Large-scale GNSS base station network data automatic processing method and device

Abstract

The invention discloses a large-scale GNSS reference station network data automatic processing method and device, and belongs to the technical field of earth science data processing. The method comprises the following steps: selecting an IGS frame station to carry out coordinate frame conversion to generate a frame station coordinate file, and automatically downloading and updating an auxiliary file; obtaining an observation station observation file, and carrying out receiver and antenna type inspection, data thinning processing and standardized naming; the method comprises the following steps: dividing a large-scale observation station network into a plurality of partitions, and automatically configuring calculation parameters of each partition and completing baseline calculation and network adjustment based on a frame station coordinate file and an auxiliary file; precise coordinates obtained through network adjustment are automatically extracted, the resolving precision is evaluated, and a timed task is set to achieve continuous automatic processing. The method has the characteristics of high efficiency, accuracy and automation, and can provide powerful technical support for large-scale geoscience research and application.

Description

Technical Field

[0001] The present invention belongs to the technical field of earth science data processing, and in particular relates to a method and device for automatically processing data of a large-scale GNSS reference station network. Background Art

[0002] With the continuous development of the Global Navigation Satellite System (GNSS), its application in fields such as earth science, surveying and mapping, and weather forecasting is becoming increasingly widespread. As a crucial infrastructure for obtaining high-precision position and time information, the GNSS base station network and its data processing technology are particularly important.

[0003] Traditional GNSS data processing methods rely heavily on manual labor, resulting in cumbersome and inefficient processes. Furthermore, with the continuous expansion of the GNSS base station network, the data processing workload has increased dramatically, making traditional methods unable to meet the demands of large-scale data processing.

[0004] GAMIT software is a widely used GNSS data processing tool with high accuracy and strong stability. However, its relatively complex operation and low degree of automation limit its application in large-scale data processing.

[0005] Therefore, developing an automatic data processing method for a large-scale GNSS reference station network has important practical application value and scientific significance. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a method and device for automatically processing data from a large-scale GNSS reference station network. By automating the processing flow, the data processing efficiency and quality are improved to meet the needs of large-scale geological research and applications.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] A method for automatically processing data from a large-scale GNSS reference station network, the method comprising:

[0009] Step S1, select the IGS frame station to perform coordinate frame conversion to generate the frame station coordinate file, and automatically download and update the auxiliary file;

[0010] Step S2: Obtain the observation file of the observation station and perform receiver and antenna type inspection, data thinning and standardized naming;

[0011] Step S3, the large-scale station network is divided into multiple partitions, based on the framework station coordinate file and auxiliary files, each partition solution parameter is automatically configured and the baseline solution and network adjustment are completed;

[0012] Step S4: Automatically extract the precise coordinates obtained by network adjustment and evaluate the solution accuracy, and set a scheduled task to achieve continuous automated processing.

[0013] In another aspect, the present invention provides a large-scale GNSS reference station network data automatic processing device, comprising:

[0014] Preparation module, used to select IGS frame station for coordinate frame conversion to generate frame station coordinate file, and automatically download and update auxiliary files;

[0015] The station data standardization processing module is used to obtain station observation files and perform receiver and antenna type checks, data thinning and standardized naming;

[0016] The partition solution processing module is used to divide the large-scale station network into multiple partitions. Based on the frame station coordinate file and auxiliary files, it automatically configures the solution parameters of each partition and completes the baseline solution and network adjustment.

[0017] The output module is used to automatically extract the precise coordinates obtained by network adjustment and evaluate the solution accuracy, and set scheduled tasks to achieve continuous automated processing.

[0018] In a third aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for automatic processing of large-scale GNSS reference station network data.

[0019] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned method for automatically processing data of a large-scale GNSS reference station network.

[0020] The beneficial effects of the present invention are:

[0021] The present invention organically combines automation technology with advanced data processing algorithms to achieve fully automated data processing. Starting from data collection, through coordinate extraction, to completing accuracy assessment, the present invention greatly improves the efficiency of the entire processing process. During the data processing stage, the present invention can automatically download and update the required auxiliary files, while performing fine processing on the observation files to ensure the integrity and accuracy of the data. In addition, by adopting a station partitioning strategy, the present invention effectively responds to the challenges of large-scale data processing and ensures the efficiency and quality of the solution. During the parameter configuration and solution process, the present invention can automatically adjust the parameters according to the characteristics of each partition to ensure the accuracy and stability of the solution. The present invention can also automatically extract precise coordinate information, and automatically evaluate the accuracy of the station position by comparing it with the data of the previous day, providing reliable data support for scientific research and practical applications. Finally, by setting timed tasks, the present invention realizes real-time processing and continuous monitoring of data, further enhancing the timeliness and reliability of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a method for automatically processing data of a large-scale GNSS reference station network according to the present invention;

[0023] Figure 2 Schematic diagram of a large-scale GNSS reference station network data automatic processing device of the present invention. DETAILED DESCRIPTION

[0024] The present invention will be further described below with reference to the accompanying drawings and examples.

[0025] like Figure 1 As shown, a large-scale GNSS reference station network data automatic processing method of the present invention includes the following steps:

[0026] Step S1, automatically obtain frame station information and complete data preparation: select the IGS frame station to perform coordinate frame conversion to generate the frame station coordinate file, and automatically download and update the auxiliary file; including:

[0027] Step S11: Obtain frame station information. Based on the station distribution information, select IGS stations with good distribution around the station as frame stations for subsequent calculations, obtain the precise coordinate information of the IGS stations, and then perform coordinate frame conversion based on the velocity information and the coordinate information under the fixed epoch to obtain the frame station coordinates that meet the user's requirements. Store them in the frame.txt file as the frame station coordinate file. The file format is: <station><x_coord> <y_coord> <z_coord> <x_vel> <y_vel> <z_vel> <epoch> The order is station name, x-coordinate under a specific frame epoch, y-coordinate under a specific frame epoch, z-coordinate under a specific frame epoch, x-velocity under a specific frame epoch, y-velocity under a specific frame epoch, z-velocity under a specific frame epoch, and a specific frame epoch. The document sample is: BADG_GPS -838281.93058 3865777.32617 4987624.58970 -0.02711 0.00017 -0.00355 2015.00 BJFS_GPS -2148744.39656 4426641.20084 4044655.84256 -0.03238 -0.00603-0.00714 2015.00 CHAN_GPS -2674427.51713 3757143.12333 4391521.57678 -0.02713 -0.00925-0.00965 2015.00 DAEJ_GPS -3120042.26632 4084614.75772 3764026.82989 -0.02701 -0.01231-0.00894 2015.00 OSN4_GPS -3068341.08234 4066863.87167 3824756.92050 -0.02627 -0.00929-0.00982 2015.00 SEJN_GPS -3110081.88381 4082093.94123 3775023.50558 -0.02786 -0.01241-0.00886 2015.00 SHAO_GPS -2831733.81522 4675665.82028 3275369.30342 -0.03025 -0.01233-0.01100 2015.00 SUWN_GPS -3062023.14578 4055447.88110 3841818.16774 -0.02796 -0.01278-0.00902 2015.00 Step S12: Download precision files and update table files. Automatically download auxiliary files, including precise orbit files (SP3 files), broadcast ephemeris files (brdc files), and IGS frame station observation files, and save them all to a specific path. Download IGS frame station observation files based on the frame station name in the frame.txt file. Automatically convert the downloaded d file type to o file using the CRX2RNX program, and then rename them to the format {xxxx}{doy}0.{yr}o. Here, {xxxx} represents the lowercase four-digit station name, {doy} is the three-digit annual day, and {yr} is the two-digit year. When updating table files, the current solution epoch and the last table file update date are automatically determined. If the tables update date is later than the current solution epoch, they will not be updated again.

[0028] Step S2: Standardization of station data: Obtaining station observation files and performing receiver and antenna type checks, data thinning, and standardized naming; including:

[0029] Obtain the standard observation file in rinex format for the desired station. First, check the receiver and antenna types and rename the file. For some private stations, the observation files may lack the receiver and antenna types. Search for the corresponding antenna and receiver types based on the station name. Automatically update the receiver information in the "REC # / TYPE / VERS" line and the antenna type in the "ANT # / TYPE" line of the observation file. Pay attention to character alignment. For data with a sampling interval of 1s, thin the data to 30s. After decompressing the data, rename all station observation files to the format {xxxx}{doy}0.{yr}o. {xxxx} represents the four-digit lowercase station name, {doy} is the three-digit day of the year, and {yr} is the two-digit year.

[0030] Step S3, partition solution processing: Divide the large-scale station network into multiple partitions, frame station coordinate files and auxiliary files, automatically configure the solution parameters of each partition and complete the baseline solution and network adjustment; including:

[0031] Step S31: Station partitioning. For large-scale GNSS reference station networks, the number of stations is first determined. If there are no more than 60 stations, no partitioning is required and the solution is performed directly. Otherwise, station partitioning is required. Each area has no more than 60 stations, and the area is divided into N partitions. The station names are stored in N files.

[0032] Step S32: Automatically configure parameters for each partition. N project files are automatically created based on the N generated partition files. The corresponding observation files are sequentially placed into the rinex files under each of the n projects. The auxiliary files (precision product files and table files) generated in step S12 are copied sequentially. Parameter configuration is performed for each partition in a loop. The station coordinates in the lfile file are updated (if this is not the first solution, the lfile file automatically updated during the previous solution is used). Fixed stations in the sites.defaults file are specified, and parameters in the sittbl file are automatically set according to user requirements. A log is generated and saved in a specific path.

[0033] Step S33: Baseline solution and network adjustment. Call the sh_gamit command to perform baseline solution partition by partition. Automatically copy the h files generated by all partitions to the first partition folder, changing the suffix to prevent duplication. Automatically configure the cmd configuration file parameters in gsoln according to user needs to perform unified adjustment solution for multiple networks.

[0034] Step S4: Result evaluation and automated execution: Automatically extract precise coordinates and evaluate solution accuracy, set scheduled tasks to achieve continuous automated processing; including:

[0035] Step S41, precise coordinate extraction and automatic update of lfile file. According to the org result file solved in step S33, the coordinates of all stations involved in the solution are obtained, wherein the precise coordinates of all stations to be solved are placed in the station_coord_{ year}{doy}.txt file. According to the station name in the frame.txt file, the precise coordinate information of all frame stations after solution is extracted, and compared with the real frame station coordinates in frame.txt to obtain the accuracy of the frame station, named frame_accu_{ year}{doy}.txt, which can be used to simply evaluate the accuracy of the solution. Among them, { year} is the four-digit year, and { doy} is the three-digit annual cumulative day. And the precise coordinates finally obtained are automatically updated in the lfile file to ensure the highest accuracy of the initial coordinates, so as to ensure the stability of subsequent solutions. The sample frame_accu_{ year}{doy}.txt document is: chan 0.00060 -0.00293 0.00849 -2674427.75989 3757143.042784391521.50052 -2674427.76049 3757143.04571 4391521.49203 daej -0.00320 0.00291 0.00120 -3120042.51719 4084614.643173764026.75855 -3120042.51399 4084614.64026 3764026.75735 sejn 0.00118 -0.00077 -0.00068 -3110082.11696 4082093.833723775023.43254 -3110082.11814 4082093.83449 3775023.43322 shao -0.00151 0.00018 -0.00316 -2831734.07597 4675665.714993275369.21367 -2831734.07446 4675665.71481 3275369.21683 wuhn 0.00137 0.00899 0.00220 -2267750.06152 5009154.191363221290.56811 -2267750.06289 5009154.18237 3221290.56591 Step S42: Automatically assess the coordinate accuracy of the large-scale GNSS reference station network. Automatically compare the precise coordinates of all stations to be measured with the precise coordinates of the stations to be measured obtained the previous day. The obtained coordinate accuracy is stored in the station_accu_{year}{doy}.txt file, which can be used as a reference for assessing the station's position accuracy and stability. The document sample is: CHAN 0.00139 -0.00568 -0.00451 -2674427.75979 3757143.045084391521.48780 -2674427.76118 3757143.05076 4391521.49231 D074 -0.00212 -0.00021 -0.00295 -2730539.19386 4278471.945463849707.52792 -2730539.19174 4278471.94567 3849707.53087 DAEJ -0.00141 0.00034 -0.00177 -3120042.51250 4084614.637483764026.75566 -3120042.51109 4084614.63714 3764026.75743 SHAO -0.00088 -0.00635 -0.00477 -2831734.07271 4675665.704943275369.21732 -2831734.07183 4675665.71129 3275369.22209 Step S43: Execute the scheduled task. Execute the scheduled task to automatically execute the automatic solution program at 0:30 every morning, automatically obtain the accuracy information of the framework station and the station to be found, complete the automatic solution of the large-scale GNSS reference station network, and fully utilize the server resources.

[0036] In summary, the present invention not only automatically downloads and updates relevant auxiliary files but also meticulously processes observation files to ensure data integrity and accuracy. During parameter configuration and solution, parameters can be automatically adjusted based on the characteristics of different partitions to ensure solution accuracy and stability. Precise coordinate information is automatically extracted, and the accuracy of the station location is automatically assessed by comparison with the previous day's data, providing powerful data support for scientific research and practical applications. By setting scheduled tasks, real-time data processing and continuous monitoring are achieved, further improving the timeliness and reliability of data processing.

[0037] On the other hand, Figure 2 As shown, the present invention provides a large-scale GNSS reference station network data automatic processing device, which includes various modules capable of implementing various steps of the above-mentioned method, specifically including:

[0038] Preparation module, used to select IGS frame station for coordinate frame conversion to generate frame station coordinate file, and automatically download and update auxiliary files;

[0039] The station data standardization processing module is used to obtain station observation files and perform receiver and antenna type checks, data thinning and standardized naming;

[0040] The partition solution processing module is used to divide the large-scale station network into multiple partitions. Based on the frame station coordinate file and auxiliary files, it automatically configures the solution parameters of each partition and completes the baseline solution and network adjustment.

[0041] The output module is used to automatically extract the precise coordinates obtained by network adjustment and evaluate the solution accuracy, and set scheduled tasks to achieve continuous automated processing.

[0042] In a third aspect, the present invention provides an electronic device comprising: one or more processors; a memory for storing one or more programs; wherein, when the one or more programs are executed by the one or more processors, the one or more processors implement the aforementioned method for automatic processing of large-scale GNSS reference station network data.

[0043] In a fourth aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a processor, enables the processor to implement the aforementioned method for automatically processing data of a large-scale GNSS reference station network.

[0044] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk drives, CD-ROMs, optical storage devices, etc.) containing computer-usable program code. The solutions in the embodiments of the present invention may be implemented using various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript.

[0045] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0046] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0047] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0048] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0049] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.< / epoch> < / station>

Claims

1. A method for automatically processing data from a large-scale GNSS reference station network, characterized in that: The method comprises: Step S1: Select an IGS frame station to perform coordinate frame conversion to generate a frame station coordinate file, and automatically download and update auxiliary files; Step S2: Obtain the observation file of the observation station and perform receiver and antenna type inspection, data thinning and standardized naming; Step S3, the large-scale station network is divided into multiple partitions, based on the framework station coordinate file and auxiliary files, each partition solution parameter is automatically configured and the baseline solution and network adjustment are completed; Step S4: Automatically extract the precise coordinates obtained by network adjustment and evaluate the solution accuracy, and set a scheduled task to achieve continuous automated processing.

2. The method for automatically processing data of a large-scale GNSS reference station network according to claim 1, characterized in that: In step S1, the generation of the frame station coordinate file includes: selecting surrounding IGS stations as frame stations based on the station distribution; obtaining the precise coordinates and velocity information of the IGS stations; and generating a frame station coordinate file in the frame.txt format containing the station name, coordinates, velocity, and epoch information through coordinate frame conversion.

3. The method for automatically processing data of a large-scale GNSS reference station network according to claim 1, characterized in that: In step S1, the auxiliary files include precise orbit files, broadcast ephemeris files, and IGS frame station observation files. The IGS frame station observation files are downloaded based on the frame station coordinate file name in the frame.txt format. The downloaded d file types are automatically converted into o files based on the CRX2RNX program, and then all are renamed to include the observation station, annual accumulation day, and year information.

4. The method for automatically processing data of a large-scale GNSS reference station network according to claim 1, characterized in that: The step S2 includes: automatically supplementing the station observation files that lack receiver and antenna type information; performing thinning processing on the 1-second sampling interval data; and renaming all the station observation files to include the station, annual accumulation day, and year information.

5. The method for automatically processing data of a large-scale GNSS reference station network according to claim 1, characterized in that: The step S3 comprises: Step S31: Divide the large-scale measurement station network into N partitions, and save the measurement station names in N files respectively; Step S32: Automatically create a project file for each partition, copy auxiliary files, and automatically configure solution parameters, including updating lfile file coordinates, setting sites.defaults file and sittbl file parameters; Step S33: Call the sh_gamit command partition by partition to perform baseline solution, automatically merge the h files of each partition, and configure the gsoln parameters to complete the multi-network unified adjustment.

6. A method for automatically processing large-scale GNSS reference station network data according to claim 5, characterized in that: In step S4, automatically extracting precise coordinates and evaluating the solution accuracy includes: obtaining the coordinates of all stations involved in the solution according to the org result file solved in step S33, extracting the precise coordinate information of all frame stations after solution according to the station names in the frame.txt file, and comparing them with the frame station coordinates in frame.txt to obtain the frame station accuracy.

7. A method for automatically processing large-scale GNSS reference station network data according to claim 6, characterized in that: The step S4 further includes: automatically performing a difference comparison between the precise coordinates of all the stations to be measured and the precise coordinates of the stations to be measured obtained the previous day, obtaining the coordinate accuracy, and evaluating the position accuracy and stability of the stations.

8. A large-scale GNSS reference station network data automatic processing device, characterized in that: include: Preparation module, used to select IGS frame stations for coordinate frame conversion to generate frame station coordinate files, and automatically download and update auxiliary files; The station data standardization processing module is used to obtain station observation files and perform receiver and antenna type checks, data thinning and standardized naming; The partition solution processing module is used to divide the large-scale station network into multiple partitions. Based on the frame station coordinate file and auxiliary files, it automatically configures the solution parameters of each partition and completes the baseline solution and network adjustment. The output module is used to automatically extract the precise coordinates obtained by network adjustment and evaluate the solution accuracy, and set scheduled tasks to achieve continuous automated processing.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs; When one or more programs are executed by the one or more processors, the one or more processors implement the method for automatically processing large-scale GNSS reference station network data as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that Executable instructions are stored thereon, and when the instructions are executed by the processor, the processor can implement the large-scale GNSS reference station network data automatic processing method described in any one of claims 1-7.

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