CN116383191A - Method for cleaning, treating and quality checking and evaluating mass data of CORS station network - Google Patents
Method for cleaning, treating and quality checking and evaluating mass data of CORS station network Download PDFInfo
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Abstract
The invention provides a method for cleaning, managing and quality checking and evaluating mass data of a CORS station network, which is oriented to mass GNSS monitoring and sensing data generated by a continuously running reference station system (Continuous Operational Reference System, CORS). The method specifically comprises the following steps: monitoring station network data file format standardization and normalization; monitoring station network data primary cleaning; monitoring station network data management and quality check; and (5) monitoring the quality evaluation and grading evaluation of the station network data assets. The invention aims at mass data characteristics and data flow attributes of the CORS station network, carries out data cleaning, treatment processing, hierarchical management and control and the like in a layered manner, forms a unified and executable data cleaning treatment standard, realizes a repeatable data management flow and has transparent flow management, thereby ensuring the data quality of the CORS station network and promoting the creation of application value of data assets close to business scenes.
Description
Technical Field
The invention relates to the technical field of satellite data processing, in particular to a method for cleaning, managing and quality checking and evaluating mass data of a (Continuous Operational Reference System, CORS) station network.
Background
The city continuous operation reference station system (CORS) can acquire the position and time information of various spaces and relevant dynamic changes, is one of hot spots of modern GNSS, and is established or is being established in more developed countries in the world at present.
With the establishment of more and more CORS stations, the generated data volume is larger and larger, and as the key of the whole CORS system to play a role, the evaluation of the data quality of the CORS is a key problem. In addition, the Beidou/GNSS receivers of different manufacturers have different design standards at present, so that the adopted received file formats are different, and the data processing of different receivers does not have format uniformity and universal applicability, so that the method has important significance for unifying the received data formats.
Disclosure of Invention
In view of the foregoing, the present invention has been developed to provide a method for cleaning and quality inspection and assessment of mass data of a CORS site network that overcomes or at least partially solves the foregoing problems.
According to one aspect of the invention, a method for cleaning, managing and evaluating mass data and quality check of a CORS station network is provided, which comprises the following steps:
s1, monitoring station network data file format standardization and normalization processing;
s2, monitoring primary cleaning of station network data;
s3, monitoring station network data management and quality check;
and S4, monitoring the quality evaluation and the grading evaluation of the station network data assets.
The step S1 specifically includes:
according to different Beidou/GNSS high-precision measurement type receiver manufacturers and different parameter settings of CORS station network receivers, one version of Rinex2.X series and Rinex3.X series can be selected as a data file storage format; the data format of multiple Rinex versions necessarily introduces the trouble of data import processing, and file format normalization and normalization processing are necessary.
The file format normalization and normalization process mainly comprises the following steps: a format checking and repairing and format converting function;
format checking and repairing: the method mainly comprises the steps of performing normalization inspection on RINEX format files, attempting automatic repair on incorrect format contents, and updating header file information according to inspection results to ensure consistency of the format and the contents;
format conversion: converting the format of any data which accords with the RINEX format standard, and normalizing the Rinex2.X format file into the RINEX2.11 format standard; the RINEX3.x format file is normalized to the RINEX3.04 format standard.
Further, the step S2 specifically includes:
the CORS station network data file is inevitably subjected to various conditions of data repetition, deletion, error and data redundancy, and special service software operation can be supported by cleaning, otherwise, the service software operation thread is extremely easy to terminate and even crash and the like.
The primary cleaning function of the monitoring station network data mainly comprises the following steps: redundant data deletion, error data correction, duplicate/invalid data deletion, logical missing data filling, and the like, specifically:
redundant data deletion: and deleting unnecessary satellite navigation system observation data in the observation file according to the subsequent service data requirement, such as only keeping Beidou/GPS observation data, deleting unnecessary navigation text files and the like.
Error data correction: checking the type of a receiver, the type of an antenna, the height of the antenna and the like in a file by using CORS station network element data, and correcting error information possibly existing; and checking the rationality of the observation epoch, satellite number and the like in the file body and attempting to correct.
Duplicate/invalid data deletion: and performing traversal checking on the data in the file according to the file header information, and identifying and deleting repeated and invalid data.
Logic missing data padding: according to the Rinex file standard, padding and filling are attempted according to logic or rules for partially missing data.
The step S3 specifically includes: data integrity rate analysis, data efficiency analysis, cycle slip rate analysis, multipath error analysis, pseudo-range noise analysis, carrier phase noise analysis and carrier-to-noise ratio analysis.
Further, the data integrity rate analysis specifically includes:
calculating any system according to a formula (1)
Any frequency signal->
Calculating the observation data integrity rate of any system according to a formula (2);
in the method, in the process of the invention,
for Beidou/GNSS system->
Frequency signal +.>
For observing the data integrity rate, the unit is; />
The total number of satellites observed in the observation period; />
For observation guardStar sequence number->
；/>
For the observation period, beidou/GNSS system +.>
Satellite->
In frequency signal->
Is a total number of actual observation epochs; />
For the observation period, beidou/GNSS system +.>
Satellite->
In frequency signal->
Is a theoretical epoch count of (2); />
For system->
Observing the data integrity rate in units of;
for the observation period, beidou/GNSS system +.>
Satellite->
Actual observation epoch Total with all observation frequencies having observation dataA number; />
For the observation period, beidou/GNSS system +.>
Satellite->
Is a theoretical epoch count of (a).
Further, the data efficiency analysis specifically includes:
the effective rate can be defined by equation (3):
wherein,,
representing a high cut-off angle->
Observing the data quantity above the degree;
representing the observed data volume of unhealthy satellites, wherein +.>
The signal to noise ratio of the data is less than the specified threshold epoch number.
Further, cycle slip analysis specifically includes:
any Beidou/GNSS system in the observation period is calculated according to a formula (4)
Cycle slip ratio of (c):
in the method, in the process of the invention,
for the observation period, beidou/GNSS system +.>
Cycle slip ratio of (2); />
For Beidou/GNSS system->
The total number of actual epochs observed during the observation period; />
For Beidou/GNSS system->
The total number of cycle slip epochs occurring during the observation period is also known as Zhou Tiaoshu. Zhou Tiaoshu->
A method of MW (Melbourne-Mubbena) combined detection and GF (Geometry-Free) combined detection is adopted.
Further, the multipath error analysis specifically includes:
calculating the multipath error value RMS of any Beidou/GNSS system, any frequency and any satellite in the observation period according to a formula (5):
in the method, in the process of the invention,
for observation frequency in observation period->
Multipath error values RMS in meters (m);
the total number of the calendar elements is observed in the observation period; />
For epoch number,/->
；/>
For observing frequency +.>
In epoch->
Multipath calculations (containing integer ambiguity effects) at time in meters (m); />
For observing frequency +.>
The average value in meters (m) is calculated for the multipath over the observation period.
Calendar element
Multipath calculation value +.>
I.e. < ->
Calculated according to the formula (6):
in the method, in the process of the invention,
is->
Multipath calculation value of any observation epoch of frequency, the unit is meter (m); />
The unit is meter (m) for the pseudo-range observation of the epoch corresponding to the first frequency; />
A carrier frequency that is the first frequency in megahertz (MHz);
a carrier frequency in megahertz (MHz) that is the second frequency; />
The unit is meter (m) for the observed carrier phase of the epoch corresponding to the first frequency; />
The unit is meter (m) for the observed carrier phase of the epoch corresponding to the second frequency; />
Is that
Multipath calculation value of any observation epoch of frequency, the unit is meter (m); />
The unit is meter (m) for the pseudo-range observation of the epoch corresponding to the second frequency;
in the method, in the process of the invention,
the total number of the calendar elements is observed in the observation period; />
For epoch number,/->
。
Specifically, the pseudo-range noise analysis specifically includes:
calculating pseudo-range noise average values of all observation satellites of any frequency of any Beidou/GNSS system in the observation period according to a formula (8):
in the method, in the process of the invention,
in the observation period, the unit of any frequency pseudo-range noise average value of any Beidou/GNSS system is meter (m); />
In the observation period, the total number of satellites observed by any frequency of any Beidou/GNSS system; />
In order to observe the satellite serial number,
；/>
for the observation period, any frequency (signal) of any Beidou/GNSS system is +.>
Pseudo-range noise RMS for a satellite is in meters (m).
wherein:
in order to observe the period, any frequency of any Beidou/GNSS system is +.>
The total number of observation epochs of the satellites; />
For epoch number,/->
；/>
For any frequency of any Beidou/GNSS system +.>
Satellite, in epoch->
Pseudo-range noise estimation at time in meters (m);
in the method, in the process of the invention,
for any frequency of any Beidou/GNSS system +.>
Satellite, in epoch->
The unit of the pseudo-range observation value of the moment is meter (m); />
For any frequency of any Beidou/GNSS system +.>
Satellite, in epoch->
The pseudorange quadratic polynomial fit value of time is given in meters (m).
Further: performing pseudo-range quadratic polynomial fitting according to a formula (11); after fitting calculation to obtain a quadratic polynomial coefficient, a pseudo-range quadratic polynomial fitting value of each epoch can be obtained
The method comprises the steps of carrying out a first treatment on the surface of the In the observation period, starting from the initial epoch, each 120 epochs are a fitting window, and the fitting windows are not overlapped; fitting according to the number of the remaining calendar elements when the number of the remaining calendar elements is not less than 3 calendar element observation data near the end of the observation period; when the number of the remaining epoch is smaller than 3 epoch observation data, the method can be abandoned; when the observation data has an interruption phenomenon, the method can be used for respectively processing the following steps:
in the method, in the process of the invention,
fitting a function for a pseudo-range quadratic polynomial; />
To fit epoch number within window, quadratic polynomial argument, ++>
；/>
Is a quadratic polynomial coefficient; />
A first order term coefficient which is a quadratic polynomial;
is a quadratic polynomial constant term.
Specifically, the carrier phase noise analysis specifically includes:
calculating the carrier-phase noise average value of all observation satellites at any frequency of any Beidou/GNSS system in the observation period according to a formula (12):
in the method, in the process of the invention,
in the observation period, the average value of phase noise of any frequency carrier of any Beidou/GNSS system is given in units of weeks; />
In the observation period, the total number of satellites observed by any frequency of any Beidou/GNSS system; />
In order to observe the satellite serial number,
；/>
in order to observe the period, any frequency of any Beidou/GNSS system is +.>
Carrier phase noise of a satellite is in units of weeks.
Further, according to the formula (13)
：
Wherein:
in order to observe the period, any frequency of any Beidou/GNSS system is +.>
The total number of observation epochs of the satellites;
for epoch number,/->
；/>
For any frequency of any Beidou/GNSS system +.>
And the noise estimation of three differences of the carrier phase observation values of adjacent epochs of the satellites is given in units of weeks.
Calculated according to the formula (14)
，
In the method, in the process of the invention,
for epoch->
Moment, any satellite->
The carrier phase observations at any frequency are in units of weeks.
Specifically, the carrier-to-noise ratio analysis specifically includes:
calculating the average value of the carrier-to-noise ratio statistics of all satellites at any frequency of any Beidou/GNSS system according to a formula (15):
in the method, in the process of the invention,
the average value of the carrier-to-noise ratio statistics values of all satellites at any frequency of any Beidou/GNSS system is expressed in decibel hertz (dBHz); />
Observing the total number of satellites in an observation period; />
For observing satellite serial number>
；/>
In order to be within the observation period, any frequency of any Beidou/GNSS system is +.>
The average carrier-to-noise ratio of a satellite is expressed in decibel hertz (dBHz).
Further, calculate according to equation (16)
：
Wherein:
the total number of the calendar elements is observed in the observation period; />
For epoch number,/->
；/>
For epoch->
Moment, satellite->
The observed amount of carrier-to-noise ratio at any frequency is in decibel hertz (dBHz).
Further, the step S4 specifically includes:
monitoring station network data asset quality assessment and grading assessment, wherein indexes such as data integrity rate, data effective rate, pseudo-range multipath, cycle slip ratio and signal to noise ratio are mainly used as indexes of a comprehensive evaluation model of a sequencing method TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approaching ideal points; the comprehensive evaluation process is to 'synthesize' a plurality of evaluation indexes into an integral comprehensive evaluation index through a mathematical model, and combine the special navigation time-frequency service type to determine the weight coefficient by forward and dimensionless methods and entropy value methods on the indexes, and determine the final comprehensive evaluation model so as to obtain the final evaluation result; and (3) carrying out grade assessment on the observation data file by utilizing the comprehensive evaluation result of the observation data and referring to a set threshold value, wherein the grade assessment is respectively four grades of excellent, good, qualified and unavailable.
According to the resources and technical characteristics utilized by the invention, the invention has the following technical advantages:
A. constructing a set of transparent, standard and standardized CORS station network data cleaning treatment flow
Aiming at mass data characteristics and data flow attributes of the CORS station network, data cleaning, treatment processing, hierarchical management and control and the like are carried out in a layered manner, a unified and executable data cleaning treatment standard is formed, a repeatable data management flow is realized, and flow management is transparent, so that the data quality of the CORS station network is ensured, and the creation of application value of data assets close to business scenes is promoted.
B. Forming a comprehensive data quality comprehensive evaluation system
The method for realizing the multidimensional evaluation of data quality by monitoring the service demand of the perceived data scene facing the CORS station network comprises the following steps: observation data integrity rate analysis, availability analysis, pseudo-range observation noise analysis, carrier phase observation noise analysis, signal-to-noise ratio analysis, cycle slip ratio analysis, multipath influence analysis and the like, and comprehensive multidimensional evaluation conclusion realizes hierarchical screening and management and control of data/site resources, and well corresponds to multi-type service requirements.
C. Ensuring balance of data governance benefits and costs
The CORS station network monitors and perceives data to generate massive data at real time, and the management of the massive data does not need to pursue extremely high data quality.
Drawings
In order that the above-recited objects, features and advantages of the present application will become more apparent and fully apparent from the following detailed description of embodiments of the invention, it should be read in connection with the accompanying drawings. Based on the embodiments of the present invention, those skilled in the art may implement other embodiments without making any inventive effort, which fall within the scope of the present invention.
FIG. 1 shows a flow chart of a method for cleaning and treating mass data and evaluating quality check of a CORS station network.
Detailed Description
The invention provides a method for cleaning, treating and quality checking and evaluating mass data of a CORS station network, which aims to overcome the defects of various formats and different data quality of the received data of the CORS station and solve the increasing demands for standardization and analysis and treatment of the CORS data.
The method realizes data standardization and cleaning, data quality analysis and evaluation and the like of the CORS station network observation data with the characteristics of high updating frequency, strong real-time performance, large data quantity, uncertain quality and the like, and forms the data quality checking capability and deep excavation data potential which cover comprehensively, have transparent and standard flows and are unified and feasible.
According to the embodiment of the invention, the method for cleaning, treating and quality checking and evaluating mass data of the CORS station network comprises the following steps:
s1, monitoring station network data file format standardization and normalization processing;
s2, monitoring primary cleaning of station network data;
s3, monitoring station network data management and quality check;
and S4, monitoring the quality evaluation and the grading evaluation of the station network data assets.
As shown in fig. 1, the detailed technical scheme of the steps is as follows:
1. data standardization and primary cleaning module
From the purpose of unifying (or reducing) the format version of the observation data file of the CORS station network, carrying out standardized normalization processing on the observation data files of different types, wherein the standardized normalization processing mainly comprises format checking and repairing, normalization format conversion and the like; the primary cleaning of the content in the observation data file mainly comprises repeated data deletion, logic missing data filling, error data identification and correction, invalid data elimination and the like in the file, and the readability and usability of the data in the file are ensured.
1.1 normalization and normalization treatments
The format checking and repairing functions are aimed at Beidou/GNSS observation data files (Obs), broadcast ephemeris files (Nav) and the like, and due to the fact that Beidou/GNSS receiver manufacturers are different and parameter settings are different, file format versions are inconsistent, and a plurality of inconveniences are introduced for later data processing and use.
The file format normalization and normalization process mainly comprises the following steps: and (5) a format checking and repairing and format conversion function. Format checking and repairing: the method mainly comprises the steps of performing normalization inspection on RINEX format files, attempting automatic repair on incorrect format contents, and updating header file information according to inspection results to ensure consistency of the format and the contents; format conversion: converting the format of any data which accords with the RINEX format standard, and normalizing the Rinex2.X format file into the RINEX2.11 format standard; the RINEX3.x format file is normalized to the RINEX3.04 format standard.
The step S2 specifically includes:
1.2 data Primary cleaning
The primary cleaning function of the monitoring station network data mainly comprises the following steps: redundant data deletion, error data correction, duplicate/invalid data deletion, logical missing data filling, and the like, specifically:
redundant data deletion: and deleting unnecessary satellite navigation system observation data in the observation file according to the subsequent service data requirement, such as only keeping Beidou/GPS observation data, deleting unnecessary navigation text files and the like.
Error data correction: checking the type of a receiver, the type of an antenna, the height of the antenna and the like in a file by using CORS station network element data, and correcting error information possibly existing; and checking the rationality of the observation epoch, satellite number and the like in the file body and attempting to correct.
Duplicate/invalid data deletion: and performing traversal checking on the data in the file according to the file header information, and identifying and deleting repeated and invalid data.
Logic missing data padding: according to the Rinex file standard, padding and filling are attempted according to logic or rules for partially missing data.
2 monitoring station network data management and quality inspection
2.1 data integrity Rate analysis
Calculating any system according to a formula (1)
Any frequency (signal)>
The observation data integrity rate of any system is calculated according to the formula (2).
In the method, in the process of the invention,
for Beidou/GNSS system->
Frequency (signal)/(frequency)>
For observing the data integrity rate, the unit is; />
The total number of satellites observed in the observation period; />
For observing satellite serial number>
；/>
For the observation period, beidou/GNSS system +.>
Satellite->
At frequency (signal)/(frequency)>
Is a total number of actual observation epochs; />
For the observation period, beidou/GNSS system +.>
Satellite->
At frequency (signal)/(frequency)>
Is a theoretical epoch count of (2); />
For system->
Observing the data integrity rate in units of; />
For the observation period, beidou/GNSS system +.>
Satellite->
The total number of actual observation epochs of the observation data exists in all the observation frequencies (signals); />
For the observation period, beidou/GNSS system +.>
Satellite->
Is a theoretical epoch count of (a).
2.2 data efficient analysis
The effective rate can be defined by equation (3):
wherein,,
indicating the height cut-off angle
Observing the data quantity above the degree;
representing the observed data volume for unhealthy satellites, wherein,
the signal to noise ratio of the data is less than the specified threshold epoch number.
2.3 cycle skip analysis
Any Beidou/GNSS system in the observation period is calculated according to a formula (4)
Cycle slip ratio of (c):
in the method, in the process of the invention,
for the observation period, beidou/GNSS system +.>
Cycle slip ratio of (2); />
For Beidou/GNSS system->
The total number of actual epochs observed during the observation period; />
For Beidou/GNSS system->
The total number of cycle slip epochs occurring during the observation period (Zhou Tiaoshu). Zhou Tiaoshu->
A method of MW (Melbourne-Mubbena) combined detection and GF (Geometry-Free) combined detection is adopted.
2.4 multipath error analysis
And calculating the multipath error value (RMS) of any Beidou/GNSS system, any frequency (signal) and any satellite in the observation period according to the formula (5).
In the method, in the process of the invention,
for the observation period, the observation frequency (signal)>
Multipath error values (RMS) in meters (m); />
The total number of the calendar elements is observed in the observation period; />
For epoch number,/->
；/>
For observing frequency (signal)>
In epoch->
Multipath calculations (containing integer ambiguity effects) at time in meters (m); />
For observing frequency (signal)>
The average value (containing integer ambiguity effects) is calculated in multipath over the observation period in meters (m).
Calendar element
Multipath calculation value +.>
I.e. < ->
Calculated according to the formula (6):
in the method, in the process of the invention,
is->
Multipath calculation value (including integer ambiguity influence) of any observation epoch of frequency (signal) in meters (m); />
The unit is meter (m) for the pseudo-range observation of the epoch corresponding to the first frequency (signal); />
A carrier frequency in megahertz (MHz) that is the first frequency (signal); />
A carrier frequency in megahertz (MHz) which is the second frequency (signal); />
The unit is meter (m) for the carrier phase observed quantity of the epoch corresponding to the first frequency (signal); />
The unit is meter (m) for the carrier phase observed quantity of the epoch corresponding to the second frequency (signal); />
Is->
Multipath calculation of any observation epoch for frequency (signal) (including integer ambiguity effect) in units ofRice (m); />
The unit is meter (m) for the pseudorange observations of the epoch corresponding to the second frequency (signal).
in the method, in the process of the invention,
the total number of the calendar elements is observed in the observation period; />
For epoch number,/->
The method comprises the steps of carrying out a first treatment on the surface of the In the process of multi-path error calculation, firstly, the influence of coarse error, clock jump and cycle slip of a repair receiver and the like should be removed from observed data.
2.5 pseudo-range noise analysis
And (3) calculating pseudo-range noise (average value) of all observation satellites of any frequency (signal) of any Beidou/GNSS system in the observation period according to a formula (8).
In the method, in the process of the invention,
in order to observe the time interval, the pseudo-range noise (average value) of any frequency (signal) of any Beidou/GNSS system is expressed in meters (m); />
The total number of satellites observed by any frequency (signal) of any Beidou/GNSS system in an observation period; />
For observing satellite serial number>
；/>
In order to observe the period, any frequency (signal) of any Beidou/GNSS system
Pseudo-range noise (RMS) of a satellite in meters (m).
wherein:
for the observation period, any frequency (signal) of any Beidou/GNSS system is +.>
The total number of observation epochs of the satellites; />
For epoch number,/->
；/>
For any frequency (signal) of any Beidou/GNSS system +.>
Satellite, in epoch->
Pseudo-range noise estimates of the time of day,the unit is meter (m).
in the method, in the process of the invention,
for any frequency (signal) of any Beidou/GNSS system +.>
Satellite, in epoch->
The pseudorange observations at time are in meters (m). />
For any frequency (signal) of any Beidou/GNSS system +.>
Satellite, in epoch->
The pseudorange quadratic polynomial fit value of time is given in meters (m).
Performing pseudo-range quadratic polynomial fitting according to a formula (11); after fitting calculation to obtain a quadratic polynomial coefficient, a pseudo-range quadratic polynomial fitting value of each epoch can be obtained
. During the observation period, starting from the starting epoch, every 120 epochs is a fit window, which does not overlap. Fitting according to the number of the remaining calendar elements when the number of the remaining calendar elements is not less than 3 calendar element observation data near the end of the observation period; when the number of remaining epochs is less than 3 epoch observation data, it may be discarded. When the observation data has an interruption phenomenon, the observation data can be processed in a sectionalized way.
In the method, in the process of the invention,
fitting a function for a pseudo-range quadratic polynomial; />
To fit epoch number within window, quadratic polynomial argument, ++>
；/>
Is a quadratic polynomial coefficient; />
A first order term coefficient which is a quadratic polynomial;
is a quadratic polynomial constant term.
2.6 Carrier phase noise analysis
And (3) calculating carrier phase noise (average value) of all observation satellites of any frequency (signal) of any Beidou/GNSS system in the observation period according to a formula (12).
In the method, in the process of the invention,
in order to observe the period, the carrier phase noise (average value) of any frequency (signal) of any Beidou/GNSS system is given in units of weeks; />
The total number of satellites observed by any frequency (signal) of any Beidou/GNSS system in an observation period; />
For observing satellite serial number>
；/>
For the observation period, any frequency (signal) of any Beidou/GNSS system is +.>
Carrier phase noise of a satellite is in units of weeks.
Calculated according to the formula (13)
。
Wherein:
for the observation period, any frequency (signal) of any Beidou/GNSS system is +.>
The total number of observation epochs of the satellites; />
For epoch number,/->
；/>
For any frequency (signal) of any Beidou/GNSS system +.>
And the noise estimation of three differences of the carrier phase observation values of adjacent epochs of the satellites is given in units of weeks.
Calculated according to the formula (14)
。
In the method, in the process of the invention,
for epoch->
Moment, any satellite->
Carrier phase observations of any frequency (signal) are in cycles. In carrier phase noise calculation, the influence of coarse difference, clock jump and cycle slip of the repair receiver should be removed from the observed data. When the observation data has an interruption phenomenon, the observation data can be processed in a sectionalized way.
2.7 analysis of the Carrier to noise ratio
And calculating the carrier-to-noise ratio statistic (average value) of all satellites at any frequency of any Beidou/GNSS system according to a formula (15).
In the method, in the process of the invention,
the carrier-to-noise ratio statistics (average) of all satellites at any frequency of any Beidou/GNSS system is in decibel hertz (dBHz); />
Observing the total number of satellites in an observation period; />
For observing satellite serial number>
；/>
In order to be within the observation period, any frequency of any Beidou/GNSS system is +.>
The average carrier-to-noise ratio of a satellite is expressed in decibel hertz (dBHz).
Calculated according to the formula (16)
。
Wherein:
the total number of the calendar elements is observed in the observation period; />
For epoch number,/->
；/>
For epoch->
Moment, satellite->
The observed amount of carrier-to-noise ratio of any frequency (signal) is in decibel hertz (dBHz).
3. Monitoring station network data asset quality assessment and grading assessment
The comprehensive evaluation analysis of the quality of the observed data adopts TOPSIS to integrate information of a plurality of indexes to comprehensively evaluate the observed data, and the index values of all the evaluated objects are examined to be close to ideal points and sequenced in sequence, wherein the ideal points are the optimal index values in the measuring station. Because the pseudo-range noise, the carrier phase noise and the carrier phase multipath have smaller values, the pseudo-range multipath contains the information of the pseudo-range noise, the carrier phase multipath and the like, and the pseudo-range multipath is used for replacing the complex degree of the comprehensive evaluation model. And taking indexes such as data integrity rate, data effective rate, pseudo-range multipath, cycle slip ratio, signal-to-noise ratio and the like as indexes of a comprehensive evaluation model, determining weight coefficients by forward and dimensionless methods and entropy value methods, and grading observation data, wherein the four grades are excellent, good, qualified and unavailable.
While preferred embodiments of the present embodiments have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the present application.
The method for cleaning, managing and evaluating mass data and quality check of the CORS station network provided by the application is described in detail, and specific examples are applied to the explanation of the principle and the implementation mode of the application, and the explanation of the examples is only used for helping to understand the method and the core idea of the application; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (17)
1. A mass data cleaning treatment and quality check evaluation method for a CORS station network is characterized by comprising the following steps:
s1, monitoring station network data file format standardization and normalization processing;
s2, monitoring primary cleaning of station network data;
s3, monitoring station network data management and quality check;
and S4, monitoring the quality evaluation and the grading evaluation of the station network data assets.
2. The mass data cleaning treatment and quality check evaluation method for the CORS station network as claimed in claim 1, wherein the mass data cleaning treatment and quality check evaluation method is characterized by comprising the following steps of: s1 specifically comprises:
according to different Beidou/GNSS high-precision measurement type receiver manufacturers, the parameter settings of the CORS station network receiver are different, the data file storage format has a plurality of Rinex version data formats, and file format standardization and normalization processing are needed;
the file format normalization and normalization process comprises the following steps: a format checking and repairing and format converting function;
format checking and repairing: performing normalization inspection on the RINEX format file, attempting automatic repair on incorrect format content, and updating header file information according to an inspection result to ensure consistency of the format and the content;
format conversion: performing format conversion on any data meeting the RINEX format standard, and normalizing the RINEX2.X format file into the RINEX2.11 format standard; the RINEX3.x format file is normalized to the RINEX3.04 format standard.
3. The mass data cleaning treatment and quality check evaluation method for the CORS station network as claimed in claim 1, wherein the mass data cleaning treatment and quality check evaluation method is characterized by comprising the following steps of: s2 specifically comprises:
monitoring station network data primary cleaning functions includes: redundant data deletion, error data correction, repeated data/invalid data deletion, and logical missing data filling, specifically:
redundant data deletion: deleting unnecessary satellite navigation system observation data in the observation file according to the subsequent service data requirement;
error data correction: checking the type of a receiver, the type of an antenna and the height of the antenna in a file by using CORS station network element data, and correcting error information possibly existing; rationality checking and correcting the observation epoch and satellite number in the file body;
duplicate/invalid data deletion: performing traversal inspection on the data in the file according to the file header information, and identifying and deleting repeated and invalid data;
logic missing data padding: and filling the partially missing data according to logic or rules according to the Rinex file standard.
4. The mass data cleaning treatment and quality check evaluation method for the CORS station network as claimed in claim 1, wherein the mass data cleaning treatment and quality check evaluation method is characterized by comprising the following steps of: s3 specifically comprises: data integrity rate analysis, data efficiency analysis, cycle slip rate analysis, multipath error analysis, pseudo-range noise analysis, carrier phase noise analysis and carrier-to-noise ratio analysis.
5. The mass data cleaning treatment and quality check evaluation method for the CORS station network according to claim 4, which is characterized in that: the data integrity rate analysis specifically comprises the following steps:
calculating any system according to a formula (1)
Any frequency signal->
Calculating the observation data integrity rate of any system according to a formula (2);
in the method, in the process of the invention,
for Beidou/GNSS system->
Frequency signal +.>
For observing the data integrity rate, the unit is; />
The total number of satellites observed in the observation period; />
For observing satellite serial number>
；/>
For the observation period, beidou/GNSS system +.>
Satellite->
In frequency signal->
Is a total number of actual observation epochs; />
For the observation period, beidou/GNSS system +.>
Satellite->
In frequency signal->
Is a theoretical epoch count of (2); />
For system->
Observing the data integrity rate in units of; />
For the observation period, beidou/GNSS system +.>
Satellite->
All the observation frequencies have the total number of actual observation epochs of the observation data; />
For the observation period, beidou/GNSS system +.>
Satellite->
Is a theoretical epoch count of (a).
6. The mass data cleaning treatment and quality check evaluation method for the CORS station network according to claim 4, which is characterized in that: the data efficiency analysis specifically comprises:
the data efficiency is defined by equation (3):
wherein,,
representing a high cut-off angle->
Observing the data quantity above the degree; />
Representing the observed data volume of unhealthy satellites, wherein +.>
The signal to noise ratio of the data is less than the specified threshold epoch number.
7. The mass data cleaning treatment and quality check evaluation method for the CORS station network according to claim 4, which is characterized in that: the cycle slip analysis specifically includes:
any Beidou/GNSS system in the observation period is calculated according to a formula (4)
Cycle slip ratio of (c):
in the formula (4), the amino acid sequence of the compound,
for the observation period, beidou/GNSS system +.>
Cycle slip ratio of (2); />
For Beidou/GNSS system->
The total number of actual epochs observed during the observation period; />
For Beidou/GNSS system->
The total number of cycle slip epochs occurring during the observation period, also known as Zhou Tiaoshu; zhou Tiaoshu->
Using the first MW groupAnd (3) a method for combined detection and then GF combined detection.
8. The mass data cleaning treatment and quality check evaluation method for the CORS station network according to claim 4, which is characterized in that: the multipath error analysis specifically includes:
calculating multipath RMS (Root Mean Square) error values of any Beidou/GNSS system, any frequency and any satellite in the observation period according to a formula (5):
in the formula (5), the amino acid sequence of the compound,
for observation frequency in observation period->
Multipath RMS error values in meters (m);
the total number of the calendar elements is observed in the observation period; />
For epoch number,/->
；/>
For observing frequency +.>
In the epoch
The multipath calculation value of the moment contains the influence of integer ambiguity, and the unit is meter (m); />
For observing frequency +.>
The average value in meters is calculated for multiple paths during the observation period.
9. The mass data cleaning treatment and quality check assessment method for a CORS station network according to claim 8, wherein the mass data cleaning treatment and quality check assessment method is characterized by comprising the following steps of:
calendar element
Multipath calculation value +.>
I.e. +.>
Calculated according to the formula (6):
in the formula (6), the amino acid sequence of the compound,
、/>
corresponding +.>
、/>
Multipath calculation value of any observation epoch of frequency, the unit is meter; />
And
carrier frequency points corresponding to the first frequency and the second frequency respectively are in megahertz; />
And->
Pseudo-range observed quantity corresponding to the epoch of the first frequency and pseudo-range observed quantity corresponding to the epoch of the second frequency are respectively measured in meters; />
And->
The unit is meter for the observed quantity of the carrier phase of the epoch corresponding to the first frequency and the observed quantity of the carrier phase of the epoch corresponding to the second frequency;
the said
Calculated according to the formula (7):
in the formula (7), the amino acid sequence of the compound,
the total number of the calendar elements is observed in the observation period; />
For epoch number,/->
。
10. The mass data cleaning treatment and quality check evaluation method for the CORS station network according to claim 4, which is characterized in that: the pseudo-range noise analysis specifically comprises:
calculating pseudo-range noise average values of all observation satellites of any frequency of any Beidou/GNSS system in the observation period according to a formula (8):
in the formula (8), the amino acid sequence of the compound,
in the observation period, the unit of any frequency pseudo-range noise average value of any Beidou/GNSS system is meter (m); />
In the observation period, the total number of satellites observed by any frequency of any Beidou/GNSS system; />
In order to observe the satellite serial number,
；/>
in order to observe the period, any frequency of any Beidou/GNSS system is +.>
Pseudo-range noise RMS for a satellite is in meters.
11. The mass data cleaning and quality check and assessment method for a CORS station network of claim 10, further comprising:
in the formula (9):
in order to observe the period, any frequency of any Beidou/GNSS system is +.>
The total number of observation epochs of the satellites; />
For epoch number,/->
；/>
For any frequency of any Beidou/GNSS system +.>
Satellite, in epoch->
Pseudo-range noise estimation of time, the unit is meter;
in the formula (10), the amino acid sequence of the compound,
for any frequency of any Beidou/GNSS system +.>
Satellite, in epoch->
The unit of the pseudo-range observation value of the moment is meter; />
For any frequency of any Beidou/GNSS system +.>
Satellite, in epoch->
And the unit of the pseudo-range quadratic polynomial fitting value of the moment is meter.
12. The mass data cleaning and inspection and assessment method for a CORS station network of claim 11, further comprising:
performing pseudo-range quadratic polynomial fitting according to a formula (11); after fitting calculation to obtain a quadratic polynomial coefficient, obtaining a pseudo-range quadratic polynomial fitting value of each epoch
The method comprises the steps of carrying out a first treatment on the surface of the In the observation period, starting from the initial epoch, each 120 epochs are a fitting window, and the fitting windows are not overlapped; fitting according to the number of the remaining calendar elements when the number of the remaining calendar elements is not less than 3 calendar element observation data near the end of the observation period; discarding the data when the number of the remaining epoch is less than 3 epoch observation data; when the observation data has an interruption phenomenon, the segmentation is respectively processed:
in the formula (11), the amino acid sequence of the compound,
fitting a function for a pseudo-range quadratic polynomial; />
To fit epoch number within window, quadratic polynomial argument, ++>
；/>
Is a quadratic polynomial coefficient; />
A first order term coefficient which is a quadratic polynomial;
is a quadratic polynomial constant term.
13. The mass data cleaning treatment and quality check evaluation method for a CORS station network as set forth in claim 4, wherein the carrier phase noise analysis specifically includes:
calculating the carrier-phase noise average value of all observation satellites at any frequency of any Beidou/GNSS system in the observation period according to a formula (12):
in the formula (12), the amino acid sequence of the compound,
in the observation period, the average value of phase noise of any frequency carrier of any Beidou/GNSS system is given in units of weeks; />
In the observation period, the total number of satellites observed by any frequency of any Beidou/GNSS system; />
In order to observe the satellite serial number,
；/>
in order to observe the period, any frequency of any Beidou/GNSS system is +.>
Carrier phase noise of a satellite is in units of weeks.
14. The mass data cleaning and inspection and assessment method for a CORS station network according to claim 13, further comprising:
calculated according to the formula (13)
：
In the formula (13):
in order to observe the period, any frequency of any Beidou/GNSS system is +.>
The total number of observation epochs of the satellites; />
For epoch number,/->
；/>
For any frequency of any Beidou/GNSS system +.>
Noise estimation of three differences of carrier phase observation values of adjacent epochs of satellites is given in units of weeks;
calculated according to the formula (14)
，
In the formula (14), the amino acid sequence of the compound,
for epoch->
Moment, any satellite->
The carrier phase observations at any frequency are in units of weeks.
15. The mass data cleaning treatment and quality check evaluation method for a CORS station network as set forth in claim 4, wherein the carrier-to-noise ratio analysis specifically includes:
calculating the average value of the carrier-to-noise ratio statistics of all satellites at any frequency of any Beidou/GNSS system according to a formula (15):
in the formula (15), the amino acid sequence of the compound,
the average value of the carrier-to-noise ratio statistics values of all satellites at any frequency of any Beidou/GNSS system is expressed in decibel hertz; />
Observing the total number of satellites in an observation period; />
For observing satellite serial number>
；/>
In order to be within the observation period, any frequency of any Beidou/GNSS system is +.>
The average value of the carrier-to-noise ratio of the satellite is expressed in decibel hertz.
16. The mass data cleaning and inspection and assessment method for a CORS station network according to claim 15, further comprising:
calculating the said according to formula (16)
：
In formula (16):
the total number of the calendar elements is observed in the observation period; />
For epoch number,/->
；/>
For epoch->
Moment, satellite->
The observed amount of carrier-to-noise ratio at any frequency is in dB hertz.
17. The mass data cleaning treatment and quality check evaluation method for the CORS station network as claimed in claim 1, wherein the mass data cleaning treatment and quality check evaluation method is characterized by comprising the following steps of: s4 specifically comprises the following steps:
the quality evaluation and grading evaluation of the data assets of the monitoring station network are used for carrying out comprehensive quality evaluation/grading on the data assets formed by the data of the monitoring station after treatment and quality verification; the specific method comprises the following steps: taking the indexes of the data integrity rate, the data effective rate, the pseudo-range multipath, the cycle slip ratio and the signal to noise ratio as the indexes of a comprehensive evaluation model of the TOPSIS of the sequencing method approaching ideal points; the comprehensive evaluation process is to synthesize a plurality of evaluation indexes into an integrated comprehensive evaluation index through a mathematical model, combine the special navigation time-frequency service types, determine weight coefficients by forward and dimensionless methods and entropy methods on the indexes, and determine a final comprehensive evaluation model so as to obtain a final evaluation result; and (3) carrying out grade assessment on the observation data file by utilizing the comprehensive evaluation result of the observation data and referring to a set threshold value, wherein the grade assessment is respectively four grades of excellent, good, qualified and unavailable.
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