GNSS differential positioning data quality control method, device, equipment and medium

Abstract

The invention provides a GNSS (Global Navigation Satellite System) differential positioning data quality control method, device, equipment and medium. The method comprises the following steps: acquiring a mean error rms and an ambiguity check rate RATIO after baseline calculation in a GNSS differential positioning process; determining a discrete discourse domain value nrms of the rms according to the discrete discourse domain Nrms of the median error, and determining a discrete discourse domain value nRATIO of the RATIO according to the discrete discourse domain NRATIO of the ambiguity check rate; according to the nrms and the nRATIO, adjusting the elevation angle of the satellite; and carrying out baseline solution again based on the adjusted satellite elevation angle. According to the method provided by the invention, after baseline calculation is carried out, the satellite elevation angle is adjusted according to the median error rms and the ambiguity check rate RATIO, calculation precision control is carried out, baseline calculation is carried out again based on the adjusted satellite elevation angle, and GNSS differential positioning precision is ensured.

Description

Technical Field

[0001] This application relates to the technical field of GNSS differential positioning, and particularly to a method, device, equipment, and medium for controlling the quality of GNSS differential positioning data. Background Art

[0002] GNSS (Global Navigation Satellite System) differential positioning can provide users with millimeter-level positioning services. The principle is to place two receivers in the monitoring area respectively, keep one of the positions relatively stationary, and synchronously observe more than 4 satellites, then the relative position of the other receiver in the protocol terrestrial coordinate system can be calculated.

[0003] However, in practical applications, due to factors such as the quality of the receiver and the observation environment, the original observation data of the receiver may have cycle slips and noise, which will lead to a decrease in the calculation accuracy, and even abnormal data may occur, affecting the accuracy of GNSS differential positioning data quality control, and further affecting the GNSS differential positioning accuracy. Summary of the Invention

[0004] To solve one of the above technical defects, this application provides a method, device, equipment, and medium for controlling the quality of GNSS differential positioning data.

[0005] In the first aspect of this application, a method for controlling the quality of GNSS differential positioning data is provided. The method includes:

[0006] Obtain the root mean square error rms and the ambiguity test rate RATIO after baseline solution in the GNSS differential positioning process;

[0007] According to the discrete domain N of the root mean square error rms Determine the discrete domain value n of rms rms , and, according to the discrete domain N of the ambiguity test rate RATIO Determine the discrete domain value n of RATIO RATIO ;

[0008] According to n rms and n RATIO , adjust the satellite elevation angle;

[0009] Re-perform baseline solution based on the adjusted satellite elevation angle.

[0010] In the second aspect of this application, a device for controlling the quality of GNSS differential positioning data is provided. The device includes:

[0011] An acquisition module, configured to acquire the root mean square error rms and the ambiguity test rate RATIO after baseline solution in the GNSS differential positioning process;

[0012] A determination module, configured to determine, according to the discrete domain N of the mean square error rms the discrete domain value n of the rms obtained by the acquisition module rms , and, according to the discrete domain N of the ambiguity test rate RATIO determine the discrete domain value n of the RATIO obtained by the acquisition module RATIO ;

[0013] An adjustment module, configured to adjust the satellite elevation angle according to n determined by the determination module rms and n RATIO ;

[0014] A control module, configured to control a re-baseline solution based on the satellite elevation angle adjusted by the adjustment module.

[0015] In a third aspect of the present application, an electronic device is provided, including:

[0016] A memory;

[0017] A processor; and

[0018] A computer program;

[0019] wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method described in the first aspect above.

[0020] In a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored; the computer program is executed by a processor to implement the method described in the first aspect above.

[0021] The present application provides a GNSS differential positioning data quality control method, apparatus, device, and medium. The method includes: obtaining the root mean square error rms and the ambiguity test rate RATIO after baseline solution during GNSS differential positioning; determining the discrete domain value n of the rms according to the discrete domain N of the mean square error rms and determining the discrete domain value n of the RATIO according to the discrete domain N of the ambiguity test rate rms , and, according to the discrete domain N of the ambiguity test rate RATIO ; adjusting the satellite elevation angle according to n RATIO and n rms ; re-performing baseline solution based on the adjusted satellite elevation angle. The method provided by the present application adjusts the satellite elevation angle according to the root mean square error rms and the ambiguity test rate RATIO after baseline solution, controls the solution accuracy, and re-performs baseline solution based on the adjusted satellite elevation angle, ensuring the GNSS differential positioning accuracy. RATIO BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0023] Figure 1 is a schematic flow chart of a GNSS differential positioning data quality control method provided by an embodiment of the present application;

[0024] Figure 2 is a schematic implementation structure diagram of a GNSS differential positioning data quality control method provided by an embodiment of the present application;

[0025] Figure 3 is a schematic structure diagram of a GNSS differential positioning data quality control device provided by an embodiment of the present application. Specific Embodiments

[0026] In order to make the technical solutions and advantages in the embodiments of the present application clearer, the following further describes the exemplary embodiments of the present application in detail with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than an exhaustive list of all embodiments. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0027] In the process of implementing the present application, the inventors found that for GNSS differential positioning, two receivers are respectively placed in the monitoring area, and one of them is kept relatively stationary, and more than 4 satellites are synchronously observed, then the relative position of the other receiver in the protocol earth coordinate system can be solved. However, in actual applications, due to factors such as the quality of the receiver and the observation environment, cycle slips and noises may occur in the original observation data of the receiver, which will lead to a decrease in the solution accuracy and even abnormal data may occur, which will affect the accuracy of GNSS differential positioning data quality control and further affect the GNSS differential positioning accuracy.

[0028] To address the above problems, an embodiment of the present application provides a GNSS differential positioning data quality control method, device, equipment, and medium. The method includes: obtaining the root mean square error rms and the ambiguity test rate RATIO after baseline solution in the GNSS differential positioning process; according to the discrete domain N of the root mean square error rms determine the discrete domain value n of rms rms , and, according to the discrete domain N of the ambiguity test rate RATIO determine the discrete domain value n of RATIO RATIO ; according to n rms and n RATIO, adjust the satellite elevation angle; re - perform baseline solution based on the adjusted satellite elevation angle. The method provided in this application adjusts the satellite elevation angle according to the root - mean - square error (rms) and the ambiguity test ratio (RATIO) after baseline solution to control the solution accuracy, and re - performs baseline solution based on the adjusted satellite elevation angle, ensuring the GNSS differential positioning accuracy.

[0029] See Figure 1 , this embodiment provides a method for controlling the quality of GNSS differential positioning data, and the implementation process of this method is as follows:

[0030] 101. Obtain the root - mean - square error (rms) and the ambiguity test ratio (RATIO) after baseline solution during GNSS differential positioning.

[0031] Among them, rms is generally greater than 0, and the unit is m. According to the distribution law of rms, it can be assumed that the value range of rms is:

[0032] RATIO is generally greater than 1. According to the distribution law of RATIO, it can be assumed that the value range of RATIO is:

[0033]

[0034] In addition, the value range of the satellite elevation angle Ag used in the baseline solution process is [8, 25] (i.e., Ag ∈ [8, 25]), and generally Ag = 15 can be set during the initial solution.

[0035] 102. According to the discrete domain N of the root - mean - square error rms Determine the discrete domain value n of rms rms , and, according to the discrete domain N of the ambiguity test ratio RATIO Determine the discrete domain value n of RATIO RATIO .

[0036] Step 102 includes two processes. One is the process of determining the discrete domain value n of rms according to the discrete domain N of the root - mean - square error rms Determine the discrete domain value n of rms rms , and one is the process of determining the discrete domain value n of RATIO according to the discrete domain N of the ambiguity test ratio RATIO Determine the discrete domain value n of RATIO RATIO The process.

[0037] Among them, the discrete domain N of the root - mean - square error rms And the discrete domain N of the ambiguity test ratio RATIO Can be the same or different.

[0038] The following will explain the two processes separately.

[0039] · According to the discrete domain N of the root - mean - square error rmsDetermine the discrete domain value n of rms rms process

[0040] Taking the discrete domain N of the mean square error rms ={-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5} as an example, the process is as follows: Determine the mapping value m of rms in N rms m = 10×(rms - 0.5). Determine n rms as the integer value of m rms rms

[0041] This example does not limit the rounding method, such as rounding by the method of rounding up or down, or ceiling or floor, etc.

[0042] For example, establish N rms ={-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5}, the mapping relationship from rms to N rms is: m rms = 10×(rms - 0.5), n rms is the rounded integer value of m rms

[0043] · Determine the discrete domain value n of RATIO according to the discrete domain N of the ambiguity test rate RATIO process RATIO

[0044] Taking the discrete domain N of the ambiguity test rate RATIO ={-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5} as an example, the process is as follows: Determine the mapping value m of RATIO in N RATIO m = RATIO - 5. Determine n RATIO as the integer value of m RATIO RATIO

[0045] This example does not limit the rounding method, such as rounding by the method of rounding up or down, or ceiling or floor, etc.

[0046] For example, establish N RATIO ={-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5}, the mapping relationship from RATIO to N RATIO is: m RATIO = RATIO - 5, n RATIO is the rounded integer value of m RATIO

[0047] 103. Adjust the satellite elevation angle according to n rms and n RATIO ​​​​​​​​

[0048] The implementation process of step 103 is as follows:

[0049] 103-1. Determine n rms The corresponding medium error fuzzy condition, and determine n RATIO The corresponding ambiguity test rate fuzzy condition.

[0050] Step 103-1 also includes two processes. One is the process of determining the medium error fuzzy condition corresponding to n rms The corresponding medium error fuzzy condition, and one is the process of determining n RATIO The corresponding ambiguity test rate fuzzy condition.

[0051] The following is an explanation of the two processes respectively.

[0052] ·n rms The corresponding medium error fuzzy condition is determined through steps 201 to 203.

[0053] 201. Obtain the fuzzy sets corresponding to each pre-set medium error fuzzy condition.

[0054] Taking N rms ={-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5} as an example, the pre-set medium error fuzzy conditions can be: the fuzzy condition of large medium error, the fuzzy condition of medium medium error, and the fuzzy condition of small medium error.

[0055] The fuzzy set corresponding to the fuzzy condition of large medium error

[0056] The fuzzy set corresponding to the fuzzy condition of medium medium error

[0057] The fuzzy set corresponding to the fuzzy condition of small medium error

[0058] That is to say, the pre-set medium error fuzzy conditions can be: the fuzzy condition of large medium error (representing a relatively large medium error), the fuzzy condition of medium medium error (representing a medium medium error), and the fuzzy condition of small medium error (representing a relatively small medium error). The fuzzy sets corresponding to each condition are:

[0059] In step 201, the following will be obtained

[0060] 202. According to the fuzzy sets corresponding to each medium error fuzzy condition, determine the membership degree of n rms Relative to each pre-set medium error fuzzy condition

[0061] Among them, x is the pre-set medium error fuzzy condition identifier. That is, x is PT, Z or NT.

[0062] 203, according to to determine n rms the corresponding medium error fuzzy condition.

[0063] For example, in step 101, rms = 1.0696 is obtained. In step 2, the obtained rms is mapped to N rms after n rms = 5. This n rms belongs to the membership degree 1 corresponding to the membership degree of 5 (that is the last element in this n rms belongs to the membership degree 0 corresponding to the membership degree of 5 (that is the last element in this n rms belongs to the membership degree 0 corresponding to the membership degree of 5 (that is the last element in Then determine that the medium error fuzzy condition corresponding to n rms is the fuzzy condition corresponding to the large medium error (representing a relatively large medium error).

[0064] ·n RATIO the fuzzy degree test rate fuzzy condition corresponding to is determined through steps 301 to 303.

[0065] 301, obtain the fuzzy sets corresponding to the pre-set fuzzy degree test rate fuzzy conditions.

[0066] Taking N RATIO = {-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5} as an example, the pre-set fuzzy degree test rate fuzzy conditions can be: the fuzzy condition with a large fuzzy degree test rate, the fuzzy condition with a medium fuzzy degree test rate, and the fuzzy condition with a small fuzzy degree test rate.

[0067] The fuzzy set corresponding to the fuzzy condition with a large fuzzy degree test rate

[0068] The fuzzy set corresponding to the fuzzy condition with a medium fuzzy degree test rate

[0069] The fuzzy set corresponding to the fuzzy condition with a small ambiguity verification rate

[0070] That is to say, the preset fuzzy conditions for the ambiguity verification rate are: the fuzzy condition with a large ambiguity verification rate (representing a relatively large ambiguity verification rate), the fuzzy condition with a medium ambiguity verification rate (representing a medium ambiguity verification rate), and the fuzzy condition with a small ambiguity verification rate (representing a relatively small ambiguity verification rate). The fuzzy sets corresponding to each condition are:

[0071] In step 301,

[0072] 302. According to the fuzzy sets corresponding to each fuzzy condition of the ambiguity verification rate, determine n RATIO The membership degrees relative to each preset fuzzy condition of the ambiguity verification rate

[0073] where y is the identifier of each preset fuzzy condition of the ambiguity verification rate. That is, y is PT, Z, or NT.

[0074] 303. According to Determine n RATIO The corresponding fuzzy condition of the ambiguity verification rate.

[0075] For example, in step 101, RATIO = 3.122 is obtained. In step 2, RATIO is mapped to N RATIO After that, n RATIO = -2. The membership degree of this n RATIO belonging to is 0.4 corresponding to the membership degree of -2 (i.e., the 4th element in), so it is determined that The membership degree of this n RATIO belonging to is 0.8 corresponding to the membership degree of -2 (i.e., the 4th element in), so it is determined that The membership degree of this n RATIO belonging to is 0.2 corresponding to the membership degree of -2 (i.e., the 4th element in), so it is determined that Then it is determined that the fuzzy condition corresponding to n RATIO is The fuzzy condition with a large ambiguity verification rate (representing a relatively large ambiguity verification rate) corresponding to The fuzzy condition corresponding to the ambiguity verification rate (representing a moderate ambiguity verification rate), The fuzzy condition corresponding to a small ambiguity verification rate (representing a relatively small ambiguity verification rate).

[0076] 103-2, according to n rms The corresponding mean error fuzzy condition and n RATIO Calculate the corresponding fuzzy value according to the corresponding ambiguity verification rate fuzzy condition.

[0077] Through step 103-1, n can be obtained rms The corresponding at least one mean error fuzzy condition. n RATIO The corresponding at least one ambiguity verification rate fuzzy condition. Each fuzzy condition corresponds to a fuzzy set. Then the implementation process of step 103-2 can be:

[0078] 401, obtain the membership degree of any mean error fuzzy condition u corresponding to n rms And the membership degree of any ambiguity verification rate fuzzy condition v corresponding to n And n RATIO The membership degree of any ambiguity verification rate fuzzy condition v corresponding to

[0079] For example, in step 103-1, it is determined that n rms The corresponding mean error fuzzy condition is the fuzzy condition of large mean error (representing a relatively large mean error), and its corresponding membership degree In step 103-1, it is determined that n RATIO The corresponding ambiguity verification rate fuzzy conditions are the fuzzy condition of large ambiguity verification rate (representing a relatively large ambiguity verification rate), the fuzzy condition of medium ambiguity verification rate (representing a moderate ambiguity verification rate), and the fuzzy condition of small ambiguity verification rate (representing a relatively small ambiguity verification rate). Among them, the membership degree corresponding to the fuzzy condition of large ambiguity verification rate The membership degree corresponding to the fuzzy condition of medium ambiguity verification rate The membership degree corresponding to the fuzzy condition of small ambiguity verification rate

[0080] 402, take As the corresponding fuzzy value ω of the mean error fuzzy condition u and the ambiguity verification rate fuzzy condition v uv .

[0081] Among them, ∧ is the minimum operator

[0082] For example, take As the corresponding fuzzy value ω of the fuzzy condition of large mean error and the fuzzy condition of large ambiguity verification rate PT,PT .

[0083] Take Determine the fuzzy value ω corresponding to the fuzzy condition with a large mean square error and the fuzzy condition in the ambiguity test rate PT,Z 。

[0084] The Determine the fuzzy value ω corresponding to the fuzzy condition with a large mean square error and the fuzzy condition with a small ambiguity test rate PT,NT 。

[0085] In addition, since the mean square error fuzzy condition and the ambiguity test rate fuzzy condition are preset, the fuzzy sets corresponding to each fuzzy condition are also known in advance. After setting, the first fuzzy relation matrix corresponding to each combination can be formed according to the combinations of various mean square error fuzzy conditions and ambiguity test rate fuzzy conditions, and the first fuzzy relation matrix is stored. When performing step 103-2, the fuzzy value can be obtained by looking up the corresponding first fuzzy relation matrix, thereby improving the execution efficiency of step 103-2. That is to say, if the first fuzzy relation matrix is pre-calculated and stored, the implementation process of step 103-2 can also be:

[0086] 501. Obtain all the first fuzzy relation matrices.

[0087] Among them, the first fuzzy relation matrix is formed based on each preset mean square error fuzzy condition and each preset ambiguity test rate fuzzy condition.

[0088] For example, before performing step 501, the first fuzzy relation matrix will be generated and stored first.

[0089] Since the fuzzy condition corresponds to the fuzzy set, the generation process of the first fuzzy relation matrix is: for any preset mean square error fuzzy condition x and any preset ambiguity test rate fuzzy condition y, form the first fuzzy relation matrix

[0090] Among them, is the fuzzy set corresponding to any mean square error fuzzy condition x, is the fuzzy set corresponding to any ambiguity test rate fuzzy condition y, T is the transpose operator, and ° is the composition operator of the fuzzy relation.

[0091] For example, the preset mean square error fuzzy conditions are: the fuzzy condition with a large mean square error, the fuzzy condition with a medium mean square error, and the fuzzy condition with a small mean square error. The preset ambiguity test rate fuzzy conditions are: the fuzzy condition with a large ambiguity test rate, the fuzzy condition with a medium ambiguity test rate, and the fuzzy condition with a small ambiguity test rate.

[0092] For the fuzzy condition with a large mean square error (the corresponding fuzzy set is ) and fuzzy conditions with a small ambiguity test rate (the corresponding fuzzy set is ), and the first fuzzy relation matrix formed thereby

[0093] Similarly, the first fuzzy relation matrix can also be formed for the fuzzy conditions with a large mean square error and the fuzzy conditions in the ambiguity test rate Form the first fuzzy relation matrix for the fuzzy conditions with a large mean square error and the fuzzy conditions with a large ambiguity test rate Form the first fuzzy relation matrix for the fuzzy conditions with a medium mean square error and the fuzzy conditions with a large ambiguity test rate Form the first fuzzy relation matrix for the fuzzy conditions with a medium mean square error and the fuzzy conditions in the ambiguity test rate Form the first fuzzy relation matrix for the fuzzy conditions with a medium mean square error and the fuzzy conditions with a small ambiguity test rate Form the first fuzzy relation matrix for the fuzzy conditions with a small mean square error and the fuzzy conditions with a large ambiguity test rate Form the first fuzzy relation matrix for the fuzzy conditions with a small mean square error and the fuzzy conditions in the ambiguity test rate Form the first fuzzy relation matrix for the fuzzy conditions with a small mean square error and the fuzzy conditions with a small ambiguity test rate

[0094] In step 501, all the first fuzzy relation matrices will be obtained.

[0095] 502. For any mean square error fuzzy condition u corresponding to n rms and any ambiguity test rate fuzzy condition v corresponding to n RATIO , select the target first fuzzy relation matrix formed based on any mean square error fuzzy condition u and any ambiguity test rate fuzzy condition v from all the first fuzzy relation matrices. And determine the fuzzy value ω rmS corresponding to n RATIO and n uv based on the target first fuzzy relation matrix.

[0096] For example, in step 103-1, it is determined that the mean square error fuzzy condition corresponding to n rms is the fuzzy condition with a large mean square error (representing a relatively large mean square error), and its corresponding membership degree In step 103-1, it is determined that the ambiguity test rate fuzzy condition corresponding to n RAtIO is the fuzzy condition with a large ambiguity test rate (representing a relatively large ambiguity test rate), the fuzzy condition in the ambiguity test rate (representing a moderate ambiguity test rate), and the fuzzy condition with a small ambiguity test rate (representing a relatively small ambiguity test rate).

[0097] Therefore, the first fuzzy relationship matrix formed based on the fuzzy condition with large mean error and the fuzzy condition with small fuzziness check rate is selected. Based on Determine the rms =5 and n RATIO = -2 corresponding to the fuzzy value ω PT,NT =0.2. Similarly, the first fuzzy relationship matrix formed based on the fuzzy condition with large mean error and the fuzzy condition in the fuzziness check rate is selected. Based on Determine the rms =5 and n RATIO = -2 corresponding to the fuzzy value ω PT,Z =0.8. Select the first fuzzy relationship matrix formed based on the fuzzy condition with large mean error and the fuzzy condition with large fuzziness check rate Based on Determine the rms =5 and n RATIO = -2 corresponding to the fuzzy value ω PT,PT =0.4.

[0098] 103-3, calculating the adjustment value and adjustment direction of the satellite altitude angle according to the fuzzy value and the corresponding satellite altitude angle adjustment rule.

[0099] Among them, the corresponding satellite altitude angle adjustment rule is based on n rms The corresponding mean error fuzzy condition and n RATIO The corresponding ambiguity check rate and fuzzy condition are jointly determined. In addition, the satellite elevation angle adjustment rule includes an adjustment direction and a fuzzy set. The fuzzy set in the satellite elevation angle adjustment rule is used to determine the adjustment value of the satellite elevation angle.

[0100] For example, the pre-set mean error ambiguity conditions are: ambiguity conditions with large mean error, ambiguity conditions with medium mean error, and ambiguity conditions with small mean error. The pre-set ambiguity check rate ambiguity conditions are: ambiguity conditions with large ambiguity check rate, ambiguity conditions with medium ambiguity check rate, and ambiguity conditions with small ambiguity check rate. Then if the discrete domain N of the satellite elevation angle is ΔAg ={-3,-2,-1,0,1,2,3}, then:

[0101] ①If n rmS The corresponding mean error fuzzy condition is the fuzzy condition with large mean error, and n RATIO The corresponding fuzzy condition of ambiguity check rate is a fuzzy condition with small ambiguity check rate, then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle positively, and based on the positive fuzzy set Determine the adjustment value of the satellite elevation angle. That is, if the rms is too large and the RATIO is too small, the satellite elevation angle change value is adjusted to be positive.

[0102] ② If n rms The corresponding medium error fuzzy condition is the fuzzy condition with a large medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition in the ambiguity test rate. Then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle positively and determine the adjustment value of the satellite elevation angle based on the positive medium fuzzy set That is, if the rms is on the high side and the RATIO is moderate, then adjust the change value of the satellite elevation angle to be positive medium.

[0103] ③ If n rms The corresponding medium error fuzzy condition is the fuzzy condition with a large medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate. Then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle positively and determine the adjustment value of the satellite elevation angle based on the positive small fuzzy set That is, if the rms is on the high side and the RATIO is on the high side, then adjust the change value of the satellite elevation angle to be positive small.

[0104] ④ If n rms The corresponding medium error fuzzy condition is the fuzzy condition with a medium medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a small ambiguity test rate. Then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle negatively and determine the adjustment value of the satellite elevation angle based on the negative medium fuzzy set That is, if the rms is moderate and the RATIO is on the low side, then adjust the change value of the satellite elevation angle to be negative medium.

[0105] ⑤ If n rmS The corresponding medium error fuzzy condition is the fuzzy condition with a medium medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a medium ambiguity test rate. Then the satellite elevation angle adjustment rule is to determine the adjustment value of the satellite elevation angle based on the zero fuzzy set That is, if the rms is moderate and the RATIO is moderate, then adjust the change value of the satellite elevation angle to be zero.

[0106] ⑥ If n rmS The corresponding medium error fuzzy condition is the fuzzy condition with a medium medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate. Then the satellite elevation angle adjustment rule is to determine the adjustment value of the satellite elevation angle based on the zero fuzzy set That is, if the rms is moderate and the RATIO is on the high side, then adjust the change value of the satellite elevation angle to be zero.

[0107] ⑦ If n rms The corresponding medium error fuzzy condition is the fuzzy condition with a small medium error, and n RATIOThe corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a small ambiguity test rate. Then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle negatively and based on the negative large fuzzy set. Determine the adjustment value of the satellite elevation angle. That is, if the rms is small and the RATIO is small, then adjust the change value of the satellite elevation angle to be negatively large.

[0108] ⑧ If n rms The corresponding mean square error fuzzy condition is the fuzzy condition with a small mean square error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a medium ambiguity test rate. Then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle negatively and based on the negative small fuzzy set. Adjust the satellite elevation angle. That is, if the rms is small and the RATIO is medium, then adjust the change value of the satellite elevation angle to be negatively small.

[0109] ⑨ If n rms The corresponding mean square error fuzzy condition is the fuzzy condition with a small mean square error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate. Then the satellite elevation angle adjustment rule is to determine the adjustment value of the satellite elevation angle based on the zero fuzzy set. Determine the adjustment value of the satellite elevation angle. That is, if the rms is small and the RATIO is large, then adjust the change value of the satellite elevation angle to be zero.

[0110] If fuzzy sets are used to represent the fuzzy conditions, the above relationships are shown in Table 1.

[0111] Table 1

[0112]

[0113]

[0114] In the specific implementation, there may be multiple fuzzy values obtained in step 103-2, and thus there can also be multiple corresponding ones.

[0115] In the case where there is at least one corresponding satellite elevation angle adjustment rule and at least one fuzzy value, the implementation process of step 103-3 is as follows:

[0116] 601. Determine the adjustment direction and fuzzy set in each corresponding satellite elevation angle adjustment rule.

[0117] For example, n rms The corresponding mean square error fuzzy condition is the fuzzy condition with a large mean square error (the corresponding fuzzy set is ), and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate (the corresponding fuzzy set is ) The ambiguity conditions in the ambiguity test rate (the corresponding fuzzy set is ) The ambiguity conditions with a small ambiguity test rate (the corresponding fuzzy set is ), and the corresponding satellite elevation angle adjustment rules are shown in Table 2:

[0118] Table 2

[0119]

[0120] 602. Determine the adjustment direction of the satellite elevation angle according to each adjustment direction, and Determine the adjustment value of the satellite elevation angle by taking three times the element in the discrete domain of the satellite elevation angle corresponding to the maximum element in the obtained set.

[0121] Among them, ω takes the small operator, and ∨ takes the large operator. i is the identification of the medium error ambiguity condition corresponding to n rms and j is the identification of the ambiguity test rate ambiguity condition corresponding to n RATIO . is the fuzzy set in the satellite elevation angle adjustment rule determined according to the medium error ambiguity condition i and the ambiguity test rate ambiguity condition j, and ω ij is the corresponding fuzzy value of the medium error ambiguity condition i and the ambiguity test rate ambiguity condition j.

[0122] For example, if ω PT,NT = 0.2, ω PT,Z = 0.8, ω PT,PT = 0.4, then:

[0123]

[0124]

[0125]

[0126] Vα = {0, 0, 0, 0.4, 0.5, 0.8, 0.5}.

[0127] The maximum element value of Vα is 0.8, and the corresponding element in the discrete domain N ΔAg = {-3, -2, -1, 0, 1, 2, 3} of the satellite elevation angle is 2. Therefore, the adjustment value of the satellite elevation angle is determined to be 2 * 3 = 6.

[0128] In addition, since the value range of Ag is [8, 25] (i.e., Ag ∈ [8, 25]), Ag can generally be set to 15 during the initial solution. Therefore, the value range of the satellite elevation angle adjustment value ΔAg is [-7, 10] (i.e., ΔAg ∈ [-7, 10]). If a discrete domain N ΔAg={-3, -2, -1, 0, 1, 2, 3}, the mapping relationship from ΔAg to N ΔAg is n ΔAg is the rounded integer value of m ΔAg Therefore, when determining the adjustment value of the satellite elevation angle, the maximum element in the obtained set is determined as n in the discrete domain of the satellite elevation angle and ΔAg = 3×n ΔAg , that is, 3 times the corresponding element in the discrete domain of the satellite elevation angle of the maximum element in the obtained set ΔAg is determined as the adjustment value of the satellite elevation angle

[0129]

[0130]

[0131] 701. Obtain all the second fuzzy relation matrices

[0132] Among them, the second fuzzy relation matrix is formed based on the pre-formed first fuzzy relation matrix and the pre-determined satellite elevation angle adjustment rules

[0133] The first fuzzy relation matrix is formed based on the pre-set various mean error fuzzy conditions and the pre-set various ambiguity test rate fuzzy conditions

[0133] The pre-determined satellite elevation angle adjustment rules are determined according to the pre-set various mean error fuzzy conditions and the pre-set various ambiguity test rate fuzzy conditions

[0134] Since the fuzzy conditions correspond to fuzzy sets, the generation process of the second fuzzy relation matrix is: for any pre-set mean error fuzzy condition x and any pre-set ambiguity test rate fuzzy condition y, form the second fuzzy relation matrix

[0135] where is the first fuzzy relation matrix formed by any mean error fuzzy condition x and any ambiguity test rate fuzzy condition y, is the horizontal expansion of, is the satellite elevation angle adjustment rule determined by any mean error fuzzy condition x and any ambiguity test rate fuzzy condition y, T is the transpose operator, and ° is the composition operator of fuzzy relations.

[0136] For example, if the first fuzzy relation matrix is According to the satellite elevation angle adjustment rule shown in Table 1, the fuzzy sets corresponding to the fuzzy condition with large mean error and the fuzzy condition with small ambiguity test rate are then the horizontal expansion of the corresponding second fuzzy relation matrix

[0137]

[0138] Similarly, other second fuzzy relation matrices formed by each first fuzzy relation matrix and the pre-determined satellite elevation angle adjustment rule can be obtained.

[0139] Step 701 is to obtain all the second fuzzy relation matrices.

[0140] 702, for any mean error fuzzy condition u corresponding to n rms and any ambiguity test rate fuzzy condition v corresponding to n RATIO select the corresponding target second fuzzy relation matrix from all the second fuzzy relation matrices.

[0141] Among them, the first fuzzy relation matrix forming the selected target second fuzzy relation matrix is formed based on any mean error fuzzy condition u and any ambiguity test rate fuzzy condition v, and the satellite elevation angle adjustment rule forming the selected target second fuzzy relation matrix is determined based on any mean error fuzzy condition u and any ambiguity test rate fuzzy condition v.

[0142] For example, n rms corresponds to the fuzzy condition with large mean error, n RATIO corresponds to the fuzzy condition with small ambiguity test rate. According to Table 1, the satellite elevation angle adjustment rule is to adjust the satellite elevation angle in the positive direction, and based on the positive large fuzzy set determine the adjustment value of the satellite elevation angle, and then the corresponding second fuzzy relation matrix obtained is

[0143] 703, determine the adjustment direction in each corresponding satellite elevation angle adjustment rule.

[0144] 704. Determine the adjustment direction of the satellite elevation angle according to each adjustment direction, and determine the adjustment value of the satellite elevation angle as three times the corresponding element in the satellite elevation angle discrete domain of the maximum element in the union of the selected target second fuzzy relation matrices.

[0145] 103-4. Adjust the satellite elevation angle by the adjustment value according to the adjustment direction.

[0146] For example, if the current satellite elevation angle Ag = 15, after obtaining the adjustment value of the satellite elevation angle as 6 and adjusting the satellite elevation angle in the positive direction, adjust Ag upward by 6 to become Ag = 21.

[0147] 104. Re-perform baseline solution based on the adjusted satellite elevation angle.

[0148] It should be noted that in addition to adjusting the satellite elevation angle, other adjustments can also be made, which are specifically determined based on the root mean square error rms and the ambiguity test ratio RATIO.

[0149] The method provided in this embodiment performs quality control based on the root mean square error rms and the ambiguity test ratio RATIO. Since the discriminant result of the baseline solution can be determined according to two indicators, namely the root mean square error rms and the ambiguity test ratio RATIO, if the two indicators do not meet the requirements, the parameters need to be adjusted and recalculated. Generally speaking, the smaller the root mean square error rms, the higher the credibility of the baseline solution result; the larger the ambiguity test ratio RATIO, the higher the credibility of the baseline solution.

[0150] Specifically, it is divided into three cases:

[0151] 1. The root mean square error rms meets the requirements, but the ambiguity test ratio RATIO does not meet the requirements.

[0152] In this case, generally, the observed data is not sufficient to determine the integer ambiguity and the final solution cannot be obtained. The reason may be that the observation time is not enough or the graph strength is poor. It can be tried to reduce the satellite elevation angle and recalculate, which may increase some original data.

[0153] 2. Both the root mean square error rms and the ambiguity test ratio RATIO do not meet the requirements.

[0154] At this time, there may be the influence of interference factors in the observed data, and the following can be considered:

[0155] a. Increase the satellite elevation angle

[0156] b. Decrease the data rejection rate

[0157] c. Change the reference satellite for the solution

[0158] 3. The ambiguity test ratio RATIO meets the requirements, but the root mean square error rms does not meet the requirements.

[0159] If the baseline is long and the observation time is also long, this situation is normal. It is also possible to increase the satellite elevation angle or delete the observation data with large residuals and recalculate.

[0160] The GNSS differential positioning data quality control method provided in this embodiment performs fuzzy processing on the root mean square error rms and the ambiguity test rate RATIO. According to three cases of the distribution of the root mean square error rms and the ambiguity test rate RATIO, fuzzy control rules are established respectively. Finally, the control parameters are defuzzified to obtain a new satellite elevation angle, and a new baseline solution is obtained after recalculation as the final positioning result.

[0161] In specific applications, it can be implemented through the Figure 2 shown structure. Set the satellite elevation angle Ag (e.g., Ag ∈ [8, 25], and the initial value of Ag is the default value, such as 15). After performing baseline solution through the solution engine based on Ag, the root mean square error rms and the ambiguity test rate RATIO are obtained (i.e., obtaining the root mean square error rms and the ambiguity test rate RATIO after baseline solution in the GNSS differential positioning process in step 101). Fuzzify the root mean square error rms and the ambiguity test rate RATIO (i.e., according to the discrete domain N of the root mean square error in step 102 rms determine the discrete domain value n of rms rms , and, according to the discrete domain N of the ambiguity test rate RATIO determine the discrete domain value n of RATIO RATIO ). Based on the pre-established fuzzy control rules and the fuzzified root mean square error rms and ambiguity test rate RATIO, perform fuzzy inference (i.e., adjusting the satellite elevation angle according to n rms and n RATIO in step 3). Based on the fuzzy inference result, defuzzify to obtain a new satellite elevation angle Ag and recalculate the baseline (i.e., performing baseline solution again based on the adjusted satellite elevation angle in step 104).

[0162] The GNSS differential positioning data quality control method provided in this embodiment adjusts the satellite elevation angle and recalculates the baseline according to three cases of the rms and RATIO distributions, so as to achieve the purpose of improving the solution quality.

[0163] This embodiment provides a GNSS differential positioning data quality control method, which obtains the root mean square error rms and the ambiguity test rate RATIO after baseline solution in the GNSS differential positioning process; according to the discrete domain N of the root mean square error rms determine the discrete domain value n of rms rms , and, according to the discrete domain N of the ambiguity test rate RATIO determine the discrete domain value n of RATIO RATIO ; according to nrms and n RATIO Adjust the satellite elevation angle; re - perform baseline solution based on the adjusted satellite elevation angle. The method provided in this embodiment, after performing baseline solution, adjusts the satellite elevation angle according to the root - mean - square error rms and the ambiguity test ratio RATIO, performs solution accuracy control, and re - performs baseline solution based on the adjusted satellite elevation angle, ensuring the GNSS differential positioning accuracy.

[0164] Based on the same inventive concept of the GNSS differential positioning data quality control method, this embodiment provides a GNSS differential positioning data quality control device. Refer to Figure 3 The device includes:

[0165] An acquisition module 301, configured to acquire the root - mean - square error rms and the ambiguity test ratio RATIO after baseline solution during GNSS differential positioning.

[0166] A determination module 302, configured to determine the discrete domain value n of the rms acquired by the acquisition module 301 according to the discrete domain N of the root - mean - square error rms and, determine the discrete domain value n of the RATIO acquired by the acquisition module 301 according to the discrete domain N of the ambiguity test ratio rms RATIO RATIO rms RATIO .

[0167] An adjustment module 303, configured to adjust the satellite elevation angle according to the n rms and n RATIO determined by the determination module 302.

[0168] A control module 304, configured to control re - performing baseline solution based on the satellite elevation angle adjusted by the adjustment module 303.

[0169] Wherein, N rms ={ - 5, - 4, - 3, - 2, - 1, 0, 1, 2, 3, 4, 5}.

[0170] The determination module 302 is configured to determine the mapping value m of rms in N rms m = 10×(rms - 0.5). Determine n rms to be the integer value of m rms rms RATIO .

[0171] Wherein, N RATIO ={ - 5, - 4, - 3, - 2, - 1, 0, 1, 2, 3, 4, 5}.

[0172] The determination module 302 is configured to determine the mapping value m of RATIO in N RATIO m = RATIO - 5. Determine n RATIO RATIo to be the integer value of mis the integer value of m RATIO The integer value taken

[0173] Among them, the adjustment module 303 is used to determine n rms The corresponding mean error ambiguity condition, and to determine n RATIO The corresponding ambiguity test rate ambiguity condition

[0174] According to n rms The corresponding mean error ambiguity condition and n RATIO The corresponding ambiguity test rate ambiguity condition to calculate the corresponding ambiguity value

[0175] According to the ambiguity value and the corresponding satellite elevation angle adjustment rule, calculate the adjustment value and adjustment direction of the satellite elevation angle. The corresponding satellite elevation angle adjustment rule is determined according to n rms The corresponding mean error ambiguity condition and n RATIO The corresponding ambiguity test rate ambiguity condition together

[0176] According to the adjustment direction, adjust the satellite elevation angle by the adjustment value

[0177] Among them, the adjustment module 303 is used to obtain the fuzzy sets corresponding to each pre-set mean error ambiguity condition

[0178] According to the fuzzy sets corresponding to each mean error ambiguity condition, determine n rms The membership degree relative to each pre-set mean error ambiguity condition Among them, x is the pre-set mean error ambiguity condition identifier

[0179] According to Determine n rms The corresponding mean error ambiguity condition

[0180] Among them, N rms ={-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5}

[0181] The pre-set mean error ambiguity conditions include: the ambiguity condition with large mean error, the ambiguity condition with medium mean error, and the ambiguity condition with small mean error

[0182] The fuzzy set corresponding to the ambiguity condition with large mean error

[0183] The fuzzy set corresponding to the ambiguity condition with medium mean error

[0184] The fuzzy set corresponding to the ambiguity condition with small mean error

[0185] Among them, the adjustment module 303 is configured to obtain the fuzzy sets corresponding to the pre-set fuzzy conditions for each ambiguity test rate.

[0186] Determine n according to the fuzzy sets corresponding to the fuzzy conditions for each ambiguity test rate RATIO The membership degrees with respect to the pre-set fuzzy conditions for each ambiguity test rate Among them, y is the identifier of the pre-set fuzzy conditions for each ambiguity test rate.

[0187] According to Determine n RATIO The corresponding fuzzy condition for the ambiguity test rate

[0188] Among them, N RATIO ={-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5}.

[0189] The pre-set fuzzy conditions for the ambiguity test rate include: the fuzzy condition with a large ambiguity test rate, the fuzzy condition with a medium ambiguity test rate, and the fuzzy condition with a small ambiguity test rate.

[0190] The fuzzy set corresponding to the fuzzy condition with a large ambiguity test rate

[0191] The fuzzy set corresponding to the fuzzy condition with a medium ambiguity test rate

[0192] The fuzzy set corresponding to the fuzzy condition with a small ambiguity test rate

[0193] Among them, the fuzzy condition corresponds to the fuzzy set. n rms The at least one corresponding mean square error fuzzy condition. n RATIO The at least one corresponding fuzzy condition for the ambiguity test rate.

[0194] Among them, the adjustment module 303 is configured to obtain the membership degree of any mean square error fuzzy condition u corresponding to n rms And the membership degree of any ambiguity test rate fuzzy condition v corresponding to n And n RATIO The membership degree of any ambiguity test rate fuzzy condition v corresponding to

[0195] Take Determine as the fuzzy value ω corresponding to the mean square error fuzzy condition u and the ambiguity test rate fuzzy condition v uv , where ∧ is the minimum operator.

[0196] Among them, n rms The at least one corresponding mean square error fuzzy condition. nRATIO corresponding to at least one ambiguity test rate fuzzy condition.

[0197] Among them, the adjustment module 303 is used to obtain all the first fuzzy relation matrices. The first fuzzy relation matrices are formed based on the pre-set various mean error fuzzy conditions and the pre-set various ambiguity test rate fuzzy conditions.

[0198] For n rms any mean error fuzzy condition u corresponding to and n RATIO any ambiguity test rate fuzzy condition v corresponding to, select the target first fuzzy relation matrix formed based on any mean error fuzzy condition u and any ambiguity test rate fuzzy condition v from all the first fuzzy relation matrices. And determine the fuzzy value ω corresponding to n rms and n RATIO based on the target first fuzzy relation matrix. uv .

[0199] Among them, the satellite elevation angle adjustment rule includes an adjustment direction and a fuzzy set. The fuzzy set in the satellite elevation angle adjustment rule is used to determine the adjustment value of the satellite elevation angle.

[0200] The corresponding satellite elevation angle adjustment rule is at least one. The fuzzy value is at least one.

[0201] Among them, the adjustment module 303 is used to determine the adjustment direction and the fuzzy set in each corresponding satellite elevation angle adjustment rule.

[0202] Determine the adjustment direction of the satellite elevation angle according to each adjustment direction, and determine 3 times the element corresponding to the maximum element in the obtained set in the discrete domain of the satellite elevation angle as the adjustment value of the satellite elevation angle.

[0203] Among them, ∧ is the minimum operator, and ∨ is the maximum operator. i is the mean error fuzzy condition identifier corresponding to n rms and j is the ambiguity test rate fuzzy condition identifier corresponding to n RATIO . is the fuzzy set in the satellite elevation angle adjustment rule determined according to the mean error fuzzy condition i and the ambiguity test rate fuzzy condition j, and ω ij is the fuzzy value corresponding to the mean error fuzzy condition i and the ambiguity test rate fuzzy condition j.

[0204] Among them, the satellite elevation angle adjustment rule includes an adjustment direction and a fuzzy set. The fuzzy set in the satellite elevation angle adjustment rule is used to determine the adjustment value of the satellite elevation angle.

[0205] The corresponding satellite elevation angle adjustment rule is at least one. The fuzzy value is at least one.

[0206] Among them, the adjustment module 303 is used to

[0207] Obtain all second fuzzy relation matrices. The second fuzzy relation matrices are formed based on the pre-formed first fuzzy relation matrices and the pre-determined satellite elevation angle adjustment rules. The first fuzzy relation matrices are formed based on the pre-set medium error fuzzy conditions and the pre-set ambiguity test rate fuzzy conditions. The pre-determined satellite elevation angle adjustment rules are determined according to the pre-set medium error fuzzy conditions and the pre-set ambiguity test rate fuzzy conditions.

[0208] For n rms Any medium error fuzzy condition u corresponding to n and n RATIO Any ambiguity test rate fuzzy condition v corresponding to, select the corresponding target second fuzzy relation matrix among all second fuzzy relation matrices. Among them, the first fuzzy relation matrix forming the selected target second fuzzy relation matrix is formed based on any medium error fuzzy condition u and any ambiguity test rate fuzzy condition v, and the satellite elevation angle adjustment rule forming the selected target second fuzzy relation matrix is determined based on any medium error fuzzy condition u and any ambiguity test rate fuzzy condition v.

[0209] Determine the adjustment direction in each corresponding satellite elevation angle adjustment rule.

[0210] Determine the adjustment direction of the satellite elevation angle according to each adjustment direction, and determine the adjustment value of the satellite elevation angle as 3 times the corresponding element in the discrete domain of the satellite elevation angle of the maximum element in the union of the selected corresponding target second fuzzy relation matrices.

[0211] Among them, the fuzzy condition corresponds to the fuzzy set.

[0212] The device further includes: a first calculation module, which is used to form a first fuzzy relation matrix for any pre-set medium error fuzzy condition x and any pre-set ambiguity test rate fuzzy condition y

[0213] Among them, is the fuzzy set corresponding to any medium error fuzzy condition x, is the fuzzy set corresponding to any ambiguity test rate fuzzy condition y, T is the transpose operator, and ° is the composition operator of the fuzzy relation.

[0214] Among them, the fuzzy condition corresponds to the fuzzy set.

[0215] The device further includes: a second calculation module, which is used to form a second fuzzy relation matrix for any pre-set medium error fuzzy condition x and any pre-set ambiguity test rate fuzzy condition y

[0216] Among them, is the first fuzzy relation matrix formed by any medium error fuzzy condition x and any ambiguity test rate fuzzy condition y, is the horizontal expansion of is the satellite elevation angle adjustment rule determined by any medium error fuzzy condition x and any ambiguity test rate fuzzy condition y, T is the transpose operator, and ° is the composition operator of fuzzy relations.

[0217] Among them, the discrete universe of discourse N of the satellite elevation angle ΔAg = {-3, -2, -1, 0, 1, 2, 3}.

[0218] The preset medium error fuzzy conditions include: the fuzzy condition of large medium error, the fuzzy condition of medium medium error, and the fuzzy condition of small medium error. The preset ambiguity test rate fuzzy conditions include: the fuzzy condition of large ambiguity test rate, the fuzzy condition of medium ambiguity test rate, and the fuzzy condition of small ambiguity test rate.

[0219] If n rms corresponds to the fuzzy condition of large medium error, and n RATIO corresponds to the fuzzy condition of small ambiguity test rate, then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle positively, and the adjustment value of the satellite elevation angle is determined based on the positive large fuzzy set

[0220] If n rms corresponds to the fuzzy condition of large medium error, and n RATIO corresponds to the fuzzy condition of medium ambiguity test rate, then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle positively, and the adjustment value of the satellite elevation angle is determined based on the positive medium fuzzy set

[0221] If n rms corresponds to the fuzzy condition of large medium error, and n RATIO corresponds to the fuzzy condition of large ambiguity test rate, then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle positively, and the adjustment value of the satellite elevation angle is determined based on the positive small fuzzy set

[0222] If n rms corresponds to the fuzzy condition of medium medium error, and n RATIO ​​​The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a small ambiguity test rate. Then, the satellite elevation angle adjustment rule is to adjust the satellite elevation angle negatively and is based on the negative medium fuzzy set. Determine the adjustment value of the satellite elevation angle.

[0223] If n rms The corresponding mean error fuzzy condition is the fuzzy condition in the mean error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition in the ambiguity test rate. Then, the satellite elevation angle adjustment rule is based on the zero fuzzy set. Determine the adjustment value of the satellite elevation angle.

[0224] If n rms The corresponding mean error fuzzy condition is the fuzzy condition in the mean error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate. Then, the satellite elevation angle adjustment rule is based on the zero fuzzy set. Determine the adjustment value of the satellite elevation angle.

[0225] If n rms The corresponding mean error fuzzy condition is the fuzzy condition with a small mean error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a small ambiguity test rate. Then, the satellite elevation angle adjustment rule is to adjust the satellite elevation angle negatively and is based on the negative large fuzzy set. Determine the adjustment value of the satellite elevation angle.

[0226] If n rms The corresponding mean error fuzzy condition is the fuzzy condition with a small mean error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition in the ambiguity test rate. Then, the satellite elevation angle adjustment rule is to adjust the satellite elevation angle negatively and is based on the negative small fuzzy set. Adjust the satellite elevation angle.

[0227] If n rms The corresponding mean error fuzzy condition is the fuzzy condition with a small mean error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate. Then, the satellite elevation angle adjustment rule is based on the zero fuzzy set. Determine the adjustment value of the satellite elevation angle.

[0228] Among them, the value range of the satellite elevation angle is [8, 25].

[0229] Among them, the default value of the satellite elevation angle is 15.

[0230] After the baseline solution is performed by the device provided in this embodiment, the satellite elevation angle is adjusted according to the root mean square error (rms) and the ambiguity test ratio (RATIO) to control the solution accuracy, and the baseline solution is re-performed based on the adjusted satellite elevation angle, ensuring the GNSS differential positioning accuracy.

[0231] Based on the same inventive concept of the GNSS differential positioning data quality control method, this embodiment provides an electronic device, which includes: a memory, a processor, and a computer program.

[0232] Among them, the computer program is stored in the memory and is configured to be executed by the processor to implement the above-mentioned GNSS differential positioning data quality control method.

[0233] For the electronic device provided in this embodiment, the computer program thereon is executed by the processor to adjust the satellite elevation angle according to the root mean square error (rms) and the ambiguity test ratio (RATIO) after the baseline solution is performed, control the solution accuracy, and re-perform the baseline solution based on the adjusted satellite elevation angle, ensuring the GNSS differential positioning accuracy.

[0234] Based on the same inventive concept of the GNSS differential positioning data quality control method, this embodiment provides a computer-readable storage medium, on which a computer program is stored. The computer program is executed by the processor to implement the above-mentioned GNSS differential positioning data quality control method.

[0235] For the computer-readable storage medium provided in this embodiment, the computer program thereon is executed by the processor to adjust the satellite elevation angle according to the root mean square error (rms) and the ambiguity test ratio (RATIO) after the baseline solution is performed, control the solution accuracy, and re-perform the baseline solution based on the adjusted satellite elevation angle, ensuring the GNSS differential positioning accuracy.

[0236] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0237] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0238] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0239] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0240] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0241] Obviously, those skilled in the art can make various changes and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. A GNSS differential positioning data quality control method, characterized in that The method includes: Obtaining the root mean square (rms) of the baseline solution and the ambiguity test ratio (RATIO) during the GNSS differential positioning process; According to the discrete domain N of the mean square error rms Determine the discrete domain value n of the rms rms , and, according to the discrete domain N of the ambiguity test rate RATIO Determine the discrete domain value n of the RATIO RATIO ; According to n rms and n RATIO , adjust the satellite elevation angle; Re-performing the baseline solution based on the adjusted satellite elevation angle.

2. The method according to claim 1, wherein The said N rms = {-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5}; The discrete domain N according to the mean square error rms Determine the discrete domain value n of the rms rms , including: Determine the mapping value m of rms at N rms where rms m = 10×(rms - 0.5); Determine n rms as the integer value of m rms .

3. The method according to claim 1, characterized in that, The said N RATIO ={-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5}; The discrete domain N according to the ambiguity test rate RATIO Determine the discrete domain value n of RATIO RATIO , including: Determine the mapping value m of RATIO at N RATIO of RATIO = RATIO - 5; Determine n RATIO as the integer value of m RATIO .

4. The method according to claim 1, wherein Said according to n rms and n RATIO , adjusting the satellite elevation angle, including: Determine n rms The corresponding mean error ambiguity condition, and determine n RATIO The corresponding ambiguity test rate ambiguity condition; According to n rms the corresponding mean square error ambiguity condition and n RATIO calculate the corresponding ambiguity value according to the ambiguity test rate ambiguity condition corresponding to it; Calculate the adjustment value and adjustment direction of the satellite elevation angle according to the fuzzy value and the corresponding satellite elevation angle adjustment rule; the corresponding satellite elevation angle adjustment rule is based on n rms The corresponding mean error fuzzy condition and n RATIO The corresponding ambiguity test rate fuzzy condition is jointly determined; Adjusting the satellite elevation angle by the adjustment value in the adjustment direction.

5. The method according to claim 4, wherein The determination of n rms The corresponding mean error ambiguity conditions include: Obtaining the fuzzy sets corresponding to each pre-set rms fuzzy condition; Determine n according to the fuzzy sets corresponding to each medium error fuzzy condition rms Membership degrees relative to each pre-set medium error fuzzy condition where x is the identifier of the pre-set medium error fuzzy condition; According to determine n rms corresponding mean square error ambiguity condition 6. The method according to claim 5, wherein The said N rms = {-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5}; The pre-set rms fuzzy conditions include: the fuzzy condition for large rms, the fuzzy condition for medium rms, and the fuzzy condition for small rms; Fuzzy sets corresponding to fuzzy conditions with large mean errors Fuzzy sets corresponding to fuzzy conditions in the mean error Fuzzy sets corresponding to fuzzy conditions with small mean errors 7. The method according to claim 4, wherein The determination of n RATIO The corresponding ambiguity test rate fuzzy condition, including: Obtaining the fuzzy sets corresponding to each pre-set ambiguity test ratio fuzzy condition; Determine n according to the fuzzy sets corresponding to the fuzzy conditions of each ambiguity test rate RATIO Membership degrees with respect to the fuzzy conditions of each ambiguity test rate set in advance where y is the identifier of the fuzzy conditions of each ambiguity test rate set in advance; According to Determine n RATIO The corresponding ambiguity test rate fuzzy condition.

8. The method according to claim 7, wherein Said N RATIO = {-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5}; The pre-set ambiguity test ratio fuzzy conditions include: the fuzzy condition for large ambiguity test ratio, the fuzzy condition for medium ambiguity test ratio, and the fuzzy condition for small ambiguity test ratio; The fuzzy set corresponding to the fuzzy condition with a large ambiguity test rate The fuzzy set corresponding to the fuzzy condition in the ambiguity test rate The fuzzy set corresponding to the fuzzy condition with a small ambiguity test rate 9. The method according to claim 4, wherein The fuzzy condition corresponds to the fuzzy set; n rms The corresponding at least one mean square error fuzzy condition; n RATIO The corresponding at least one ambiguity test rate fuzzy condition; The said according to n rms The corresponding mean square error ambiguity condition and n RATIO Calculate the corresponding ambiguity values according to the corresponding ambiguity test rate ambiguity condition, including: Obtain n rms The membership degree corresponding to any medium error fuzzy condition u corresponding to n And n RATIO The membership degree corresponding to any ambiguity test rate fuzzy condition v corresponding to n Let the fuzzy value ω corresponding to the medium error fuzzy condition u and the ambiguity test rate fuzzy condition v be determined uv , where ∧ is the minimum operator.

10. The method according to claim 4, wherein n rms corresponding to at least one medium error ambiguity condition; n RATIO corresponding to at least one ambiguity test rate ambiguity condition The said according to n rms The corresponding mean error ambiguity condition and n RATIO Calculate the corresponding ambiguity value according to the ambiguity test rate ambiguity condition corresponding to n, including: Obtaining all the first fuzzy relation matrices; the first fuzzy relation matrices are formed based on each pre-set rms fuzzy condition and each pre-set ambiguity test ratio fuzzy condition; For n rms For any medium error fuzzy condition u and n corresponding thereto RATIO For any ambiguity test rate fuzzy condition v corresponding thereto, select, from all the first fuzzy relation matrices, a target first fuzzy relation matrix formed based on the any medium error fuzzy condition u and the any ambiguity test rate fuzzy condition v; and determine, based on the target first fuzzy relation matrix, the fuzzy value ω corresponding to n rms and n RATIO corresponding uv .

11. The method according to claim 4, wherein The satellite elevation angle adjustment rule includes an adjustment direction and a fuzzy set; the fuzzy set in the satellite elevation angle adjustment rule is used to determine the adjustment value of the satellite elevation angle; There is at least one corresponding satellite elevation angle adjustment rule; There is at least one fuzzy value; Calculating the adjustment value and adjustment direction of the satellite elevation angle according to the fuzzy value and the corresponding satellite elevation angle adjustment rule includes: Determining the adjustment direction and the fuzzy set in each corresponding satellite elevation angle adjustment rule; Determine the adjustment direction of the satellite elevation angle according to each adjustment direction, and Determine the adjustment value of the satellite elevation angle by taking three times the element corresponding to the maximum element in the obtained set in the discrete domain of the satellite elevation angle; Wherein, ∧ is the minimum operator, and V is the maximum operator; i is the middle error fuzzy condition identifier corresponding to n rms The corresponding middle error fuzzy condition identifier, and j is the ambiguity test rate fuzzy condition identifier corresponding to n RATIO The corresponding ambiguity test rate fuzzy condition identifier Is the fuzzy set in the satellite elevation angle adjustment rule determined according to the middle error fuzzy condition i and the ambiguity test rate fuzzy condition j, ω ij Is the fuzzy value corresponding to the middle error fuzzy condition i and the ambiguity test rate fuzzy condition j 12. The method according to claim 4, wherein The satellite elevation angle adjustment rule includes an adjustment direction and a fuzzy set; the fuzzy set in the satellite elevation angle adjustment rule is used to determine the adjustment value of the satellite elevation angle; There is at least one corresponding satellite elevation angle adjustment rule; There is at least one fuzzy value; Calculating the adjustment value and adjustment direction of the satellite elevation angle according to the fuzzy value and the corresponding satellite elevation angle adjustment rule includes: Obtaining all the second fuzzy relation matrices; the second fuzzy relation matrices are formed based on the pre-formed first fuzzy relation matrices and the pre-determined satellite elevation angle adjustment rules; the first fuzzy relation matrices are formed based on each pre-set rms fuzzy condition and each pre-set ambiguity test ratio fuzzy condition; the pre-determined satellite elevation angle adjustment rules are determined according to each pre-set rms fuzzy condition and each pre-set ambiguity test ratio fuzzy condition; For n rms For any medium error ambiguity condition u and n corresponding thereto RATIO For any ambiguity test rate ambiguity condition v corresponding thereto, select the corresponding target second fuzzy relation matrix from all the second fuzzy relation matrices; wherein, the first fuzzy relation matrix forming the selected target second fuzzy relation matrix is formed based on the any medium error ambiguity condition u and the any ambiguity test rate ambiguity condition v, and the satellite elevation angle adjustment rule forming the selected target second fuzzy relation matrix is determined based on the any medium error ambiguity condition u and the any ambiguity test rate ambiguity condition v; Determining the adjustment direction in each corresponding satellite elevation angle adjustment rule; Determining the adjustment direction of the satellite elevation angle according to each adjustment direction, and determining three times the element corresponding to the maximum element in the union of the selected target second fuzzy relation matrices in the discrete universe of discourse of the satellite elevation angle as the adjustment value of the satellite elevation angle.

13. The method according to claim 10 or 12, characterized in that The fuzzy condition corresponds to the fuzzy set; The method further includes: Form a first fuzzy relation matrix for any pre-set medium error fuzzy condition x and any pre-set ambiguity test rate fuzzy condition y Among them, is the fuzzy set corresponding to any medium error fuzzy condition x, is the fuzzy set corresponding to any ambiguity test rate fuzzy condition y, and T is the transpose operator. is the composition operator of fuzzy relations.

14. The method according to claim 12, wherein The fuzzy condition corresponds to the fuzzy set; The method further includes: For any preset medium error ambiguity condition x and any preset ambiguity test rate ambiguity condition y, a second fuzzy relation matrix is formed Among them, is the first fuzzy relation matrix formed by any mean error fuzzy condition x and any ambiguity test rate fuzzy condition y, is the horizontal expansion of, is the satellite elevation angle adjustment rule determined by any mean error fuzzy condition x and any ambiguity test rate fuzzy condition y, T is the transpose operator, is the composition operator of fuzzy relations.

15. The method according to claim 4, wherein Discrete universe of discourse N for satellite elevation angle ΔAg = {-3, -2, -1, 0, 1, 2, 3}; The pre-set rms fuzzy conditions include: the fuzzy condition for large rms, the fuzzy condition for medium rms, and the fuzzy condition for small rms; the pre-set ambiguity test ratio fuzzy conditions include: the fuzzy condition for large ambiguity test ratio, the fuzzy condition for medium ambiguity test ratio, and the fuzzy condition for small ambiguity test ratio; If rms The corresponding mean error fuzzy condition is the fuzzy condition with large mean error, and n RATIO The corresponding fuzzy condition of ambiguity check rate is a fuzzy condition with small ambiguity check rate, then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle positively, and based on the positive fuzzy set Determine the adjustment value of the satellite elevation angle; If n rms The corresponding mean error ambiguity condition is the ambiguity condition with a large mean error, and n RATIO The corresponding ambiguity test rate ambiguity condition is the ambiguity condition in the ambiguity test rate. Then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle positively, and based on the positive middle fuzzy set Determine the adjustment value of the satellite elevation angle; If n rms The corresponding medium error fuzzy condition is the fuzzy condition with a large medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate, then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle in the positive direction, and based on the positive small fuzzy set Determine the adjustment value of the satellite elevation angle; If n rms The corresponding mean error fuzzy condition is the fuzzy condition in the mean error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a small ambiguity test rate. Then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle negatively, and based on the negative medium fuzzy set Determine the adjustment value of the satellite elevation angle; If n rms The corresponding mean error fuzzy condition is the fuzzy condition in the mean error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition in the ambiguity test rate, then the satellite elevation angle adjustment rule is based on the zero fuzzy set to determine the adjustment value of the satellite elevation angle; If n rms The corresponding medium error fuzzy condition is the fuzzy condition in the medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate, then the satellite elevation angle adjustment rule is based on the zero fuzzy set Determine the adjustment value of the satellite elevation angle; If n rms The corresponding mean error fuzzy condition is the fuzzy condition with a small mean error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a small ambiguity test rate, then the satellite elevation angle adjustment rule is to negatively adjust the satellite elevation angle, and based on the negative large fuzzy set Determine the adjustment value of the satellite elevation angle; If n rms The corresponding medium error fuzzy condition is the fuzzy condition with a small medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition in the ambiguity test rate, then the satellite elevation angle adjustment rule is to adjust the satellite elevation angle negatively and based on the negative small fuzzy set Adjust the satellite elevation angle; If n rms The corresponding medium error fuzzy condition is the fuzzy condition with a small medium error, and n RATIO The corresponding ambiguity test rate fuzzy condition is the fuzzy condition with a large ambiguity test rate, then the satellite elevation angle adjustment rule is based on the zero fuzzy set to determine the adjustment value of the satellite elevation angle.

16. The method according to claim 4, wherein The value range of the satellite elevation angle is [8, 25].

17. The method according to claim 4, characterized in that, The default value of the satellite elevation angle is 15.

18. A GNSS differential positioning data quality control device, characterized in that, The device includes: An acquisition module, configured to acquire the root mean square error (rms) and the ambiguity test ratio (RATIO) after baseline solution during GNSS differential positioning; A determination module, configured to determine according to the discrete domain N of the mean square error rms to determine the discrete domain value n of the rms obtained by the obtaining module rms , and to determine according to the discrete domain N of the ambiguity test rate RATIO to determine the discrete domain value n of the RATIO obtained by the obtaining module RATIO ; An adjustment module, configured to adjust the satellite elevation angle according to n determined by the determination module rms and n RATIO , where n is determined by the determination module A control module, configured to control re - performing baseline solution based on the satellite elevation angle adjusted by the adjustment module.

19. An electronic device, characterized in that, It includes: A memory; A processor; And A computer program; Wherein, the computer program is stored in the memory and is configured to be executed by the processor to implement the method according to any one of claims 1 - 17.

20. A computer-readable storage medium, characterized in that, A computer program is stored thereon; the computer program is executed by the processor to implement the method according to any one of claims 1 - 17.

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