A method, device and apparatus for identifying abnormality of state domain space correction number

Abstract

The embodiments of the present application provide a method, apparatus, device, and computer storage medium for identifying abnormalities in state-domain spatial corrections, which relate to the field of satellite positioning and are used to improve the accuracy of SSR anomaly identification. The method comprises: obtaining state-domain spatial corrections (SSRs) and global satellite navigation system (GNSS) observation information, wherein the SSRs include pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections, and the GNSS observation information includes pseudorange, carrier phase, Doppler observations, and broadcast ephemeris; and testing the target SSR based on the difference in target SSRs under consecutive epochs to determine whether an abnormal SSR exists, wherein the target SSR is at least one of the pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections.

Description

Technical Field

[0001] The present application relates to the field of satellite positioning, and in particular to a method, device, and apparatus for identifying anomalies in state-domain spatial correction numbers. Background Art

[0002] State Space Representation (SSR) corrections are typically used to determine the instantaneous fixed solution when performing Precise Point Positioning (PPP) and Real-time Kinematic (RTK) positioning. However, due to the limited accuracy of SSR, abnormal SSRs can affect the positioning results.

[0003] In existing technologies, integrity information is generally used to mark abnormal SSRs and send the corresponding marking results to users, so that users can use SSRs in a more reasonable way. When SSRs have large deviations, integrity information can mark them to help users avoid the positioning errors caused by abnormal SSRs. However, integrity information is difficult to identify SSRs with smaller deviations.

[0004] In summary, the accuracy of abnormality identification of SSR in the existing technology is insufficient. Summary of the Invention

[0005] The embodiments of the present application provide a method, apparatus, and device for identifying anomalies in a state-domain spatial correction number, which are used to improve the accuracy of SSR anomaly identification.

[0006] In a first aspect, an embodiment of the present application provides a method for identifying anomalies in a state domain space correction number, the method comprising:

[0007] Obtain state domain spatial corrections (SSRs) and global satellite navigation system (GNSS) observation information. SSRs include pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections. GNSS observation information includes pseudorange, carrier phase, Doppler observations, and broadcast ephemeris.

[0008] Based on the difference of the target SSR in consecutive epochs, the target SSR is checked to determine whether there is an abnormal SSR, where the target SSR is at least one of the pseudorange deviation correction number, the phase deviation correction number, the tropospheric delay correction number and the ionospheric delay correction number.

[0009] In a second aspect, an embodiment of the present application provides a device for identifying anomalies in a state domain space correction number, the device comprising:

[0010] An acquisition module is used to obtain state domain spatial corrections (SSRs) and global satellite navigation system (GNSS) observation information. SSRs include pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections. GNSS observation information includes pseudorange, carrier phase, Doppler observations, and broadcast ephemeris.

[0011] The verification module is used to verify the target SSR based on the difference of the target SSR in consecutive epochs to determine whether there is an abnormal SSR, wherein the target SSR is at least one of the pseudorange deviation correction number, the phase deviation correction number, the tropospheric delay correction number and the ionospheric delay correction number.

[0012] In a third aspect, an embodiment of the present application provides a device for identifying anomalies of a state domain space correction number, the device comprising:

[0013] A processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method for identifying anomalies of state domain space correction numbers provided in the first aspect of the embodiment of the present application.

[0014] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, a method for identifying anomalies in a state domain space correction number as provided in the first aspect of the embodiment of the present application is implemented.

[0015] The method, device, and equipment for identifying anomalies in the state domain space correction numbers of the embodiments of the present application obtain SSR and global satellite navigation system GNSS observation information. The above-mentioned SSR includes pseudorange deviation correction numbers, phase deviation correction numbers, tropospheric delay correction numbers, and ionospheric delay correction numbers. Since the SSR is inspected based on the continuity or stability of the SSR itself, whether it is abnormal is judged according to the data characteristics of the SSR at different epochs, rather than relying on other information related to the SSR to indirectly judge whether it is abnormal, compared with the existing technology, it can detect abnormal deviations of smaller orders of magnitude, thereby improving the accuracy of SSR anomaly identification. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0017] Figure 1 1 is a flow chart of a method for identifying anomalies in a state domain space correction number provided in an embodiment of the present application;

[0018] Figure 2This is a flow chart of a data transmission link provided by an embodiment of the present application;

[0019] Figure 3 This is a flow chart of a device for identifying anomalies in a state domain space correction number provided by an embodiment of the present application;

[0020] Figure 4 This is a structural diagram of a state domain space correction number anomaly identification device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0022] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0023] State Space Representation (SSR) is usually used to determine the instantaneous fixed solution when performing Precise Point Positioning (PPP) and Real-time Kinematic (RTK) positioning technology. However, due to the limited accuracy of SSR, abnormal SSR will affect the positioning results.

[0024] In the existing technology, the following two solutions can be used to identify SSR anomalies:

[0025] Solution 1: Use integrity information to monitor the availability of SSR corrections. When SSR corrections deviate significantly, integrity information can flag them to help users mitigate the impact of these anomalous SSR corrections, ensuring that user positioning results do not experience extreme anomalies. However, integrity information cannot provide information on satellite fixability or identify SSR corrections with minor deviations.

[0026] Solution 2 uses quality indicators to provide quality factors for SSR corrections, helping users use them more rationally. However, since the quality factors for some SSR corrections are calculated independently and fail to account for their self-consistency, some normal SSR corrections are abnormally excluded.

[0027] In summary, the accuracy of abnormality identification of SSR in the existing technology is insufficient.

[0028] In response to the above problems, an embodiment of the present application provides a method for identifying anomalies in state domain space correction numbers, which judges whether the SSR is abnormal based on its data characteristics at different epochs, rather than relying on other SSR-related information to indirectly judge whether it is abnormal. It can thus detect abnormal deviations of smaller orders of magnitude and improve the accuracy of SSR anomaly identification.

[0029] like Figure 1 As shown, the embodiment of the present application provides a method for identifying anomalies of state domain space correction numbers, the method comprising:

[0030] S101, obtain state domain spatial corrections (SSRs) and global satellite navigation system (GNSS) observation information, where the SSRs include pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections, and the GNSS observation information includes pseudorange, carrier phase, Doppler observations, and broadcast ephemeris.

[0031] It should be noted that if Figure 2 As shown in the figure, GNSS observation information and SSR correction numbers can be obtained by using terminal equipment. The GNSS observation information includes observation values such as carrier phase, pseudorange, Doppler, and broadcast ephemeris. The SSR correction numbers include orbit correction numbers, clock correction numbers, pseudorange deviation correction numbers, phase deviation correction numbers, tropospheric delay correction numbers, and ionospheric delay correction numbers. The SSR correction numbers can be obtained through satellite-based links or Internet links.

[0032] S102: Check the target SSR based on the difference of the target SSR in consecutive epochs to determine whether there is an abnormal SSR, where the target SSR is at least one of a pseudorange deviation correction, a phase deviation correction, a tropospheric delay correction, and an ionospheric delay correction.

[0033] In satellite positioning applications, PPP-RTK using GNSS observation information and SSR corrections can achieve instantaneous centimeter-level positioning. However, since SSR may be anomaly, which may cause deviations in positioning results, it is necessary to verify whether the SSR corrections are abnormal before using them for PPP-RTK.

[0034] The above is a specific implementation method of the state domain space correction number anomaly identification method provided in the embodiment of the present application. In this specific implementation method, SSR and GNSS observation information is obtained, and whether the SSR is abnormal is judged based on the data characteristics of the SSR at different epochs. Since the SSR is tested based on the continuity or stability of the SSR itself, rather than relying on other SSR-related information to indirectly judge whether it is abnormal, the data properties of the SSR are fully considered, and abnormal deviations of smaller orders of magnitude can be detected, thereby improving the accuracy of SSR anomaly identification.

[0035] In some embodiments, where the target SSR includes a pseudorange bias correction,

[0036] Based on the difference in the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

[0037] For each satellite at each frequency point, perform the following steps for the pseudorange bias corrections of two adjacent epochs:

[0038] Calculate the difference between the pseudorange bias correction at the Kth epoch and the pseudorange bias correction at the K+1th epoch to determine the change in the pseudorange bias correction, where K is a positive integer.

[0039] When the change in the pseudorange bias correction number is greater than a first preset threshold, it is determined that the pseudorange bias correction number of the K+1 epoch is abnormal.

[0040] In some embodiments, when the change in the pseudorange bias correction is greater than a first preset threshold, and when it is determined that the pseudorange bias correction at the K+1 epoch is abnormal, the method may further include:

[0041] Eliminate the pseudorange bias correction of the K+1th epoch;

[0042] Calculate the difference between the pseudorange bias correction number at the Kth epoch and the pseudorange bias correction number at the K+2th epoch to determine the change in the first pseudorange bias correction number;

[0043] When the change in the first pseudorange bias correction number is not greater than the first preset threshold, it is determined that the pseudorange bias correction number of the K+2 epoch is normal.

[0044] It should be noted that in the above example, when the pseudorange bias correction is tested based on the difference in corresponding corrections of consecutive epochs, since the pseudorange bias correction is relatively stable and will not change continuously in a short period of time, when the difference between the pseudorange bias correction of the Kth epoch and the pseudorange bias correction of the K+1th epoch is greater than the first preset threshold, it is first assumed that the pseudorange bias correction of the subsequent epoch in the adjacent epochs, that is, the pseudorange bias correction of the K+1th epoch, is abnormal. At the same time, the pseudorange bias correction of the previous epoch in the adjacent epoch is abnormal. The difference correction number, that is, the pseudorange bias correction number of the Kth epoch is normal. In addition, in actual applications, there is a very small probability that the pseudorange bias correction numbers of the K+1th epoch and the K+2th epoch, that is, adjacent epochs, are both abnormal. Therefore, the above situation is not considered. Finally, when it is determined that the pseudorange bias correction number of the K+1th epoch is abnormal, the above abnormal data is eliminated, and the pseudorange bias correction number of the K+2th epoch is compared with the pseudorange bias correction number of the Kth epoch to continue the abnormality test of the pseudorange bias correction numbers between adjacent epochs.

[0045] In one example, when K=1, calculating the difference between the pseudorange bias correction number at the Kth epoch and the pseudorange bias correction number at the K+1th epoch, and determining the change in the pseudorange bias correction number may include:

[0046] Calculate the difference between the pseudorange bias corrections of the first epoch and the second epoch, and determine the difference as the change in pseudorange bias correction. If the change in pseudorange bias correction is greater than 0.5 (unit: m), the pseudorange bias correction of the second epoch is determined to be abnormal; otherwise, the pseudorange bias correction of the second epoch is normal.

[0047] The above-mentioned 0.5m is a first preset threshold value determined by those skilled in the art based on experience. The first preset threshold value may also be other experience values, which is not limited in this application.

[0048] The above-mentioned method for detecting anomalies in the pseudorange bias correction number provided in the embodiment of the present application is based on the fact that the pseudorange bias correction number has good stability and does not change much in a short period of time. Therefore, based on this characteristic, the difference between the pseudorange bias correction numbers of two adjacent epochs for each satellite at each frequency point is calculated. The difference is used to determine whether the pseudorange bias correction number is abnormal, and then the accuracy of anomaly identification is improved by setting an anomaly threshold.

[0049] In some embodiments, where the target SSR includes a phase deviation correction number,

[0050] Based on the difference in the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

[0051] Step A1: Calculate the difference between two phase deviation correction numbers of adjacent epochs for each satellite at each frequency point, and determine N phase deviation correction number changes, where N is an integer greater than 1.

[0052] It's important to note that because satellites belong to different observation systems, phase deviation correction anomaly detection requires separate processing for each observation system. For example, in the current epoch, 30 satellites are observed: 10 GPS satellites, 10 BeiDou satellites, and 10 Galileo satellites. Phase deviation corrections are received for two frequencies per satellite. Phase deviation correction anomaly detection requires separate testing for each of these observation systems. To avoid repetition, the following example only uses the phase deviation anomaly detection for 10 GPS satellites as a reference.

[0053] In one example, step A1 may include:

[0054] At the current epoch, 10 GPS satellites are observed. The difference between the two phase deviation corrections of adjacent epochs at each frequency point of each of the 10 GPS satellites is calculated. The above difference can be expressed by formula 1:

[0055]

[0056] Among them, t k ,t k+1 For receiving time, is the difference in phase deviation correction, is the phase deviation correction number, i is the frequency number, s is the satellite terminal identifier, and m is the satellite number.

[0057] Using the above formula 1, the 20 phase deviation correction value changes of the above 10 GPS satellites at 2 frequencies can be obtained.

[0058] Step A2, execute the first update step: calculate the first mean and first standard deviation of the first set, where the initial data of the first set is N phase deviation correction number changes; when the first standard deviation is greater than the second preset threshold, eliminate the phase deviation correction number change with the largest difference from the first mean in the first set to obtain the remaining multiple phase deviation correction number changes; update the first set according to the remaining multiple phase deviation correction number changes, and return to calculate the first mean and first standard deviation of the first set until the first standard deviation is less than the second preset threshold.

[0059] Based on the above example, step A2 is described in detail:

[0060] The first mean of the 20 phase deviation correction value changes is calculated using Formula 2, and the first standard deviation is calculated. Formula 2 is expressed as follows:

[0061]

[0062] If the first standard deviation is greater than a second preset threshold, such as 0.01, the phase deviation correction number change with the largest difference from the first mean among the 20 phase deviation correction number changes is eliminated, and the first standard deviation of the remaining phase deviation correction number changes is recalculated to be less than the second preset threshold.

[0063] In step A3, the phase deviation correction number corresponding to the phase deviation correction number variation eliminated in the first updating step is identified as abnormal.

[0064] Specifically, in combination with the above example, if the absolute value of the difference between the variation and the mean of any phase deviation correction number in step A3 is greater than the second preset threshold, the phase deviation correction number is considered unusable.

[0065] The above-mentioned method for detecting abnormalities in the phase deviation correction number provided in the embodiment of the present application, since the mean and standard deviation can reflect the stability and consistency between multiple data, is combined with the continuity of the phase deviation correction number itself, and the above two statistical data are used to determine whether it is abnormal. The abnormal thresholds of the mean and standard deviation are further set to improve the precision and accuracy of identifying abnormalities in the phase deviation correction number, and effectively eliminate abnormal data in the phase deviation correction number.

[0066] In some embodiments, where the target SSR includes a phase deviation correction number,

[0067] Based on the difference in the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

[0068] Based on the sliding window, linear fitting is performed on multiple phase deviation corrections of each satellite at each frequency point and multiple epochs to obtain the fitting results.

[0069] According to the fitting results, determine whether there is a slow drift anomaly in the phase deviation correction number;

[0070] When it is determined that the phase deviation correction number has a slow drift anomaly, it is determined that the phase deviation correction number is abnormal.

[0071] The above-mentioned method for detecting anomalies in phase deviation correction numbers obtains as many phase deviation correction numbers as possible based on a sliding window, determines the quantitative relationship of mutual dependence between multiple phase deviation correction numbers of consecutive epochs by performing linear fitting on them, and predicts the expected data based on the current data, so as to facilitate comparison of the actual data with the expected data and determine the phase deviation correction numbers with abnormal deviations in the actual data, thereby further improving the accuracy of identifying anomalies in phase deviation correction numbers.

[0072] In some embodiments, linear fitting is performed on multiple phase deviation correction values for each satellite at each frequency point at multiple epochs based on a sliding window, and determining whether the phase deviation correction value has a slow drift anomaly based on the fitting result may include:

[0073] Step B1: Perform linear fitting on multiple phase deviation correction values of each satellite at each frequency point for multiple epochs to determine P fitting parameters, where P is an integer greater than 1.

[0074] It should be noted that based on the sliding window, phase deviation correction numbers of multiple epochs can be obtained. The longer the sliding window duration, the more phase deviation correction numbers can be obtained.

[0075] In one example, step B1 may include:

[0076] Using a sliding window of 1-2 minutes, the phase deviation corrections of the two frequency points of the 10 satellites are linearly fitted, as shown in Formula 3:

[0077] b=a0+a1t Formula 3

[0078] Among them, a0 is a constant, a1 is a trend term, and t is the fitting time.

[0079] Using formula 3, we can obtain 20 fitting parameters of the phase deviation correction number of 10 satellites and 2 frequency points.

[0080] Step B2, execute the second update step: calculate the second mean and the second standard deviation of the second set, where the initial data of the second set is P fitting parameters; when the second standard deviation is greater than the third preset threshold, eliminate the fitting parameter in the second set with the largest difference from the second mean to obtain the remaining multiple fitting parameters; update the second set according to the remaining multiple fitting parameters, and return to calculate the second mean and the second standard deviation of the second set until the second standard deviation is less than the third preset threshold.

[0081] In combination with the above example, step B2 is described in detail below:

[0082] The second mean and the second standard deviation of the above 20 fitting parameters are calculated. If the second standard deviation is greater than the third preset threshold, the fitting parameter with the largest difference from the second mean among the 20 fitting parameters is eliminated, and the second standard deviation of the remaining fitting parameters is recalculated until the second standard deviation is less than the third preset threshold.

[0083] In step B3, the phase deviation correction number corresponding to the fitting parameter eliminated in the second updating step is identified as abnormal.

[0084] Specifically, in combination with the above example, if the absolute value of the difference between any fitting parameter and the second mean is greater than the third preset threshold in step B3, it is considered that the phase deviation correction value is unusable.

[0085] In another example, if the absolute value of the difference between the fitting parameter and the second mean of any phase deviation correction is greater than three times the second standard deviation, the phase deviation correction is considered inconsistent with the trend of the other corrections and is marked as unusable. The three times is a ratio determined by those skilled in the art based on experience, and may be another empirical value, which is not limited in this application.

[0086] In some embodiments, where the target SSR includes a tropospheric delay correction,

[0087] Based on the difference in the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

[0088] For each satellite at each frequency point, perform the following steps for the tropospheric delay corrections for two adjacent epochs:

[0089] Calculate the difference between the tropospheric delay correction at the Lth epoch and the tropospheric delay correction at the L+1th epoch to determine the change in the tropospheric delay correction, where L is a positive integer;

[0090] When the change in the tropospheric delay correction number is greater than a fourth preset threshold, it is determined that the tropospheric delay correction number of the L+1th epoch is abnormal.

[0091] In some embodiments, when the change in the tropospheric delay correction number is greater than a fourth preset threshold, when it is determined that the tropospheric delay correction number at the L+1 epoch is abnormal, the method may further include:

[0092] Eliminate the tropospheric delay correction number of the L+1th epoch;

[0093] Calculate the difference between the tropospheric delay correction number at the Lth epoch and the tropospheric delay correction number at the L+2th epoch to determine the change in the first tropospheric delay correction number;

[0094] When the change in the first tropospheric delay correction number is not greater than a fourth preset threshold, it is determined that the tropospheric delay correction number of the L+2 epoch is normal.

[0095] In one example, when L=1, calculating the difference between the tropospheric delay correction number at the Lth epoch and the tropospheric delay correction number at the L+1th epoch to determine the change in the tropospheric delay correction number may include:

[0096] Calculate the difference between the tropospheric delay corrections of the first epoch and the second epoch, and determine the difference as the change in the tropospheric delay correction. If the change in the tropospheric delay correction is greater than 0.02 (unit: m), the tropospheric delay correction of the second epoch is determined to be abnormal; otherwise, the tropospheric delay correction of the second epoch is normal.

[0097] The above-mentioned 0.02m is a fourth preset threshold value determined by those skilled in the art based on experience. The fourth preset threshold value may also be other experience values, which is not limited in this application.

[0098] The anomaly detection method for the tropospheric delay correction number provided in the above-mentioned embodiment of the present application, since the troposphere has spatiotemporal continuity, is based on this characteristic to calculate the difference between the tropospheric delay correction numbers of two adjacent epochs for each satellite at each frequency point. The tropospheric delay correction number is judged to be abnormal based on the above difference, and then the accuracy of anomaly identification is improved by setting an anomaly threshold.

[0099] In some embodiments, where the target SSR includes an ionospheric delay correction,

[0100] Based on the difference in the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

[0101] Calculate the difference between two ionospheric delay corrections for each satellite at adjacent epochs to determine M ionospheric delay correction changes, where M is an integer greater than 1;

[0102] It should be noted that the ionosphere is continuous in time and space. Therefore, to effectively identify specific satellites, the ionospheric delay correction values of two consecutive epochs must be checked between epochs. Each system must be processed separately. Assume that 10 GPS satellites can be observed at the current time, and each satellite has an ionospheric delay correction value. The difference between two consecutive broadcast ionospheric delay correction values can be expressed by Formula 4:

[0103]

[0104] Among them, t k ,t k+1 For receiving time, is the change in ionospheric delay correction, I m is the ionospheric delay correction number.

[0105] Executing a third updating step: calculating a third mean and a third standard deviation of the third set, wherein initial data of the third set is M ionospheric delay correction number changes; when the third standard deviation is greater than a fifth preset threshold, removing the ionospheric delay correction number change with the largest difference from the third mean in the third set, to obtain the remaining multiple phase ionospheric delay correction number changes; updating the third set based on the remaining multiple ionospheric delay correction number changes, and returning to calculate the third mean and third standard deviation of the third set until the third standard deviation is less than the fifth preset threshold;

[0106] The ionospheric delay correction number corresponding to the ionospheric delay variation eliminated in the third updating step is identified as abnormal.

[0107] The anomaly detection method for the ionospheric correction number provided in the above-mentioned embodiment of the present application, since the ionosphere has spatiotemporal continuity, is based on this characteristic to calculate the difference between the ionospheric delay correction numbers of each satellite in two adjacent epochs. Based on the above difference, it is determined whether the ionospheric delay correction number is abnormal, and then by setting an anomaly threshold, the accuracy of anomaly identification is improved.

[0108] In some embodiments, the SSR also includes orbit corrections and clock corrections. The method for identifying anomalies in state-domain space corrections provided in the embodiments of the present application may further include:

[0109] In step C1, the inter-satellite single-difference observation equation is constructed using the tropospheric delay correction, ionospheric delay correction, satellite orbit correction, satellite clock correction, and GNSS observation information, and the posterior residual of each satellite is determined by the least squares method.

[0110] Step C2: When the posterior-validation residual is greater than a sixth preset threshold, it is determined that an abnormality exists in the orbit correction number and the clock correction number of the corresponding satellite.

[0111] The above-mentioned embodiment of the present application provides a method for identifying anomalies in the state domain space correction number. In addition to the pseudorange deviation correction number, phase deviation correction number, tropospheric delay correction number, and ionospheric delay correction number, the SSR also includes orbit correction numbers and clock correction numbers. An SSR observation equation is established based on GNSS observation data, and all SSR data are cross-checked through the observation equation. This can take into account the self-consistency between different SSR data in the correction number anomaly identification process, thereby improving the accuracy of SSR anomaly identification.

[0112] In the above step C1, in one example, the inter-satellite single-difference observation equation is constructed using the tropospheric delay correction, the ionospheric delay correction, the satellite orbit correction, the satellite clock correction, and the GNSS observation information. The inter-satellite single-difference observation equation can be expressed by Formula 5:

[0113]

[0114] Where i is the frequency number, t r is the user-side clock error, t s is the satellite clock error, L i is the carrier phase corresponding to the i-th frequency point, ρ is the distance between the phase centers of the satellite end and the user end; λ i is the wavelength, N i is the whole-cycle ambiguity, I1 is the ionospheric delay corresponding to the first frequency point; β i is the ionospheric delay proportional coefficient of the I-th frequency point, is the phase wrap, in cycles; T is the tropospheric delay.

[0115] Considering the user end clock difference t r It is not possible to perform correction directly, and it is necessary to construct an inter-satellite single difference to eliminate it. Assuming that satellite m is selected as the reference satellite, the inter-satellite single difference combination residual can be expressed by formula 6 as:

[0116]

[0117] Where n is the non-reference star marker.

[0118] Before linearizing Equation 6, the satellite orbit calculated from the broadcast ephemeris is first corrected using the orbit correction number; the satellite clock error calculated from the broadcast ephemeris is corrected using the clock correction number; the tropospheric delay is corrected using the tropospheric delay correction number; and the ionospheric delay is corrected using the ionospheric delay correction number.

[0119] Currently, we observe the carrier phase observations of 10 GPS satellites at two frequencies. After constructing the inter-satellite single difference using only the carrier phase observations of the first frequency, we can obtain 9 observation equations. After linearizing them, we can obtain the corresponding linearization matrix H, weight matrix P, and residual matrix V, which are expressed by Formula 7, Formula 8, and Formula 10 respectively:

[0120]

[0121] Among them, X r ,Y r ,Z r is the user terminal location, is the position of the non-reference star, 1 to 9 are the numbers of the non-reference stars, X s,m ,Y s,m ,Z s,m is the position of reference star m.

[0122]

[0123] Among them, p1…p9 are the weight ratios of each inter-satellite single-difference combination, which can be expressed by Formula 9:

[0124]

[0125]

[0126] in, is the residual of each inter-satellite single-difference combination.

[0127] Least squares is used to check whether the post-test residuals exceed the limit. If the post-test residuals exceed a certain threshold, such as 0.03m, it is considered that there is an anomaly in the orbit correction and clock correction.

[0128] It should be noted that all preset thresholds in the embodiments of the present application are set by those skilled in the art based on empirical values, and the present application does not limit this.

[0129] Based on the same inventive concept as above, an embodiment of the present application provides an abnormality identification device for state domain space correction numbers.

[0130] like Figure 3 As shown, an embodiment of the present application provides an apparatus for identifying anomalies of a state domain space correction number, and the apparatus may include:

[0131] An acquisition module 301 is configured to acquire state domain spatial corrections (SSRs) and global satellite navigation system (GNSS) observation information, wherein the SSRs include pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections, and the GNSS observation information includes pseudorange, carrier phase, Doppler observations, and broadcast ephemeris.

[0132] The verification module 302 is used to verify the target SSR based on the difference of the target SSR in consecutive epochs to determine whether there is an abnormal SSR, where the target SSR is at least one of the pseudorange deviation correction number, the phase deviation correction number, the tropospheric delay correction number, and the ionospheric delay correction number.

[0133] The anomaly identification device for state domain space correction numbers provided in the embodiment of the present application is used to obtain SSR and GNSS observation information, and judge whether the SSR is abnormal based on the data characteristics of the SSR at different epochs. Since the SSR is inspected based on the continuity or stability of the SSR itself, rather than relying on other SSR-related information to indirectly judge whether it is abnormal, the data properties of the SSR are fully considered, and abnormal deviations of smaller orders of magnitude can be detected, thereby improving the accuracy of SSR anomaly identification.

[0134] In some embodiments, the inspection module may be specifically used to:

[0135] For each satellite at each frequency point, perform the following steps for the pseudorange bias corrections of two adjacent epochs:

[0136] Calculate the difference between the pseudorange bias correction at the Kth epoch and the pseudorange bias correction at the K+1th epoch to determine the change in the pseudorange bias correction, where K is a positive integer.

[0137] When the change in the pseudorange bias correction number is greater than a first preset threshold, it is determined that the pseudorange bias correction number of the K+1 epoch is abnormal.

[0138] The verification module provided in the embodiment of the present application calculates the difference between the pseudorange bias correction numbers of two adjacent epochs for each satellite at each frequency point during the abnormality test of the pseudorange bias correction number. Since the pseudorange bias correction number has good stability and does not change much in a short period of time, based on this characteristic, the difference between the pseudorange bias correction numbers of two adjacent epochs at each frequency point is calculated. Based on the above difference, it is determined whether the pseudorange bias correction number is abnormal, and then the accuracy of abnormality identification is improved by setting an abnormality threshold.

[0139] In some embodiments, the inspection module may be specifically used to:

[0140] Calculate the difference between two phase bias correction numbers of adjacent epochs for each satellite at each frequency point, and determine N phase bias correction number changes, where N is an integer greater than 1;

[0141] Performing a first updating step: calculating a first mean and a first standard deviation of a first set, wherein initial data of the first set is the N phase deviation correction number changes; if the first standard deviation is greater than a second preset threshold, eliminating the phase deviation correction number change with the largest difference from the first mean in the first set to obtain a remaining plurality of phase deviation correction number changes; updating the first set based on the remaining plurality of phase deviation correction number changes, and returning to the step of calculating the first mean and the first standard deviation of the first set until the first standard deviation is less than the second preset threshold;

[0142] The phase deviation correction number corresponding to the phase deviation correction number variation eliminated in the first updating step is identified as abnormal.

[0143] The inspection module provided in the embodiment of the present application, in the process of performing abnormality inspection on the phase deviation correction number, since the mean and standard deviation can reflect the stability and consistency between multiple data, combined with the continuity of the phase deviation correction number itself, uses the above two statistical data to determine whether it is abnormal, and further sets abnormal thresholds for the mean and standard deviation to improve the precision and accuracy of identifying abnormal phase deviation correction numbers, and effectively eliminate abnormal phase deviation correction number data.

[0144] In some embodiments, the inspection module may be specifically used to:

[0145] Based on the sliding window, linear fitting is performed on multiple phase deviation corrections of each satellite at each frequency point and multiple epochs to obtain the fitting results.

[0146] According to the fitting results, determine whether there is a slow drift anomaly in the phase deviation correction number;

[0147] When it is determined that the phase deviation correction number has a slow drift anomaly, it is determined that the phase deviation correction number is abnormal.

[0148] In some embodiments, the inspection module may be specifically used to:

[0149] Perform linear fitting on multiple phase deviation corrections of each satellite at multiple epochs at each frequency point to determine P fitting parameters, where P is an integer greater than 1;

[0150] Performing a second updating step: calculating a second mean and a second standard deviation of a second set, wherein initial data of the second set are the P fitting parameters; if the second standard deviation is greater than a third preset threshold, eliminating the fitting parameter in the second set with the largest difference from the second mean to obtain a plurality of remaining fitting parameters; updating the second set according to the plurality of remaining fitting parameters, and returning to the step of calculating the second mean and second standard deviation of the second set until the second standard deviation is less than the third preset threshold;

[0151] The phase deviation correction number corresponding to the fitting parameter eliminated in the second updating step is identified as abnormal.

[0152] The inspection module provided in the embodiment of the present application obtains as many phase deviation correction numbers as possible based on a sliding window during the process of performing anomaly inspection on the phase deviation correction number, determines the quantitative relationship of mutual dependence between multiple phase deviation correction numbers of consecutive epochs by performing linear fitting on them, predicts the expected data based on the current data, so as to compare the actual data with the expected data, determine the phase deviation correction numbers with abnormal deviations in the actual data, and further improve the accuracy of identifying anomalies in the phase deviation correction numbers.

[0153] In some embodiments, the inspection module may be specifically used to:

[0154] Calculate the difference between the tropospheric delay correction at the Lth epoch and the tropospheric delay correction at the L+1th epoch to determine the change in the tropospheric delay correction, where L is a positive integer;

[0155] When the change in the tropospheric delay correction number is greater than a fourth preset threshold, it is determined that the tropospheric delay correction number of the L+1th epoch is abnormal.

[0156] The verification module provided in the embodiment of the present application calculates the difference between the tropospheric delay correction numbers of two adjacent epochs for each satellite at each frequency point based on the spatiotemporal continuity of the troposphere during the process of performing anomaly detection on the tropospheric delay correction number. The difference is used to determine whether the pseudorange bias correction number is abnormal, and the accuracy of anomaly identification is improved by setting an anomaly threshold.

[0157] In some embodiments, the inspection module may be specifically used to:

[0158] Calculate the difference between two ionospheric delay corrections for each satellite at adjacent epochs to determine M ionospheric delay correction changes, where M is an integer greater than 1;

[0159] Executing a third updating step: calculating a third mean and a third standard deviation of the third set, wherein initial data of the third set is M ionospheric delay correction number changes; when the third standard deviation is greater than a fifth preset threshold, removing the ionospheric delay correction number change with the largest difference from the third mean in the third set, to obtain the remaining multiple phase ionospheric delay correction number changes; updating the third set based on the remaining multiple ionospheric delay correction number changes, and returning to calculate the third mean and third standard deviation of the third set until the third standard deviation is less than the fifth preset threshold;

[0160] The ionospheric delay correction number corresponding to the ionospheric delay variation eliminated in the third updating step is identified as abnormal.

[0161] The verification module provided in the embodiment of the present application calculates the difference between the ionospheric delay correction numbers of each satellite in two adjacent epochs based on the spatiotemporal continuity of the ionosphere during the process of checking for anomalies in the ionospheric delay correction number. The ionospheric delay correction number is judged to be abnormal based on the difference, and the accuracy of anomaly identification is improved by setting an anomaly threshold.

[0162] In some embodiments, the inspection module may be specifically used to:

[0163] Using the tropospheric delay correction, ionospheric delay correction, satellite orbit correction, satellite clock correction and GNSS observation information, an inter-satellite single-difference observation equation is constructed, and the posterior residual of each satellite is determined by the least squares method.

[0164] When the posterior-test residual is greater than a sixth preset threshold, it is determined that an abnormality exists in the orbit correction number and the clock correction number of the corresponding satellite.

[0165] The verification module provided in the embodiment of the present application is used to establish an SSR observation equation based on GNSS observation data and to cross-check all SSR data through the observation equation. Therefore, the self-consistency between different SSR data can be taken into account during the correction number anomaly identification process, thereby improving the accuracy of SSR anomaly identification.

[0166] Other details of the abnormality identification device for the state domain space correction number provided in the embodiment of the present application are combined with the above Figure 1 The method for identifying anomalies in the state domain space correction number according to the embodiment of the present application is similar and will not be repeated here.

[0167] Figure 4 A schematic diagram of the hardware structure of the abnormal state domain space correction number provided by an embodiment of the present application is shown.

[0168] Combine Figure 1 、 Figure 3 The described method and apparatus for identifying anomalies of state-domain spatial correction numbers provided in accordance with the embodiments of the present application may be implemented by an apparatus for identifying anomalies of state-domain spatial correction numbers. Figure 4 FIG4 is a schematic diagram showing a hardware structure 400 of an abnormality identification device for state domain space correction numbers according to an embodiment of the present invention.

[0169] The device for identifying anomalies of state domain space correction numbers may include a processor 401 and a memory 402 storing computer program instructions.

[0170] Specifically, the processor 401 may include a central processing unit (CPU) or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0171] Memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In one example, memory 402 may include removable or non-removable (or fixed) media, or memory 402 may be a non-volatile solid-state memory. Memory 402 may be inside or outside the integrated gateway disaster recovery device.

[0172] In one example, the memory 402 may be a read-only memory (ROM). In one example, the ROM may be a mask-programmable ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or a flash memory, or a combination of two or more of these.

[0173] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement Figure 1 The method / steps S101 to S102 in the embodiment shown, and achieving Figure 1 The corresponding technical effects achieved by executing the methods / steps in the illustrated example will not be repeated here for the sake of brevity.

[0174] In one example, the abnormality identification device for the state domain space correction number may further include a communication interface 403 and a bus 410. Figure 4 As shown, the processor 401 , the memory 402 , and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0175] The communication interface 403 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0176] Bus 410 includes hardware, software or both, and couples the components of the online data traffic billing device to each other. For example, and not limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus or other suitable buses or a combination of two or more of these. Where appropriate, bus 410 may include one or more buses. Although the present application describes and illustrates a specific bus, the present application considers any suitable bus or interconnect.

[0177] The state domain space correction number anomaly identification device provided in the embodiment of the present application, on the one hand, in the process of anomaly identification of pseudorange deviation correction number, phase deviation correction number, tropospheric delay correction number and ionospheric delay correction number, judges whether it is abnormal based on the data characteristics of SSR at different epochs, rather than relying on other information related to SSR to indirectly judge whether it is abnormal, thereby being able to detect abnormal deviations of smaller orders of magnitude and improve the accuracy of SSR anomaly identification. On the other hand, an SSR observation equation is established based on GNSS observation data, and all SSR data are mutually checked through the observation equation. In the process of correction number anomaly identification, the self-consistency between different SSR data can be taken into account, thereby improving the accuracy of SSR anomaly identification.

[0178] In addition, in conjunction with the state-domain space correction number anomaly identification method in the above-mentioned embodiments, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the state-domain space correction number anomaly identification methods in the above-mentioned embodiments is implemented.

[0179] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0180] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link via a data signal carried in a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0181] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0182] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor or a field programmable logic circuit. It is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0183] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A method for identifying anomalies in state domain space correction numbers, characterized in that: The method comprises: Acquiring state domain spatial corrections (SSRs) and global satellite navigation system (GNSS) observation information, wherein the SSRs include pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections, and the GNSS observation information includes pseudorange, carrier phase, Doppler observations, and broadcast ephemeris; Testing the target SSR based on differences in target SSRs at consecutive epochs to determine whether an abnormal SSR exists, wherein the target SSR is at least one of a pseudorange bias correction, a phase bias correction, a tropospheric delay correction, and an ionospheric delay correction; In the case where the target SSR includes the phase deviation correction number, The target SSR is tested based on the difference between the target SSRs in consecutive epochs to determine whether there is an abnormal SSR, including: performing linear fitting on a plurality of phase deviation correction values of each satellite at a plurality of epochs at each frequency point based on a sliding window to obtain a fitting result; Determining whether there is a slow drift anomaly in the phase deviation correction number based on the fitting result; When it is determined that the phase deviation correction number has the slow drift anomaly, it is determined that the phase deviation correction number is abnormal.

2. The method according to claim 1, characterized in that In the case where the target SSR includes the pseudorange bias correction number, The step of testing the target SSR based on the difference between the target SSRs in consecutive epochs to determine whether an abnormal SSR exists includes: For each satellite at each frequency point, perform the following steps for the pseudorange bias corrections of two adjacent epochs: Calculate the difference between the pseudorange bias correction at the Kth epoch and the pseudorange bias correction at the K+1th epoch to determine the change in the pseudorange bias correction, where K is a positive integer. When the change in the pseudorange bias correction number is greater than a first preset threshold, it is determined that the pseudorange bias correction number of the K+1 epoch is abnormal.

3. The method according to claim 1, characterized in that In the case where the target SSR includes the phase deviation correction number, The step of testing the target SSR based on the difference between the target SSRs in consecutive epochs to determine whether an abnormal SSR exists includes: Calculate the difference between two phase bias correction numbers of adjacent epochs for each satellite at each frequency point, and determine N phase bias correction number changes, where N is an integer greater than 1; Performing a first updating step: calculating a first mean and a first standard deviation of a first set, wherein initial data of the first set is the N phase deviation correction number changes; if the first standard deviation is greater than a second preset threshold, eliminating the phase deviation correction number change with the largest difference from the first mean in the first set to obtain a remaining plurality of phase deviation correction number changes; updating the first set based on the remaining plurality of phase deviation correction number changes, and returning to the step of calculating the first mean and the first standard deviation of the first set until the first standard deviation is less than the second preset threshold; The phase deviation correction number corresponding to the phase deviation correction number variation eliminated in the first updating step is identified as abnormal.

4. The method according to claim 3, characterized in that The method of performing linear fitting on the phase deviation correction values of each satellite at each frequency point for multiple epochs based on the sliding window, and determining whether the phase deviation correction values have a slow drift anomaly based on the fitting results, includes: Performing linear fitting on the multiple phase deviation correction values of each satellite at each frequency point for multiple epochs to determine P fitting parameters, where P is an integer greater than 1; Performing a second updating step: calculating a second mean and a second standard deviation of a second set, wherein initial data of the second set are the P fitting parameters; if the second standard deviation is greater than a third preset threshold, eliminating the fitting parameter in the second set with the largest difference from the second mean to obtain a plurality of remaining fitting parameters; updating the second set according to the plurality of remaining fitting parameters, and returning to the step of calculating the second mean and second standard deviation of the second set until the second standard deviation is less than the third preset threshold; The phase deviation correction number corresponding to the fitting parameter eliminated in the second updating step is identified as abnormal.

5. The method according to claim 1, wherein In the case where the target SSR includes the tropospheric delay correction number, The step of testing the target SSR based on the difference between the target SSRs in consecutive epochs to determine whether an abnormal SSR exists includes: For each satellite at each frequency point, perform the following steps for the tropospheric delay corrections for two adjacent epochs: Calculate the difference between the tropospheric delay correction at the Lth epoch and the tropospheric delay correction at the L+1th epoch to determine the change in the tropospheric delay correction, where L is a positive integer; When the change in the tropospheric delay correction number is greater than a fourth preset threshold, it is determined that the tropospheric delay correction number of the L+1 epoch is abnormal.

6. The method according to claim 1, characterized in that In the case where the target SSR includes the ionospheric delay correction number, The step of testing the target SSR based on the difference between the target SSRs in consecutive epochs to determine whether an abnormal SSR exists includes: Calculating the difference between two ionospheric delay correction values for each satellite at adjacent epochs to determine M ionospheric delay correction value changes, where M is an integer greater than 1; Performing a third updating step: calculating a third mean and a third standard deviation of a third set, wherein initial data of the third set are the M ionospheric delay correction number changes; if the third standard deviation is greater than a fifth preset threshold, removing the ionospheric delay correction number change with the largest difference from the third mean in the third set to obtain a remaining plurality of phase ionospheric delay correction number changes; updating the third set based on the remaining plurality of ionospheric delay correction number changes, and returning to the step of calculating the third mean and third standard deviation of the third set until the third standard deviation is less than the fifth preset threshold; The ionospheric delay correction number corresponding to the ionospheric delay variation eliminated in the third updating step is identified as abnormal.

7. The method according to claim 1, characterized in that The SSR also includes an orbit correction number and a clock correction number, and the method further includes: constructing an inter-satellite single-difference observation equation using the tropospheric delay correction, the ionospheric delay correction, the orbit correction, the clock correction, and the GNSS observation information, and determining a posterior residual for each satellite using a least squares method; When the posterior-test residual is greater than a sixth preset threshold, it is determined that an abnormality exists in the orbit correction number and the clock correction number of the corresponding satellite.

8. A device for identifying abnormalities in state domain space correction numbers, characterized in that: The device comprises: an acquisition module, configured to acquire state domain spatial corrections (SSRs) and global satellite navigation system (GNSS) observation information, wherein the SSRs include pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections; and the GNSS observation information includes pseudorange, carrier phase, Doppler observations, and broadcast ephemeris; a verification module, configured to verify the target SSR based on differences in target SSRs at consecutive epochs to determine whether an abnormal SSR exists, wherein the target SSR is at least one of a pseudorange deviation correction, a phase deviation correction, a tropospheric delay correction, and an ionospheric delay correction; In a case where the target SSR includes the phase deviation correction number, the verification module is specifically configured to: perform linear fitting on a plurality of the phase deviation correction numbers of each satellite at a plurality of epochs at each frequency point based on a sliding window to obtain a fitting result; Determining whether there is a slow drift anomaly in the phase deviation correction number based on the fitting result; When it is determined that the phase deviation correction number has the slow drift anomaly, it is determined that the phase deviation correction number is abnormal.

9. A device for identifying abnormalities in state domain space correction numbers, characterized in that: The device includes: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method for identifying anomalies of state domain space correction numbers according to any one of claims 1 to 7.

10. A computer storage medium, characterized in that The computer storage medium stores computer program instructions, which, when executed by a processor, implement the method for identifying anomalies in a state domain space correction number according to any one of claims 1 to 7.

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