Pseudo-period detection method and device for coordinate time sequence, electronic equipment and medium

Abstract

The present application relates to the technical field of signal processing, and particularly relates to a pseudo-period detection method and device for coordinate time sequence, electronic equipment and medium, comprising: performing data preprocessing on a coordinate time sequence of a target navigation satellite system, and performing period signal detection to obtain a plurality of target period signals satisfying a preset detection condition; calculating a reference Akaike information criterion of the preprocessed coordinate time sequence under a zero hypothesis; fitting a single period model of each target period signal and the preprocessed coordinate time sequence to obtain a first new coordinate time sequence; and obtaining a real period signal of the coordinate time sequence based on a comparison result of a target Akaike information criterion corresponding to each target period signal and the reference Akaike information criterion. Thus, the problems of pseudo-period interference caused by residual gross errors, incomplete elimination of step signals and poor accuracy of period detection methods in related technologies are solved.

Description

Technical Field

[0001] The present invention relates to the field of signal processing technology, and in particular to a method, device, electronic equipment and medium for detecting pseudo-periods of a coordinate time series. Background Art

[0002] GNSS (Global Navigation Satellite System) coordinate time series, as core observational data in modern geodesy, play an irreplaceable role in fields such as plate motion monitoring and glacial isostatic adjustment. By recording the millimeter-level evolution of station three-dimensional coordinates, these series provide critical data for revealing Earth's internal dynamics and constructing a high-precision reference frame. However, the complex periodic signals in these time series contain both useful information reflecting real geophysical mechanisms (such as thermoelastic deformation of the crust and hydrological loading effects), as well as interference components caused by systematic errors (such as antenna phase center variations and orbital errors) and environmental noise. This coupling of multiple signals has made effectively isolating the real physical periodic signals a major and long-standing challenge for the international geodetic community.

[0003] Among the related technologies, for the problem of periodic signal extraction, in the time domain methods, LSHE (Least Squares Harmonic Estimation) and its improved version MLSHE (Modified Least Square Harmonics Estimation) are representative, which improve the single-station period detection capability through joint time-frequency domain analysis; in the time-frequency analysis methods, CEEMD (Complete Ensemble Empirical Mode Decomposition) is good at processing non-stationary signals; in the detection of anniversary and semi-anniversary signals, MCSSA (Monte Carlo Singular Spectrum Analysis) shows advantages.

[0004] However, the engineering applicability of the above methods still has significant limitations: LSHE is not sensitive enough to abnormal signals, CEEMD is prone to produce false long-period components in high-noise environments, and MCSSA has a systematic deviation of about 10 days in detecting annual signals (355 days vs. the theoretical value of 365.25 days).

[0005] Therefore, the study shows that the signal detection methods of related technologies have three key defects in engineering applications: (1) The incomplete elimination of gross errors and step signals will lead to the generation of abnormal pseudo-cycles. For example, the residual outliers of the IQR (Interquartile Range) method will induce 8-15 months of false signals in the Lomb-Scargle spectrum analysis; (2) The period length deviation type pseudo-cycle seriously affects the modeling accuracy. The annual signal deviation detected by MCSSA can increase the seasonal deformation inversion error by 12%; (3) Traditional evaluation indicators such as spectral index (Kappa) have the risk of failure. Both the real period and the pseudo-period may make the Kappa value approach zero, resulting in the inability to effectively distinguish between the two. At the same time, the aliasing of pseudo-periodic signals and real physical periods will cause cascading errors. For example, in the Greenland ice sheet area, the estimated deviation of the post-glacial rebound rate caused by such errors can reach 0.8mm / yr, which needs to be solved urgently. Summary of the Invention

[0006] The present invention provides a pseudo-period detection method, device, electronic device and medium for coordinate time series, so as to solve the problems of pseudo-period interference caused by residual gross errors, incomplete elimination of step signals and poor accuracy of period detection methods in related technologies.

[0007] A first embodiment of the present invention provides a method for detecting pseudo-periods in a coordinate time series, comprising the following steps:

[0008] Acquiring a coordinate time series of a target navigation satellite system, and performing data preprocessing on the coordinate time series to obtain a preprocessed coordinate time series;

[0009] Performing periodic signal detection on the preprocessed coordinate time series to obtain a plurality of target periodic signals that meet preset detection conditions, performing noise analysis on the preprocessed coordinate time series based on a null hypothesis, and calculating a benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis;

[0010] Performing single-cycle modeling on each target periodic signal to obtain a single-cycle model of each target periodic signal, fitting the single-cycle model of each target periodic signal to the preprocessed coordinate time series to obtain a first new coordinate time series, and calculating a target Akaike information criterion corresponding to each target periodic signal under the first new coordinate time series;

[0011] Each target Akaike information criterion is compared with the benchmark Akaike information criterion, and a true periodic signal of the coordinate time series is obtained according to the comparison result, and a pseudo-period detection result of the coordinate time series is obtained according to the true periodic signal.

[0012] According to one embodiment of the present invention, performing data preprocessing on the coordinate time series to obtain a preprocessed coordinate time series includes:

[0013] Determining whether there are abnormal values ​​and / or missing values ​​in the coordinate time series;

[0014] If the abnormal values ​​and / or the missing values ​​exist in the coordinate time series, the abnormal values ​​are cleared and / or the missing values ​​are interpolated to obtain a preliminarily processed coordinate time series;

[0015] Identifying a step signal and a target trend signal in the initially processed coordinate time series, and correcting the step signal to obtain a target step signal;

[0016] The target trend signal and the target step signal are fitted and removed to obtain a preprocessed coordinate time series.

[0017] According to one embodiment of the present invention, comparing each target Akaike information criterion with the benchmark Akaike information criterion, and obtaining the true periodic signal of the coordinate time series according to the comparison results, includes:

[0018] Detecting the period length between each periodic signal and determining whether the period length is less than or equal to a preset period length;

[0019] If the cycle length is less than or equal to the preset cycle length, calculating a first difference between each target Akaike information criterion and the benchmark Akaike information criterion to obtain a first calculation result, and determining whether there is a first target difference that meets a preset difference condition in the first calculation result;

[0020] If the first target difference that satisfies the preset difference condition exists in the first calculation result, the minimum target difference among the first target differences is identified, and the target period signal corresponding to the minimum target difference is used as the real period signal, and the real period signal is stored in the target real period set.

[0021] According to one embodiment of the present invention, after determining whether there is a first target difference that meets a preset difference condition in the first calculation result, the method further includes:

[0022] If the first calculation result does not contain the first target difference value satisfying the preset difference value condition, other target periodic signals not satisfying the preset difference value condition and other target difference values except the minimum target difference value are iteratively calculated, the step of performing single-period modeling on each target periodic signal to obtain a single-period model of the target periodic signal is re-executed until the real periodic signal is detected in two consecutive iterations, and the iterative calculation is ended.

[0023] According to an embodiment of the present application, after judging whether the period length is less than or equal to the preset period length, the method further comprises:

[0024] If the period length is greater than the preset period length, a second difference value between the Akaike information criterion of each target and the reference Akaike information criterion is calculated to obtain a second calculation result, and it is judged whether the second calculation result contains a second target difference value satisfying the preset difference value condition;

[0025] If the second calculation result contains the second target difference value satisfying the preset difference value condition, the target periodic signal corresponding to the second target difference value is taken as the real periodic signal, and the real periodic signal is stored in the target real periodic set.

[0026] If the second calculation result does not contain the second target difference value satisfying the preset difference value condition, other target periodic signals not satisfying the preset difference value condition are iteratively calculated, the step of performing single-period modeling on each target periodic signal to obtain a single-period model of the target periodic signal is re-executed until the real periodic signal is detected in two consecutive iterations, and the iterative calculation is ended.

[0027] According to an embodiment of the present application, the pseudo-period detection result of the coordinate time sequence obtained according to the real periodic signal comprises:

[0028] The real periodic signal is subjected to period modeling, and a period modeling result of the real periodic signal is outputted;

[0029] Based on the period modeling result, a periodic signal analysis is performed to obtain a pseudo-period detection result of the coordinate time sequence.

[0030] According to the pseudo-period detection method of the coordinate time series of an embodiment of the present invention, the coordinate time series of the target navigation satellite system is preprocessed and periodic signal detection is performed to obtain multiple target periodic signals that meet the preset detection conditions, and the benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis is calculated. The single-period model of each target periodic signal and the preprocessed coordinate time series are fitted to obtain a first new coordinate time series, and the target Akaike information criterion corresponding to each target periodic signal is compared with the benchmark Akaike information criterion to obtain the true periodic signal of the coordinate time series. Thus, the problems of pseudo-period interference caused by residual gross errors, incomplete elimination of step signals, and poor accuracy of period detection methods in the related art are solved.

[0031] A second embodiment of the present invention provides a pseudo-period detection device for a coordinate time series, comprising:

[0032] A data processing module is used to obtain a coordinate time series of a target navigation satellite system and perform data preprocessing on the coordinate time series to obtain a preprocessed coordinate time series;

[0033] a first calculation module, configured to perform periodic signal detection on the preprocessed coordinate time series to obtain a plurality of target periodic signals that meet preset detection conditions, perform noise analysis on the preprocessed coordinate time series based on a null hypothesis, and calculate a benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis;

[0034] a second computing module, configured to perform single-cycle modeling on each target periodic signal to obtain a single-cycle model of each target periodic signal, and fit the single-cycle model of each target periodic signal to the preprocessed coordinate time series to obtain a first new coordinate time series, and calculate a target Akaike information criterion corresponding to each target periodic signal under the first new coordinate time series;

[0035] A comparison module is used to compare each target Akaike information criterion with the benchmark Akaike information criterion, and obtain a true periodic signal of the coordinate time series according to the comparison result, and obtain a pseudo-period detection result of the coordinate time series according to the true periodic signal.

[0036] According to one embodiment of the present invention, the data processing module includes:

[0037] A first judgment unit is used to judge whether there are abnormal values ​​and / or missing values ​​in the coordinate time series;

[0038] a preliminary processing unit, configured to, if the abnormal values ​​and / or the missing values ​​exist in the coordinate time series, remove the abnormal values ​​and / or interpolate the missing values ​​to obtain a preliminary processed coordinate time series;

[0039] a correction unit, configured to identify a step signal and a target trend signal in the initially processed coordinate time series, and correct the step signal to obtain a target step signal;

[0040] The fitting unit is used to fit and remove the target trend signal and the target step signal to obtain a preprocessed coordinate time series.

[0041] According to one embodiment of the present invention, the comparison module includes:

[0042] A second judging unit, configured to detect a period length between each periodic signal and judge whether the period length is less than or equal to a preset period length;

[0043] a third judgment unit, configured to calculate a first difference between each target Akaike information criterion and the benchmark Akaike information criterion if the cycle length is less than or equal to the preset cycle length, obtain a first calculation result, and determine whether there is a first target difference that meets a preset difference condition in the first calculation result;

[0044] an identification unit for identifying a minimum target difference among the first target differences if the first calculation result contains the first target difference that satisfies the preset difference condition, and taking the target period signal corresponding to the minimum target difference as the real period signal, and storing the real period signal in the target real period set.

[0045] According to one embodiment of the present invention, after determining whether there is a first target difference that meets a preset difference condition in the first calculation result, the third determination unit further includes:

[0046] The first iterative subunit is used to iteratively calculate other target periodic signals that do not meet the preset difference condition and other target periodic signals in the target difference except the minimum target difference if the first target difference that meets the preset difference condition does not exist in the first calculation result, and re-execute the step of single-period modeling of each target periodic signal to obtain a single-period model of each target periodic signal, until the real periodic signal is detected after two consecutive iterative calculations, and then terminate the iterative calculation.

[0047] According to one embodiment of the present invention, after determining whether the cycle length is less than or equal to a preset cycle length, the second determining unit further includes:

[0048] a judging subunit, configured to, if the cycle length is greater than the preset cycle length, calculate a second difference between each target Akaike information criterion and the benchmark Akaike information criterion to obtain a second calculation result, and determine whether there is a second target difference that meets the preset difference condition in the second calculation result;

[0049] a storage unit, configured to, if a second target difference value that satisfies the preset difference condition exists in the second calculation result, use the target period signal corresponding to the second target difference value as the real period signal, and store the real period signal in the target real period set;

[0050] The second iterative sub-unit is used to iteratively calculate other target periodic signals that do not meet the preset difference condition if there is no second target difference that meets the preset difference condition in the second calculation result, and re-execute the step of single-period modeling of each target periodic signal to obtain the single-period model of each target periodic signal, until the real periodic signal is detected after two consecutive iterative calculations, and then end the iterative calculation.

[0051] According to one embodiment of the present invention, the comparison module includes:

[0052] A modeling unit, configured to perform period modeling on the real periodic signal and output a period modeling result of the real periodic signal;

[0053] A signal analysis unit is used to perform periodic signal analysis based on the periodic modeling result to obtain a pseudo-period detection result of the coordinate time series.

[0054] According to the pseudo-period detection device of the coordinate time series of the embodiment of the present invention, the coordinate time series of the target navigation satellite system is preprocessed and periodic signal detection is performed to obtain multiple target periodic signals that meet the preset detection conditions, and the benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis is calculated. The single-period model of each target periodic signal and the preprocessed coordinate time series are fitted to obtain a first new coordinate time series, and the target Akaike information criterion corresponding to each target periodic signal is compared with the benchmark Akaike information criterion to obtain the true periodic signal of the coordinate time series. Thus, the problems of pseudo-period interference caused by residual gross errors, incomplete elimination of step signals, and poor accuracy of period detection methods in the related art are solved.

[0055] A third aspect of the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pseudo-period detection method for a coordinate time series as described in the above embodiment.

[0056] A fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the method for detecting pseudo-periods of a coordinate time series as described in the above embodiment.

[0057] A fifth aspect of the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements the pseudo-period detection method for a coordinate time series as described in the above embodiment.

[0058] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0060] Figure 1 A flowchart of a pseudo-period detection method for a coordinate time series according to an embodiment of the present invention;

[0061] Figure 2 1. A flowchart of a process for detecting an AIC (Akaike Information Criterion)_diff pseudo-periodic signal according to an embodiment of the present invention;

[0062] Figure 3 Schematic diagram of pseudo-period detection results of MLSHE, Lomb-Scargle, MCSSA, and CEEMD in synthetic time series of different abnormal signals according to one embodiment of the present invention;

[0063] Figure 4 Schematic diagram of pseudo-periodic detection results of MLSHE, Lomb-Scargle, MCSSA, and CEEMD in a synthetic time series at a noise level of 1 mm for multiple periods according to one embodiment of the present invention;

[0064] Figure 5 Schematic diagram of pseudo-periodic detection results of MLSHE, Lomb-Scargle, MCSSA, and CEEMD in a synthetic time series under a noise level of 8 mm for multiple periods according to one embodiment of the present invention;

[0065] Figure 6 2. A schematic diagram of pseudo-periodic detection results of MLSHE, Lomb-Scargle, MCSSA, and CEEMD in the long-term GNSS coordinate time series of the KOUR station according to one embodiment of the present invention;

[0066] Figure 7 Schematic diagram of fitting results before and after removing pseudo-periods in MLSHE, Lomb-Scargle, MCSSA, and CEEMD according to one embodiment of the present invention;

[0067] Figure 8 2. It is an exemplary diagram of a pseudo-period detection device for a coordinate time series according to an embodiment of the present invention;

[0068] Figure 9 FIG. 1 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention, but are not to be construed as limiting the present invention.

[0070] The following describes a pseudo-period detection method, device, electronic device, and medium for a coordinate time series according to an embodiment of the present invention with reference to the accompanying drawings. In response to the problem of pseudo-period interference caused by residual gross errors, incomplete elimination of step signals, and poor accuracy of period detection methods in the related art mentioned in the above background technology, the present invention provides a pseudo-period detection method for a coordinate time series, in which data preprocessing is performed on the coordinate time series of a target navigation satellite system, and periodic signal detection is performed to obtain multiple target periodic signals that meet preset detection conditions, and the baseline Akaike information criterion of the preprocessed coordinate time series under the null hypothesis is calculated. A single-period model of each target periodic signal and the preprocessed coordinate time series are fitted to obtain a first new coordinate time series, and the target Akaike information criterion corresponding to each target periodic signal is compared with the baseline Akaike information criterion to obtain the true periodic signal of the coordinate time series. As a result, problems such as pseudo-periodic interference caused by residual gross errors, incomplete elimination of step signals, and poor accuracy of period detection methods in related technologies are solved. By constructing a full-process closed loop of "data preprocessing, period detection, pseudo-periodic identification, and modeling optimization and analysis", accurate elimination of pseudo-periodic signals is achieved.

[0071] Specifically, Figure 1A schematic flow chart of a pseudo-period detection method for a coordinate time series provided by an embodiment of the present invention.

[0072] like Figure 1 As shown, the pseudo-period detection method of the coordinate time series includes the following steps:

[0073] In step S101 , a coordinate time series of a target navigation satellite system is acquired, and data preprocessing is performed on the coordinate time series to obtain a preprocessed coordinate time series.

[0074] According to one embodiment of the present invention, data preprocessing is performed on a coordinate time series to obtain a preprocessed coordinate time series, including: determining whether there are abnormal values ​​and / or missing values ​​in the coordinate time series; if there are abnormal values ​​and / or missing values ​​in the coordinate time series, clearing the abnormal values ​​and / or interpolating the missing values ​​to obtain a preliminarily processed coordinate time series; identifying a step signal and a target trend signal in the preliminarily processed coordinate time series, and correcting the step signal to obtain a target step signal; fitting and removing the target trend signal and the target step signal to obtain a preprocessed coordinate time series.

[0075] Specifically, the present invention addresses the problem of detecting and weakening pseudo-periodic signals in GNSS coordinate time series. By constructing a full-process closed loop of "time series data preprocessing, periodic signal detection, pseudo-periodic signal identification, and periodic signal modeling optimization and analysis", the pseudo-periodic signals can be accurately eliminated.

[0076] Specifically, if Figure 2 As shown, a target navigation satellite system (e.g., a GNSS coordinate time series) is obtained from a public data source (e.g., CDDIS (Crustal Dynamics Data Information System), GGOS (Global Geodetic Observing System), etc.), and data preprocessing is performed on the GNSS coordinate time series. First, it is determined whether there are abnormal values ​​and / or missing values ​​in the GNSS coordinate time series. If there are abnormal values ​​and / or missing values ​​in the GNSS coordinate time series, the abnormal values ​​need to be removed and / or the missing values ​​need to be interpolated. For example, the IQR method can be used to remove abnormal values ​​in the GNSS coordinate time series, and the missing values ​​can be interpolated by linear interpolation, thereby obtaining a GNSS coordinate time series after preliminary processing. The method for determining abnormal values ​​is expressed as follows:

[0077] ;

[0078] in, is the first quartile, is the third quartile, is the interquartile range.

[0079] Linear interpolation can be expressed as:

[0080] ;

[0081] in, is the timestamp of the most recent valid data before the missing value, The timestamp of the most recent valid data after the missing value. y is the GNSS coordinate time series value at the corresponding timestamp, t Timestamp for missing values.

[0082] Secondly, using information from the Jet Propulsion Laboratory (JPL), including the time when antenna or receiver replacement or technical factors such as earthquakes occurred, we identify step signals in the initially processed coordinate time series. We then use the sequential T-test analysis method to further identify and mark additional target trend signals, such as additional offsets and trend change points. The expression formulas for step signals and target trend signals can be:

[0083] ;

[0084] in, is the mean value before the step signal, is the mean value after the step signal, is the standard deviation, is the amount of data in the window, is the variance of the time window after the step, is the variance of the time window before the step, is the number of samples after the step, is the number of samples before the step, when It is determined that there is a potential step signal when

[0085] Thirdly, the detected step signal and target trend signal are visually corrected in combination with the Gazeaux criterion (2013) to eliminate false detection points and obtain the target step signal.

[0086] Finally, the target trend signal and target step signal in the GNSS coordinate time series are fitted and removed, and the preprocessed GNSS coordinate time series is finally obtained. The fitting formula of the GNSS coordinate time series can be expressed as:

[0087] ;

[0088] where is the coordinate displacement at time , which can be in any direction of north, east, up; is the Julian coordinate time epoch (floating point year); is the position of the reference epoch of the station; is the polynomial fitting order in the GNSS coordinate time series; is the coefficient of the polynomial, if is 1, then it has a linear trend; is the number of harmonics; is the constant of the sine term of the periodic signal with angular frequency ; is the constant of the cosine term of the periodic signal with angular frequency ; is the number of offsets; is the offset function; is the time of the occurrence of the offset; is the time of sudden change of the equipment or seismic activity; is the number of classes of the random process, where the subscript , , and only distinguish n and have no other practical meaning; is the observation noise.

[0089] Thus, after fitting and removing the target trend signal and the target step signal in the GNSS coordinate time series, the GNSS coordinate time series only contains periodic signals and random process signals, and its expression is as follows:

[0090] ;

[0091] where, is the number of harmonics; is the constant of the sine term of the periodic signal with angular frequency ; is the constant of the cosine term of the periodic signal with angular frequency ; is the number of classes of the random process; is the Julian coordinate time epoch (floating point year); is the observation noise.

[0092] In step S102, periodic signal detection is performed on the preprocessed coordinate time series to obtain multiple target periodic signals that meet the preset detection conditions, and noise analysis is performed on the preprocessed coordinate time series based on the null hypothesis to calculate the benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis.

[0093] The preset detection conditions may be set by those skilled in the art according to actual pseudo-periodic signal detection requirements and are not specifically limited herein.

[0094] Specifically, if Figure 2 As shown, in an embodiment of the present invention, after preprocessing the GNSS coordinate time series, a GNSS coordinate time series containing only periodic signals and random process signals is obtained, and then periodic signal detection is performed on the GNSS coordinate time series containing only periodic signals and random process signals to obtain multiple periodic signals, and multiple target periodic signals that meet preset detection conditions are selected from the multiple periodic signals obtained. For example, the 20 most significant target periodic signals are selected from the multiple periodic signals obtained. In an embodiment of the present invention, the Lomb-Scargle spectrum analysis periodic signal detection method is used as an example to detect the GNSS coordinate time series containing only periodic signals and random process signals. For period detection, the expression can be:

[0095] ;

[0096] in, is the epoch number in the GNSS coordinate time series; For the The time corresponding to the epoch; For the The coordinate displacement value of the epoch; is the angular frequency to be detected; is the phase shift parameter, satisfying , sort the filtered peaks in descending order of power value, and select the frequencies corresponding to the first 20 extreme points of the power spectrum , and convert it into period length .

[0097] Furthermore, pseudo-periodic signal detection is performed on the GNSS coordinate time series, and the Akaike information criterion difference (i.e., AIC_diff value) is obtained. In the process of obtaining the AIC_diff value, first, a null hypothesis is established, and noise analysis is performed on the preprocessed GNSS coordinate time series based on the null hypothesis, and the benchmark Akaike information criterion (i.e., AIC0) of the preprocessed GNSS coordinate time series under the null hypothesis is calculated. The expression of the benchmark Akaike information criterion under the null hypothesis is:

[0098] ;

[0099] in, is the total number of noise model parameters (including the power-law noise parameter κ), is the likelihood function value.

[0100] In step S103, single-cycle modeling is performed on each target periodic signal to obtain a single-cycle model of each target periodic signal, and the single-cycle model of each target periodic signal is fitted with the preprocessed coordinate time series to obtain a first new coordinate time series, and the target Akaike information criterion corresponding to each target periodic signal under the first new coordinate time series is calculated.

[0101] Specifically, if Figure 2 As shown in Figure 2, after completing the calculation of the benchmark Akaike information criterion under the above null hypothesis, each target periodic signal is further modeled into a single period to obtain a single period model of each target periodic signal. The single period model can be expressed as:

[0102] ;

[0103] in, is the cycle length, is the amplitude of the sinusoidal component (amplitude coefficient), is the amplitude of the cosine component (amplitude coefficient), is the pi constant, usually 3.14159, Solve for the epoch (floating point year) for day coordinates.

[0104] Then, the single-cycle model of each target periodic signal is fitted with the preprocessed GNSS coordinate time series to obtain the first new coordinate time series , and calculate the target Akaike information criterion corresponding to each target period signal under the first new coordinate time series (i.e. ), and the calculated Akaike Information Criterion for each target The differences between the two methods are calculated respectively with the benchmark Akaike Information Criterion AIC0 under the null hypothesis, so as to finally obtain the detection results of the true period and pseudo period of the GNSS coordinate time series.

[0105] in, is the preprocessed GNSS coordinate time series, is a single-period model, Solve for the epoch (floating point year) for day coordinates.

[0106] In step S104, each target Akaike information criterion is compared with the benchmark Akaike information criterion, and the true periodic signal of the coordinate time series is obtained according to the comparison result, and the pseudo-period detection result of the coordinate time series is obtained according to the true periodic signal.

[0107] According to one embodiment of the present invention, each target Akaike information criterion is compared with the benchmark Akaike information criterion, and a true periodic signal of the coordinate time series is obtained based on the comparison result, including: detecting the period length between each periodic signal, and judging whether the period length is less than or equal to a preset period length; if the period length is less than or equal to the preset period length, calculating the first difference between each target Akaike information criterion and the benchmark Akaike information criterion to obtain a first calculation result, and judging whether there is a first target difference that meets the preset difference condition in the first calculation result; if there is a first target difference that meets the preset difference condition in the first calculation result, identifying the minimum target difference in the first target difference, and taking the target periodic signal corresponding to the minimum target difference as the true periodic signal, and storing the true periodic signal in the target true period set.

[0108] The preset period length and the preset difference condition can be set by those skilled in the art according to actual detection requirements and are not specifically limited here.

[0109] Specifically, in order to ensure the detection accuracy of pseudo-periodic signals and true periodic signals, the present invention compares each target Akaike information criterion with the benchmark Akaike information criterion and obtains the difference between the two. , that is, the first difference between each target Akaike Information Criterion and the benchmark Akaike Information Criterion , its specific expression is:

[0110]

[0111] in, is the target Akaike information criterion corresponding to each target periodic signal under the first new coordinate time series, is the benchmark Akaike Information Criterion under the null hypothesis.

[0112] Specifically, in the process of comparing each target Akaike information criterion with the benchmark Akaike information criterion, first, the period length between each periodic signal is detected, that is, the time interval between each periodic signal is detected, and then the target Akaike information criterion is compared with the benchmark Akaike information criterion based on the period length between each periodic signal and the difference between each target Akaike information criterion and the benchmark Akaike information criterion. Further analysis can effectively distinguish true periodic components from pseudo-periodic components and optimize the processing and analysis of GNSS coordinate time series.

[0113] First, determine whether the period length between each periodic signal is less than or equal to the preset period length (for example, 10). If the period length is less than or equal to the preset period length, that is, When , it indicates that the period lengths between each periodic signal are similar, and it is necessary to further calculate the first difference between each target Akaike information criterion and the benchmark Akaike information criterion Secondly, according to the first calculation result, it is determined whether there is a difference condition (for example ≤-5), if there is a first target difference that meets the preset difference condition in the first calculation result, the minimum target difference in the first target difference is identified, and the target period signal corresponding to the minimum target difference is used as the real period signal, and the real period signal is stored in the target real period set .

[0114] That is, the period length between each periodic signal is less than or equal to 10, that is, When , it indicates that the period lengths between each periodic signal are similar, and it is necessary to further calculate the first difference between each target Akaike information criterion and the benchmark Akaike information criterion , if in the first calculation result of the first difference, there is ≤-5, it means that there is The first target difference of ≤-5, at this time, identification The minimum target difference among the first target differences of ≤-5 is selected, and the target periodic signal corresponding to the minimum target difference is taken as the true periodic signal. After all 20 periodic signals are detected, all true periodic signals obtained after the first screening are deducted from the GNSS coordinate time series, and a new GNSS coordinate time series is generated based on all true periodic signals. .

[0115] According to one embodiment of the present invention, after determining whether there is a first target difference that meets the preset difference condition in the first calculation result, it also includes: if there is no first target difference that meets the preset difference condition in the first calculation result, iteratively calculating other target periodic signals that do not meet the preset difference condition and other target periodic signals in the target difference except the minimum target difference, re-executing the step of single-period modeling of each target periodic signal to obtain a single-period model of each target periodic signal, until the real periodic signal is detected after two consecutive iterative calculations, and then terminating the iterative calculation.

[0116] Specifically, if there is no first target difference that meets the preset difference condition in the first calculation result, that is, the first difference between each target Akaike information criterion and the benchmark Akaike information criterion middle, , then it indicates that there is no first target difference that meets the preset difference condition in the first calculation result, then the other target periodic signals that do not meet the preset difference condition and the other target periodic signals except the minimum target difference in the target difference are iteratively calculated, and the difference between the benchmark Akaike information criterion under the null hypothesis and the target Akaike information criterion of the other remaining periodic signals is recalculated. And then perform single-cycle modeling on each target periodic signal to obtain a single-cycle model of each target periodic signal, until all real periodic signals are detected in two consecutive iterative calculations, end the iterative calculation, and store all detected real periodic signals in the target real period set.

[0117] According to one embodiment of the present invention, after determining whether the cycle length is less than or equal to the preset cycle length, it also includes: if the cycle length is greater than the preset cycle length, calculating the second difference between each target Akaike information criterion and the benchmark Akaike information criterion to obtain a second calculation result, and determining whether there is a second target difference that meets the preset difference condition in the second calculation result; if there is a second target difference that meets the preset difference condition in the second calculation result, the target cycle signal corresponding to the second target difference is used as the real cycle signal, and the real cycle signal is stored in the target real cycle set at the same time; if there is no second target difference that meets the preset difference condition in the second calculation result, iterative calculation is performed on other target cycle signals that do not meet the preset difference condition, and the step of single-cycle modeling of each target cycle signal is re-executed to obtain a single-cycle model of each target cycle signal, until the real cycle signal is detected after two consecutive iterative calculations, and the iterative calculation is terminated.

[0118] Specifically, if the cycle length is greater than the preset cycle length, that is, >10, the second difference between each target Akaike information criterion and the benchmark Akaike information criterion is calculated, and it is determined whether there is a value in the second calculation result that meets the preset difference condition (for example ≤-5), if the second target difference value that meets the preset difference condition exists in the second calculation result, that is, the second calculation result exists ≤-5, the target period signal corresponding to the second target difference is taken as the real period signal, and the real period signal is stored in the target real period set. If it does not exist in the second calculation result ≤-5, that is, in the second calculation result, if > -5, then iterative calculation will be performed on other target periodic signals that do not meet the preset difference conditions, that is, >-5, and then iterate the calculation for other target periodic signals, and recalculate the difference between the benchmark Akaike information criterion under the null hypothesis and the target Akaike information criterion of the other remaining periodic signals. And then perform single-cycle modeling on each target periodic signal to obtain a single-cycle model of each target periodic signal, until all real periodic signals are detected in two consecutive iterative calculations, end the iterative calculation, and store all detected real periodic signals in the target real period set.

[0119] According to one embodiment of the present invention, a pseudo-period detection result of a coordinate time series is obtained based on a real periodic signal, including: performing periodic modeling on the real periodic signal and outputting the periodic modeling result of the real periodic signal; performing periodic signal analysis based on the periodic modeling result to obtain a pseudo-period detection result of the coordinate time series.

[0120] Specifically, the target real cycle set obtained above is The real periodic signal is incorporated into the harmonic model for periodic modeling, and its expression can be:

[0121] ;

[0122] in, is the number of harmonics; is the number of categories of the random process; is the angular frequency of the periodic signal The constant of the sine term, is the angular frequency of the periodic signal The constant of the cosine term; is the cycle length; Solve the epoch (floating point year) for the day coordinates; is the observation noise.

[0123] Furthermore, after establishing a harmonic model containing significant periodic terms, the periodic modeling results of the real periodic signal are output, such as the amplitude, phase, optimal noise type, kappa, etc. of the real periodic signal, and periodic signal analysis is performed based on the periodic modeling results, that is, the physical sources of each periodic component are systematically explored.

[0124] First, integrate multi-source geophysical time series data, including regional precipitation series , abnormal temperature , GRACE (Gravity Recovery and Climate Experiment) equivalent water height and atmospheric pressure Then, a time-varying coupling analysis framework is constructed based on the obtained multi-source geophysical time series data.

[0125] Further, for the detected typical period , the corresponding frequency band time series of GNSS coordinate time series is extracted by band-pass filtering and the same frequency component of geophysical data , the time lag cross-correlation function of the two is calculated, and the expression is:

[0126] ;

[0127] Wherein, is the standard deviation of the corresponding frequency band signal sequence, is the corresponding frequency band time series of GNSS coordinate time series extracted by band-pass filtering; is the standard deviation of the geophysical data sequence, is the same frequency component of the geophysical data; is the mean of the corresponding frequency band signal sequence, is the mean of the geophysical data sequence; is the time delay (unit: day), and the search range is usually When and the corresponding match the physical process response time, it is considered that there is a causal relationship; is the length of the time series; is the coordinate epoch (floating point year), wherein, is used to distinguish and , to show that is the corresponding frequency band time series of GNSS coordinate time series extracted by band-pass filtering, and has no other practical significance.

[0128] Therefore, according to a large number of simulation experiments and experimental results of real stations of GNSS coordinate time series, an AIC_diff pseudo-period detection method is proposed, one of the advantages of which is to construct a universal pseudo-period stripping framework, which can be compatible with the results of mainstream detection methods such as Lomb-Scargle, MLSHE, CEEMD and MCSSA; the second advantage is to realize the synchronous detection and elimination of abnormal signal interference type pseudo-period (such as false period caused by gross error and step signal) and detection accuracy insufficient type pseudo-period (such as period length deviation caused by harmonic model parameter deviation), which systematically reduces the signal distortion risk in period modeling under the premise of ensuring the complete retention of real periods, and provides a method guarantee for the high-precision application of GNSS time series in geodynamics inversion.

[0129] In summary, the systematic verification of synthetic time series and measured data from 52 JPL stations confirms the significant advantages of the proposed method in pseudo-period detection and modeling optimization. Figure 3 The figure shows that in a synthetic sequence containing abnormal signals (preset anniversary / semi-anniversary periods), the pseudo-periods in the MLSHE, Lomb-Scargle, MCSSA, and CEEMD methods can be accurately identified by the present invention, verifying the applicability of the present invention to pseudo-periods generated by abnormal signals. The gray color represents the pseudo-periods detected by the present invention, and the other colors represent the periodic signals detected by the above four period detection methods. The determined periods are marked in the figure.

[0130] Further, Figure 4 and Figure 5 The detection effect of multi-periodic signals under 1mm and 8mm noise levels was further compared, indicating that the present invention can still effectively identify more than 90% of pseudo-periodics in complex signal scenarios, and is only weakly sensitive to low-frequency signals with noise amplitudes exceeding 8mm and periods greater than 500 days. Figure 4 In the detection effect of multi-periodic signals under 1mm noise level, the gray dots are pseudo-periodic signals detected by the present invention, and the gray dotted line marks the simulated determined period. Figure 5 In the detection effect of multi-periodic signals under 8mm noise level, the gray dots are pseudo-periodic signals detected by the present invention, and the gray dotted line marks the simulated determined period.

[0131] Further, Figure 6 Figure 3 is the Akaike Information Criterion difference of the periodic detection results in the northing, easting, and celestial components at the KOUR station. Measured data from the northing, easting, and celestial components at the KOUR station demonstrate that the periodic detection method of the present invention can fully preserve known geophysical periods (such as annual and semi-annual signals) while eliminating 83% of pseudo-periodic interference. The gray dots represent pseudo-periodic signals detected by the present invention, and the gray dotted line marks the mainstream periodic signal in the current GNSS coordinate time series.

[0132] Further, Figure 7Four modeling strategies were compared for the northing, easting, and celestial components of the KOUR station: true cycle modeling based on the screening of the present invention, full cycle modeling (modeling of all cycles), seasonal signal modeling, and higher-order harmonic modeling. The gray dots represent the original GNSS coordinate time series, the green line represents the modeling of all periodic signals detected at a single station, the orange line represents the modeling of only the true periodic signal, the blue line represents the modeling of the seasonal signal, and the red line represents the modeling of the seasonal signal, the intersection year, the 383-day cycle, the annual cycle, and its higher-order harmonics. The results show that the true cycle model exhibits the best fitting characteristics, with the standard deviation of its residual series reduced by 42% compared with the seasonal signal model. It also avoids the overfitting problem of the full cycle model and the noise aliasing problem of the higher-order harmonic model, thus confirming the core value of the present invention in improving the accuracy and physical interpretability of periodic modeling.

[0133] According to the pseudo-period detection method of the coordinate time series of an embodiment of the present invention, the coordinate time series of the target navigation satellite system is preprocessed and periodic signal detection is performed to obtain multiple target periodic signals that meet the preset detection conditions, and the benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis is calculated. The single-period model of each target periodic signal and the preprocessed coordinate time series are fitted to obtain a first new coordinate time series, and the target Akaike information criterion corresponding to each target periodic signal is compared with the benchmark Akaike information criterion to obtain the true periodic signal of the coordinate time series. Thus, the problems of pseudo-period interference caused by residual gross errors, incomplete elimination of step signals, and poor accuracy of period detection methods in the related art are solved.

[0134] Next, a pseudo-period detection device for a coordinate time series according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0135] Figure 8 4 is a block diagram of a pseudo-period detection device for a coordinate time series according to an embodiment of the present invention.

[0136] like Figure 8 As shown, the coordinate time series pseudo-period detection device 10 includes: a data processing module 100 , a first calculation module 200 , a second calculation module 300 and a comparison module 400 .

[0137] The data processing module 100 is used to obtain the coordinate time series of the target navigation satellite system and perform data preprocessing on the coordinate time series to obtain a preprocessed coordinate time series;

[0138] The first calculation module 200 is configured to perform periodic signal detection on the preprocessed coordinate time sequence to obtain a plurality of target periodic signals satisfying a preset detection condition, and perform noise analysis on the preprocessed coordinate time sequence based on a zero hypothesis to calculate a baseline Akaike information criterion of the preprocessed coordinate time sequence under the zero hypothesis.

[0139] The second calculation module 300 is configured to perform single-period modeling on each target periodic signal to obtain a single-period model of each target periodic signal, fit the single-period model of each target periodic signal with the preprocessed coordinate time sequence to obtain a first new coordinate time sequence, and calculate a target Akaike information criterion corresponding to each target periodic signal under the first new coordinate time sequence.

[0140] The comparison module 400 is configured to compare each target Akaike information criterion with the baseline Akaike information criterion respectively, and obtain a real periodic signal of the coordinate time sequence according to a comparison result, and obtain a pseudo-period detection result of the coordinate time sequence according to the real periodic signal.

[0141] According to an embodiment of the present application, the data processing module 100 comprises:

[0142] The first judging unit is configured to judge whether there is an abnormal value and / or a missing value in the coordinate time sequence.

[0143] The preliminary processing unit is configured to, if there is an abnormal value and / or a missing value in the coordinate time sequence, perform cleaning processing on the abnormal value and / or interpolation processing on the missing value to obtain a preliminarily processed coordinate time sequence.

[0144] The correction unit is configured to identify a step signal and a target trend signal in the preliminarily processed coordinate time sequence, and correct the step signal to obtain a target step signal.

[0145] The fitting unit is configured to fit and remove the target trend signal and the target step signal to obtain the preprocessed coordinate time sequence.

[0146] According to an embodiment of the present application, the comparison module 400 comprises:

[0147] The second judging unit is configured to detect the period length between each periodic signal, and judge whether the period length is less than or equal to a preset period length.

[0148] The third judging unit is configured to, if the period length is less than or equal to the preset period length, calculate a first difference value between each target Akaike information criterion and the baseline Akaike information criterion to obtain a first calculation result, and judge whether there is a first target difference value satisfying a preset difference value condition in the first calculation result.

[0149] An identification unit is used to identify the minimum target difference among the first target differences if there is a first target difference that meets the preset difference condition in the first calculation result, and to use the target period signal corresponding to the minimum target difference as the real period signal, and to store the real period signal in the target real period set.

[0150] According to one embodiment of the present invention, after determining whether there is a first target difference that meets a preset difference condition in the first calculation result, the third determination unit further includes:

[0151] The first iterative subunit is used to iteratively calculate other target periodic signals that do not meet the preset difference condition and other target periodic signals in the target difference except the minimum target difference if there is no first target difference that meets the preset difference condition in the first calculation result, and re-execute the step of single-period modeling of each target periodic signal to obtain a single-period model of each target periodic signal, until the real periodic signal is detected after two consecutive iterative calculations, and then terminate the iterative calculation.

[0152] According to one embodiment of the present invention, after determining whether the cycle length is less than or equal to the preset cycle length, the second determining unit further includes:

[0153] a judging subunit, configured to calculate a second difference between each target Akaike information criterion and the reference Akaike information criterion if the period length is greater than a preset period length, obtain a second calculation result, and judge whether there is a second target difference that satisfies a preset difference condition in the second calculation result;

[0154] a storage unit configured to, if a second target difference value that satisfies a preset difference condition exists in the second calculation result, use the target period signal corresponding to the second target difference value as the real period signal, and store the real period signal in the target real period set;

[0155] The second iterative subunit is used to iteratively calculate other target periodic signals that do not meet the preset difference condition if there is no second target difference that meets the preset difference condition in the second calculation result, and re-execute the step of single-period modeling of each target periodic signal to obtain a single-period model of each target periodic signal, until the real periodic signal is detected after two consecutive iterative calculations, and then terminate the iterative calculation.

[0156] According to one embodiment of the present invention, the comparison module 400 includes:

[0157] A modeling unit, configured to perform period modeling on a real periodic signal and output a period modeling result of the real periodic signal;

[0158] The signal analysis unit is used to perform periodic signal analysis based on the periodic modeling results to obtain pseudo-period detection results of the coordinate time series.

[0159] According to the pseudo-period detection device of the coordinate time series of the embodiment of the present invention, the coordinate time series of the target navigation satellite system is preprocessed and periodic signal detection is performed to obtain multiple target periodic signals that meet the preset detection conditions, and the benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis is calculated. The single-period model of each target periodic signal and the preprocessed coordinate time series are fitted to obtain a first new coordinate time series, and the target Akaike information criterion corresponding to each target periodic signal is compared with the benchmark Akaike information criterion to obtain the true periodic signal of the coordinate time series. Thus, the problems of pseudo-period interference caused by residual gross errors, incomplete elimination of step signals, and poor accuracy of period detection methods in the related art are solved.

[0160] Figure 9 This is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0161] A memory 901 , a processor 902 , and a computer program stored in the memory 901 and executable on the processor 902 .

[0162] When the processor 902 executes the program, the pseudo-period detection method of the coordinate time series provided in the above embodiment is implemented.

[0163] Furthermore, the electronic device further includes:

[0164] The communication interface 903 is used for communication between the memory 901 and the processor 902 .

[0165] The memory 901 is used to store computer programs that can be run on the processor 902 .

[0166] The memory 901 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0167] If the memory 901, processor 902, and communication interface 903 are implemented independently, the communication interface 903, memory 901, and processor 902 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc. For ease of representation, Figure 9 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0168] Optionally, in a specific implementation, if the memory 901, the processor 902 and the communication interface 903 are integrated on a chip, the memory 901, the processor 902 and the communication interface 903 can communicate with each other through an internal interface.

[0169] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0170] This embodiment further provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the above-mentioned method for detecting pseudo-periods of a coordinate time series is implemented.

[0171] An embodiment of the present invention further provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the pseudo-period detection method for a coordinate time series as described in the above embodiment.

[0172] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0173] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "N" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0174] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing a custom logical function or step of a process, and the scope of the preferred embodiments of the invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of the invention pertain.

[0175] The logic and / or steps represented in a flowchart or otherwise described herein, for example, can be considered a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" is any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (not exhaustive) of computer-readable media include: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or otherwise processing it in a suitable manner if necessary, and then storing it in a computer memory.

[0176] It should be understood that various components of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0177] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0178] Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0179] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and are not to be construed as limiting the present invention. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for detecting pseudo-periods in a coordinate time series, characterized in that: The following steps are involved: Acquiring a coordinate time series of a target navigation satellite system, and performing data preprocessing on the coordinate time series to obtain a preprocessed coordinate time series; Performing periodic signal detection on the preprocessed coordinate time series to obtain a plurality of target periodic signals that meet preset detection conditions, performing noise analysis on the preprocessed coordinate time series based on a null hypothesis, and calculating a benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis; Performing single-cycle modeling on each target periodic signal to obtain a single-cycle model of each target periodic signal, fitting the single-cycle model of each target periodic signal to the preprocessed coordinate time series to obtain a first new coordinate time series, and calculating a target Akaike information criterion corresponding to each target periodic signal under the first new coordinate time series; Each target Akaike information criterion is compared with the benchmark Akaike information criterion, and a true periodic signal of the coordinate time series is obtained according to the comparison result, and a pseudo-period detection result of the coordinate time series is obtained according to the true periodic signal.

2. The method for detecting pseudo-periods of coordinate time series according to claim 1, characterized in that: The performing data preprocessing on the coordinate time series to obtain a preprocessed coordinate time series includes: Determining whether there are abnormal values ​​and / or missing values ​​in the coordinate time series; If the abnormal values ​​and / or the missing values ​​exist in the coordinate time series, the abnormal values ​​are cleared and / or the missing values ​​are interpolated to obtain a preliminarily processed coordinate time series; Identifying a step signal and a target trend signal in the initially processed coordinate time series, and correcting the step signal to obtain a target step signal; The target trend signal and the target step signal are fitted and removed to obtain a preprocessed coordinate time series.

3. The method for detecting pseudo-periods of coordinate time series according to claim 1, characterized in that: The step of comparing each target Akaike information criterion with the benchmark Akaike information criterion and obtaining a true periodic signal of the coordinate time series according to the comparison results includes: Detecting the period length between each periodic signal and determining whether the period length is less than or equal to a preset period length; If the cycle length is less than or equal to the preset cycle length, calculating a first difference between each target Akaike information criterion and the benchmark Akaike information criterion to obtain a first calculation result, and determining whether there is a first target difference that meets a preset difference condition in the first calculation result; If the first target difference that satisfies the preset difference condition exists in the first calculation result, the minimum target difference among the first target differences is identified, and the target period signal corresponding to the minimum target difference is used as the real period signal, and the real period signal is stored in the target real period set.

4. The method for detecting pseudo-periods of coordinate time series according to claim 3, characterized in that: After determining whether there is a first target difference that meets a preset difference condition in the first calculation result, the method further includes: If the first target difference that meets the preset difference condition does not exist in the first calculation result, the other target periodic signals that do not meet the preset difference condition and the other target periodic signals in the target difference except the minimum target difference are iteratively calculated, and the step of performing single-period modeling on each target periodic signal to obtain the single-period model of each target periodic signal is re-executed until the real periodic signal is detected after two consecutive iterative calculations, and the iterative calculation is terminated.

5. The method for detecting pseudo-periods of coordinate time series according to claim 3, characterized in that: After determining whether the cycle length is less than or equal to the preset cycle length, the method further includes: If the cycle length is greater than the preset cycle length, calculating a second difference between each target Akaike information criterion and the benchmark Akaike information criterion to obtain a second calculation result, and determining whether there is a second target difference that meets the preset difference condition in the second calculation result; If a second target difference value that satisfies the preset difference condition exists in the second calculation result, taking the target period signal corresponding to the second target difference value as the real period signal, and storing the real period signal in the target real period set; If there is no second target difference that meets the preset difference condition in the second calculation result, the other target periodic signals that do not meet the preset difference condition will be iteratively calculated, and the step of single-cycle modeling of each target periodic signal will be re-executed to obtain the single-cycle model of each target periodic signal, until the real periodic signal is detected after two consecutive iterative calculations, and the iterative calculation is terminated.

6. The method for detecting pseudo-periods of coordinate time series according to claim 1, characterized in that: The obtaining of a pseudo-period detection result of a coordinate time series according to the real periodic signal includes: Performing period modeling on the real periodic signal and outputting a period modeling result of the real periodic signal; A periodic signal analysis is performed based on the periodic modeling result to obtain a pseudo-period detection result of the coordinate time series.

7. A pseudo-period detection device for coordinate time series, characterized in that: include: A data processing module is used to obtain a coordinate time series of a target navigation satellite system and perform data preprocessing on the coordinate time series to obtain a preprocessed coordinate time series; a first calculation module, configured to perform periodic signal detection on the preprocessed coordinate time series to obtain a plurality of target periodic signals that meet preset detection conditions, perform noise analysis on the preprocessed coordinate time series based on a null hypothesis, and calculate a benchmark Akaike information criterion of the preprocessed coordinate time series under the null hypothesis; a second computing module, configured to perform single-cycle modeling on each target periodic signal to obtain a single-cycle model of each target periodic signal, and fit the single-cycle model of each target periodic signal to the preprocessed coordinate time series to obtain a first new coordinate time series, and calculate a target Akaike information criterion corresponding to each target periodic signal under the first new coordinate time series; A comparison module is used to compare each target Akaike information criterion with the benchmark Akaike information criterion, and obtain a true periodic signal of the coordinate time series according to the comparison result, and obtain a pseudo-period detection result of the coordinate time series according to the true periodic signal.

8. The pseudo-period detection device for coordinate time series according to claim 7, characterized in that: The data processing module includes: a judgment unit, configured to judge whether there are abnormal values ​​and / or missing values ​​in the coordinate time series; a preliminary processing unit, configured to, if the abnormal values ​​and / or the missing values ​​exist in the coordinate time series, remove the abnormal values ​​and / or interpolate the missing values ​​to obtain a preliminary processed coordinate time series; a correction unit, configured to identify a step signal and a target trend signal in the initially processed coordinate time series, and correct the step signal to obtain a target step signal; The fitting unit is used to fit and remove the target trend signal and the target step signal to obtain a preprocessed coordinate time series.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the pseudo-period detection method for a coordinate time series according to any one of claims 1 to 6.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program is executed by a processor to implement the pseudo-period detection method for a coordinate time series according to any one of claims 1 to 6.

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