Observation data quality analysis management system and method based on Beidou base station network

Abstract

The invention discloses an observation data quality analysis management system and method based on a Beidou base station network, and the system comprises a data conversion module which is used for carrying out the data conversion of original Beidou observation data collected by a base station; the data splicing module is used for splicing the hour file of the observation data into a day file; the data checking module is used for storing the data file in a text form; the data quality analysis module is used for reading base station GNSS observation data and navigation ephemeris data; the visual display module is used for displaying the reference station quality image and the index value information; and the data editing module is used for editing the data. According to the method, efficient management and storage of the Beidou observation data can be realized, user-defined output and processing of the data according to actual requirements are also supported, the flexibility and efficiency of data processing are greatly improved, and technical support is provided for wide application of a Beidou system.

Description

Technical Field

[0001] The present invention belongs to the field of satellite positioning technology, and in particular is a management system and method for quality analysis of observation data based on Beidou reference station network. Background Art

[0002] The Base Station Network of the Beidou Reference Service System (CORS) is the cornerstone of the basic urban geographic information framework. It is not only used to build and maintain high-precision mapping benchmarks in the region, but also provides high-precision positioning services for regional network RTK users. In addition, raw observation data can provide data support for many key areas such as the update and maintenance of control points, the construction of precision control networks, three-dimensional laser scanning, and high-precision aerial photography. However, in the face of the rapid development of the Beidou satellite navigation system, existing data processing and analysis tools face new challenges. Specifically, TEQC software cannot be directly applied to the quality analysis of Beidou base station data, and Anubis software has limitations in detecting the new B1c / B2a signal cycle slips of Beidou, and fails to fully cover the characteristics of these new signals, which to a certain extent limits the accuracy and efficiency of data processing.

[0003] At the data management level, the current system also exposes several problems that need to be solved. When data is missing, the system often fails to issue warnings in time, making it difficult for managers to respond and take remedial measures in a timely manner; at the same time, manual data extraction is cumbersome and prone to errors. Therefore, strengthening the intelligence and automation level of data management and building a more efficient data monitoring and warning system have become the key to improving the overall performance and service quality of the CORS base station network. Summary of the invention

[0004] In view of the above problems, the present invention is proposed to provide a system and method for quality analysis and management of observation data based on Beidou reference station network that overcomes the above problems or at least partially solves the above problems.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A Beidou reference station network observation data quality analysis and management system, the system comprising:

[0007] Data conversion module, used to convert the original Beidou observation data collected by the base station;

[0008] The data splicing module is used to splice the hourly files of observation data into a daily file;

[0009] The data verification module is used to store data files in text form and conduct a normative review of the file header;

[0010] Data quality analysis module, used to read base station GNSS observation data and navigation ephemeris data, and implement data quality analysis of BDS / GPS / Galileo / GLONASS navigation systems and all public navigation frequencies;

[0011] Visual display module, used to display the base station quality image and index value information;

[0012] Data editing module, used for data editing operations.

[0013] Optionally, the data conversion module calls convertToRinex software to convert the data in T02 format into Rinex 3.04 standard format, which is a universal format for GNSS observation data.

[0014] Optionally, the file header stores the station name, antenna information, approximate coordinates of the station, the number and type of observation values, and the observation epoch interval.

[0015] Optionally, the data quality analysis module includes a data integrity analysis unit, a signal-to-noise ratio analysis unit, a multipath effect analysis unit and a cycle slip ratio analysis unit.

[0016] Optionally, the data integrity analysis unit includes:

[0017] Read broadcast ephemeris and observation data files to obtain pseudorange and carrier observations at different frequencies of a single satellite;

[0018] The pseudorange and carrier of the visible satellites in each epoch are judged to be non-zero;

[0019] Determine the number of epochs valid for each frequency point of each satellite;

[0020] According to the data integrity of each satellite and each frequency point, the data integrity of all satellites after integration is given;

[0021] The data integrity of the observation data collected by the GNSS receiver is calculated, which is divided into single-frequency data integrity rate and single-system data integrity, and is calculated according to the following formulas:

[0022]

[0023] DI f =(∑DI satf / nsat)×100% (2)

[0024] Where: DI sat，f Indicates the data integrity of a single satellite and a single frequency point; DI f Indicates the integrity of the single-frequency data of the system; n indicates the number of epochs in a period of time; nsat indicates the total number of satellites in the system; A iIndicates the total theoretical epoch data volume of a satellite continuous arc at a certain frequency point within a period of time; B i It indicates the total amount of actual observation epoch data of a satellite continuous arc at a certain frequency point within a period of time.

[0025] Optionally, the signal-to-noise ratio analysis unit comprises:

[0026] According to the set signal-to-noise ratio sampling interval, the signal-to-noise ratio of each satellite and each frequency point in the original observation file is sampled, and the results are stored uniformly to provide data basis for subsequent signal-to-noise ratio graphic display;

[0027] Calculate the altitude angle of each epoch according to the broadcast ephemeris, analyze the sequence of signal-to-noise ratio and altitude angle of the same epoch, and obtain the signal-to-noise ratio at different altitude angles by statistics;

[0028] After reading the original observation data and configuration information, the signal-to-noise ratio data of each frequency point is obtained through the original observation data, and the temporal and spatial variation characteristics of the signal-to-noise ratio are analyzed. The analysis content includes the time series analysis of the signal-to-noise ratio and the height angle series analysis of the signal-to-noise ratio. The calculation formula is:

[0029]

[0030] Where SNR represents the signal-to-noise ratio of each satellite, P_signal represents the power of the signal, and P_noise represents the power of the noise.

[0031] Optionally, the multipath effect analysis unit includes:

[0032] Select the pseudo-range and carrier phase data of two frequency points of the same satellite to calculate the multipath effect;

[0033] The multipath effect result of each epoch is statistically calculated to obtain the multipath effect result corresponding to the satellite;

[0034] The multipath effect results of all satellites are counted to obtain the receiver multipath effect evaluation results;

[0035] The multipath delay calculation mainly relies on dual-frequency observation data. The pseudorange observation equation and the carrier phase observation equation are combined to eliminate the tropospheric delay and ionospheric delay, ignore the carrier phase multipath size, and obtain the pseudorange multipath information. The pseudorange multipath expressions of the B1 and B2 frequency points of the Beidou satellite navigation system are as follows:

[0036] For B1 frequency:

[0037]

[0038] For B2 frequency:

[0039]

[0040] Where s is the satellite in a certain period of time, r is the receiver, MP1 and MP2 are the multipaths of B1 and B2 frequency points respectively, P1 and P2, B1 and B2, f1 and f2 are the corresponding pseudorange observation values, carrier phase observation values, and frequency point frequency values;

[0041] Multipath error evaluation value MP k The calculation method is as follows:

[0042]

[0043] In the formula, It represents the amount of computation required when the receiver observes the satellite at frequency k at the tth epoch, including multipath errors and integer ambiguity information.

[0044] Optionally, the cycle slip ratio analysis unit includes:

[0045] Use the observed quantities to construct phase combinations, pseudorange combinations and MW detection quantities;

[0046] Use GF combination and MW combination to detect large cycle slips respectively;

[0047] The phase combination and pseudorange combination detection quantities are subtracted, and then the difference is performed between epochs to obtain the residual. The MW detection quantity is subtracted between epochs, and then the secondary difference between epochs is performed to obtain the residual.

[0048] The residuals obtained above are judged by thresholds respectively. If the judgment conditions are met, a cycle slip occurs and is marked, and the next epoch is processed for judgment. If the conditions are not met, the next epoch is directly processed;

[0049] After processing all epochs, count the number of cycle slips and calculate the cycle slip ratio;

[0050] The observation data collected by the GNSS receiver is used to detect cycle slips. The ionospheric residual method and the wide-narrow phase narrow lane pseudo-range method are used together, and the MW combined observations and the GF combined observations are used for joint analysis and judgment. The gross error of the cycle slip is detected according to the cycle slip judgment method. If there is no cycle slip, the GF combined observations are used for supplementary detection. The main steps are:

[0051] Construct the recursive formula according to the formula:

[0052]

[0053] Where: is the MW combined observation value of the i-th epoch, in meters; is the mean of the MW combined observations in the previous i-1 epochs, in meters; is the mean of the combined MW observations of the previous i epochs, in meters; σi is the RMS error of the combined MW observations of the previous i epochs, in meters;

[0054] For the ith epoch MW combined observation like If , it is out of limit, and epoch i may be a cycle slip or a gross error;

[0055] In order to determine whether epoch i is a gross error or a cycle slip, the data characteristics between the three adjacent epochs i-1, i and i+1 are further analyzed; the wide lane ambiguity and variance of epoch i+1 are calculated according to the formula based on the wide lane ambiguity and variance of epoch i-1:

[0056]

[0057] If the widelane ambiguity at epoch i+1 is within the limit, or the widelane ambiguity at epochs i and i+1 are both exceeded and epoch i is considered a gross error; if the wide lane ambiguity of epochs i and i+1 exceeds the limit and It is considered that there is a cycle slip at epoch i; the previous i-1 epochs are regarded as an arc segment, and the Value and variance For subsequent processing, it is divided into a new arc segment starting from epoch i;

[0058] Constructing combined observables;

[0059] According to the following formula, the GF phase combination observation is constructed and pseudorange ionospheric residual combination ρ GF :

[0060]

[0061] ρ GF =ρ1-ρ2 (12)

[0062] Among them, subscript 1 and subscript 2 represent frequency numbers, and λ represents the carrier phase observation value. The corresponding wavelength, ρ represents the geometric distance between the satellite and the ground;

[0063] Let x be the total number of observation epochs, x = 0, 1, 2, ...., X corresponds to the sequence number of consecutive observation epochs, according to the formula:

[0064] ρ GF (x) = a m x m +a m-1 x m-1 +...+a1x+a0 (13)

[0065] Among them, X is the ambiguity vector, and the parameters of each order term a m am-1 , ..., a1, a0 are the design matrices that connect the unknown parameters and the observed values. The data of these epochs are fitted to obtain the parameters of each order. When X / 100 ≥ 6, the polynomial order m = 6, otherwise it is m = X / 100 + 1. After determining the parameter value, substitute x into the formula to obtain q GF The theoretical truth value q GF ;

[0066] ρ GF (x) = a m x m +a m- 1x m-1 +...+a1x+a0 (14)

[0067] If the formula is satisfied:

[0068]

[0069] Where λ represents the carrier phase observation value The corresponding wavelength, is the GF phase combination observation value of the i-th epoch; is the extrapolated value of the fitting polynomial of the i-th epoch, it can be determined that there is a cycle slip on the i-th epoch, and a new arc segment is divided starting from the i-th epoch, sliding backward to the next epoch.

[0070] Optionally, the data editing module includes:

[0071] Extract observation data according to the sampling interval and modify the data sampling rate information in the observation file header;

[0072] Extract observation data according to operation time;

[0073] If the job is performed every other day, it is necessary to merge the data across days and modify the file header of the merged file.

[0074] The present invention also provides a method for analyzing and managing the quality of observation data based on the Beidou reference station network, using the above-mentioned method for analyzing and managing the quality of observation data based on the Beidou reference station network, the method comprising:

[0075] Perform data conversion on the original Beidou observation data collected by the base station;

[0076] Concatenate the hourly files of observation data into a daily file;

[0077] Store data files in text format and conduct a standard review of file headers;

[0078] Read the base station GNSS observation data and navigation ephemeris data to perform data quality analysis of BDS / GPS / Galileo / GLONASS navigation systems and all public navigation frequencies;

[0079] Display base station quality images and index value information;

[0080] Edit the data.

[0081] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0082] 1. The present invention covers a full range of processes from format conversion of raw observation data, data splicing, information accuracy verification, to detailed data quality analysis, and finally data editing and optimization; at the data quality analysis level, the present invention particularly focuses on key indicators such as data integrity, signal-to-noise ratio evaluation, multipath delay, and cycle slip, ensuring the reliability and accuracy of the data.

[0083] 2. The present invention effectively solves the complex problems of Beidou observation data editing, quality analysis, etc., and constructs an efficient and flexible base station network data management system; the system can not only realize the efficient management and storage of Beidou observation data, but also support users to customize the output and processing of data according to actual needs, which greatly improves the flexibility and efficiency of data processing and provides technical support for the widespread application of the Beidou system. BRIEF DESCRIPTION OF THE DRAWINGS

[0084] Figure 1 A schematic diagram of an observation data quality analysis and management system based on the Beidou reference station network provided in an embodiment of the present application. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0086] Embodiment 1:

[0087] See also Figure 1 This embodiment provides a Beidou reference station network observation data quality analysis and management system, the system comprising:

[0088] The data conversion module is used to convert the original Beidou observation data collected by the base station.

[0089] The original Beidou observation data collected by the base station is usually stored in T02 format and organized by hour. To facilitate subsequent data processing and analysis, the data conversion module calls the convertToRinex software to convert the T02 format data into the Rinex 3.04 standard format, which is the universal format of GNSS observation data.

[0090] The data splicing module is used to splice the hourly files of observation data into a daily file.

[0091] The data verification module is used to store data files in text form and perform a normative review of the file header.

[0092] The data files in Rinex format are stored in text form and consist of two sections: file header and data record. The file header contains information such as the station name, antenna information, approximate coordinates of the station, number and type of observation values, and observation epoch interval. The file header is reviewed for standardization, and the data after review is stored in accordance with the prescribed storage and management rules.

[0093] The data quality analysis module is used to read the base station GNSS observation data and navigation ephemeris data and other data to realize data quality analysis of BDS / GPS / Galileo / GLONASS navigation systems and all public navigation frequencies.

[0094] The data quality analysis module includes a data integrity analysis unit, a signal-to-noise ratio analysis unit, a multipath effect analysis unit and a cycle slip ratio analysis unit.

[0095] The data integrity analysis unit includes:

[0096] Read the broadcast ephemeris and observation data files to obtain the pseudorange and carrier observations of different frequencies of a single satellite.

[0097] The pseudorange and carrier of the visible satellites at each epoch are judged to be non-zero.

[0098] Determine the number of epochs that are valid for each frequency point of each satellite.

[0099] Based on the data integrity of each frequency point of each satellite, the data integrity of all satellites combined is given.

[0100] The data integrity of the observation data collected by the GNSS receiver is calculated, which is divided into single-frequency data integrity rate and single-system data integrity, and is calculated according to the following formulas respectively;

[0101]

[0102] Dl f =(∑DI satf / nsat)×100% (2)

[0103] Where: DI sat，f Indicates the data integrity of a single satellite and a single frequency point; DI f Indicates the integrity of the single-frequency data of the system; n indicates the number of epochs in a period of time; nsat indicates the total number of satellites in the system; A i Indicates the total theoretical epoch data volume of a satellite continuous arc at a certain frequency point within a period of time; B i It indicates the total amount of actual observation epoch data of a satellite continuous arc at a certain frequency point within a period of time.

[0104] The signal-to-noise ratio analysis unit includes:

[0105] According to the set signal-to-noise ratio sampling interval, the signal-to-noise ratio of each satellite and each frequency point in the original observation file is sampled, and the results are stored uniformly to provide data basis for subsequent signal-to-noise ratio graphic display.

[0106] The altitude angle of each epoch is calculated according to the broadcast ephemeris, and the sequence of the signal-to-noise ratio and altitude angle of the same epoch is analyzed to obtain the signal-to-noise ratio at different altitude angles.

[0107] After reading the original observation data and configuration information, the signal-to-noise ratio data of each frequency point is obtained through the original observation data, and the temporal and spatial variation characteristics of the signal-to-noise ratio are analyzed. The analysis content includes the time series analysis of the signal-to-noise ratio and the height angle series analysis of the signal-to-noise ratio. The calculation formula is:

[0108]

[0109] Where SNR represents the signal-to-noise ratio of each satellite, P_signal represents the power of the signal, and P_noise represents the power of the noise.

[0110] The multipath effect analysis unit includes:

[0111] The pseudorange and carrier phase data of two frequency points of the same satellite are selected to calculate the multipath effect.

[0112] The multipath effect results of each epoch are statistically calculated to obtain the multipath effect results corresponding to the satellite.

[0113] The multipath effect results of all satellites are statistically analyzed to obtain the receiver multipath effect evaluation results.

[0114] The multipath delay calculation mainly relies on dual-frequency observation data. The pseudorange observation equation and the carrier phase observation equation are combined to eliminate the tropospheric delay and ionospheric delay, ignore the carrier phase multipath size, and obtain the pseudorange multipath information. The pseudorange multipath expressions of the B1 and B2 frequency points of the Beidou satellite navigation system are as follows:

[0115] For B1 frequency:

[0116]

[0117] For B2 frequency:

[0118]

[0119] Among them, s is the satellite in a certain period of time, r is the receiver, MP1 and MP2 are the multipaths of B1 and B2 frequency points respectively, P1 and P2, B1 and B2, f1 and f2 are the corresponding pseudorange observation values, carrier phase observation values, and frequency point frequency values.

[0120] Multipath error evaluation value MP k The calculation method is as follows:

[0121]

[0122] In the formula, It represents the amount of computation required when the receiver observes the satellite at frequency k at the tth epoch, including multipath errors and integer ambiguity information.

[0123] The cycle slip ratio analysis unit includes:

[0124] The observed quantities are used to construct phase combinations, pseudorange combinations and MW detection quantities.

[0125] The GF combination and the MW combination are used to detect large cycle slips respectively.

[0126] The phase combination and pseudorange combination detection quantities are subtracted, and then the differences are differentiated between epochs to obtain the residuals. The MW detection quantities are subtracted between epochs, and then the secondary differences between epochs are performed to obtain the residuals.

[0127] The residuals obtained above are respectively subjected to threshold judgment. If the judgment conditions are met, a cycle slip occurs and is marked, and the next epoch is processed for judgment. If the conditions are not met, the next epoch is directly processed.

[0128] After processing all epochs, count the number of cycle slips and calculate the cycle slip ratio.

[0129] The observation data collected by the GNSS receiver is used to detect cycle slips. The ionospheric residual method and the wide-narrow phase narrow lane pseudo-range method are used together, and the MW combined observations and the GF combined observations are used for joint analysis and judgment. The gross error of the cycle slip is detected according to the cycle slip judgment method. If there is no cycle slip, the GF combined observations are used for supplementary detection. The main steps are:

[0130] Construct the recursive formula according to the formula:

[0131]

[0132] Where: is the MW combined observation value of the i-th epoch, in meters; is the mean of the MW combined observations in the previous i-1 epochs, in meters; is the mean of the combined MW observations of the previous i epochs, in meters; σ i is the RMS error of the MW combined observations in the previous i epochs, in meters.

[0133] For the ith epoch MW combined observation like If , it is out of limit, and epoch i may be a cycle slip or a gross error.

[0134] In order to determine whether epoch i is a gross error or a cycle slip, the data characteristics between the three adjacent epochs i-1, i and i+1 are further analyzed; the wide lane ambiguity and variance of epoch i+1 are calculated according to the formula based on the wide lane ambiguity and variance of epoch i-1:

[0135]

[0136] If the widelane ambiguity at epoch i+1 is within the limit, or the widelane ambiguity at epochs i and i+1 are both exceeded and epoch i is considered a gross error; if the wide lane ambiguity of epochs i and i+1 exceeds the limit and It is considered that there is a cycle slip at epoch i; the previous i-1 epochs are regarded as an arc segment, and the Value and variance For subsequent processing, it is divided into a new arc segment starting from epoch i.

[0137] Construct a combined observable:

[0138] According to the following formula, the GF phase combination observation is constructed and pseudorange ionospheric residual combination ρ GF :

[0139]

[0140] ρ GF =ρ1-ρ2 (12)

[0141] Among them, subscript 1 and subscript 2 represent frequency numbers, and λ represents the carrier phase observation value. The corresponding wavelength, ρ represents the geometric distance between the satellite and the earth.

[0142] Let x be the total number of observation epochs, x = 0, 1, 2, ...., X corresponds to the sequence number of consecutive observation epochs, according to the formula:

[0143] ρ GF (x) = am x m +a m-1 x m-1 +...+a1x+a0 (13)

[0144] Among them, X is the ambiguity vector, and the parameters of each order term a m a m-1 , ..., a1, a0 are the design matrices that connect the unknown parameters and the observed values. The data of these epochs are fitted to obtain the parameters of each order. When X / 100 ≥ 6, the polynomial order m = 6, otherwise it is m = X / 100 + 1; after determining the parameter value, substitute x into the formula to obtain q GF The theoretical truth value q GF :

[0145] ρ GF (x) = a m x m +a m-1 x m-1 +...+a1x+a0 (14)

[0146] If the formula is satisfied:

[0147]

[0148] Where λ represents the carrier phase observation value The corresponding wavelength, is the GF phase combination observation value of the i-th epoch; is the extrapolated value of the fitting polynomial of the i-th epoch, it can be determined that there is a cycle slip on the i-th epoch, and a new arc segment is divided starting from the i-th epoch, sliding backward to the next epoch.

[0149] Visual display module, used to display base station quality images and indicator value information.

[0150] The visual display of data quality analysis automatically displays the base station quality image and indicator value information according to the set time span and the selected base station name.

[0151] Data editing module, used for data editing operations.

[0152] Data editing operations can be performed according to requirements. The data editing module includes:

[0153] Extract observation data according to the sampling interval and modify the data sampling rate information in the observation file header.

[0154] Extract observation data according to operation time.

[0155] If the job is performed every other day, it is necessary to merge the data across days and modify the file header of the merged file.

[0156] This embodiment covers a full range of processes from format conversion of raw observation data, data splicing, information accuracy verification, to detailed data quality analysis, and finally data editing and optimization. At the data quality analysis level, the present invention particularly focuses on key indicators such as data integrity, signal-to-noise ratio evaluation, multipath delay, and cycle slips to ensure data reliability and accuracy.

[0157] This embodiment effectively solves complex problems such as editing and quality analysis of Beidou observation data, and builds an efficient and flexible base station network data management system; the system can not only realize the efficient management and storage of Beidou observation data, but also support users to customize the output and processing of data according to actual needs, which greatly improves the flexibility and efficiency of data processing and provides technical support for the widespread application of the Beidou system.

[0158] Embodiment 2:

[0159] Embodiment 2 provides a method for quality analysis and management of observation data based on Beidou reference station network, using a method for quality analysis and management of observation data based on Beidou reference station network provided by Embodiment 1, the method comprises the following steps:

[0160] S1. Perform data conversion on the original Beidou observation data collected by the base station.

[0161] S2. Concatenate the hourly files of observation data into a daily file.

[0162] S3. Store the data files in text format and conduct a regulatory review of the file headers.

[0163] S4. Read the base station GNSS observation data and navigation ephemeris data to perform data quality analysis of BDS / GPS / Galileo / GLONASS navigation systems and all public navigation frequencies.

[0164] S5. Display the base station quality image and index value information.

[0165] S6. Edit the data.

[0166] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A Beidou reference station network observation data quality analysis and management system, characterized in that: The system comprises: Data conversion module, used to convert the original Beidou observation data collected by the base station; The data splicing module is used to splice the hourly files of observation data into a daily file; The data verification module is used to store data files in text form and conduct a normative review of the file header; Data quality analysis module, used to read the base station GNSS observation data and navigation ephemeris data, and implement data quality analysis of BDS / GPS / Galileo / GLONASS navigation systems and all public navigation frequencies; Visual display module, used to display the base station quality image and index value information; Data editing module, used for data editing operations.

2. The Beidou reference station network observation data quality analysis and management system according to claim 1, characterized in that: The data conversion module calls convertToRinex software to convert the data in T02 format into the Rinex 3.04 standard format, a universal format for GNSS observation data.

3. The Beidou reference station network observation data quality analysis and management system according to claim 1, characterized in that: The file header stores the station name, antenna information, approximate coordinates of the station, the number and type of observation values, and the observation epoch interval.

4. The Beidou reference station network observation data quality analysis and management system according to claim 1, characterized in that: The data quality analysis module includes a data integrity analysis unit, a signal-to-noise ratio analysis unit, a multipath effect analysis unit and a cycle slip ratio analysis unit.

5. The Beidou reference station network observation data quality analysis and management system according to claim 4, characterized in that: The data integrity analysis unit includes: Read broadcast ephemeris and observation data files to obtain pseudorange and carrier observations at different frequencies of a single satellite; The pseudorange and carrier of the visible satellites in each epoch are judged to be non-zero; Determine the number of epochs valid for each frequency point of each satellite; According to the data integrity of each satellite and each frequency point, the data integrity of all satellites after integration is given; The data integrity of the observation data collected by the GNSS receiver is calculated, which is divided into single-frequency data integrity rate and single-system data integrity, and is calculated according to the following formulas: IN f =(∑DI sat，f / nsat)×100% (2) Where: DI sat，f Indicates the data integrity of a single satellite and a single frequency point; DI f Indicates the integrity of the single-frequency data of the system; n indicates the number of epochs in a period of time; nsat indicates the total number of satellites in the system; A i Indicates the total theoretical epoch data volume of a satellite continuous arc at a certain frequency point within a period of time; B i It indicates the total amount of actual observation epoch data of a satellite continuous arc at a certain frequency point within a period of time.

6. The Beidou reference station network observation data quality analysis and management system according to claim 4, characterized in that: The signal-to-noise ratio analysis unit includes: According to the set signal-to-noise ratio sampling interval, the signal-to-noise ratio of each satellite and each frequency point in the original observation file is sampled, and the results are stored uniformly to provide data basis for subsequent signal-to-noise ratio graphic display; Calculate the altitude angle of each epoch according to the broadcast ephemeris, analyze the sequence of signal-to-noise ratio and altitude angle of the same epoch, and obtain the signal-to-noise ratio at different altitude angles by statistics; After reading the original observation data and configuration information, the signal-to-noise ratio data of each frequency point is obtained through the original observation data, and the temporal and spatial variation characteristics of the signal-to-noise ratio are analyzed. The analysis content includes the time series analysis of the signal-to-noise ratio and the height angle series analysis of the signal-to-noise ratio. The calculation formula is: Where SNR represents the signal-to-noise ratio of each satellite, P_signal represents the power of the signal, and P_noise represents the power of the noise.

7. The Beidou reference station network observation data quality analysis and management system according to claim 4, characterized in that: The multipath effect analysis unit includes: Select the pseudo-range and carrier phase data of two frequency points of the same satellite to calculate the multipath effect; The multipath effect result of each epoch is statistically calculated to obtain the multipath effect result corresponding to the satellite; The multipath effect results of all satellites are counted to obtain the receiver multipath effect evaluation results; The multipath delay calculation mainly relies on dual-frequency observation data. The pseudorange observation equation and the carrier phase observation equation are combined to eliminate the tropospheric delay and ionospheric delay, ignore the carrier phase multipath size, and obtain the pseudorange multipath information. The pseudorange multipath expressions of the B1 and B2 frequency points of the Beidou satellite navigation system are as follows: For B1 frequency: For B2 frequency: Where s is the satellite in a certain period of time, r is the receiver, MP1 and MP2 are the multipaths of B1 and B2 frequency points respectively, P1 and P2, B1 and B2, f1 and f2 are the corresponding pseudorange observation values, carrier phase observation values, and frequency point frequency values; Multipath error evaluation value MP k The calculation method is as follows: In the formula, It represents the amount of computation required when the receiver observes the satellite at frequency k at the tth epoch, including multipath errors and integer ambiguity information.

8. The Beidou reference station network observation data quality analysis and management system according to claim 4, characterized in that: The cycle slip ratio analysis unit includes: Use the observed quantities to construct phase combinations, pseudorange combinations and MW detection quantities; Use GF combination and MW combination to detect large cycle slips respectively; The phase combination and pseudorange combination detection quantities are subtracted, and then the difference is performed between epochs to obtain the residual. The MW detection quantity is subtracted between epochs, and then the secondary difference between epochs is performed to obtain the residual. The residuals obtained above are judged by thresholds respectively. If the judgment conditions are met, a cycle slip occurs and is marked, and the next epoch is processed for judgment. If the conditions are not met, the next epoch is directly processed; After processing all epochs, count the number of cycle slips and calculate the cycle slip ratio; The observation data collected by the GNSS receiver is used to detect cycle slips. The ionospheric residual method and the wide-narrow phase narrow lane pseudo-range method are used together, and the MW combined observations and the GF combined observations are used for joint analysis and judgment. The gross error of the cycle slip is detected according to the cycle slip judgment method. If there is no cycle slip, the GF combined observations are used for supplementary detection. The main steps are: Construct the recursive formula according to the formula: Where: is the MW combined observation value of the i-th epoch, in meters; is the mean of the MW combined observations in the previous i-1 epochs, in meters; is the mean of the combined MW observations of the previous i epochs, in meters; σ i is the RMS error of the combined MW observations of the previous i epochs, in meters; For the ith epoch MW combined observation like If , it is out of limit, and epoch i may be a cycle slip or a gross error; In order to determine whether epoch i is a gross error or a cycle slip, the data characteristics between the three adjacent epochs i-1, i and i+1 are further analyzed; the wide lane ambiguity and variance of epoch i+1 are calculated according to the formula based on the wide lane ambiguity and variance of epoch i-1: If the widelane ambiguity at epoch i+1 is within the limit, or the widelane ambiguity at epochs i and i+1 are both exceeded and epoch i is considered a gross error; if the wide lane ambiguity of epochs i and i+1 exceeds the limit and It is considered that there is a cycle slip at epoch i; the previous i-1 epochs are regarded as an arc segment, and the Value and variance For subsequent processing, it is divided into a new arc segment starting from epoch i; Constructing combined observables; According to the following formula, the GF phase combination observation is constructed and pseudorange ionospheric residual combination ρ GF : r GF =ρ1-ρ2 (12) Among them, subscript 1 and subscript 2 represent frequency numbers, and λ represents the carrier phase observation value. The corresponding wavelength, ρ represents the geometric distance between the satellite and the ground; Let x be the total number of observation epochs, x = 0, 1, 2, ...., X corresponds to the sequence number of consecutive observation epochs, according to the formula: r GF (x)=a m x m +a m-1 x m-1 +...+a1x+a0 (13) Among them, X is the ambiguity vector, and the parameters of each order term a m a m-1 , ..., a1, a0 are the design matrices that connect the unknown parameters and the observed values. The data of these epochs are fitted to obtain the parameters of each order. When X / 100 ≥ 6, the polynomial order m = 6, otherwise it is m = X / 100 + 1. After determining the parameter value, substitute x into the formula to obtain q GF The theoretical truth value q GF ; r GF (x)=a m x m +a m-1 x m-1 +...+a1x+a0 (14) If the formula is satisfied: Where λ represents the carrier phase observation value The corresponding wavelength, is the GF phase combination observation value of the i-th epoch; is the extrapolated value of the fitting polynomial of the i-th epoch, it can be determined that there is a cycle slip on the i-th epoch, and a new arc segment is divided starting from the i-th epoch, sliding backward to the next epoch.

9. The Beidou reference station network observation data quality analysis and management system according to claim 1, characterized in that: The data editing module comprises: Extract observation data according to the sampling interval and modify the data sampling rate information in the observation file header; Extract observation data according to operation time; If the job is performed every other day, it is necessary to merge the data across days and modify the file header of the merged file.

10. A method for analyzing and managing observation data quality based on Beidou reference station network, characterized in that: Using a Beidou reference station network observation data quality analysis and management system as described in any one of claims 1 to 9, the method comprises: Perform data conversion on the original Beidou observation data collected by the base station; Concatenate the hourly files of observation data into a daily file; Store data files in text format and conduct a standard review of file headers; Read the base station GNSS observation data and navigation ephemeris data to perform data quality analysis of BDS / GPS / Galileo / GLONASS navigation systems and all public navigation frequencies; Display base station quality images and index value information; Edit the data.

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