Ultra-short-term world time forecasting method based on large optical gyroscope

Abstract

The invention discloses an ultra-short-term world time forecasting method based on a large optical gyroscope, which comprises the following steps: obtaining a day length variation sequence according to an angular velocity sequence of the optical gyroscope, and determining a day length variation sequence without a high-frequency term and a tidal long-period term; obtaining an autoregression residual model according to a fitted residual sequence obtained by fitting the day length change sequence in which the high-frequency term and the tidal long-period term are removed; obtaining a day length change forecast value sequence of 24 points according to the fitted linear combination model and the auto-regression residual model; obtaining a day length change forecast value sequence with a high-frequency item and a tidal long-period item according to the day length change forecast value sequence and the high-frequency item forecast value sequence and the tidal long-period item forecast value sequence which are in the same period as the day length change forecast value sequence; and obtaining a UT1-UTC forecast value sequence of 24 points according to the day length change forecast value sequence with the high-frequency item and the tidal long-period item. According to the method, the problem of low time resolution of current world time forecast result release is solved.

Description

Technical Field

[0001] This invention relates to the field of world time forecasting technology, and specifically to an ultra-short-term world time forecasting method based on a large optical gyroscope. Background Technology

[0002] Universal Time (UT1) is a core parameter of Earth's orientation parameters. It is a time system defined based on the Earth's rotation and is essential for coordinate transformation between the International Earth Reference System and the Geocentric Celestial Reference System. Due to modern space geodetic techniques, such as Very Long Baseline Interferometry (VLBI) and Global Navigation Satellite Systems (GNSS), measured UUT data often has a delay of several hours to several days, making real-time acquisition impossible. In certain fields, such as satellite navigation and space exploration, real-time UUT data is required for parameter calculations; therefore, UUT forecasts are indispensable in these areas.

[0003] Currently, the datasets used in UTC forecasting research generally come from the International Earth Rotation and Reference System Service (IERS). IERS updates Bulletin A weekly, containing the latest weekly UTC observations and forecasts for the next year, with a time resolution of one day. IERS also updates Bulletin B monthly, providing post-processed, more accurate UTC data with a one-month lag, also with a one-day time resolution. UTC forecasting research primarily focuses on the high-precision UTC time series published by IERS with a daily time resolution, using different mathematical models to forecast UTC results for 1-365 days.

[0004] In recent years, with the continuous development of satellite navigation, aerospace and other technologies, the requirements for the accuracy and time resolution of UTC forecasts have become increasingly higher. However, current UTC forecasts based on IERS still suffer from low time resolution and do not consider the high-frequency variations within the daily time diurnal cycle of UTC. This makes it difficult to meet the requirements of some applications that require high-time-resolution UTC forecasts. This study investigates the high-frequency variation patterns within the daily time diurnal cycle of UTC based on real-time UTC observation data from large optical gyroscopes, establishes a high-frequency variation model for UTC within the daily time diurnal cycle, and develops an ultra-short-term UTC forecasting method that takes into account these variations. This method is expected to provide high-precision, high-time-resolution ultra-short-term UTC forecasts for satellite navigation, aerospace and other technologies.

[0005] The research on UTC forecasting mainly focuses on the high-precision UTC / day length variation data released by IERS with a time resolution of days, and makes short-term (1-30 days) and medium-term (30-90 days) UTC forecasts at daily intervals.

[0006] However, current UTC forecasts have a low time resolution (1 day), and they do not take into account the fine structure of UTC on a daily scale. Summary of the Invention

[0007] To address the aforementioned problems in the existing technology, this invention provides an ultra-short-term cosmic time prediction method based on a large optical gyroscope. The technical problem to be solved by this invention is achieved through the following technical solution: This invention provides an ultra-short-term cosmic time prediction method based on a large optical gyroscope, comprising: Based on the diurnal variation sequence obtained from the angular velocity sequence of the optical gyroscope, a diurnal variation sequence after removing high-frequency terms and long-period tidal terms is determined, wherein the time resolution of the angular velocity is 1 hour. Based on the fitted residual sequence obtained by fitting the diurnal variation sequence after removing the high-frequency term and the long-period tidal term, an autoregressive residual model is obtained. Based on the fitted linear combination model and the autoregressive residual model, a sequence of predicted daily length variations for 24 points on a predetermined day is obtained. Based on the predicted value sequence of day length variation and the predicted value sequence of high frequency term and the predicted value sequence of long tidal period term that are concurrent with the predicted value sequence of day length variation, a predicted value sequence of day length variation with high frequency term and long tidal period term is obtained. Based on the diurnal variation forecast value sequence with high frequency term and long tidal period term, a preset UT1-UTC forecast value sequence for 24 points within a day is obtained, where UT1 is Universal Time and UTC is Coordinated Universal Time.

[0008] In one embodiment of the present invention, determining a diurnal variation sequence after removing high-frequency terms and long-period tidal terms based on a diurnal variation sequence obtained from an optical gyroscope angular velocity sequence includes: The angular velocities in the angular velocity sequence of the optical gyroscope are converted into diurnal variations using a conversion formula, and all diurnal variations are combined into the diurnal variation sequence. The conversion formula is expressed as follows:

[0009] in, This is the change in day length. Based on the length of day, The angular velocity of Earth's rotation. The Earth's rotational angular velocity measured by an optical gyroscope; Remove the high-frequency terms from the diurnal variation sequence to obtain the diurnal variation sequence with high-frequency terms removed; Subtracting the diurnal variation sequence after removing the high-frequency term from the long-period tidal term in the Earth tidal model yields the diurnal variation sequence after removing both the high-frequency term and the long-period tidal term.

[0010] In one embodiment of the present invention, removing the high-frequency term sequence from the diurnal variation sequence to obtain a diurnal variation sequence with high-frequency terms removed includes: Perform a Fourier transform on the diurnal variation sequence to generate a spectrum. The amplitude and phase of the daily term and the amplitude and phase of the semi-daily term in the spectrum are identified by using least squares to obtain the high-frequency term sequence; Subtracting the high-frequency term sequence from the daily length variation sequence yields the daily length variation sequence with the high-frequency term removed. In one embodiment of the present invention, an autoregressive residual model is obtained based on the fitted residual sequence obtained by fitting the diurnal variation sequence after removing the high-frequency term and the long-period tidal term, including: Based on the linear combination model, the deterministic trend, linear term and periodic term in the diurnal variation sequence after removing high-frequency term and long-period tidal term are fitted by least squares to obtain the fitted value sequence. Subtract the fitted value sequence from the diurnal length variation sequence after removing the high-frequency term and the long-period tidal term to obtain the fitted residual sequence; The fitted residual sequence is modeled for randomness to obtain an autoregressive residual model. In one embodiment of the present invention, the linear combination model is represented as:

[0011] in, These are the fitted values. For trend items, For linear terms, and All are amplitude values. For time, n The total number of periodic components. For the first i Each periodic component. In one embodiment of the present invention, n The periodic components are 9.13 days, 13.7 days, 27.4 days, 121.75 days, 182.62 days, 365.24 days, 1095.72 days and 3396.73 days, respectively. In one embodiment of the present invention, a sequence of predicted diurnal variations for 24 points on a predetermined day is obtained based on the fitted linear combination model and the autoregressive residual model, including: Based on the fitted linear combination model, a sequence of predicted values ​​for 24 points on a predetermined day is obtained, and based on the autoregressive residual model, a sequence of predicted values ​​for 24 points on a predetermined day is obtained. The diurnal variation forecast sequence for the 24 points on a predetermined day is obtained based on the linear combination model forecast sequence of 24 points on a predetermined day and the autoregressive residual forecast sequence of 24 points on a predetermined day. In one embodiment of the present invention, a daily length variation forecast sequence having both high-frequency and long-period tidal terms is obtained based on the daily length variation forecast sequence and the high-frequency term forecast sequence and the long-period tidal term forecast sequence contemporaneous with the daily length variation forecast sequence, including: The high-frequency term forecast sequence that is concurrent with the day length variation forecast sequence is obtained by using the fitted high-frequency variation model, and the long-period tidal term forecast sequence that is concurrent with the day length variation forecast sequence is obtained by using the Earth tidal model. The high-frequency term forecast sequence and the long-period tidal term forecast sequence, which are contemporaneous with the diurnal variation forecast sequence, are superimposed on the diurnal variation forecast sequence to obtain the diurnal variation forecast sequence with high-frequency term and long-period tidal term. In one embodiment of the present invention, a preset UTI-UTC forecast sequence for 24 points within a day is obtained based on the diurnal variation forecast sequence having a high-frequency term and a long-period tidal term, including: Based on the differential relationship between UTC and day length variation, the UT1-UTC observation value of the moment before the forecast start time is used as the initial reference. The day length variation forecast value sequence with high frequency term and long tidal period term is converted into a preset UT1-UTC forecast value sequence of 24 points within a day through numerical integration. In one embodiment of the present invention, the differential relationship between the changes in cosmic time and day length is expressed as:

[0012] in, This is a preset forecast value for the change in day length at a certain point within a day. Based on the length of day, For time.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: To address the issues of low time resolution (1 day) and lack of consideration for fine structure on the diurnal scale of UTC forecasts in existing forecasts, this invention provides an ultra-short-term forecasting method based on high-precision, real-time UTC data measured by a large optical gyroscope. This method can provide hourly UTC ultra-short-term forecasts within a single day, taking into account high-frequency variations within the diurnal scale of UTC, effectively solving the problem of low time resolution in current UTC forecasts and improving forecast accuracy within the diurnal scale of UTC.

[0014] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating an ultra-short-term cosmic time prediction method based on a large optical gyroscope provided in an embodiment of the present invention; Figure 2 The optical gyroscope provided in this embodiment of the invention has an accuracy of 4×10⁻⁶. -14 Simulation sequence of ideal Earth rotation angular velocity in rad / s; Figure 3 The optical gyroscope provided in this embodiment of the invention has an accuracy of 4×10⁻⁶. -14 Simulation sequence of diurnal variation in rad / s; Figure 4 The optical gyroscope provided in this embodiment of the invention has an accuracy of 4×10⁻⁶. -14 MAE distribution map of UT1-UTC 24-hour forecast at rad / s (based on 365 sliding forecasts). Detailed Implementation

[0016] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following describes in detail a method for ultra-short-term cosmic time prediction based on a large optical gyroscope, in conjunction with the accompanying drawings and specific embodiments.

[0017] The foregoing and other technical contents, features, and effects of the present invention will be clearly presented in the following detailed description of specific embodiments in conjunction with the accompanying drawings. Through the description of the specific embodiments, a more in-depth and concrete understanding can be gained of the technical means and effects adopted by the present invention to achieve its intended purpose. However, the accompanying drawings are for reference and illustration only and are not intended to limit the technical solutions of the present invention.

[0018] Example 1 Please see Figure 1 , Figure 1This is a flowchart illustrating an ultra-short-term cosmic time (UTC) forecasting method based on a large optical gyroscope, provided by an embodiment of the present invention. The UTC forecasting method includes: Step 1: Based on the diurnal variation sequence obtained from the angular velocity sequence of the optical gyroscope, determine the diurnal variation sequence after removing high-frequency terms and long-period tidal terms, where the time resolution of the angular velocity is 1 hour.

[0019] In one specific embodiment, step 1 may include: Step 1.1: Use the conversion formula to convert each angular velocity in the angular velocity sequence of the optical gyroscope into a diurnal variation, so as to combine all the diurnal variations into a diurnal variation sequence.

[0020] Specifically, a set of angular velocities with a time resolution of 1 hour is obtained by a large optical gyroscope. This set of angular velocities is called the angular velocity sequence. Then, the conversion formula is used to convert each angular velocity in the angular velocity sequence into a daily length change. All the daily length changes are combined to form a daily length change sequence. The time interval between all daily length changes is equal. Since the time resolution of the angular velocity is 1 hour, the time resolution of the daily length change is also 1 hour.

[0021] Here, the conversion formula is expressed as:

[0022] in, This is the change in day length. Based on the length of day, =3600s, The angular velocity of Earth's rotation. =7.292115×10 -5 rad / s, The Earth's rotational angular velocity measured by an optical gyroscope.

[0023] Step 1.2: Remove the high-frequency terms from the daily length variation sequence to obtain the daily length variation sequence with high-frequency terms removed.

[0024] Step 1.21: Perform a Fourier transform on the diurnal variation sequence and establish a spectrum.

[0025] Here, the spectrum is a line graph plotted with frequency on the horizontal axis and the corresponding amplitude on the vertical axis. In the spectrum, you can see obvious peaks around frequencies of 1 week / day and 2 weeks / day; these are the daily (24-hour) and semi-daily (12-hour) signals.

[0026] Step 1.22: Use least squares (LS) to identify the amplitude and phase of the daily term and the semi-daily term in the spectrogram to obtain the high-frequency term sequence.

[0027] Specifically, firstly, based on the observation results of the spectrum diagram, confirm that the dominant frequency to be fitted is the angular frequency of 1 week / day (daily term). ω Angular frequencies of 1 and 2 weeks / day (semi-diurnal term) ω 2. Next, construct a basis function consisting of sine and cosine functions (sin( ω 1 t ), cos( ω 1 t ), sin( ω 2 t ), cos( ω 2 t A linear combination model is constructed using the diurnal variation sequence as observed values. Then, the combination coefficients of this linear combination model are solved using the least squares principle to minimize the sum of squared residuals between the model and the observed values. The linear combination model corresponding to the coefficients obtained at this point is denoted as the fitted linear combination model. Finally, the amplitude and phase of each component are calculated from the solved coefficients to obtain the amplitude of the diurnal term. A 1 and phase φ 1 and the amplitude of the semi-day term A 2 and phase φ 2. Compare them with their corresponding standard angular frequencies. ω 1. ω 2. Combine and substitute into the sinusoidal superposition model (i.e., the fitted high-frequency variation model). Then, for each time point in the daily variation sequence, the corresponding function value is calculated. This yields a corresponding high-frequency term, thus generating a complete time series, which is the reconstructed high-frequency term sequence.

[0028] Step 1.23: Subtract the high-frequency term sequence from the daily length variation sequence to obtain the daily length variation sequence with high-frequency term removed.

[0029] Specifically, for each point in the daily length change sequence, the daily length change is subtracted from the high-frequency term in the corresponding high-frequency term sequence to obtain the daily length change sequence after removing the high-frequency term.

[0030] Step 1.3: Subtract the diurnal variation sequence after removing the high-frequency term from the long-period tidal term in the Earth tidal model to obtain the diurnal variation sequence after removing the high-frequency term and the long-period tidal term.

[0031] Specifically, the long-period tidal term corresponding to each point in the diurnal length variation sequence after removing high-frequency terms is obtained using the Earth tidal model, so that the time reference, sampling interval and length of the long-period tidal term sequence obtained from the Earth tidal model are consistent with those of the diurnal length variation sequence after removing high-frequency terms. Then, the long-period tidal term of the corresponding point is subtracted from the change of each point in the diurnal length variation sequence after removing high-frequency terms, thereby obtaining the diurnal length variation sequence after removing high-frequency terms and long-period tidal terms.

[0032] Step 2: Based on the fitted residual sequence obtained by removing the high-frequency term and the long-period tidal term from the diurnal variation sequence, the autoregressive residual model is obtained.

[0033] In one specific embodiment, step 2 may include: Step 2.1: Based on the linear combination model, use least squares to fit the deterministic trend, linear term and periodic term in the diurnal variation sequence after removing high-frequency term and long-period tidal term to obtain the fitted value sequence.

[0034] Specifically, a linear combination model is first constructed. Then, using the diurnal variation sequence after removing high-frequency terms and long-period tidal terms as observed data, the optimal coefficients of each basis function in the linear combination model are solved by least squares to minimize the sum of squared residuals between the fitted value of the linear combination model and each variation value in the diurnal variation sequence after removing high-frequency terms and long-period tidal terms. Finally, the complete fitted linear combination model is reconstructed using the solved coefficients, and the fitted value corresponding to the variation value of each point in the diurnal variation sequence after removing high-frequency terms and long-period tidal terms is obtained. All fitted values ​​form a fitted value sequence.

[0035] Here, the linear combination model is expressed as:

[0036] in, These are the fitted values. For trend items, For linear terms, and All are amplitude values. For time, n The total number of periodic components. For the first i Each periodic component, n Take 8, n The periodic components are 9.13 days, 13.7 days, 27.4 days, 121.75 days, 182.62 days, 365.24 days, 1095.72 days and 3396.73 days, respectively.

[0037] Step 2.2: Subtract the fitted value sequence from the diurnal length variation sequence after removing the high-frequency term and the long-period tidal term to obtain the fitted residual sequence.

[0038] Specifically, for each point in the diurnal variation sequence after removing high-frequency terms and long-period tidal terms, the diurnal variation is subtracted from the fitted value of the corresponding point in the fitted value sequence to obtain the fitted residual sequence.

[0039] Step 2.3: Perform randomness modeling on the fitted residual sequence to obtain the autoregressive residual model.

[0040] Specifically, the optimal order is selected by analyzing the partial autocorrelation function of the fitted residual sequence. p Using maximum likelihood estimation to estimate and This process aims to make the model fit the fitted residual sequence as closely as possible, ultimately resulting in an autoregressive residual model.

[0041] Here, the autoregressive residual model is expressed as:

[0042] in, For the fitted residual sequence at the current time t The value, For autoregressive coefficients, 1 ≤ j ≤ p , , … For the fitted residual sequence in the past p The value at each moment, For random error term, This is a constant term. Step 3: Based on the fitted linear combination model and autoregressive residual model, obtain the predicted sequence of diurnal variation values ​​for 24 points on a certain day.

[0043] Here, the 24 points for a certain day are the 24 points within the first day after the end time of the day length variation data. The time resolution of the 24 points is 1 hour. For example, the 24 points within the first day after the end time.

[0044] In one specific embodiment, step 3 may include: Step 3.1: Obtain the linear combination model forecast sequence of 24 points on a predetermined day based on the fitted linear combination model, and obtain the autoregressive residual forecast sequence of 24 points on a predetermined day based on the autoregressive residual model.

[0045] Specifically, 24 points on a predetermined day are substituted into the fitted linear combination model to obtain the forecast value corresponding to each point, and the 24 forecast values ​​corresponding to the 24 points form the forecast value sequence of the linear combination model; 24 points on a predetermined day are substituted into the autoregressive residual model to obtain the autoregressive residual forecast value corresponding to each point, and the 24 autoregressive residual forecast values ​​corresponding to the 24 points form the autoregressive residual forecast value sequence.

[0046] Step 3.2: Based on the preset linear combination model forecast value sequence of 24 points on a certain day and the preset autoregressive residual forecast value sequence of 24 points on a certain day, obtain the preset daily length variation forecast value sequence of 24 points on a certain day.

[0047] Specifically, the forecast value of each point in the linear combination model forecast value sequence is added to the corresponding autoregressive residual forecast value of each point in the autoregressive residual forecast value sequence to obtain the diurnal variation forecast value sequence.

[0048] Step 4: Based on the diurnal variation forecast value sequence and the high-frequency term forecast value sequence and the tidal long-period term forecast value sequence that are in the same period as the diurnal variation forecast value sequence, obtain the diurnal variation forecast value sequence with high-frequency term and tidal long-period term.

[0049] In one specific embodiment, step 4 may include: Step 4.1: Use the fitted high-frequency variation model to obtain the high-frequency term forecast value sequence that is concurrent with the diurnal variation forecast value sequence, and use the Earth tidal model to obtain the tidal long-period term forecast value sequence that is concurrent with the diurnal variation forecast value sequence.

[0050] Specifically, firstly, the amplitude and phase of the daily and semi-daily terms are calculated using the fitted linear combination model in step 1.22. Then, the high-frequency term forecast value sequence, which is concurrent with the daily length variation forecast value sequence, is obtained using the fitted high-frequency variation model. The high-frequency term forecast value sequence and the daily length variation forecast value sequence have the same time reference, time interval, and length. The Earth tidal model is used to calculate the tidal long-period term forecast values ​​for 24 points on a preset day. All tidal long-period terms form the tidal long-period term forecast value sequence.

[0051] Step 4.2: Superimpose the high-frequency term forecast value sequence and the long-period tidal term forecast value sequence that are concurrent with the diurnal variation forecast value sequence onto the diurnal variation forecast value sequence to obtain a diurnal variation forecast value sequence with both high-frequency and long-period tidal terms.

[0052] Specifically, the forecast values ​​of the high-frequency term, the long-period tidal term, and the diurnal variation of 24 points in the forecast value sequence of high-frequency term, the forecast value sequence of long-period tidal term, and the forecast value of diurnal variation are added together to obtain the diurnal variation forecast value sequence with high-frequency term and long-period tidal term.

[0053] Step 5: Based on the diurnal variation forecast value sequence with high frequency term and long tidal period term, obtain the preset UT1-UTC forecast value sequence for 24 points within a day, where UT1 is Universal Time and UTC is Coordinated Universal Time.

[0054] Specifically, based on the differential relationship between UTC and day length variation, the UT1-UTC observation value of the moment before the forecast start time is used as the initial reference. Through numerical integration, the day length variation forecast value sequence with high frequency term and long tidal period term is converted into a preset UT1-UTC forecast value sequence of 24 points within a day.

[0055] Here, the differential relationship between the changes in cosmic time and day length is expressed as:

[0056] in, This is a preset forecast value for the change in day length at a certain point within a day. Based on the length of day, For time.

[0057] Finally, the UTC forecast results provided by the embodiments of the present invention can be verified based on the observations published by IERS, using the mean absolute error. MAE As an evaluation index for forecast accuracy.

[0058] Here, mean absolute error MAE Represented as:

[0059] in, These are IERS observations. This is a predicted value for UTC.

[0060] This invention provides an embodiment to illustrate the ultra-short-term cosmic time forecasting method provided by the present invention.

[0061] (1) Using an optical gyroscope with an accuracy of 4×10 -14 Taking the ideal Earth rotation angular velocity simulation sequence in rad / s as an example (e.g.) Figure 2 As shown), after transformation using the conversion formula, the time series of diurnal variation is obtained (e.g. Figure 3 (As shown). Figure 2 and Figure 3 All data are from 4 years of simulation, with a time resolution of 1 hour.

[0062] (2) Using the time series of daily length variation, construct a high-frequency term sequence of daily length variation within the day, mainly consisting of daily and half-day terms. Subtract the high-frequency term sequence from the original daily length variation time series to obtain the daily length variation sequence with high-frequency terms removed.

[0063] (3) Remove the daily length variation sequence with high frequency terms within the day according to the long period of tidal terms provided by IERS to highlight the non-tidal signal components.

[0064] (4) After preprocessing, a combined LS and AR model is used for forecast modeling. The LS part is used to fit the deterministic trend and periodicity in the sequence, and the periodicity includes eight significant periodic components: 9.13 days, 13.7 days, 27.4 days, 121.75 days, 182.62 days, 365.24 days, 1095.72 days, and 3396.73 days. The AR part is used to model and forecast the stochasticity of the residual sequence after LS fitting.

[0065] (5) The forecast experiment used 4 years of daily length variation data as the training set, forecasted 24 points (corresponding to 1 day) each time, and made 365 independent forecasts along the time axis to comprehensively evaluate the model’s performance in different time periods.

[0066] (6) The predicted values ​​of the high-frequency term and the extrapolated values ​​of the long-period tidal term are superimposed on the predicted diurnal variation sequence to restore its complete variation characteristics. Then, based on the differential relationship between the Earth's rotation parameters, the UT1-UTC observation value at the moment before the forecast start time is used as the initial reference, and the diurnal variation forecast sequence is converted into the UT1-UTC forecast sequence through numerical integration.

[0067] (7) Compare the obtained UT1-UTC forecast sequence with the actual UT1-UTC observations published by IERS, and use MAE as the accuracy evaluation index to evaluate the forecast accuracy of UT1-UTC.

[0068] like Figure 4 As shown, when the optical gyroscope noise level is 4×10 -14 At rad / s, the UT1-UTC prediction accuracy can reach 32.4 μs over a 24-hour time span. This accuracy advantage provides crucial technical support for applications such as real-time orbit determination and high-precision timekeeping in deep space exploration.

[0069] This invention is based on high-time-resolution large optical gyroscope UTC data to study the high-frequency variation pattern of UTC within a day and proposes an ultra-short-term UTC forecasting method that takes into account the high-frequency variation pattern of UTC. This improves the time resolution of UTC forecasting from the traditional 1-day level to the hour level (1 hour), significantly enhancing the time resolution of UTC forecasting and providing forecast data for applications that require hourly UTC within a day.

[0070] It should be noted that the terms "first," "second," etc., 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. The embodiments described in the above exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatuses and methods consistent with some aspects of the present invention.

[0071] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0072] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings and the disclosure in carrying out the claimed invention. In the description of the invention, the word "comprising" does not exclude other components or steps, "a" or "an" does not exclude a plurality, and "a plurality" means two or more, unless otherwise explicitly specified. Furthermore, while different embodiments may describe certain measures, this does not mean that these measures cannot be combined to produce good results.

[0073] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A method for ultra-short-term cosmic time forecasting based on a large optical gyroscope, characterized in that, include: Based on the diurnal variation sequence obtained from the angular velocity sequence of the optical gyroscope, a diurnal variation sequence after removing high-frequency terms and long-period tidal terms is determined, wherein the time resolution of the angular velocity is 1 hour. Based on the fitted residual sequence obtained by fitting the diurnal variation sequence after removing the high-frequency term and the long-period tidal term, an autoregressive residual model is obtained. Based on the fitted linear combination model and the autoregressive residual model, a sequence of predicted daily length variations for 24 points on a predetermined day is obtained. Based on the predicted value sequence of day length variation and the predicted value sequence of high frequency term and the predicted value sequence of long tidal period term that are concurrent with the predicted value sequence of day length variation, a predicted value sequence of day length variation with high frequency term and long tidal period term is obtained. Based on the diurnal variation forecast value sequence with high frequency term and long tidal period term, a preset UT1-UTC forecast value sequence for 24 points within a day is obtained, where UT1 is Universal Time and UTC is Coordinated Universal Time.

2. The ultra-short-term cosmic time forecasting method according to claim 1, characterized in that, Based on the diurnal variation sequence obtained from the angular velocity sequence of optical gyroscopes, a diurnal variation sequence after removing high-frequency terms and long-period tidal terms is determined, including: The angular velocities in the angular velocity sequence of the optical gyroscope are converted into diurnal variations using a conversion formula, and all diurnal variations are combined into the diurnal variation sequence. The conversion formula is expressed as follows: in, This is the change in day length. Based on the length of day, The angular velocity of Earth's rotation. The Earth's rotational angular velocity measured by an optical gyroscope; Remove the high-frequency terms from the diurnal variation sequence to obtain the diurnal variation sequence with high-frequency terms removed; Subtracting the diurnal variation sequence after removing the high-frequency term from the long-period tidal term in the Earth tidal model yields the diurnal variation sequence after removing both the high-frequency term and the long-period tidal term.

3. The ultra-short-term cosmic time forecasting method according to claim 2, characterized in that, Removing high-frequency terms from the diurnal variation sequence yields a diurnal variation sequence with high-frequency terms removed, including: Perform a Fourier transform on the diurnal variation sequence to create a spectrum. The amplitude and phase of the daily term and the amplitude and phase of the semi-daily term in the spectrum are identified by using least squares to obtain the high-frequency term sequence; Subtracting the high-frequency term sequence from the daily length variation sequence yields the daily length variation sequence with the high-frequency term removed.

4. The ultra-short-term cosmic time forecasting method according to claim 1, characterized in that, Based on the fitted residual sequence obtained by fitting the diurnal variation sequence after removing the high-frequency term and the long-period tidal term, an autoregressive residual model is obtained, including: Based on the linear combination model, the deterministic trend, linear term and periodic term in the diurnal variation sequence after removing high-frequency term and long-period tidal term are fitted by least squares to obtain the fitted value sequence. Subtract the fitted value sequence from the diurnal length variation sequence after removing the high-frequency term and the long-period tidal term to obtain the fitted residual sequence; The fitted residual sequence is modeled for randomness to obtain an autoregressive residual model.

5. The ultra-short-term cosmic time forecasting method according to claim 4, characterized in that, The linear combination model is expressed as follows: in, These are the fitted values. For trend items, For linear terms, and All are amplitude values. For time, n The total number of periodic components. For the first i Each periodic component.

6. The ultra-short-term cosmic time forecasting method according to claim 1, characterized in that, n The periodic components are 9.13 days, 13.7 days, 27.4 days, 121.75 days, 182.62 days, 365.24 days, 1095.72 days and 3396.73 days, respectively.

7. The ultra-short-term cosmic time forecasting method according to claim 1, characterized in that, Based on the fitted linear combination model and the autoregressive residual model, a preset sequence of diurnal variation forecast values ​​for 24 points on a given day is obtained, including: Based on the fitted linear combination model, a sequence of predicted values ​​for 24 points on a predetermined day is obtained, and based on the autoregressive residual model, a sequence of predicted values ​​for 24 points on a predetermined day is obtained. The diurnal variation forecast sequence for the 24 points on a predetermined day is obtained based on the linear combination model forecast sequence of 24 points on a predetermined day and the autoregressive residual forecast sequence of 24 points on a predetermined day.

8. The ultra-short-term cosmic time forecasting method according to claim 1, characterized in that, Based on the predicted sequence of day length variations and the predicted sequences of high-frequency terms and long-period tidal terms that are contemporaneous with the predicted sequence of day length variations, a predicted sequence of day length variations with both high-frequency and long-period tidal terms is obtained, including: The high-frequency term forecast sequence that is concurrent with the day length variation forecast sequence is obtained by using the fitted high-frequency variation model, and the long-period tidal term forecast sequence that is concurrent with the day length variation forecast sequence is obtained by using the Earth tidal model. The high-frequency term forecast sequence and the long-period tidal term forecast sequence, which are contemporaneous with the diurnal variation forecast sequence, are superimposed on the diurnal variation forecast sequence to obtain the diurnal variation forecast sequence with high-frequency term and long-period tidal term.

9. The ultra-short-term cosmic time forecasting method according to claim 1, characterized in that, Based on the diurnal variation forecast value sequence with high-frequency term and long-period tidal term, a preset UT1-UTC forecast value sequence for 24 points within a day is obtained, including: Based on the differential relationship between UTC and day length variation, the UT1-UTC observation value of the moment before the forecast start time is used as the initial reference. The day length variation forecast value sequence with high frequency term and long tidal period term is converted into a preset UT1-UTC forecast value sequence of 24 points within a day through numerical integration.

10. The ultra-short-term cosmic time forecasting method according to claim 1, characterized in that, The differential relationship between the changes in cosmic time and day length is expressed as: in, This is a preset forecast value for the change in day length at a certain point within a day. Based on the length of day, For time.

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