A health diagnosis method suitable for GNSS receivers

Abstract

The application provides a health diagnosis method suitable for a GNSS receiver, and the method comprises the following steps: determining to-be-diagnosed sites, and acquiring calculation data and original data of GNSS receivers of each to-be-diagnosed site within a preset time period; acquiring a standard deviation of the calculation data of the GNSS receivers of each to-be-diagnosed site within the preset time period; acquiring a data average value of each quality parameter in the original data of the GNSS receivers of each to-be-diagnosed site within the preset time period; acquiring a correlation coefficient of each quality parameter and the standard deviation; acquiring quality parameters corresponding to the correlation coefficients in the top 50%; obtaining a normalized data average value of each quality parameter; and judging an influencing factor of GNSS receiver accuracy based on the normalized data average value of each quality parameter, so as to complete the health diagnosis of the GNSS receiver. The application can solve the technical problem that it takes too long to judge the authenticity of ground surface deformation by using manual work in the prior art.

Description

Technical Field

[0001] This invention relates to the field of GNSS receiver technology, and more particularly to a health diagnosis method applicable to GNSS receivers. Background Technology

[0002] When GNSS receivers are used as disaster monitoring equipment, the accuracy and stability of the solution results are critical. The methods typically employed are GNSS relative solutions based on carrier phase, which are essentially solutions for integer ambiguities. When satellite observation conditions are poor, the mean square error of the real-valued parameter ambiguity of the integer ambiguity will inevitably be large, meaning the confidence interval will also be large, containing multiple integers as candidate solutions for that ambiguity parameter. Combining all the candidate solutions for the ambiguity parameters of all observed satellites forms a candidate set of integer ambiguity vector N. Substituting each combination of integer ambiguities from the candidate set into the normal equations for calculation, the solution that minimizes the sum of squared residuals of the observed values ​​is considered the optimal solution. The integer ambiguity obtained in this way is, in high probability, the correct solution for that ambiguity, but there is still a certain probability that it is an incorrect solution. Therefore, the analysis of the raw data and the correlation analysis of the solution accuracy for each set of real-time solutions has a large margin of error. These errors, mixed in with the data of actual deformation, can interfere with or even misjudge the determination of surface deformation.

[0003] Currently, common parameters for evaluating the quality of raw data from GNSS receivers include multipath reference values ​​(MP1, MP2), data integrity rate, cycle slips, and signal-to-noise ratio (SNR). Multipath effect can reflect the environmental conditions of the receiver's location to some extent; a smaller multipath value indicates better multipath resistance. Cycle slip ratio, the ratio of observations to cycle slips, reflects the frequency of data cycle slips; a smaller O / Slip value indicates more severe cycle slips. SNR, the ratio of signal power to noise power, measures the quality of the ranging signal; a higher SNR indicates better signal quality. Data integrity rate reflects the proportion of observed epochs obtained from analysis to the total number of epochs that should be observed. A higher data integrity rate indicates more consistent and higher-quality data. These parameters, to some extent, reflect the receiver's observation environment and influence its normal processing.

[0004] Currently, the authenticity of surface deformation is determined manually. However, manually determining the authenticity of surface deformation is time-consuming, requiring maintenance personnel to analyze historical data and raw data. Due to the large amount of data, a large number of personnel are needed, which is also time-consuming. Summary of the Invention

[0005] This invention provides a health diagnosis method for GNSS receivers, which can solve the technical problem that the time-consuming manual judgment of the authenticity of surface deformation is too long in the prior art.

[0006] According to one aspect of the present invention, a health diagnosis method suitable for a GNSS receiver is provided, the method comprising:

[0007] Identify the sites to be diagnosed and obtain the calculated and raw data of the GNSS receiver for each site within a preset time period;

[0008] Obtain the standard deviation of the solution data of the GNSS receiver of each site to be diagnosed within a preset time period;

[0009] Obtain the average value of each quality parameter in the raw data of the GNSS receiver of each site to be diagnosed within a preset time period;

[0010] The correlation coefficient between each quality parameter and the standard deviation is obtained based on the standard deviation of the solution data of the GNSS receiver of each site to be diagnosed within a preset time period and the average value of each quality parameter.

[0011] Sort the correlation coefficients of each quality parameter with the standard deviation in descending order, and obtain the quality parameters corresponding to the top 50% of the correlation coefficients.

[0012] The average data of each quality parameter corresponding to the top 50% correlation coefficients is normalized to obtain the normalized average data of each quality parameter.

[0013] The factors affecting the accuracy of the GNSS receiver are determined based on the average value of the data after normalization of each quality parameter, so as to complete the health diagnosis of the GNSS receiver.

[0014] Preferably, determining the factors affecting GNSS receiver accuracy based on the average value of data normalized for each quality parameter, in order to complete the health diagnosis of the GNSS receiver, includes:

[0015] The average value of the normalized data for each quality parameter is compared with a preset threshold.

[0016] If the average value of the data after normalization of the current quality parameters is greater than the preset threshold, it is determined that the current quality parameters are not a factor affecting the accuracy of the GNSS receiver.

[0017] If the average value of the normalized data of the current quality parameter is less than or equal to a preset threshold, it is determined that the current quality parameter is a factor affecting the accuracy of the GNSS receiver.

[0018] The data average values ​​of all quality parameters after normalization are traversed to complete the health diagnosis of the GNSS receiver.

[0019] Preferably, the quality parameters include multipath effect value, cycle slip ratio, signal-to-noise ratio, and data integrity rate.

[0020] Preferably, when the quality parameter cannot be represented by a numerical value, the data of the quality parameter is assigned a value.

[0021] Preferably, the correlation coefficient is a Pearson simple correlation coefficient, a Spearman rank correlation coefficient, or a Kendall τ correlation coefficient.

[0022] According to another aspect of the present invention, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0023] By applying the technical solution of this invention, a health diagnosis of GNSS receivers is performed through analysis of the raw data quality and correlation analysis with the resolved data. This health diagnosis method can quickly complete the raw data quality analysis of a large number of GNSS receivers, reducing personnel and time costs. Attached Figure Description

[0024] The accompanying drawings, which form part of this specification, are provided to further illustrate embodiments of the invention and, together with the textual description, explain the principles of the invention. It is obvious that the drawings described below are merely some embodiments of the invention, and those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0025] Figure 1 A flowchart of a health diagnosis method for a GNSS receiver according to an embodiment of the present invention is shown;

[0026] Figure 2 A radar chart of mass parameters provided according to an embodiment of the present invention is shown. Detailed Implementation

[0027] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0029] Unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. It should also be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale. Techniques, methods, and devices known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and devices should be considered part of the specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values. It should be noted that similar reference numerals and letters in the following figures denote similar items; therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0030] like Figure 1 As shown, the present invention provides a health diagnosis method suitable for GNSS receivers, the method comprising:

[0031] S10. Determine the sites to be diagnosed and obtain the solution data and raw data of the GNSS receiver of each site to be diagnosed within a preset time period;

[0032] S20. Obtain the standard deviation of the solution data of the GNSS receiver of each site to be diagnosed within a preset time period;

[0033] S30. Obtain the average value of each quality parameter in the raw data of the GNSS receiver of each site to be diagnosed within a preset time period;

[0034] S40. Based on the standard deviation of the solution data of each GNSS receiver at each site to be diagnosed within a preset time period and the average value of each quality parameter, obtain the correlation coefficient between each quality parameter and the standard deviation.

[0035] S50. Sort the correlation coefficients of each quality parameter with the standard deviation in descending order, and obtain the quality parameters corresponding to the top 50% of the correlation coefficients.

[0036] S60. Normalize the average data of each quality parameter corresponding to the correlation coefficients of the top 50% of the ranking to obtain the normalized average data of each quality parameter.

[0037] S70. Determine the factors affecting the accuracy of the GNSS receiver based on the average value of the data after normalization of each quality parameter, so as to complete the health diagnosis of the GNSS receiver.

[0038] This invention performs health diagnostics on GNSS receivers by analyzing the quality of raw data and its correlation with the processed data. This health diagnostic method can quickly complete the raw data quality analysis of a large number of GNSS receivers, reducing personnel and time costs.

[0039] According to one embodiment of the present invention, in S70, determining the influencing factors of GNSS receiver accuracy based on the average value of the data after normalization of each quality parameter, in order to complete the health diagnosis of the GNSS receiver, includes:

[0040] S71. Compare the average value of the normalized data for each quality parameter with the preset threshold respectively;

[0041] S72. If the average value of the normalized data of the current quality parameter is greater than the preset threshold, it is determined that the current quality parameter is not a factor affecting the accuracy of the GNSS receiver.

[0042] S73. If the average value of the data after normalization of the current quality parameter is less than or equal to a preset threshold, determine whether the current quality parameter is a factor affecting the accuracy of the GNSS receiver.

[0043] S74. Iterate through the average values ​​of all normalized quality parameters to complete the health diagnosis of the GNSS receiver.

[0044] According to one embodiment of the present invention, the quality parameters include multipath effect value, cycle slip ratio, signal-to-noise ratio, and data integrity rate.

[0045] According to one embodiment of the present invention, when the quality parameter cannot be represented by a numerical value, the data of the quality parameter is assigned a value.

[0046] For example, if there is no direct data for satellite distribution variables, we can assign values ​​to them. For instance, by observing a star chart, we can assign values ​​from 1 to 5 to the satellite distribution conditions of the receiver, namely: 1 = poor satellite distribution, 2 = relatively poor satellite distribution, 3 = average satellite distribution, 4 = relatively good satellite distribution, and 5 = good satellite distribution.

[0047] According to one embodiment of the present invention, the correlation coefficient is a Pearson simple correlation coefficient, a Spearman rank correlation coefficient, or a Kendall τ correlation coefficient.

[0048] The method of the present invention will be described in detail below through specific embodiments, specifically including the following steps:

[0049] Step 1: Enter the name of the site to be diagnosed into the system through the input module, such as 5200001764; the input module is the interaction module between the human and the system.

[0050] Step 2: The input module passes the keyword "5200001764" to the data acquisition module. The data acquisition module searches the database for the GNSS receiver's calculated and raw data for the past 5 days. It converts the raw data into the standard Rinex 2.11 format and passes it to the data analysis module along with the calculated data.

[0051] Step 3: The data analysis module first statistically analyzes the results of the receiver's calculations over 5 days to obtain the standard deviation of the receiver's calculated data; it then calls the TEQC command-line software to analyze the raw data within this time period to obtain the average value of each quality parameter and calculate the correlation coefficient between each quality parameter and the standard deviation. The quality parameters include multipath effect value, cycle slip ratio, signal-to-noise ratio, and data integrity rate.

[0052] Step 4: Sort the correlation coefficients of each quality parameter with the standard deviation in descending order, and obtain the quality parameters corresponding to the top 50% of the correlation coefficients. The larger the correlation coefficient, the greater the impact of the corresponding quality parameter on the solution accuracy.

[0053] Step 5: Normalize the average data of each quality parameter corresponding to the top 50% correlation coefficients to obtain the normalized average data of each quality parameter;

[0054] Step Six: Compare the normalized average data of each quality parameter with a preset threshold. If the normalized average data of the current quality parameter is greater than the preset threshold, determine that the current quality parameter is not a factor affecting the accuracy of the GNSS receiver. If the normalized average data of the current quality parameter is less than or equal to the preset threshold, determine that the current quality parameter is a factor affecting the accuracy of the GNSS receiver. Repeat this process for all normalized average data of all quality parameters to complete the health diagnosis of the GNSS receiver.

[0055] Step 7: The output module will output the factors affecting the accuracy of the GNSS receiver and generate a simple report.

[0056] Figure 2 A radar chart of mass parameters provided according to an embodiment of the present invention is shown. Figure 2 In this context, MP1 and MP2 both represent multipath effect values, SLIP represents cycle slip ratio, SNR1 and SNR2 represent signal-to-noise ratio, and COMP represents data integrity rate.

[0057] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0058] In the description of this invention, it should be understood that the orientation or positional relationship indicated by directional terms such as "front, back, up, down, left, right", "horizontal, vertical, horizontal" and "top, bottom" is generally based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing this invention and simplifying the description. Unless otherwise stated, these directional terms do not indicate or imply that the device or element referred to must have a specific orientation or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on the scope of protection of this invention; the directional terms "inner" and "outer" refer to the inner and outer contours relative to the outline of each component itself.

[0059] For ease of description, spatial relative terms such as "above," "on top of," "on the upper surface of," "above," etc., are used herein to describe the spatial positional relationship of a device or feature as shown in the figures to other devices or features. It should be understood that spatial relative terms are intended to encompass different orientations in use or operation beyond the orientation of the device as described in the figures. For example, if the device in the figures were inverted, a device described as "above" or "on top of" other devices or structures would subsequently be positioned as "below" or "under" other devices or structures. Thus, the exemplary term "above" can include both "above" and "below." The device may also be positioned in other different ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used herein will be interpreted accordingly.

[0060] Furthermore, it should be noted that the use of terms such as "first" and "second" to define components is merely for the purpose of distinguishing the corresponding components. Unless otherwise stated, the above terms have no special meaning and therefore should not be construed as limiting the scope of protection of this invention.

[0061] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A health diagnosis method suitable for a GNSS receiver, characterized in that, The method includes: Identify the sites to be diagnosed and obtain the calculated and raw data of the GNSS receiver for each site within a preset time period; Obtain the standard deviation of the solution data of the GNSS receiver of each site to be diagnosed within a preset time period; Obtain the average value of each quality parameter in the raw data of the GNSS receiver of each site to be diagnosed within a preset time period; The correlation coefficient between each quality parameter and the standard deviation is obtained based on the standard deviation of the solution data of the GNSS receiver of each site to be diagnosed within a preset time period and the average value of each quality parameter. Sort the correlation coefficients of each quality parameter with the standard deviation in descending order, and obtain the quality parameters corresponding to the top 50% of the correlation coefficients. The average data of each quality parameter corresponding to the top 50% correlation coefficients is normalized to obtain the normalized average data of each quality parameter; The factors affecting the accuracy of the GNSS receiver are determined based on the average value of the data after normalization of each quality parameter, so as to complete the health diagnosis of the GNSS receiver. The quality parameters include multipath effect value, cycle slip ratio, signal-to-noise ratio, and data integrity rate. The correlation coefficients are Pearson simple correlation coefficients, Spearman rank correlation coefficients, or Kendall τ correlation coefficients.

2. The method of claim 1, wherein, The factors affecting GNSS receiver accuracy are determined based on the average value of the normalized data for each quality parameter, in order to complete the health diagnosis of the GNSS receiver, including: The average value of the normalized data for each quality parameter is compared with a preset threshold. If the average value of the data after normalization of the current quality parameters is greater than the preset threshold, it is determined that the current quality parameters are not a factor affecting the accuracy of the GNSS receiver. If the average value of the normalized data of the current quality parameter is less than or equal to a preset threshold, it is determined that the current quality parameter is a factor affecting the accuracy of the GNSS receiver. The data average values ​​of all quality parameters after normalization are traversed to complete the health diagnosis of the GNSS receiver.

3. The method of claim 1, wherein, When the quality parameter cannot be represented by a numerical value, the data of the quality parameter is assigned a value.

4. A computer device, comprising: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-3.

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