppression coefficient to achieve filtering performance across the entire temperature range. At the same time, this filtering method mainly relies on the historical data of the fiber optic gyroscope and the given judgment threshold, and has no requirements on the noise of the entire gyroscope system, which means it has a wide range of applications and can be used in situations where Kalman filtering is not suitable.

[0033] (2) The algorithm is highly flexible and can adjust multiple parameters to adjust the filtering performance. By setting the filtering parameters appropriately, data that fully reflects the characteristics of the gyroscope itself can be obtained.

[0034] (3) The operation is simple and the parameter adjustment method is simple and clear, making it easy to apply in engineering practice. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 A flowchart illustrating the steps of a dynamic filtering method for fiber optic gyroscopes provided in this application.

[0037] Figure 2 A flowchart illustrating the steps of another embodiment of a dynamic filtering method for fiber optic gyroscopes provided in this application.

[0038] Figure 3 This application provides an embodiment of a dynamic filtering method for fiber optic gyroscopes, which is a working logic diagram.

[0039] Figure 4 A flowchart illustrating the steps of another embodiment of a dynamic filtering method for fiber optic gyroscopes provided in this application.

[0040] Figure 5 A working logic diagram of another embodiment of a dynamic filtering method for fiber optic gyroscopes provided in this application;

[0041] Figure 6 This is a structural block diagram of an embodiment of a dynamic filtering device suitable for fiber optic gyroscopes, provided in this application. Detailed Implementation

[0042] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0043] The terms "first," "second," "third," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0044] In fiber optic gyroscope data filtering algorithms, Kalman filtering is widely used due to its excellent filtering capabilities and broad application models. Kalman filtering is an algorithm that uses the state equations of a linear system and observed data to optimally estimate the system state. It makes two assumptions: the dynamic model of the system is known, and the noise is assumed to be Gaussian white noise. Although most noise in nature is white noise, when it is affected by environmental factors and its statistical characteristics are altered, meaning it is no longer white noise, Kalman filtering loses its effectiveness. This is why Kalman filtering fails in some situations. Therefore, this invention addresses the shortcomings of current mainstream fiber optic gyroscope data filtering algorithms and proposes a more effective filtering method and filter, which has significant engineering implications.

[0045] Reference Figure 1 The diagram illustrates a flowchart of a dynamic filtering method for fiber optic gyroscopes according to an embodiment of the present invention. The method involves acquiring sample data output from the fiber optic gyroscope over a period of time, establishing a dynamic filtering algorithm model with a temperature trigger threshold and trigger and threshold variables that evolve based on changes in the gyroscope output data, setting preset parameters for the filtering model based on the sample data, inputting the sample data into the filtering model, performing iterative calculations and filtering, and obtaining the filtered fiber optic gyroscope output data.

[0046] Step S1: Obtain sample data of the fiber optic gyroscope's output after a period of stillness. The sample data includes at least one of the following: the gyroscope output data before filtering, the zero position of the fiber optic gyroscope, the temperature information of the surrounding environment, and the preset parameters of the filtering model.

[0047] Furthermore, based on the sample data of the 10-minute static output of the fiber optic gyroscope, the preset parameters of the filtering model are set, wherein the preset parameters include at least one of the initial values ​​of the trigger variable, the initial values ​​of the threshold variable, the filtering parameters, and the temperature suppression parameters.

[0048] It is worth noting that the filtering parameters are set based on the sample data measured in step S1. The specific value is generally set as follows: The specific value is on the same order of magnitude as one-squared zero. The absolute value is generally less than 1. When the temperature change is small, the temperature suppression coefficient can be set to 0, that is, the effect of temperature change on the output is not considered.

[0049] Step S2: Establish a dynamic filtering algorithm model with a temperature trigger threshold and historical data based on gyroscope output data, including trigger variables and threshold variables. In a preferred embodiment, the trigger variable is the number of times the data is updated during filtering, and the threshold variable is intermediate data evolving based on changes in the gyroscope output data. The temperature trigger threshold is obtained by taking the difference between the current temperature and a preset reference temperature and substituting it into a temperature suppression reference coefficient as a filtering reference condition to suppress temperature changes. However, users can choose other data names as intermediate variables as needed; this is not limited here.

[0050] Furthermore, the following dynamic filtering algorithm is preferred for filtering the output of the optical gyroscope, and the dynamic filtering algorithm model is as follows:

[0051]

[0052] in, y ( k ) is a spinning top k The original output at any given moment. y ( slThe output is the filtered result. It is the first The next data update serves as the trigger variable. It is the first k Threshold variable at time, Here are the filter parameters, and Δ represents the current temperature. p This is the temperature suppression coefficient. This is the reference temperature.

[0053] Step S3: Input the preset parameters and sample data into the filtering model, perform iterative calculations and filtering, and obtain the filtered fiber optic gyroscope output data. Further, refer to... Figure 2 The flowchart illustrates an embodiment of the dynamic filtering method for fiber optic gyroscopes according to the present invention, including the following steps:

[0054] Step S301. Input the preset parameters and the gyroscope output data before filtering into the filtering model to calculate the threshold variable and the trigger variable before updating, and construct the first judgment condition based on the filtering model;

[0055] Specifically, refer to Figure 3 This diagram illustrates the operational logic of an embodiment of a dynamic filtering method for fiber optic gyroscopes according to the present invention. Preset parameters and the gyroscope output data before filtering are input into the filtering model to calculate the threshold variable before updating. The threshold variable before updating is obtained through the filtering model... Calculate before update The first condition can be set as follows: .

[0056] S302. Substitute the threshold variable and the trigger variable before the update into the first judgment condition to obtain the first judgment result; the first judgment result includes whether the condition is met or not.

[0057] S303. When the first judgment result is not satisfied, the iteration factor is updated to obtain the updated iteration factor, and the updated iteration factor is substituted into the filtering model again to calculate and obtain the updated threshold variable;

[0058] Specifically, if the first judgment result is not satisfied, the iteration factor is updated, for example, let k = k +1, and update the threshold variable. Then, substitute the updated iteration factor back into the filtering model to calculate the updated threshold variable. Repeat steps S301 to S303 above to calculate the updated threshold variable.

[0059] S304. If the first judgment result is satisfied, then update the trigger variable and obtain the filtered gyroscope output data.

[0060] Specifically, if the first judgment result is satisfied, then let Update Output , l =1,2,..., n This means obtaining the filtered output data from the fiber optic gyroscope.

[0061] The above embodiments are preferably applicable to a real-time dynamic filtering application scenario, such as an online filtering mode where the filter performs real-time filtering on the gyroscope output data. Additionally, the present invention provides another embodiment, preferably applicable to an offline filtering application scenario, such as directly filtering gyroscope output data after acquiring data over a fixed period.

[0062] Reference Figure 4 The diagram shows a flowchart of an embodiment of a dynamic filtering method for fiber optic gyroscopes according to the present invention. Further, step three may also include step: S305. Substituting the iteration factor before update into the second judgment condition to obtain the second judgment result; the second judgment result includes satisfying and not satisfying.

[0063] S306. When the second judgment result is satisfied, the iteration factor before the update is updated to obtain the iteration factor after the update, and the above steps S301 to S305 are repeated.

[0064] S307. If the second judgment result is not satisfied, then output the filtered gyroscope output data.

[0065] Specifically, S305 will update the previous iteration factor, that is... Substitute the second judgment condition to obtain the second judgment result; where the second judgment condition is: ,in For time, as an iteration factor, the calculation is performed. This is the moment when the fiber optic gyroscope sample data obtained in step S1 is at its maximum.

[0066] S306. When the result of the second judgment is satisfied, let k = k +1 Repeat steps S301 to S305 above to calculate the updated threshold variable. and the updated trigger variables Continue until a second judgment result that is not satisfied is obtained, then output. , l =1,2,...,n, which means obtaining the filtered fiber optic gyroscope output data.

[0067] As a preferred embodiment, refer to Figure 5This diagram illustrates the operational logic of an embodiment of a dynamic filtering method for fiber optic gyroscopes according to the present invention. The following uses a single-axis fiber optic gyroscope powered on for 10 minutes as sample data to illustrate steps S301 to S307 in an application scenario for offline filtering. The diagram records the average zero-point value of the single-axis fiber optic gyroscope after 10 minutes of power-on, which is -17984, and the maximum temperature change, which is 0.1℃. This includes setting the filtering parameters using a reference temperature. The weighting coefficients are respectively set as follows: Set the initial value of the threshold variable, for example, one-tenth of the zero value after one minute of power-on. Set the initial value of the trigger variable, for example... Set the temperature suppression parameters, and take a reference temperature, for example... T s =25℃. Considering that the temperature change range is small when the fiber optic gyroscope is stable at room temperature, the filtering algorithm in this embodiment removes the temperature suppression part, i.e., sets it to 25℃. p =0, and then continuously collect the output data of the fiber optic gyroscope for 10 minutes as sample data.

[0068] It should be noted that the filtering parameters selected in the preferred embodiments described herein are only applicable to the specific fiber optic gyroscope selected in the embodiments. For different fiber optic gyroscopes, due to differences in overall design, components, scaling, etc., different parameters can be set, and this solution does not limit this.

[0069] S301, based on y(1) and formula

[0070] Calculate threshold variable ,in, =17624.32, then according to the formula The trigger threshold can be obtained. .

[0071] S302, y(2) and Substitute them together into the first condition. The first judgment result is generated in the middle, which is easy to know. Not valid.

[0072] S303, if the first judgment result is not satisfied, update the iteration factor, let k = k + 1, and repeat the above steps S301 to S304 to recalculate. Continue to substitute the first judgment condition to obtain the first judgment result until the first judgment result is satisfied;

[0073] S304, if the first judgment result is satisfied, and if k=m at this time, then let ,

[0074] S305, when the second judgment result is satisfied, the iteration factor before the update is updated, letting... k = k +1, and repeat steps S301 to S304 above.

[0075] S306, Repeat steps S301 to S305 until all collected sample data are filtered to obtain... as well as .

[0076] S307, if the second judgment result is not satisfied, then output... , l =1,2,..., n This means obtaining the filtered output data from the fiber optic gyroscope.

[0077] Reference Figure 6 This is a structural block diagram of a dynamic filtering device embodiment for fiber optic gyroscopes according to an embodiment of the present invention, comprising:

[0078] The data acquisition module is used to acquire sample data output by the fiber optic gyroscope over a period of time.

[0079] A filtering model generation module is used to establish a dynamic filtering algorithm model with a temperature trigger threshold and trigger and threshold variables that evolve based on changes in gyroscope output data.

[0080] The filtering module is used to acquire the preset parameters and the sample data, perform iterative calculations and filtering, and generate filtered fiber optic gyroscope output data.

[0081] The implementation principle and technical effects of this device are similar to those of the methods described above, and will not be repeated here.

[0082] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method. The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, DVDs, CD-ROMs, microdrives, as well as magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0083] Based on the above analysis, this solution is highly flexible and can adjust multiple parameters to modify the filtering performance. By appropriately setting the filtering parameters, data that fully reflects the characteristics of the gyroscope itself can be obtained. At the same time, it is simple to operate, and the parameter adjustment method is simple and clear, making it easy to apply in practical engineering.

[0084] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0085] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0086] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some service interface; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0087] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0088] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0089] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0090] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0091] The foregoing description is merely an exemplary embodiment of this disclosure and should not be construed as limiting the scope of this disclosure. Any equivalent changes and modifications made in accordance with the teachings of this disclosure shall still fall within the scope of this disclosure. Those skilled in the art will readily conceive of embodiments of this disclosure upon considering the specification and practicing the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of this disclosure are defined by the claims.

[0092] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0093] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A dynamic filtering method suitable for fiber-optic gyroscope, for processing the output data of fiber-optic gyroscope, characterized in that, The method comprises the following steps: S1, obtaining sample data output by a fiber-optic gyroscope for a period of time; S2, establishing a dynamic filtering algorithm model provided with a temperature trigger threshold and a trigger variable and a threshold variable evolved based on changes in the sample data; the dynamic filtering algorithm model is: wherein, y k is the raw output of the gyroscope k at the time t, is the filtered output, is the nth update data as a trigger variable, is the threshold variable at the time t, is the threshold variable at the time t, k is the filter parameter, and is the current temperature, p is the temperature suppression coefficient, is the reference temperature.​ S3, inputting the sample data into the dynamic filtering algorithm model, performing iterative calculation and filtering, and obtaining filtered fiber-optic gyroscope output data; The sample data comprises preset parameters of the dynamic filtering algorithm model.

2. A dynamic filtering method for a fiber optic gyroscope as claimed in claim 1, wherein, The preset parameters comprise at least one of an initial value of the trigger variable, an initial value of the threshold variable, a filtering parameter, and a temperature suppression parameter.

3. A dynamic filtering method for a fiber optic gyroscope as claimed in claim 1, wherein, The preset parameters and the pre-filtering gyroscope output data are input into the dynamic filtering algorithm model to perform iterative calculation, comprising the following steps: S301. The preset parameters and the pre-filtering gyroscope output data are input into the dynamic filtering algorithm model to obtain an updated threshold variable and an updated trigger variable, and a first judgment condition is constructed based on the dynamic filtering algorithm model; S302. The updated threshold variable and the updated trigger variable are input into the first judgment condition to obtain a first judgment result; the first judgment result comprises satisfaction and dissatisfaction; S303. When the first judgment result is dissatisfaction, an iteration factor is updated to obtain an updated iteration factor, and the updated iteration factor is input into the dynamic filtering algorithm model again to perform calculation and obtain an updated threshold variable; S304. When the first judgment result is satisfaction, the trigger variable is updated to obtain filtered gyroscope output data.

4. A dynamic filtering method for a fiber optic gyroscope as claimed in claim 3, wherein, Further comprising the following steps: S305. An updated iteration factor is input into a second judgment condition to obtain a second judgment result; the second judgment result comprises satisfaction and dissatisfaction; S306. When the second judgment result is satisfaction, the updated iteration factor is updated to obtain an updated iteration factor, and the above steps S301 to S305 are repeated; S307. When the second judgment result is dissatisfaction, the filtered gyroscope output data is output.

5. A dynamic filtering method for a fiber optic gyroscope as claimed in claim 3, wherein, The first judgment condition is: wherein, y k is the raw output of the gyroscope k at the time t, is the filtered output, is the nth update data as a trigger variable, is the threshold variable at the time t, is the threshold variable at the time t, k is the filter parameter, and is the current temperature, p is the temperature suppression coefficient, is the reference temperature.​ 6. A dynamic filtering method for a fiber optic gyroscope as claimed in claim 4, wherein, The second judging condition is: , wherein is the time, as an iteration factor calculation, the is the maximum time of the fiber-optic gyroscope sample data obtained in the S1 step.

7. A dynamic filtering device suitable for use in a fiber optic gyroscope, characterized in that, The method comprises the following steps: A data acquisition module is configured to acquire sample data output by a fiber-optic gyroscope for a period of time; A filtering model generation module is configured to establish a dynamic filtering algorithm model provided with a temperature trigger threshold and a trigger variable and a threshold variable evolved based on changes in the gyroscope output data; the dynamic filtering algorithm model is: in, y ( k ) is a spinning top k The original output at any given moment. This is the filtered output. It is the first The next data update serves as the trigger variable. It is the first k Threshold variable at time, Here are the filter parameters, and Δ represents the current temperature. p This is the temperature suppression coefficient. Reference temperature; A filtering module is configured to input the sample data into the filtering model, perform iterative calculation and filtering, and obtain filtered fiber-optic gyroscope output data.

8. A filtering device suitable for use in a fiber optic gyroscope, characterized in that, The computer program is executed by the processing unit, so that the processing unit executes the steps of the method according to any one of claims 1 to 6.

9. A storage medium, characterized by The computer program is executed by the processing unit, so that the processing unit executes the steps of the method according to any one of claims 1 to 6.

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