CN115480270A - Method, device and equipment for identifying abnormity of state domain space correction number - Google Patents

Abstract

Embodiments of the present application provide a method, device, device, and computer storage medium for identifying anomalies of state domain space correction numbers, which relate to the field of satellite positioning and are used to improve the accuracy of SSR anomaly identification. The method includes: obtaining state domain space correction number SSR and global satellite navigation system GNSS observation information, wherein the SSR includes pseudorange deviation correction number, phase deviation correction number, tropospheric delay correction number and ionospheric delay correction number, so The GNSS observation information includes pseudorange, carrier phase, Doppler observation value and broadcast ephemeris; according to the difference of the target SSR in consecutive epochs, the target SSR is checked to determine whether there is an abnormal SSR, wherein, The target SSR is at least one of pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, and ionospheric delay corrections.

Description

A method, device, and equipment for abnormal identification of state domain space correction numbers

technical field

The application belongs to the field of satellite positioning, and in particular relates to a method, device and equipment for identifying abnormality of a state domain space correction number.

Background technique

State Space Representation (SSR) is usually used to determine the instantaneous fixed position when performing precise point positioning (Precise point positioning, PPP) and real-time dynamic carrier phase differential positioning technology (Real-time kinematic, RTK). However, due to the limited accuracy of SSR, abnormal SSR will affect the positioning results.

In the prior art, the integrity information is generally used to mark abnormal SSRs, and the corresponding marking results are sent to users, so that users can use SSRs in a more reasonable manner. When there is a large deviation in the SSR, the integrity information can mark it to help users avoid the impact of positioning errors caused by abnormal SSRs, but the integrity information is difficult to identify SSRs with small deviations.

To sum up, the accuracy of anomaly identification for SSR in the prior art is insufficient.

Contents of the invention

Embodiments of the present application provide an anomaly identification method, device, and equipment for a state domain space correction number, which are used to improve the accuracy of SSR anomaly identification.

In the first aspect, the embodiment of the present application provides a method for identifying anomalies in state domain space correction numbers, the method including:

Obtain the state domain space correction number SSR and the GNSS observation information of the global satellite navigation system, wherein, the SSR includes the pseudorange deviation correction number, the phase deviation correction number, the troposphere delay correction number and the ionosphere delay correction number, and the GNSS observation information includes the pseudorange, carrier wave phase, Doppler observations, and broadcast ephemeris;

According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, where the target SSR is the pseudorange bias correction number, phase bias correction number, tropospheric delay correction number and ionospheric delay correction number at least one of the numbers.

In the second aspect, the embodiment of the present application provides an abnormality identification device for a state domain space correction number, the device includes:

The acquisition module is used to obtain the state domain space correction number SSR and the GNSS observation information of the global satellite navigation system, wherein the SSR includes the pseudorange deviation correction number, the phase deviation correction number, the troposphere delay correction number and the ionosphere delay correction number, and the GNSS observation information Including pseudorange, carrier phase, Doppler observations and broadcast ephemeris;

The inspection module is used to inspect the target SSR according to the difference of the target SSR in consecutive epochs to determine whether there is an abnormal SSR, wherein the target SSR is a pseudorange deviation correction number, a phase deviation correction number, and a tropospheric delay correction number and at least one of ionospheric delay corrections.

In the third aspect, the embodiment of the present application provides an abnormality identification device for a correction number in a state domain space, and the device includes:

A processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions, so as to realize the abnormality identification method of the state domain space correction number provided by the first aspect of the embodiment of the present application.

In the fourth aspect, the embodiment of the present application provides a computer storage medium, on which computer program instructions are stored. When the computer program instructions are executed by the processor, the state domain space correction as provided in the first aspect of the embodiment of the present application is implemented. Anomaly detection method for numbers.

The abnormal identification method, device and equipment of the state domain space correction number of the embodiment of the present application obtain SSR and GNSS observation information of the global satellite navigation system. The above SSR includes pseudorange deviation correction number, phase deviation correction number, tropospheric delay correction number and ionization Layer delay correction number, because the process of checking SSR is based on the continuity or stability of SSR itself, and judges whether it is abnormal according to the data characteristics of SSR in different epochs, rather than relying on other SSR-related The information indirectly judges whether it is abnormal, so compared with the existing technology, it can detect abnormal deviations of smaller orders of magnitude and improve the accuracy of SSR abnormal recognition.

Description of drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the accompanying drawings that need to be used in the embodiments of the present application. Additional figures can be derived from these figures.

FIG. 1 is a schematic flowchart of a method for identifying anomalies in state domain space correction numbers provided by an embodiment of the present application;

FIG. 2 is a schematic flow diagram of a data transmission link provided by an embodiment of the present application;

Fig. 3 is a schematic flow diagram of an abnormal identification device for a state domain space correction number provided by an embodiment of the present application;

Fig. 4 is a schematic structural diagram of a device for identifying anomalies in state domain space correction numbers provided by an embodiment of the present application.

detailed description

The characteristics and exemplary embodiments of various aspects of the application will be described in detail below. In order to make the purpose, technical solution and advantages of the application clearer, the 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 here are only intended to explain the present application rather than limit the present application. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by showing examples of the present application.

It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is a relationship between these entities or operations. There is no such actual relationship or order between them. Furthermore, the term "comprises", "comprises" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus comprising a set of elements includes not only those elements, but also includes elements not expressly listed. other elements of or also include elements inherent in such a process, method, article, or device. Without further limitations, an element defined by the statement "comprising..." does not exclude the presence of additional same elements in the process, method, article or device comprising said element.

State Space Representation (SSR) is usually used to determine the instantaneous fixed solution when performing precise point positioning (Precise point positioning, PPP) and real-time dynamic carrier phase differential positioning technology (Real-time kinematic, RTK) , but due to the limited accuracy of SSR, abnormal SSR will affect the positioning results.

In the prior art, the SSR can be identified abnormally through the following two schemes:

Solution 1. Use the integrity information to monitor the availability of SSR correction numbers. When the SSR correction numbers deviate by a large magnitude, the integrity information can be marked to help users avoid the error impact of abnormal SSR correction numbers, so as to ensure that the user positioning results are not accurate. Extreme exceptions occur. However, the integrity information is difficult to provide information on whether the satellite can be fixed, and it is difficult to identify SSRs with small deviations.

Solution 2: Use the quality index to provide the quality factor of the SSR correction number, and assist users to use the SSR correction number in a more reasonable way. However, since the calculation of the quality factors of some SSR corrections is independent, the self-consistency of the SSR corrections cannot be considered, resulting in some normal SSR corrections being abnormally eliminated.

To sum up, the accuracy of anomaly identification for SSR in the prior art is insufficient.

In view of the above problems, the embodiment of the present application provides an anomaly identification method of the state domain space correction number, which judges whether it is abnormal according to the data characteristics of the SSR in different epochs, instead of relying on other SSR-related information to indirectly determine Whether it is abnormal is judged, and then abnormal deviations of smaller orders of magnitude can be detected, and the accuracy of abnormal identification of SSR can be improved.

As shown in Figure 1, the embodiment of the present application provides a method for identifying abnormality of the correction number in the state domain space, the method includes:

S101, obtain the state domain space correction number SSR and the global satellite navigation system GNSS observation information, wherein, the SSR includes the pseudorange deviation correction number, the phase deviation correction number, the troposphere delay correction number and the ionosphere delay correction number, and the GNSS observation information includes the pseudorange deviation correction number and the ionosphere delay correction number. , carrier phase, Doppler observations, and broadcast ephemeris.

It should be noted that, as shown in Figure 2, terminal equipment can be used to obtain GNSS observation information and SSR correction numbers, where GNSS observation information includes carrier phase, pseudorange, Doppler and other observations and broadcast ephemeris, and SSR correction numbers include Orbit corrections, clock corrections, pseudorange bias corrections, phase bias corrections, tropospheric delay corrections, ionospheric delay corrections, and SSR corrections can be obtained through satellite-based links or Internet links.

S102, according to the difference of the target SSR in consecutive epochs, check the target SSR to determine whether there is an abnormal SSR, wherein the target SSR is the pseudorange deviation correction number, the phase deviation correction number, the tropospheric delay correction number and the ionosphere At least one of delay correction numbers.

In satellite positioning applications, using GNSS observation information and SSR corrections to perform PPP-RTK can achieve instantaneous centimeter-level positioning. However, due to possible abnormalities in SSR, there may be deviations in positioning results. Therefore, when using SSR corrections to perform PPP-RTK Before, it is necessary to check whether the SSR correction number is abnormal.

The above is the specific implementation manner of the method for identifying abnormality of the correction number in the state domain space provided by the embodiment of the present application. In this specific embodiment, the SSR and GNSS observation information is obtained, and whether it is abnormal is judged according to the data characteristics of the SSR in different epochs, because the process of checking the SSR is based on the continuity or stability of the SSR itself , instead of relying on other SSR-related information to indirectly judge whether it is abnormal, so the data nature of SSR is fully considered, and abnormal deviations of small orders of magnitude can be detected, which improves the accuracy of SSR abnormal recognition.

In some embodiments, where the target SSR includes pseudorange bias corrections,

According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

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

Calculate the difference between the pseudorange deviation correction number of the K epoch and the K+1 epoch pseudorange deviation correction number, and determine the variation of the pseudorange deviation correction number, wherein K is a positive integer;

In a case where the change amount of the pseudorange deviation correction number is greater than the first preset threshold, it is determined that the pseudorange deviation correction number of the K+1th epoch is abnormal.

In some embodiments, in the case where the variation of the pseudorange deviation correction number is greater than the first preset threshold, and it is determined that the pseudorange deviation correction number of the K+1th epoch is abnormal, the method may further include:

Eliminate the pseudo-range deviation correction number of the K+1th epoch;

Calculate the difference between the pseudorange deviation correction number of the Kth epoch and the K+2th epoch pseudorange deviation correction number, and determine the change amount of the first pseudorange deviation correction number;

In the case that the change amount of the first pseudorange deviation correction number is not greater than the first preset threshold, it is determined that the pseudorange deviation correction number of the K+2th epoch is normal.

It should be noted that in the above example, when the pseudo-range deviation correction number is tested according to the corresponding correction number difference of consecutive epochs, due to the good stability of the pseudo-range deviation correction number, there will be no continuity in a short period of time. Therefore, when the difference between the pseudorange deviation correction number of the Kth epoch and the pseudorange deviation correction number of the K+1th epoch is greater than the first preset threshold, the following epochs in the adjacent epochs are first defaulted The pseudo-range deviation correction number of the K+1 epoch is abnormal, and at the same time, the pseudo-range deviation correction number of the previous epoch in the adjacent epoch, that is, the pseudo-range deviation correction number of the K-th epoch is normal; in addition, In practical applications, there will be a very small probability that the K+1th epoch and the K+2th epoch, that is, the pseudorange deviation correction numbers of adjacent epochs, are abnormal, so the above situation will not be considered; finally, determine In the case of an abnormality in the pseudorange deviation correction number of the K+1 epoch, the above-mentioned abnormal data will be eliminated, and the pseudorange deviation correction number of the K+2 epoch will be compared with the pseudorange deviation correction number of the K epoch, so as to continue the relevant process. Anomaly testing for pseudorange bias corrections between adjacent epochs.

In one example, in the case of K=1, the difference between the pseudorange deviation correction number of the K epoch and the pseudorange deviation correction number of the K+1 epoch is calculated, and the change amount of the pseudorange deviation correction number can be determined include:

Calculate the difference between the first epoch and the second epoch pseudo-range deviation correction number, and determine the above-mentioned difference as the pseudo-range deviation correction number change; if the pseudo-range deviation correction number change is greater than 0.5 (unit m), Then it is determined that the pseudorange bias correction number in the second epoch is abnormal; otherwise, the pseudorange bias correction number in the second epoch is normal data.

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

The abnormality inspection method of the pseudo-range deviation correction number provided in the above-mentioned embodiment of the present application, because the stability of the pseudo-range deviation correction number is good, it will not change too much in a short time, so based on this characteristic, each satellite in Calculate the difference between the pseudorange deviation correction numbers of two adjacent epochs at each frequency point, and judge whether the pseudorange deviation correction number is abnormal according to the above difference, and then set the abnormal threshold to improve the accuracy of abnormal recognition.

In some embodiments, where the target SSR includes phase offset corrections,

According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

Step A1, calculate the difference between two phase deviation correction numbers in 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.

It should be noted that since the satellites belong to different observation systems, in order to detect the abnormality of the phase deviation correction number, each observation system needs to be processed separately. For example, 30 satellites are observed in the current epoch, including 10 GPS satellites, 10 Beidou satellites, and 10 Galileo satellites, and the phase deviation correction numbers of 2 frequency points of each satellite are received. When performing anomaly detection on the phase deviation correction number, it is necessary to detect the above-mentioned different observation systems separately. In order to avoid repeated expressions, the following only uses the identification of phase deviation anomalies of 10 GPS satellites as a reference.

In an example, the above step A1 may include:

In the current epoch, 10 satellites in the GPS satellite system are observed, and the difference between the two phase deviation correction numbers of the adjacent epochs at each frequency point of each satellite in the 10 GPS satellites is calculated respectively. The above The difference can be expressed by Equation 1:

为相位偏差改正数之差，

为相位偏差改正数，i为频率号，s表示卫星端标识符，m表示卫星号。Among them, t _k , t _k+1 are the receiving time,

is the difference of phase deviation correction numbers,

is the phase deviation correction number, i is the frequency number, s is the identifier of the satellite terminal, and m is the satellite number.

Using the above formula 1, the 20 phase deviation correction number variations of the above 10 GPS satellites at 2 frequencies can be obtained.

Step A2, perform the first update step: calculate the first mean value and the first standard deviation of the first set, wherein 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 In the case of setting a threshold value, remove the phase deviation correction number variation with the largest difference from the first mean value in the first set, and obtain the remaining multiple phase deviation correction number variations; update according to the remaining multiple phase deviation correction number variations the first set, and return to calculate the first mean and the first standard deviation of the first set until the first standard deviation is smaller than the second preset threshold.

Combined with the above examples, step A2 is described in detail:

Calculate the first mean value of the above-mentioned 20 phase deviation correction numbers by formula 2, and calculate the first standard deviation, where formula 2 is expressed as:

If the first standard deviation is greater than the second preset threshold, such as 0.01, remove the 20 phase deviation correction changes and the phase deviation correction change with the largest first mean value difference, and recalculate the remaining phase deviation correction change The first standard deviation of the quantity is less than a second preset threshold.

Step A3, identifying the phase deviation correction number corresponding to the variation of the phase deviation correction number eliminated in the first updating step as abnormal.

Specifically, with reference to the above example, in step A3, if the absolute value of the difference between the change amount of any phase deviation correction number and the mean value is greater than the second preset threshold, the phase deviation correction number is considered unavailable.

The abnormality inspection method of the phase deviation correction number provided in the above-mentioned embodiment of the present application, because the mean value and standard deviation can reflect the stability and consistency between multiple data, so combined with the continuity of the phase deviation correction number itself, through the above two To determine whether it is abnormal, further set the abnormal threshold value of the mean and standard deviation, improve the precision and accuracy of identifying the abnormal phase deviation correction number, and effectively eliminate the abnormal data of the phase deviation correction number.

In some embodiments, where the target SSR includes phase offset corrections,

According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

Based on the sliding window, linear fitting is performed on multiple phase deviation correction numbers of multiple epochs of each satellite at each frequency point, and the fitting results are obtained;

According to the fitting result, determine whether there is slow drift anomaly in the phase deviation correction number;

In a case where it is determined that the phase deviation correction number has an abnormal slow drift, it is determined that the phase deviation correction number is abnormal.

The above-mentioned method for abnormal detection of phase deviation correction numbers obtains as many phase deviation correction numbers as possible based on the sliding window, and determines the interdependent quantitative relationship between multiple phase deviation correction numbers of consecutive epochs by linear fitting , predict the expected data through the current data, so as to compare the actual data with the expected data, and determine the phase deviation correction number with abnormal deviation in the actual data, so as to further improve the accuracy of abnormal identification of the phase deviation correction number.

In some embodiments, based on the sliding window, linear fitting is performed on multiple phase deviation correction numbers of multiple epochs for each satellite at each frequency point, and according to the fitting results, it is determined whether the phase deviation correction numbers are slow or not. Floating exceptions can include:

Step B1, perform linear fitting on multiple phase deviation correction numbers of multiple epochs for each satellite at each frequency point, and determine P fitting parameters, where P is an integer greater than 1.

It should be noted that, based on the sliding window, the phase deviation correction numbers of multiple epochs can be obtained, and the longer the sliding window is, the more phase deviation correction numbers can be obtained.

In an example, the above step B1 may include:

Using a sliding window of 1-2 minutes, the phase deviation correction numbers of the 2 frequency points of the 10 satellites are linearly fitted, as shown in formula 3:

b＝a ₀ +a ₁ t Formula 3

Among them, a ₀ is a constant, a ₁ is the trend item, and t is the fitting time.

Using formula 3, 20 fitting parameters of phase deviation correction numbers of 10 satellites and 2 frequency points can be obtained.

Step B2, execute the second update step: calculate the second mean and the second standard deviation of the second set, wherein the initial data of the second set is P fitting parameters; when the second standard deviation is greater than the third preset threshold In this case, remove the fitting parameter with the largest difference from the second mean in the second set to obtain the remaining multiple fitting parameters; update the second set according to the remaining multiple fitting parameters, and return to calculate the second set of the second set Two mean values and a second standard deviation until the second standard deviation is less than the third preset threshold.

Combined with the above examples, step B2 is described in detail below:

Calculate the second mean and second standard deviation of the above 20 fitting parameters, if the second standard deviation is greater than the third preset threshold, then remove the fitting parameter with the largest difference between the 20 fitting parameters and the second mean, and The second standard deviation of the remaining fitting parameters is recalculated until the second standard deviation is smaller than the third preset threshold.

Step B3, identifying the phase deviation correction numbers corresponding to the fitting parameters eliminated in the second updating step as abnormal.

Specifically, with reference to the above example, in step B3, if the absolute value of the difference between any fitting parameter and the second mean value is greater than a third preset threshold, it is considered that the phase deviation correction value is not available.

In another example, if the absolute value of the difference between the fitting parameter of any phase deviation correction number and the second mean value is greater than 3 times the second standard deviation, it is considered that the phase deviation correction number is inconsistent with other correction numbers, And mark the phase deviation correction number as unavailable. Wherein, the above-mentioned 3 times is a proportional value determined by those skilled in the art based on empirical values, and it may also be other empirical values, which are not limited in this application.

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

According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

For the tropospheric delay corrections of two adjacent epochs at each frequency point for each satellite, perform the following steps respectively:

Calculate the difference between the tropospheric delay correction number in the Lth epoch and the tropospheric delay correction number in the L+1 epoch, and determine the change in the tropospheric delay correction number, where L is a positive integer;

In a case where the change amount of the tropospheric delay correction number is greater than the fourth preset threshold, it is determined that the tropospheric delay correction number of the L+1th epoch is abnormal.

In some embodiments, in the case where the tropospheric delay correction number variation is greater than the fourth preset threshold, and it is determined that the tropospheric delay correction number of the L+1th epoch is abnormal, the method may further include:

Remove the tropospheric delay correction for the L+1th epoch;

Calculate the difference between the tropospheric delay correction number of the Lth epoch and the tropospheric delay correction number of the L+2 epoch, and determine the variation of the first tropospheric delay correction number;

In a case where the change amount of the first tropospheric delay correction is not greater than the fourth preset threshold, it is determined that the tropospheric delay correction at the L+2th epoch is normal.

In an example, in the case of L=1, calculating the difference between the tropospheric delay correction number in the L epoch and the tropospheric delay correction number in the L+1 epoch, and determining the change amount of the tropospheric delay correction number may include:

Calculate the difference between the tropospheric delay correction number in the first epoch and the second epoch, and determine the above difference as the change in the tropospheric delay correction number; if the change in the tropospheric delay correction number is greater than 0.02 (unit m), determine the first The tropospheric delay correction number in the second epoch is abnormal, otherwise, the tropospheric delay correction number in the second epoch is normal data.

The above-mentioned 0.02m is the fourth preset threshold determined by those skilled in the art based on empirical values, and the fourth preset threshold may also be other empirical values, which are not limited in this application.

The abnormality inspection method of the tropospheric delay correction number provided in the above-mentioned embodiment of the present application, because the troposphere has the continuity of time and space, so based on this characteristic, the difference between the tropospheric delay correction number of two adjacent epochs at each frequency point Calculate the difference between them, judge whether the tropospheric delay correction number is abnormal according to the above difference, and then set the abnormal threshold to improve the accuracy of abnormal identification.

In some embodiments, where the target SSR includes ionospheric delay corrections,

According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, which may include:

Calculate the difference between the two ionospheric delay correction numbers for each satellite in adjacent epochs, and determine the variation of M ionospheric delay correction numbers, where M is an integer greater than 1;

It should be noted that the ionosphere has time-space continuity, so the ionospheric delay corrections of two consecutive epochs are checked between epochs. In order to effectively identify specific satellites, each system needs to be processed separately. Assume that 10 GPS satellites can be observed at the current moment, and each satellite has an ionospheric delay correction number. The difference between the ionospheric delay corrections broadcasted twice in a row can be expressed by formula 4:

为电离层延迟改正数变化量，I^m为电离层延迟改正数。Among them, t _k , t _k+1 are the receiving time,

is the change amount of the ionospheric delay correction number, and I ^m is the ionospheric delay correction number.

Execute the third update step: calculate the third mean value and the third standard deviation of the third set, wherein the initial data of the third set is the amount of change of M ionospheric delay correction numbers; when the third standard deviation is greater than the fifth preset threshold In the case of , the ionospheric delay correction number variation with the largest difference between the third set and the third mean value is eliminated to obtain the remaining multi-phase ionospheric delay correction number variation; according to the remaining multiple ionospheric delay correction number changes Quantitatively update the third set, and return to calculate the third mean and the third standard deviation of the third set, until the third standard deviation is less than the fifth preset threshold;

Identifying the ionospheric delay correction number corresponding to the ionospheric delay variation eliminated in the third updating step as abnormal.

In the above-mentioned anomaly inspection method of the ionospheric correction number provided in the embodiment of the present application, since the ionosphere has the continuity of time and space, the difference between the ionospheric delay correction number of each satellite in two adjacent epochs is based on this characteristic. Calculate the difference, judge whether the ionospheric delay correction number is abnormal according to the above difference, and then set the abnormal threshold to improve the accuracy of abnormal identification.

In some embodiments, the SSR also includes an orbit correction number and a clock correction number, and the abnormal identification method of the state domain space correction number provided in the embodiment of the present application may also include:

Step C1, using tropospheric delay corrections, ionospheric delay corrections, satellite orbit corrections, satellite clock corrections and GNSS observation information to construct inter-satellite single-difference observation equations, and determine the posterior residual.

Step C2, when the post-test residual is greater than the sixth preset threshold, it is determined that the orbit correction and the clock correction of the corresponding satellite are abnormal.

In the method for identifying abnormality of the state domain space correction number provided by the above-mentioned embodiment of the present application, in addition to the correction number of the pseudorange deviation, the correction number of the phase deviation, the correction number of the tropospheric delay, and the correction number of the ionospheric delay, the SSR also includes the orbital correction number and the clock error The correction number, based on the GNSS observation data, establishes the SSR observation equation, and conducts mutual inspection of all SSR data through the observation equation, which can take into account the self-consistency between different SSR data during the correction number anomaly identification process, thereby improving the accuracy of SSR anomaly identification sex.

In the above step C1, in an example, using the tropospheric delay correction number, the ionosphere delay correction number, the satellite orbit correction number, the satellite clock error correction number and GNSS observation information, the inter-satellite single-difference observation equation is constructed, wherein the inter-satellite single-difference The difference observation equation equation can be expressed by Equation 5:

为相位缠绕，单位为周；T为对流层延迟。Wherein, i is the frequency number, t _r is the clock difference of the user terminal, t ^s is the clock difference of the satellite terminal, L _i is the carrier phase corresponding to the i-th frequency point, and ρ is the distance between the phase center of the satellite terminal and the user terminal; λ _i is the wavelength, N _i is the integer ambiguity, I ₁ 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 winding, the unit is cycle; T is the tropospheric delay.

Considering that the clock difference t _r at the user end cannot be corrected directly, it is necessary to construct an inter-satellite single difference to eliminate it. Assuming that satellite m is selected as the reference star, the inter-satellite single-difference combination residual can be expressed by Equation 6 as:

Among them, n is a non-reference star mark.

Before linearizing Equation 6, first use the orbit correction number to correct the satellite orbit calculated by the broadcast ephemeris; use the clock error correction number to correct the satellite clock error calculation calculated by the broadcast ephemeris; Perform correction; use the ionospheric delay correction number to correct the ionospheric delay.

The carrier phase observations of 2 frequency points of 10 GPS satellites are currently observed. After constructing the inter-satellite single difference using only the carrier phase observation value of the first frequency point, 9 observation equations can be obtained, and the corresponding linearization can be obtained. The linearization matrix H, weight matrix P and residual matrix V of , which are expressed by formula 7, formula 8 and formula 10 respectively:

为非参考星的位置，1到9为非参考星的标号，X^s,m,Y^s,m,Z^s,m为m号参考星的位置。Among them, X _r , Y _r , Z _r are the positions of the user end,

is the position of the non-reference star, 1 to 9 is the label of the non-reference star, X ^s,m ,Y ^s,m ,Z ^s,m is the position of the m reference star.

Among them, p ₁ ... p ₉ is the weight ratio of each inter-satellite single-difference combination, which can be expressed by formula 9:

为每个星间单差组合的残差。in,

is the residual error of each inter-satellite single-difference combination.

Using least squares, check whether the post-test residuals are out of bounds. If the post-test residual exceeds a certain threshold, such as 0.03m, it is considered that the orbit correction and clock correction are abnormal.

It should be noted that all the preset thresholds in the embodiments of the present application are set by those skilled in the art based on empirical values, which is not limited in the present application.

Based on the same inventive concept as above, an embodiment of the present application provides an abnormality identification device for a correction number in a state domain space.

As shown in Figure 3, the embodiment of the present application provides an abnormal identification device for the state domain space correction number, and the device may include:

The obtaining module 301 is used to obtain the state domain space correction number SSR and the GNSS observation information of the global satellite navigation system, wherein the SSR includes the pseudorange deviation correction number, the phase deviation correction number, the troposphere delay correction number and the ionosphere delay correction number, and the GNSS observation Information includes pseudorange, carrier phase, Doppler observations, and broadcast ephemeris;

The inspection module 302 is used to inspect the target SSR according to the difference of the target SSR in consecutive epochs to determine whether there is an abnormal SSR, wherein the target SSR is a pseudorange deviation correction number, a phase deviation correction number, and a tropospheric delay correction number. At least one of the number and the ionospheric delay correction number.

The abnormal identification device of the state domain space correction number provided by the embodiment of the present application is used to obtain SSR and GNSS observation information, and judge whether it is abnormal according to the data characteristics of the SSR in different epochs. , is 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. Therefore, the data nature of the SSR is fully considered, and it can detect abnormal deviations of a small order of magnitude and improve improved the accuracy of SSR anomaly recognition.

In some embodiments, the verification module can specifically be used for:

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

Calculate the difference between the pseudorange deviation correction number of the K epoch and the K+1 epoch pseudorange deviation correction number, and determine the variation of the pseudorange deviation correction number, wherein K is a positive integer;

In a case where the change amount of the pseudorange deviation correction number is greater than the first preset threshold, it is determined that the pseudorange deviation correction number of the K+1th epoch is abnormal.

In the inspection module provided in the embodiment of the present application, in the process of abnormal inspection of the pseudorange deviation correction number, due to the stability of the pseudorange deviation correction number is good, there will not be too much change in a short period of time, so based on This feature calculates the difference between the pseudo-range bias corrections of two adjacent epochs for each satellite at each frequency point, and judges whether the pseudo-range bias corrections are abnormal according to the above difference, and then sets the abnormal threshold , to improve the accuracy of anomaly recognition.

In some embodiments, the verification module can specifically be used for:

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 variations, where N is an integer greater than 1;

Execute the first update step: calculate the first mean value and the first standard deviation of the first set, wherein the initial data of the first set is the variation of the N phase deviation correction numbers; when the first standard deviation is greater than In the case of the second preset threshold value, the phase deviation correction number change amount with the largest difference from the first mean value in the first set is eliminated to obtain the remaining multiple phase deviation correction number change amounts; according to the remaining multiple The amount of phase deviation correction number update the first set, and return the first mean value and first standard deviation of the calculated first set, until the first standard deviation is less than a second preset threshold;

The phase deviation correction number corresponding to the change amount of the phase deviation correction number eliminated in the first updating step is identified as abnormal.

In the inspection module provided in the embodiment of this application, in the process of abnormal inspection of the phase deviation correction number, since the mean value and standard deviation can reflect the stability and consistency between multiple data, it is combined with the phase deviation correction number itself The continuity of the above two statistical data is used to judge whether it is abnormal, and the abnormal threshold of the mean value and standard deviation is further set to improve the precision and accuracy of identifying the abnormal phase deviation correction number, and effectively eliminate the abnormal data of the phase deviation correction number.

In some embodiments, the verification module can specifically be used for:

Based on the sliding window, linear fitting is performed on multiple phase deviation correction numbers of multiple epochs of each satellite at each frequency point, and the fitting results are obtained;

According to the fitting result, determine whether there is slow drift anomaly in the phase deviation correction number;

In a case where it is determined that the phase deviation correction number has an abnormal slow drift, it is determined that the phase deviation correction number is abnormal.

In some embodiments, the verification module can specifically be used for:

Carry out linear fitting to multiple phase deviation correction numbers of multiple epochs of each satellite at each frequency point respectively, and determine P fitting parameters, wherein, P is an integer greater than 1;

Execute the second update step: calculate the second mean and the second standard deviation of the second set, wherein the initial data of the second set is the P fitting parameters; when the second standard deviation is greater than the third preset In the case of setting a threshold value, removing the fitting parameter with the largest difference from the second mean value in the second set to obtain the remaining multiple fitting parameters; updating the second set according to the remaining multiple fitting parameters , and returning to the calculation of the second mean and second standard deviation of the second set, until the second standard deviation is less than a third preset threshold;

The phase deviation correction numbers corresponding to the fitting parameters eliminated in the second update step are identified as abnormal.

The inspection module provided in the embodiment of this application obtains as many phase deviation correction numbers as possible based on the sliding window in the process of abnormal inspection of the phase deviation correction numbers, and determines the number of consecutive epochs by performing linear fitting on them. The interdependent quantitative relationship between multiple phase deviation correction numbers, the current data is used to predict the expected data, so that the actual data can be compared with the expected data, and the phase deviation correction numbers with abnormal deviations in the actual data can be determined to further improve Accuracy of phase deviation correction number anomaly identification.

In some embodiments, the verification module can specifically be used for:

Calculate the difference between the tropospheric delay correction number in the Lth epoch and the tropospheric delay correction number in the L+1 epoch, and determine the change in the tropospheric delay correction number, where L is a positive integer;

In a case where the change amount of the tropospheric delay correction number is greater than the fourth preset threshold, it is determined that the tropospheric delay correction number of the L+1th epoch is abnormal.

In the inspection module provided in the embodiment of this application, in the process of abnormal inspection of the tropospheric delay correction number, since the troposphere has the continuity of time and space, based on this characteristic, two adjacent historical data at each frequency point of each satellite are analyzed. Calculate the difference between the tropospheric delay corrections of each element, judge whether the pseudorange deviation correction is abnormal according to the above difference, and then set the abnormal threshold to improve the accuracy of abnormal recognition.

In some embodiments, the verification module can specifically be used for:

Calculate the difference between the two ionospheric delay correction numbers for each satellite in adjacent epochs, and determine the variation of M ionospheric delay correction numbers, where M is an integer greater than 1;

Execute the third update step: calculate the third mean value and the third standard deviation of the third set, wherein the initial data of the third set is the amount of change of M ionospheric delay correction numbers; when the third standard deviation is greater than the fifth preset threshold In the case of , the ionospheric delay correction number variation with the largest difference between the third set and the third mean value is eliminated to obtain the remaining multi-phase ionospheric delay correction number variation; according to the remaining multiple ionospheric delay correction number changes Quantitatively update the third set, and return to calculate the third mean and the third standard deviation of the third set, until the third standard deviation is less than the fifth preset threshold;

Identifying the ionospheric delay correction number corresponding to the ionospheric delay variation eliminated in the third update step as abnormal.

In the inspection module provided in the embodiment of the present application, in the process of abnormal inspection of the ionospheric delay correction number, since the ionosphere has temporal-spatial continuity, based on this characteristic, each satellite under two adjacent epochs Calculate the difference between the ionospheric delay correction numbers, and judge whether the ionospheric delay correction number is abnormal according to the above difference, and then set the abnormal threshold to improve the accuracy of abnormal identification.

In some embodiments, the verification module can specifically be used for:

Using tropospheric delay corrections, ionospheric delay corrections, satellite orbit corrections, satellite clock corrections, and GNSS observation information, construct inter-satellite single-difference observation equations, and determine the posterior residuals of each satellite through the least square method;

In the case where the post-test residual is greater than the sixth preset threshold, it is determined that the orbit correction number and the clock error correction number of the corresponding satellite are abnormal.

The inspection module provided in the embodiment of the present application is used to establish an SSR observation equation based on GNSS observation data, and perform mutual inspection on all SSR data through the observation equation. Therefore, the self-consistency between different SSR data can be considered in the process of abnormal identification of correction numbers, thereby improving the accuracy of abnormal identification of SSR.

Other details of the device for identifying abnormalities of correction numbers in state domain space according to the embodiment of the present application are similar to the method for identifying abnormalities of correction numbers in state domain space according to the embodiment of the present application described above in conjunction with FIG. 1 , and will not be repeated here.

FIG. 4 shows a schematic diagram of the hardware structure of the abnormality of the correction number in the state domain space provided by the embodiment of the present application.

The method and device for identifying anomalies of correction numbers in state domain space described in conjunction with FIG. 1 and FIG. 3 according to the embodiments of the present application may be implemented by an abnormal identification device for correction numbers in state domain space. Fig. 4 is a schematic diagram showing a hardware structure 400 of an anomaly identification device for state domain space correction numbers according to an embodiment of the invention.

The device for identifying abnormality of correction numbers in the state domain space may include a processor 401 and a memory 402 storing computer program instructions.

Specifically, the above-mentioned processor 401 may include a central processing unit (Central Processing Unit, CPU), or a specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application .

Memory 402 may include mass storage for data or instructions. By way of example and not limitation, memory 402 may include a hard disk drive (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 (Universal Serial Bus, USB) drive or two or more Combinations of multiple of the above. 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. The storage 402 can be inside or outside the comprehensive gateway disaster recovery device.

In one example, the memory 402 may be a read only memory (Read Only Memory, ROM). In one example, the ROM can be mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or both. A combination of one or more of the above.

The processor 401 reads and executes the computer program instructions stored in the memory 402 to realize the methods/steps S101 to S102 in the embodiment shown in FIG. The effect is not described here for brevity.

In an example, the device for identifying anomalies of correction numbers in the state domain space may further include a communication interface 403 and a bus 410 . Wherein, as shown in FIG. 4 , the processor 401 , the memory 402 , and the communication interface 403 are connected through a bus 410 to complete mutual communication.

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

The bus 410 includes hardware, software or both, and couples the components of the online data traffic charging device to each other. By way of example and not limitation, a bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Super Transmission (Hyper Transport, HT) interconnect, Industry Standard Architecture (Industry Standard Architecture, ISA) bus, InfiniBand interconnect, Low Pin Count (LPC) bus, memory bus, Micro Channel Architecture (MCA) bus, peripheral component interconnect PCI bus, PCI-Express (PCI-X) bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association local (VLB) bus, or other suitable bus or a combination of two or more of these combination. Bus 410 may comprise one or more buses, where appropriate. Although the embodiments of this application describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.

The abnormal identification device of the state domain space correction number provided by the embodiment of the present application, on the one hand, during the abnormal identification process of the pseudorange deviation correction number, phase deviation correction number, tropospheric delay correction number and ionospheric delay correction number, according to the SSR in The data characteristics under different epochs can judge whether it is abnormal, instead of relying on other information related to SSR to indirectly judge whether it is abnormal, and then can detect abnormal deviations of small orders of magnitude, and improve the ability to identify abnormalities in SSR. precision. On the other hand, the SSR observation equation is established based on GNSS observation data, and all SSR data are cross-checked through the observation equation, which can take into account the self-consistency between different SSR data in the process of abnormal correction number identification, thereby improving the accuracy of SSR anomaly identification. sex.

In addition, in combination with the method for identifying anomalies in the state domain space correction numbers in the above embodiments, the embodiments of the present application may provide a computer storage medium for implementation. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, any one of the abnormality identification methods of the state domain space correction number in the above-mentioned embodiments is implemented.

It is to be understood that the application is not limited to the specific configurations and processes described above and shown in the figures. For conciseness, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art may make various changes, modifications and additions, or change the order of the steps after understanding the spirit of the present application.

The functional blocks shown in the structural block diagrams described above may be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an Application Specific Integrated Circuit (ASIC), appropriate firmware, a plug-in, a function card, and the like. When implemented in software, the elements of the present application are the programs or code segments employed to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted over transmission media or communication links by data signals carried in carrier waves. "Machine-readable medium" may include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, Radio Frequency (RF) links, etc. . Code segments may be downloaded via a computer network such as the Internet, an Intranet, or the like.

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, the present application is not limited to the order of the above steps, that is, the steps may be performed in the order mentioned in the embodiment, or may be different from the order in the embodiment, or several steps may be performed simultaneously.

Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present disclosure. It will be understood that each block of the flowchart and/or block diagrams, and combinations of blocks in the flowchart and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine such that execution of these instructions via the processor of the computer or other programmable data processing apparatus enables Implementation of the functions/actions specified in one or more blocks of the flowchart and/or block diagrams. Such processors may be, but are not limited to, general purpose processors, special purpose processors, application specific processors, or field programmable logic circuits. It can also be understood that each block in the block diagrams and/or flowcharts and combinations of blocks in the block diagrams and/or flowcharts can also be realized by dedicated hardware for performing specified functions or actions, or can be implemented by dedicated hardware and Combination of computer instructions to achieve.

The above is only a specific implementation of the present application, and those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process of the above-described systems, modules and units can refer to the foregoing method embodiments The corresponding process in , will not be repeated here. It should be understood that the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the application, and these modifications or replacements should cover all Within the protection scope of this application.

Claims (11)

1. An abnormal identification method of a state domain space correction number, characterized in that the method comprises: Obtain the state domain space correction number SSR and the global satellite navigation system GNSS observation information, wherein the SSR includes pseudorange deviation correction number, phase deviation correction number, troposphere delay correction number and ionosphere delay correction number, and the GNSS observation information includes Pseudorange, carrier phase, Doppler observations, and broadcast ephemeris; According to the difference of the target SSR in consecutive epochs, the target SSR is checked to determine whether there is an abnormal SSR, wherein the target SSR is the pseudorange deviation correction number, the phase deviation correction number, the tropospheric delay correction number and At least one of the ionospheric delay corrections. 2. The method according to claim 1, wherein, in the case where the target SSR includes the pseudorange deviation correction number, According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, including: For the pseudorange bias corrections of two adjacent epochs for each satellite at each frequency point, perform the following steps respectively: Calculate the difference between the pseudorange deviation correction number of the K epoch and the K+1 epoch pseudorange deviation correction number, and determine the variation of the pseudorange deviation correction number, wherein K is a positive integer; In a case where the change amount of the pseudorange deviation correction number is greater than a first preset threshold, it is determined that the pseudorange deviation correction number of the K+1th epoch is abnormal. 3. The method according to claim 1, wherein, in the case where the target SSR includes the phase deviation correction number, According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, including: 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 variations, where N is an integer greater than 1; Executing the first update step: calculating the first mean and the first standard deviation of the first set, wherein the initial data of the first set is the variation of the N phase deviation correction numbers; when the first standard deviation is greater than In the case of the second preset threshold value, the phase deviation correction number change amount with the largest difference from the first mean value in the first set is eliminated to obtain the remaining multiple phase deviation correction number change amounts; according to the remaining multiple The amount of phase deviation correction number update the first set, and return the first mean value and first standard deviation of the calculated first set, until the first standard deviation is less than a second preset threshold; The phase deviation correction number corresponding to the change amount of the phase deviation correction number eliminated in the first updating step is identified as abnormal. 4. The method according to claim 1, wherein, in the case where the target SSR includes the phase deviation correction number, The said target SSR is tested according to the difference of the target SSR in consecutive epochs to determine whether there is an abnormal SSR, including: based on the sliding window, multiple epochs for each satellite at each frequency point A plurality of said phase deviation correction numbers are linearly fitted to obtain a fitting result; According to the fitting result, it is determined whether there is a slow drift anomaly in the phase deviation correction number; If it is determined that the slow drift anomaly exists in the phase deviation correction number, it is determined that the phase deviation correction number is abnormal. 5. The method according to claim 4, wherein, based on the sliding window, a plurality of phase deviation correction numbers of multiple epochs of each satellite at each frequency point are respectively linearly fitted, According to the fitting result, it is determined whether there is a slow drift anomaly in the phase deviation correction number, including: Carry out linear fitting to the multiple phase deviation correction numbers of multiple epochs of each satellite at each frequency point respectively, and determine P fitting parameters, where P is an integer greater than 1; Execute the second update step: calculate the second mean and the second standard deviation of the second set, wherein the initial data of the second set is the P fitting parameters; when the second standard deviation is greater than the third preset In the case of setting a threshold value, removing the fitting parameter with the largest difference from the second mean value in the second set to obtain the remaining multiple fitting parameters; updating the second set according to the remaining multiple fitting parameters , and returning to the calculation of the second mean and second standard deviation of the second set, until the second standard deviation is less than a third preset threshold; The phase deviation correction numbers corresponding to the fitting parameters eliminated in the second updating step are identified as abnormal. 6. The method according to claim 1, wherein, in the case where the target SSR includes the tropospheric delay correction number, According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, including: For the tropospheric delay corrections of two adjacent epochs at each frequency point for each satellite, perform the following steps respectively: Calculate the difference between the tropospheric delay correction number in the Lth epoch and the tropospheric delay correction number in the L+1 epoch, and determine the change in the tropospheric delay correction number, where L is a positive integer; In a case where the variation of 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. 7. The method according to claim 1, wherein, in the case where the target SSR includes the ionospheric delay correction number, According to the difference of the target SSR in consecutive epochs, the target SSR is tested to determine whether there is an abnormal SSR, including: Calculating the difference between the two ionospheric delay corrections for each satellite in adjacent epochs, and determining the variation of M ionospheric delay corrections, where M is an integer greater than 1; Execute the third update step: calculate the third mean and the third standard deviation of the third set, wherein the initial data of the third set is the variation of the M ionospheric delay correction numbers; in the third standard deviation In the case of greater than the fifth preset threshold value, the ionospheric delay correction number variation with the largest difference from the third mean value in the third set is eliminated to obtain the remaining multi-phase ionospheric delay correction number variation; according to the The remaining multiple ionospheric delay correction number changes update the third set, and return the third mean value and third standard deviation of the calculated third set, until the third standard deviation is less than the fifth preset threshold; Identifying the ionospheric delay correction number corresponding to the ionospheric delay variation eliminated in the third updating step as abnormal. 8. The method according to claim 1, wherein the SSR also includes an orbit correction number and a clock correction number, and the method also includes: Using the tropospheric delay correction number, the ionosphere delay correction number, the satellite orbit correction number, the satellite clock error correction number and the GNSS observation information to construct an inter-satellite single-difference observation equation, through the least square method, determine the a posteriori residuals for each satellite; In a case where the post-test residual is greater than a sixth preset threshold, it is determined that the orbit correction and the clock correction of the corresponding satellite are abnormal. 9. An abnormal identification device of a state domain space correction number, characterized in that the device comprises: The acquisition module is used to obtain the state domain space correction number SSR and the global satellite navigation system GNSS observation information, wherein the SSR includes a pseudorange deviation correction number, a phase deviation correction number, a tropospheric delay correction number and an ionospheric delay correction number, so The GNSS observation information includes pseudorange, carrier phase, Doppler observation and broadcast ephemeris; The inspection module is used to inspect the target SSR according to the difference of the target SSR in consecutive epochs to determine whether there is an abnormal SSR, wherein the target SSR is a pseudorange deviation correction number, a phase deviation correction number, At least one of a tropospheric delay correction and an ionospheric delay correction. 10. A device for identifying abnormality of a state domain space correction number, 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 The method for identifying abnormality of the correction number in the state domain space according to any one of claims 1-8 is realized. 11. A computer storage medium, characterized in that computer program instructions are stored on the computer storage medium, and when the computer program instructions are executed by a processor, the state domain space according to any one of claims 1-8 is realized Anomaly identification method for correction numbers.

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