System for obtaining dynamic attitude of ship body through high-frequency GNSS data

Abstract

The invention relates to the technical field of data analysis, in particular to a system for solving the dynamic attitude of a ship body through high-frequency GNSS data, and the system comprises a data preprocessing module, a data pre-calculation module, a data integration module and a data analysis module. A target data set is established through a data preprocessing module, a theoretical rotation angle and an actual error value of a target vector are obtained through a data pre-calculation module, the theoretical rotation angle and the actual error value are combined through a data integration module to obtain an analysis array, and data analysis is performed on the analysis array through a data analysis module. According to the method, a theoretical rotation angle is obtained, related types of the theoretical rotation angle and an actual error value are obtained, an error accounting model is established, a theoretical error value is obtained according to the error accounting model, an inclination angle is calculated and solved according to the theoretical error value, and the method is used for reflecting the inclination angle attitude of the ship body.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a system for obtaining a dynamic posture of a ship using high-frequency GNSS data. Background Art

[0002] The hull posture refers to the movement state of the hull when sailing. It is used to reflect the tilt angle and sway state of the hull. The hull posture plays an important role in navigation safety, transportation efficiency and the development of ship intelligence.

[0003] In the prior art, relevant information about the hull is usually obtained through inclinometers, spirit levels, etc., and the attitude information of the hull is obtained by processing the relevant information. However, the inclinometer has obvious disadvantages, such as slow dynamic response. When the hull attitude changes rapidly, it is impossible to capture the angle change in time, thereby causing a lag error. At the same time, in the case of impact interference, the measurement data of the inclinometer is prone to fluctuations or errors, and the accuracy is poor, and it is impossible to make a timely and effective response to the attitude of the hull. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose a system for obtaining the dynamic posture of a ship using high-frequency GNSS data.

[0005] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a system for obtaining the dynamic posture of a ship using high-frequency GNSS data, comprising:

[0006] As a further solution of the present invention, three target points are respectively arranged at three different positions of the hull, and a total station is used to respectively collect initial position information of the three target points, and initial data is established according to the three position information, and the initial data is uploaded to the data preprocessing module; the heights of the three target points are the same horizontal height;

[0007] A data preprocessing module is used to preprocess the initial data to obtain a standard vector set, analyze the standard vector set, and determine the target data set;

[0008] A data pre-calculation module is used to obtain a theoretical rotation angle according to a target data set and a measured rotation angle according to existing equipment, and to calculate an actual error value of a target vector according to the measured rotation angle and the theoretical rotation angle, wherein the theoretical rotation angle corresponds to the actual error value one by one;

[0009] A data integration module, taking the theoretical rotation angle and the corresponding actual error value as the first sub-item and the second sub-item respectively, and combining the first sub-item and the second sub-item to obtain an analysis array;

[0010] The data analysis module splits and arranges several analysis arrays to obtain reference sequences and feature sequences, performs data analysis on the reference sequences and feature sequences, obtains the correlation type between the theoretical rotation angle and the actual error value, establishes an error accounting model based on the correlation type, and calculates the inclination angle of the hull based on the error accounting model.

[0011] As a further solution of the present invention, the method for preprocessing the initial data is:

[0012] Three target point vectors formed by the three target point coordinates are calculated respectively by a vector calculation method, and the three target point vectors are combined to obtain a standard vector set;

[0013] The modulus length of the target vector is calculated according to the vector modulus formula to obtain the vector modulus length, the vector modulus lengths are compared, the maximum vector modulus length is obtained and the corresponding target vector is marked as a non-target vector, and the non-target vector is eliminated from the standard vector set;

[0014] The vector modulus corresponding to the target vector is marked as the target modulus L, the corresponding two coordinates in the target vector are obtained and marked as the target coordinates, the height difference between the two target coordinates is obtained by calculating the difference between the height values ​​of the two target coordinates, and the height difference is marked as the target height h. The target data set {L, h} is constructed according to the target modulus L and the target height h.

[0015] As a further solution of the present invention, the method for calculating the actual error value is:

[0016] The actual error value SWθ of the target vector is calculated by the training formula SWθ=Cθ-Jθ; Cθ is the standard rotation angle, Jθ is the theoretical rotation angle, and is calculated by the inverse sine function Jθ=arcsin(h / L);

[0017] Several different postures of the hull are set, that is, different measurement rotation angles, and the above method of calculating the actual error value is repeated to obtain several analysis arrays.

[0018] As a further solution of the present invention, the method for constructing the error calculation model is:

[0019] Numbering a plurality of analysis arrays, assigning the analysis array numbers to the first sub-item and the second sub-item in the analysis array, splitting the first sub-item and the second sub-item in the analysis array, and sorting the first sub-item from small to large to obtain a reference sequence;

[0020] The number of the first sub-item in the reference sequence is obtained and used as the reference number, the reference number sorting is obtained, the second sub-item is sorted according to the reference number sorting, a feature sequence is obtained, and the correlation type between the first sub-item and the second sub-item is determined.

[0021] As a further solution of the present invention, the first sequence element of the feature sequence is set as the starting element, the second sequence element of the feature sequence is used as the target element, the size relationship between the target element and the starting element is compared, the second sequence element is then updated as the starting element, the third sequence element is updated as the target element, the size relationship between the target element and the starting element is compared, the feature sequence is traversed in sequence, and a comparison model is established according to the size relationship between the starting element and the target element;

[0022] The expression of the comparison model is: BX(i, i+1) is the comparative analysis value between the starting element and the target element, (i, i+1) represents the comparative relationship between the starting element and the target element, i is the element number of the feature sequence, i is a positive integer, i∈[1, N-1], and N is the total number of elements in the feature sequence.

[0023] As a further solution of the present invention, the comparative analysis value BX(i, i+1) is monitored. If the analysis value BX(i, i+1) is the same, the traversal is continued; if BX(i, i+1) changes, the sequence element with the element number i+1 corresponding to the current comparative analysis value BX(i, i+1) is marked as an abnormal element, until the traversal of the feature sequence is completed and the traversal result is obtained;

[0024] By judging the formula The monitoring and analysis value JX is calculated and data analysis is performed on the monitoring and analysis value JX; if the monitoring and analysis value JX is not less than the positive correlation threshold ZY and the monitoring and analysis value JX is not greater than the negative correlation threshold FY, a decision instruction is generated; if the monitoring and analysis instruction JX∈(FY, ZY), it is determined that the first sub-item and the second sub-item are not related.

[0025] 7. A system for obtaining the dynamic posture of a ship using high-frequency GNSS data according to claim 6, characterized in that: according to the decision instruction, two sequence elements adjacent to the abnormal element are marked as decision elements, the decision elements are arranged from small to large according to the element number i of the decision element to obtain a decision sequence, and it is judged whether the sequence elements in the decision sequence are linearly changed. If they are linearly changed, the correlation type between the first sub-item and the second sub-item is judged to be linearly correlated; otherwise, it is judged that the first sub-item and the second sub-item are unrelated.

[0026] As a further solution of the present invention, the calculation method of the correlation coefficient is:

[0027] By using the year-on-year parameter formula Calculate the ratio parameter between two adjacent elements in the reference sequence and two adjacent elements in the feature sequence Wherein, CKh and TZj represent the sequence element of the reference sequence and the sequence element of the characteristic sequence respectively, h and j represent the sequence element number of the reference sequence and the sequence element number of the characteristic sequence respectively, m=j, m∈[1,N-1];

[0028] Calculated by the year-on-year coefficient formula; The year-on-year coefficient TX of the first sub-item and the second sub-item;

[0029] The year-on-year parameter The difference between the reference value and the year-on-year coefficient TX is calculated to obtain the reference value, and the minimum reference value is obtained and its corresponding year-on-year parameter Mark as target year-on-year parameter, mark two adjacent elements in the reference sequence corresponding to the target year-on-year parameter and two adjacent elements in the feature sequence as the first key element and the second key element respectively, calculate the mean of the two first key elements and use the result as the first standard reference sub-item, calculate the mean of the two second key elements and use the result as the second standard reference sub-item;

[0030] By geometric formula The standard error value Bθ is calculated; FC is the first standard reference sub-item, and SC is the second standard reference sub-item.

[0031] As a further solution of the present invention, if the correlation type is no correlation, multiple second sub-items of the same first sub-item are obtained through testing, and the average of the multiple second sub-items is calculated to obtain an average second sub-item, and the first sub-item and the average second sub-item are recorded; wherein, each time the corresponding second sub-item of the same first sub-item is obtained, the average second sub-item is updated; and the average second sub-item is marked as the characteristic error value Tθ e , e is the number of the first sub-item, e is a positive integer, e∈[1, N-1];

[0032] According to the first sub-term and the characteristic error value Tθ e Establish a mapping function F(e), and obtain the characteristic error value Tθ of the first sub-item according to the mapping function F(e) e ;

[0033] The expression of the error accounting model is:

[0034] LWθ is the theoretical error value;

[0035] The solved inclination angle Sθ of the hull is calculated by the solving formula Sθ=Jθ+LWθ, and the inclination posture of the hull is reflected according to the solved inclination angle Sθ.

[0036] Compared with the existing technology, the advantages of the present invention are: a target data set is established through a data preprocessing module, the theoretical rotation angle and the actual error value of the target vector are obtained through a data precalculation module, the theoretical rotation angle and the actual error value are combined through a data integration module to obtain an analysis array, data analysis is performed on the analysis array through a data analysis module to obtain the correlation type of the theoretical rotation angle and the actual error value, and an error accounting model is established, the theoretical error value is obtained according to the error accounting model, and the inclination angle is calculated and solved according to the theoretical error value to reflect the inclination attitude of the hull, which effectively solves the problems of poor accuracy and low frequency of traditional hull attitude determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a module flow chart of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0039] Reference Figure 1 , a system for obtaining the dynamic posture of a ship using high-frequency GNSS data, including three target points, which are respectively set at three different positions of the ship, a total station for respectively collecting the initial position information of the three target points, establishing initial data according to the three position information, and uploading the initial data to a data preprocessing module; the heights of the three target points are the same horizontal height;

[0040] The data preprocessing module is used to preprocess the initial data to obtain a standard vector set, analyze the standard vector set, and determine the target data set; it should be noted that the lateral spacing between the three target points is not less than 2 / 3 of the ship's width, and the longitudinal spacing is not less than 1 / 2 of the ship's length;

[0041] The method for preprocessing the initial data is:

[0042] The three target vectors formed by the three target coordinates are calculated respectively by a vector calculation method, and the three target vectors are combined to obtain a standard vector set; the initial position coordinates of the target are realized by obtaining satellite positioning data with a frequency of 1559 MHz to 1610 MHz by three high-frequency GNSS receivers, and the target vector is calculated by a vector calculation formula. Satellite positioning and vector calculation are both existing technologies and will not be described in detail here;

[0043] The modulus length of the target vector is calculated according to the vector modulus formula to obtain the vector modulus length, the vector modulus lengths are compared, the maximum vector modulus length is obtained and the corresponding target vector is marked as a non-target vector, and the non-target vector is eliminated from the standard vector set; the remaining target vectors in the standard vector set are marked as target vectors; the vector modulus length calculation can be implemented using the existing calculation formula, which will not be repeated here;

[0044] The vector modulus corresponding to the target vector is marked as the target modulus L, the corresponding two coordinates in the target vector are obtained and marked as the target coordinates, the height difference between the two target coordinates is obtained by performing a difference calculation on the height values ​​of the two target coordinates, the height difference is marked as the target height h, and the target data set {L, h} is constructed according to the target modulus L and the target height h;

[0045] A data pre-calculation module is used to obtain a theoretical rotation angle according to a target data set and a measured rotation angle according to existing equipment, and to calculate an actual error value of a target vector according to the measured rotation angle and the theoretical rotation angle, wherein the theoretical rotation angle corresponds to the actual error value one by one;

[0046] A data integration module, taking the theoretical rotation angle and the corresponding actual error value as the first sub-item and the second sub-item respectively, and combining the first sub-item and the second sub-item to obtain an analysis array;

[0047] The method to calculate the actual error value is:

[0048] The actual error value SWθ of the target vector is calculated by the training formula SWθ=Cθ-Jθ; Cθ is the standard rotation angle, Jθ is the theoretical rotation angle, and is calculated by the inverse sine function Jθ=arcsin(h / L);

[0049] Set several different postures of the hull, that is, different measurement rotation angles, and repeat the above method of calculating the actual error value to obtain several analysis arrays;

[0050] The data analysis module splits and arranges several analysis arrays to obtain reference sequences and feature sequences, performs data analysis on the reference sequences and feature sequences, obtains the correlation type between the theoretical rotation angle and the actual error value, establishes an error accounting model according to the correlation type, and calculates the inclination angle of the hull according to the error accounting model;

[0051] The method to construct the error accounting model is:

[0052] S101, numbering a plurality of analysis arrays, assigning the analysis array numbers to the first sub-item and the second sub-item in the analysis array, splitting the first sub-item and the second sub-item in the analysis array, and sorting the first sub-item from small to large to obtain a reference sequence;

[0053] Obtaining the number of the first sub-item in the reference sequence and using it as the reference number, obtaining the reference number sorting, sorting the second sub-item according to the reference number sorting, obtaining the feature sequence, and determining the correlation type between the first sub-item and the second sub-item;

[0054] S102, setting the first sequence element of the feature sequence as the starting element, setting the second sequence element of the feature sequence as the target element, comparing the size relationship between the target element and the starting element, then updating the second sequence element as the starting element, updating the third sequence element as the target element, comparing the size relationship between the target element and the starting element, completing the traversal of the feature sequence in sequence, and establishing a comparison model according to the size relationship between the starting element and the target element;

[0055] The expression of the comparison model is: BX(i, i+1) is the comparative analysis value between the starting element and the target element, (i, i+1) represents the comparative relationship between the starting element and the target element, i is the element number of the feature sequence, i is a positive integer, i∈[1, N-1], N is the total number of elements in the feature sequence; it should be noted that the total number of elements in the reference sequence is the same as the total number of elements in the feature sequence;

[0056] Monitor the comparative analysis value BX(i, i+1). If the analysis value BX(i, i+1) is the same, continue to traverse; if BX(i, i+1) changes, mark the sequence element with element number i+1 corresponding to the current comparative analysis value BX(i, i+1) as an abnormal element, until the feature sequence traversal is completed and the traversal result is obtained;

[0057] By judging the formula Calculate the monitoring and analysis value JX, and perform data analysis on the monitoring and analysis value JX; if the monitoring and analysis value JX is not less than the positive correlation threshold ZY, and the monitoring and analysis value JX is not greater than the negative correlation threshold FY, then generate a decision instruction; if the monitoring and analysis index JX∈(FY, ZY), then it is determined that the first sub-item and the second sub-item are not related; the positive correlation threshold ZY and the negative correlation threshold FY are both obtained based on historical test big data, the positive correlation threshold ZY can be 0.8, and the negative correlation threshold FY can be 0.2;

[0058] According to the decision instruction, the two sequence elements adjacent to the abnormal element are marked as decision elements, and the decision elements are arranged from small to large according to the element number i of the decision element to obtain a decision sequence, and it is determined whether the sequence elements in the decision sequence are linearly changed. If they are linearly changed, the correlation type between the first sub-item and the second sub-item is determined to be linearly related; otherwise, it is determined that the first sub-item and the second sub-item are not related; it should be noted that the linear change can be increasing or decreasing;

[0059] If the correlation type is linear correlation, the sequence elements of the reference sequence are calculated to obtain a reference ratio sequence, the sequence elements of the characteristic sequence are actuated to obtain a characteristic ratio sequence, and the correlation coefficient between the first sub-item and the second sub-item is calculated based on the reference ratio sequence and the characteristic ratio sequence;

[0060] The calculation method of correlation coefficient is:

[0061] By using the year-on-year parameter formula Calculate the ratio parameter between two adjacent elements in the reference sequence and two adjacent elements in the feature sequence Wherein, CKh and TZj represent the sequence element of the reference sequence and the sequence element of the characteristic sequence respectively, h and j represent the sequence element number of the reference sequence and the sequence element number of the characteristic sequence respectively, m=j, m∈[1,N-1];

[0062] Calculated by the year-on-year coefficient formula; The year-on-year coefficient TX of the first sub-item and the second sub-item;

[0063] The year-on-year parameter The difference between the reference value and the year-on-year coefficient TX is calculated to obtain the reference value, and the minimum reference value is obtained and its corresponding year-on-year parameter Mark as target year-on-year parameter, mark two adjacent elements in the reference sequence corresponding to the target year-on-year parameter and two adjacent elements in the feature sequence as the first key element and the second key element respectively, calculate the mean of the two first key elements and use the result as the first standard reference sub-item, calculate the mean of the two second key elements and use the result as the second standard reference sub-item;

[0064] By geometric formula The standard error value Bθ is calculated; FC is the first standard reference sub-item, and SC is the second standard reference sub-item;

[0065] If the correlation type is no correlation, multiple second sub-items of the same first sub-item are obtained through testing, and the average of the multiple second sub-items is calculated to obtain the average second sub-item, and the first sub-item and the average second sub-item are recorded; wherein, each time the corresponding second sub-item of the same first sub-item is obtained, the average second sub-item is updated; and the average second sub-item is marked as the characteristic error value Tθ e , e is the number of the first sub-item, e is a positive integer, e∈[1, N-1];

[0066] According to the first sub-term and the characteristic error value Tθ e Establish a mapping function F(e), and obtain the characteristic error value Tθ of the first sub-item according to the mapping function F(e) e ;

[0067] The expression of the error accounting model is:

[0068] LWθ is the theoretical error value;

[0069] The solved inclination angle Sθ of the hull is calculated by the solving formula Sθ=Jθ+LWθ, and the inclination posture of the hull is reflected according to the solved inclination angle Sθ.

[0070] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A system for obtaining dynamic posture of a ship using high-frequency GNSS data, characterized in that: It includes three target points, which are respectively set at three different positions of the hull, and a total station for respectively collecting initial position information of the three target points, establishing initial data according to the three position information, and uploading the initial data to a data preprocessing module; The three targets are at the same level. A data preprocessing module is used to preprocess the initial data to obtain a standard vector set, analyze the standard vector set, and determine the target data set; A data pre-calculation module is used to obtain a theoretical rotation angle according to a target data set and a measured rotation angle according to existing equipment, and to calculate an actual error value of a target vector according to the measured rotation angle and the theoretical rotation angle, wherein the theoretical rotation angle corresponds to the actual error value one by one; A data integration module, taking the theoretical rotation angle and the corresponding actual error value as the first sub-item and the second sub-item respectively, and combining the first sub-item and the second sub-item to obtain an analysis array; The data analysis module splits and arranges several analysis arrays to obtain reference sequences and feature sequences, performs data analysis on the reference sequences and feature sequences, obtains the correlation type between the theoretical rotation angle and the actual error value, establishes an error accounting model based on the correlation type, and calculates the inclination angle of the hull based on the error accounting model.

2. The system for obtaining dynamic posture of a ship using high-frequency GNSS data according to claim 1, characterized in that: The method for preprocessing the initial data is: Three target point vectors formed by the three target point coordinates are calculated respectively by a vector calculation method, and the three target point vectors are combined to obtain a standard vector set; The modulus length of the target vector is calculated according to the vector modulus formula to obtain the vector modulus length, the vector modulus lengths are compared, the maximum vector modulus length is obtained and the corresponding target vector is marked as a non-target vector, and the non-target vector is eliminated from the standard vector set; The vector modulus corresponding to the target vector is marked as the target modulus L, the corresponding two coordinates in the target vector are obtained and marked as the target coordinates, the height difference between the two target coordinates is obtained by calculating the difference between the height values ​​of the two target coordinates, and the height difference is marked as the target height h. The target data set {L, h} is constructed according to the target modulus L and the target height h.

3. The system for obtaining dynamic posture of a ship using high-frequency GNSS data according to claim 2, characterized in that: The method to calculate the actual error value is: The actual error value SWθ of the target vector is calculated by the training formula SWθ=Cθ-Jθ; Cθ is the standard rotation angle, Jθ is the theoretical rotation angle, and is calculated by the inverse sine function Jθ=arcsin(h / L); Several different postures of the hull are set, that is, different measurement rotation angles, and the above method of calculating the actual error value is repeated to obtain several analysis arrays.

4. The system for obtaining dynamic posture of a ship using high-frequency GNSS data according to claim 3, characterized in that: The method to construct the error accounting model is: Numbering a plurality of analysis arrays, assigning the analysis array numbers to the first sub-item and the second sub-item in the analysis array, splitting the first sub-item and the second sub-item in the analysis array, and sorting the first sub-item from small to large to obtain a reference sequence; The number of the first sub-item in the reference sequence is obtained and used as the reference number, the reference number sorting is obtained, the second sub-item is sorted according to the reference number sorting, a feature sequence is obtained, and the correlation type between the first sub-item and the second sub-item is determined.

5. The system for obtaining dynamic posture of a ship using high-frequency GNSS data according to claim 4, characterized in that: The first sequence element of the feature sequence is set as the starting element, the second sequence element of the feature sequence is used as the target element, the size relationship between the target element and the starting element is compared, the second sequence element is updated as the starting element, the third sequence element is updated as the target element, the size relationship between the target element and the starting element is compared, the feature sequence is traversed in sequence, and a comparison model is established according to the size relationship between the starting element and the target element; The expression of the comparison model is: BX(i, i+1) is the comparative analysis value between the starting element and the target element, (i, i+1) represents the comparative relationship between the starting element and the target element, i is the element number of the feature sequence, i is a positive integer, i∈[1, N-1], and N is the total number of elements in the feature sequence.

6. The system for obtaining dynamic posture of a ship using high-frequency GNSS data according to claim 5, characterized in that: Monitor the comparative analysis value BX(i, i+1). If the analysis value BX(i, i+1) is the same, continue to traverse; if BX(i, i+1) changes, mark the sequence element with element number i+1 corresponding to the current comparative analysis value BX(i, i+1) as an abnormal element, until the feature sequence traversal is completed and the traversal result is obtained; By judging the formula The monitoring and analysis value JX is calculated and data analysis is performed on the monitoring and analysis value JX; if the monitoring and analysis value JX is not less than the positive correlation threshold ZY and the monitoring and analysis value JX is not greater than the negative correlation threshold FY, a decision instruction is generated; if the monitoring and analysis instruction JX∈(FY, ZY), it is determined that the first sub-item and the second sub-item are not related.

7. The system for obtaining dynamic posture of a ship using high-frequency GNSS data according to claim 6, characterized in that: According to the decision instruction, the two sequence elements adjacent to the abnormal element are marked as decision elements, and the decision elements are arranged from small to large according to the element number i of the decision element to obtain a decision sequence, and it is determined whether the sequence elements in the decision sequence are linearly changed. If they are linearly changed, the correlation type between the first sub-item and the second sub-item is determined to be linearly correlated; otherwise, it is determined that the first sub-item and the second sub-item are not correlated.

8. The system for obtaining dynamic posture of a ship using high-frequency GNSS data according to claim 7, characterized in that: The calculation method of the correlation coefficient is: By using the year-on-year parameter formula Calculate the ratio parameter between two adjacent elements in the reference sequence and two adjacent elements in the feature sequence Wherein, CKh and TZj represent the sequence element of the reference sequence and the sequence element of the characteristic sequence respectively, h and j represent the sequence element number of the reference sequence and the sequence element number of the characteristic sequence respectively, m=j, m∈[1,N-1]; Calculated by the year-on-year coefficient formula; The year-on-year coefficient TX of the first sub-item and the second sub-item; The year-on-year parameter The difference between the reference value and the year-on-year coefficient TX is calculated to obtain the reference value, and the minimum reference value is obtained and its corresponding year-on-year parameter Mark as target year-on-year parameter, mark two adjacent elements in the reference sequence corresponding to the target year-on-year parameter and two adjacent elements in the feature sequence as the first key element and the second key element respectively, calculate the mean of the two first key elements and use the result as the first standard reference sub-item, calculate the mean of the two second key elements and use the result as the second standard reference sub-item; By geometric formula The standard error value Bθ is calculated; FC is the first standard reference sub-item, and SC is the second standard reference sub-item.

9. The system for obtaining dynamic posture of a ship using high-frequency GNSS data according to claim 8, characterized in that: If the correlation type is no correlation, multiple second sub-items of the same first sub-item are obtained through testing, and the average of the multiple second sub-items is calculated to obtain the average second sub-item, and the first sub-item and the average second sub-item are recorded; wherein, each time the corresponding second sub-item of the same first sub-item is obtained, the average second sub-item is updated; and the average second sub-item is marked as the characteristic error value Tθ e , e is the number of the first sub-item, e is a positive integer, e∈[1, N-1]; According to the first sub-term and the characteristic error value Tθ e Establish a mapping function F(e), and obtain the characteristic error value Tθ of the first sub-item according to the mapping function F(e) e ; The expression of the error accounting model is: LWθ is the theoretical error value; The solved inclination angle Sθ of the hull is calculated by the solving formula Sθ=Jθ+LWθ, and the inclination posture of the hull is reflected according to the solved inclination angle Sθ.

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