GNSS Network RTK Reference Station Distributed Data Processing Method and Device

Abstract

The present invention discloses a distributed data processing method and device for GNSS network RTK base stations, which belongs to the field of satellite navigation and positioning technology. It is used to solve key problems such as base station networking, baseline solution, error correction number calculation and model establishment in large GNSS network RTK systems. It is particularly suitable for efficient calculation, task allocation and dynamic load balancing processing of large-scale base station networks. The method of the present invention includes: 1) the core server dynamically networks and determines the baseline to be solved, generates baseline solution tasks and establishes error models; 2) multiple data processing servers solve all baselines in parallel and extract baseline error correction numbers; 3) each base station calculates the relative correction numbers of other stations relative to the current station based on the relevant baseline correction numbers, and establishes an error model. 4) Dynamically allocate baseline solution tasks and error modeling tasks. The present invention significantly improves the task processing efficiency and resource utilization of large-scale base station networks through multi-threading and distributed parallel computing.

Description

Technical Field

[0001] The present invention belongs to the technical field of satellite navigation and positioning, and particularly relates to a distributed data processing method and device for a large-scale reference station for GNSS network real-time kinematic (RTK) positioning. Background Art

[0002] GNSS network RTK (Real-Time Kinematic) technology is a high-precision positioning method based on carrier phase observations, which is widely used in fields such as surveying and mapping, engineering monitoring, and geological disaster warning. Its core is to calculate the precise positioning information of the user terminal through the cooperation of multiple reference stations. In this process, the reference station data processing includes multiple links such as network formation and baseline solution, error correction number extraction and integration, error model establishment and data broadcast. The baseline solution task needs to calculate the baseline vectors between reference stations and extract error correction numbers, and then integrate the correction numbers of multiple baselines into relative correction numbers to establish a global error model and generate differential data. However, with the continuous expansion of the scale of the reference station network, the existing centralized processing architecture gradually exposes problems such as low efficiency, insufficient scalability, and poor system stability.

[0003] Currently, the data processing of large-scale reference stations in GNSS network RTK mainly adopts a centralized architecture, that is, the core server uniformly processes all reference station data. This method is more applicable to small-scale reference station networks in the early stage, but when facing hundreds or even thousands of reference stations, its computing power and task processing efficiency are difficult to meet the requirements. The computing bottleneck is one of the main problems of the centralized architecture. The large amount of baseline solution tasks and error model establishment calculations are huge, and the core server is prone to overloading, resulting in reduced real-time performance. In addition, the centralized system lacks the ability of dynamic management for task allocation and resource utilization, with uneven load distribution and difficulty in expansion. When the scale of the reference station network expands or the core server fails, the system may become paralyzed, seriously affecting the reliability of the positioning service. Summary of the Invention

[0004] In view of the above problems, the distributed data processing technology provides an efficient solution for GNSS network RTK. By introducing a distributed computing architecture and distributing the baseline solution and error model establishment tasks to multiple processing servers, parallel processing of tasks can be achieved, effectively improving the computing efficiency. In addition, through a dynamic task scheduling and load balancing mechanism, the distributed architecture makes full use of the computing resources of each server while reducing the pressure on the core server. Through the message bus to achieve efficient transmission of reference station data, task allocation and result interaction, the distributed system can also flexibly adapt to the needs of the expansion of the reference station network scale. In summary, the application of distributed data processing in GNSS network RTK has significant performance improvement and technical advantages, and has gradually become the mainstream direction of large-scale reference station network data processing.

[0005] The present invention proposes a distributed data processing method and system for GNSS network RTK reference stations. Through the distributed architecture design and task dynamic allocation mechanism, it solves the problems existing in the existing centralized processing and subnet processing modes, such as computing bottlenecks, low task scheduling efficiency, poor scalability, and complex coordination, and realizes the efficient processing of large-scale reference station network data and real-time error correction services.

[0006] The technical solution of the present invention for the distributed data processing method of GNSS network RTK reference stations includes the following steps:

[0007] Step 1, the core server receives the real-time observation data of all reference stations through the message bus, and determines the available reference stations; based on the available reference stations, the baselines are dynamically grouped based on Denaunay triangles, the number of baselines and geometric conditions are optimized to obtain a baseline set; the baseline set is used as the task to be solved and assigned to the baseline solution server; the available reference station set is used as the error modeling task and assigned to the error model establishment server;

[0008] Step 2, the baseline solution server first calculates the real-time resource occupancy assessment of the baseline solution server, and then performs baseline solution to calculate the double-difference ionospheric delay and double-difference tropospheric delay;

[0009] Step 3, the error model establishment server calculates the real-time resource occupancy assessment of the error model establishment server, then parallelly solves all baselines, extracts the baseline error correction numbers, calculates the relative correction numbers of other stations relative to the local station, and thus establishes the master station error model;

[0010] Step 4, the core server calculates the corresponding remaining computing capabilities according to the real-time resource occupancy assessments of the baseline solution server and the error model establishment server respectively, and dynamically allocates baseline solution tasks and error modeling tasks according to the corresponding remaining computing capabilities.

[0011] Furthermore, in Step 1, the specific implementation method of obtaining the baseline set is as follows:

[0012] Step 1.2.1, input the coordinates of all reference stations to form a point set , refers to the horizontal and vertical coordinates of the i-th point;

[0013] Step 1.2.2, initialize the triangle, and select a super triangle containing all points from the point set to cover the entire point set;

[0014] Step 1.2.3, sequentially insert the point into the current triangulation, which is the result of the initialized triangle, and check the point Whether it is within the circumcircle of a certain triangle. If satisfied, reconstruct the relevant triangle;

[0015] Step 1.2.4. If a certain side does not meet the Delaunay condition, that is, other points are included in the circumcircle, exchange the side and update the triangle structure;

[0016] Step 1.2.5. Remove the sides of the super triangle and output the remaining triangle set and baseline set.

[0017] Furthermore, in Step 2, the real-time resource occupancy evaluation calculation method of the baseline processing server is as follows:

[0018] Set the number of baselines processed by the baseline processing server to , and the CPU and memory resources occupied by each baseline solution are equal. The CPU occupancy rate of the baseline solution process in the current server is , and the memory occupancy rate is , then the CPU occupancy rate of a single baseline is and the memory occupancy rate is respectively:

[0019]

[0020] .

[0021] Furthermore, in Step 2, the calculation processes of the double-difference ionospheric delay and the double-difference tropospheric delay are as follows:

[0022] Step 2.2.1. The baseline solution server obtains the coordinate and observation value information of the baseline to be solved from the message bus;

[0023] Step 2.2.2. Based on the HMW combination method, determine the Beidou ultra-wide lane ambiguity, then based on the TCAR method of the ionosphere-free combination, determine the wide lane ambiguity, and finally determine the original ambiguity of the satellite based on the wide lane and the double-frequency ionosphere-free combined ambiguity;

[0024] Step 2.2.3. According to the already-fixed ambiguity and the reference station coordinate information, extract the error information related and unrelated to the baseline geometry. According to the L1 and L2 carrier double-difference observation equations:

[0025]

[0026]

[0027] Where , , , are the frequencies of carriers L1 and L2 respectively, , are the wavelengths of carrier waves L1 and L2 respectively, are the double-difference phase observations on carrier waves L1 and L2, is the double-difference geometric distance, are the L1 and L2 double-difference ambiguities, is the double-difference tropospheric delay, is the double-difference ionospheric delay; finally, the double-difference ionospheric delay and double-difference tropospheric delay with centimeter-level accuracy at the reference station are obtained, and their estimation formulas are as follows:

[0028]

[0029] .

[0030] Furthermore, in step 3, the calculation method for real-time resource occupancy evaluation of the error model establishment server is as follows:

[0031] Set the current number of sites processed by the error model establishment server to , and the CPU and memory resources occupied by the relative error calculation for each site are equal. The CPU occupancy rate of the main station error model establishment process in the current error model establishment server is , and the memory occupancy rate is , then the CPU occupancy rate and memory occupancy rate for the establishment of 1 main station error model are respectively:

[0032]

[0033] .

[0034] Furthermore, in step 3, the specific implementation method for establishing the main station error model is as follows:

[0035] Step 3.2.1, the error model establishment server obtains the relative error information of the baselines related to the site to be solved and several surrounding sites from the message bus;

[0036] Step 3.2.2, use the overall adjustment algorithm of baseline error corrections to extract baseline error corrections, which are carried out in three steps: 1) Use the triangle network closure difference test to judge whether there are gross errors; 2) Use the error corrections of each baseline to perform the overall adjustment solution of the relative error corrections; 3) Based on the adjustment results, locate and repair the gross errors and perform adjustment again;

[0037] Step 3.2.3, integrate the baseline error corrections of all main stations to construct the main station error model, which is expressed by the interpolation method as:

[0038]

[0039] where is the total number of slave stations, is the serial number of the slave station, is the slave station correction, is the slave station interpolation coefficient, is the slave station coordinate value relative to the interpolation position to be interpolated, is the correction of the position to be interpolated.

[0040] Furthermore, in step 3.2.2 (1), the closure error of the baseline error corrections of each side within the same triangle is within the minimum threshold range. Therefore, if the closure error of the triangular network exceeds the minimum threshold range, there must be at least one baseline error correction in the triangular network that is incorrect.

[0041] Furthermore, in step 3.2.2 (2), according to the basic principle of indirect adjustment, the baseline vector adjustment method is used to solve the corrections for a specified master station. According to the relationship of double differences and baseline transmission, the following error equations describing the relationships of each baseline vector are listed:

[0042] (2)

[0043] where are the error corrections of each baseline in the reference station triangular network, is the adjusted estimate of the double-difference relative error correction from the master station to other reference stations, is the coefficient matrix describing the linear relationship between the error corrections of each baseline in the triangular network and the relative error correction of the master station, is the residual of the error correction.

[0044] Furthermore, set the memory and CUP occupancy rates of all the baseline processing servers to and , and the maximum memory and CUP occupancy are both P%. Then the remaining computing power of the baseline processing server is:

[0045]

[0046] where and are the CUP occupancy rate and memory occupancy rate of a single baseline respectively;

[0047] Set the memory and CUP occupancy rates of all the error model establishment servers to and , and the maximum memory and CUP occupancy are both P%. Then the remaining computing power is:

[0048] Wherein, and are respectively the CPU occupancy rate and memory occupancy rate established by one master station error model;

[0049] And and meet the following conditions:

[0050] ,

[0051] The core server dynamically allocates baseline solution tasks and error modeling tasks according to and , that is, preferentially assign tasks with a large number of processed baselines.

[0052] The present invention also provides GNSS network RTK reference station distributed data processing, including:

[0053] A processor and a memory, the memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute the GNSS network RTK reference station distributed data processing method as described in the above technical solution.

[0054] The present invention provides a GNSS network RTK reference station distributed data processing method and system, which overcomes the deficiencies of the existing centralized processing and subnet processing modes through a distributed architecture and a task dynamic scheduling mechanism, and significantly improves the system performance and reliability. The specific beneficial effects are as follows:

[0055] 1. The calculation efficiency is significantly improved

[0056] Adopting a distributed computing architecture, the baseline solution and error model establishment tasks are assigned to multiple data processing servers, making full use of the computing resources of multiple servers, realizing parallel processing of tasks, and significantly reducing the calculation time. The dynamic task scheduling mechanism allocates tasks based on the real-time resource status of the servers, avoiding waste of computing resources and improving the overall computing efficiency of the system.

[0057] 2. The system scalability is enhanced

[0058] The distributed architecture supports flexible expansion and can quickly access new data processing servers according to the increase in the scale of the reference station network. Whether the number of reference stations increases or the complexity of the calculation tasks increases, the system can maintain efficient operation by adding computing nodes. It supports dynamic networking and task allocation strategies, so that the addition or deletion of reference stations or changes in the quality of observation data will not affect the normal operation of the system.

[0059] 3. The reliability and robustness are improved

[0060] Through task distributed allocation and multi-server collaboration, the system avoids the risk of single-point failure. Even if some servers fail, other servers can still continue to complete tasks, ensuring the stability of the system. The core server is mainly responsible for task allocation and result integration, without directly participating in calculations, significantly reducing the load pressure and failure risk of the core server.

[0061] 4. Enhanced real-time processing

[0062] Use the message bus to achieve efficient data interaction, support multi-threaded asynchronous communication, and ensure the rapid upload of reference station data, real-time task allocation, and rapid feedback of calculation results.

[0063] Through distributed parallel processing, the system shortens the processing time of baseline solution and error model establishment, and can meet the requirements of the GNSS network RTK system for real-time high-precision positioning services.

[0064] 5. Improved data processing accuracy

[0065] The dynamic networking algorithm optimizes baseline selection according to the observation data quality and geometric conditions of reference stations, reduces redundant baselines, and improves the accuracy of baseline solution.

[0066] The error correction integration algorithm is based on multi-baseline data and generates a more accurate error model through weighted average or least squares optimization, further improving the positioning accuracy of the user terminal.

[0067] 6. Optimization of data interaction and resource utilization efficiency

[0068] The message bus mechanism simplifies the data transmission process between the core server and data processing servers, improving data interaction efficiency.

[0069] Task allocation is dynamically adjusted based on the resource utilization rate and load of processing servers, ensuring uniform distribution of tasks and improving the resource utilization rate of servers.

[0070] 7. Enhanced adaptability and flexibility

[0071] The system can adjust the networking scheme and task allocation strategy according to the dynamic changes in reference station distribution and observation data quality, adapting to the needs of different scenarios.

[0072] Supports multiple task priority settings, can prioritize high-priority tasks, and meet the real-time requirements of specific scenarios (such as disaster warning).

[0073] 8. Practical application value

[0074] The present invention is widely applicable to fields such as high-precision surveying and mapping, intelligent driving, disaster warning, precision agriculture, etc., and is of great significance for enhancing the service capabilities of GNSS network RTK systems.

[0075] The distributed processing mode reduces the costs of hardware upgrade and system maintenance, and provides an economical and efficient solution for the popularization and application of large-scale GNSS networks.

[0076] Through an innovative distributed computing architecture and task scheduling mechanism, the present invention solves the technical bottlenecks of the centralized processing and sub-network processing modes in existing GNSS network RTK systems in terms of computing efficiency, real-time performance, scalability, and reliability, and has significant technical advantages and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flowchart of an embodiment of the present invention.

[0078] Figure 2 It is a structural diagram of a reference station network distributed data processing system in an embodiment of the present invention.

[0079] Figure 3 It is a data processing flowchart of a baseline solution server in an embodiment of the present invention.

[0080] Figure 4 It is a data processing flowchart of an error model establishment server in an embodiment of the present invention.

[0081] Figure 5 It is a data processing flowchart of a core server in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0082] The following further illustrates the implementation process of the present invention in conjunction with the drawings and specific embodiments.

[0083] In a GNSS network RTK system, the data processing modes are mainly divided into two types: centralized processing and processing in multiple sub-networks. These two methods have played a certain role in reference station networks of different scales, but there are technical problems when dealing with the requirements of modern large-scale GNSS networks. The following analyzes from the core characteristics and limitations of the two methods.

[0084] In the GNSS network RTK system, the centralized processing mode relies on a single core server to uniformly process the data of the entire reference station network. All the observation data of the reference stations are uploaded to the core server for networking, baseline solution, and calculation of error correction numbers. However, with the expansion of the scale of the reference station network, this mode exposes obvious computational bottlenecks and poor scalability problems. The computational power of the core server is difficult to meet the real-time requirements of a large-scale reference station network. Especially when facing a large number of baseline solution and error model construction tasks, it is easy to cause task accumulation and processing delays. In addition, the centralized transmission of all data to the core server will cause network bandwidth pressure, further exacerbating the system efficiency problem. At the same time, the design relying on a single server increases the risk of single-point failure.

[0085] The mode of processing in multiple subnets divides the entire reference station network into several subnets, and each subnet is responsible for processing by an independent server. This mode alleviates the computational pressure of a single server but brings new problems. On the one hand, the data coordination between subnets is relatively complex, which is easy to lead to inconsistencies in error correction numbers and model calculations. On the other hand, the calculation results of each subnet need to be aggregated to a global server, which still forms a new computational bottleneck when the number of subnets is large. In addition, the subnet division is usually static and cannot be adjusted according to the dynamic distribution of reference stations and task requirements, resulting in insufficient flexibility and adaptability of the system.

[0086] Neither of the two modes can effectively cope with the high computational requirements and real-time requirements of large-scale GNSS network RTK. In practical applications, centralized processing is difficult to expand, and processing in subnets has coordination and consistency problems. Therefore, a more efficient distributed processing architecture is needed to achieve dynamic allocation of computational tasks, load balancing, and network expansion capabilities to meet the development needs of modern GNSS network RTK systems.

[0087] The embodiment of the present invention proposes a large-scale reference station distributed data processing method for GNSS network RTK. Through distributed architecture design and task dynamic allocation mechanism, it solves the problems of computational bottlenecks, low task scheduling efficiency, poor scalability, and complex coordination existing in the existing centralized processing and subnet processing modes, and realizes the efficient processing of large-scale reference station network data and real-time error correction services. Specifically, it includes: core server reference station networking and task list generation; baseline solution server baseline solution and real-time resource occupancy assessment; error model establishment service relative error modeling of the master station and real-time resource occupancy assessment; dynamic allocation of baseline solution and error modeling. The connection methods between each step are as Figure 1 shown, the structure of the reference station network distributed data processing system is as Figure 2 shown, the data processing flow of the baseline solution server is as Figure 3 shown, and the data processing flow of the error model establishment server is as Figure 4As shown, the networking and task allocation process of the core server reference station is as follows Figure 5 as shown.

[0088] As Figure 2 shown, the GNSS network RTK large-scale reference station distributed data processing system provided by the embodiment of the present invention includes:

[0089] 1) The system consists of a core server, a baseline solution server, an error model establishment server, and a message bus.

[0090] 2) Core server: Responsible for reference station networking, task generation and allocation, integration of network-wide error correction numbers, task scheduling, and load balancing.

[0091] 3) Data processing server: Responsible for receiving tasks assigned by the core server, performing tasks such as baseline solution, etc., and feeding back the calculation results to the core server through the message bus.

[0092] 4) Error model establishment server: Responsible for receiving tasks assigned by the core server, performing tasks such as error correction number calculation and error model establishment, and feeding back the calculation results to the core server through the message bus.

[0093] 5) Message bus: Used for data transmission and task interaction between the core server and the data processing server, ensuring the real-time and reliability of task allocation, data upload, and result feedback.

[0094] The data processing process of the GNSS network RTK large-scale reference station distributed data processing system is as follows:

[0095] 1) Reference station networking and task generation. The core server receives the observation data of the reference station through the message bus, and dynamically generates a baseline networking plan for the entire network according to the geographical distribution, observation data quality, and geometric structure of the reference station. The core server generates a task list for baseline solution and error model establishment.

[0096] 2) Task dynamic allocation. The core server obtains the real-time resource usage of all data processing servers (such as CPU occupancy rate, memory usage rate) through the message bus. According to the task volume and the resource status of each server, the core server adopts a load balancing strategy to dynamically allocate baseline solution tasks and error model establishment tasks to different data processing servers.

[0097] 3) Baseline solution and error correction number calculation. The baseline solution server receives the observation data of the baseline to be solved, independently completes the baseline solution task, extracts the baseline error correction number, and uploads the calculation results to the core server through the message bus.

[0098] 4) Error model establishment. The processing server obtains relevant baseline corrections from the message bus according to the tasks, calculates the relative corrections of other reference stations relative to the local station, constructs a regional error model, and sends it to the message bus for use by the data broadcast module.

[0099] 5) Distributed task scheduling and data interaction mechanism

[0100] 51) Dynamic task scheduling: The core server dynamically allocates baseline solution and error model establishment tasks according to the priorities of the tasks, the computational complexity, and the real-time status of the processing servers (such as CPU usage rate, task load), ensuring balanced distribution of tasks and avoiding overload of computing resources.

[0101] 52) Efficient data interaction: Use the message bus to achieve data sharing and result interaction between the core server and the data processing servers. The message bus supports multi-threaded asynchronous communication, improves data transmission efficiency, and ensures the real-time nature of the upload of observation data, task assignment, and result feedback.

[0102] Based on the above GNSS network RTK large-scale reference station distributed data processing system, the specific implementation of the embodiment of the present invention includes the following steps:

[0103] Step 1: Core server reference station network formation and task list generation

[0104] All reference stations of the GNSS network RTK system form a triangular network, determine the baselines to be solved in the system, and generate a task list for baseline solution for task assignment to the solution server.

[0105] Step 1.1, Core server reference station data reception

[0106] The core server receives the real-time observation data of all reference stations through the message bus, including coordinate information, signal strength, observation data quality indicators, etc., and determines the available reference stations.

[0107] Step 1.2, Delaunay triangle reference station network formation

[0108] Based on the available reference stations obtained in Step 1.1, dynamically form baselines based on Delaunay triangles, optimize the number of baselines and geometric conditions, and ensure the solution quality and efficiency.

[0109] Step 1.2.1, Input point set. Input the coordinates of all reference stations to form a point set , refers to the horizontal and vertical coordinates of the i-th point.

[0110] Step 1.2.2, Initialize triangles. Select a super triangle containing all points from the point set containing all points , covering the entire point set.

[0111] Step 1.2.3, insert point by point. Insert the points into the current triangulation in sequence (the result of initializing the triangles in 1.2.2 is the triangulation), and check whether the point is inside the circumcircle of a certain triangle. If so, reconstruct the relevant triangles.

[0112] Step 1.2.4, edge swap optimization. If an edge does not satisfy the Delaunay condition (i.e., there are other points in its circumcircle), swap the edge and update the triangle structure.

[0113] In Delaunay triangulation, edge swap optimization is a step used to ensure that the triangulation satisfies the Delaunay condition (i.e., the empty circle property). Specifically, the purpose of edge swapping is to adjust the edges of the triangles so that there are no other points inside the circumcircle of any triangle. The following is the specific process of edge swapping:

[0114] Steps of edge swap optimization:

[0115] Judge whether the Delaunay condition is satisfied: For two adjacent triangles △ ABC and △ ABD , they share an edge AB .

[0116] Check whether there are other points in the circumcircles of these two triangles. If there are other points inside the circumcircle, the Delaunay condition is not satisfied.

[0117] (2) Swap the edge:

[0118] If the Delaunay condition is not satisfied, swap the shared edge AB for the diagonal CD , that is, replace the edge AB with the edge CD .

[0119] After swapping, the original two triangles △ ABC and △ ABD are replaced by the new two triangles △ ACD and △ BCD .

[0120] (3) Update the triangle structure:

[0121] After swapping the edge, update the triangle structure in the triangulation to ensure that the new triangles still satisfy the Delaunay condition.

[0122] If the new triangle still does not satisfy the Delaunay condition, edge swapping may need to be further performed. (4) Repeated checking:

[0123] Check again whether the swapped triangle satisfies the Delaunay condition until all triangles satisfy it.

[0124] Step 1.2.5, output the triangulation result. Remove the edges of the super triangle and output the remaining set of triangles and the baseline set , where all the edges forming the triangles are the baselines.

[0125] Step 1.3, task list generation

[0126] Mainly divided into two types of tasks: 1) baseline solution 2) master station error model establishment. Take the baseline set as the task to be solved and allocate it to the baseline solution server; take the available reference station set as the error modeling task and allocate it to the error model establishment server, including specific information such as whether to allocate the server and resource occupancy.

[0127] Step 2: Real-time resource occupancy assessment and baseline solution of the baseline processing server

[0128] Step 2.1, real-time resource occupancy assessment of the baseline processing server

[0129] Let the number of baselines processed by the server be , and assume that the CPU and memory resources occupied by each baseline solution are equal. Let the CPU occupancy rate of the baseline solution process in the current server be , and the memory occupancy rate be , then the CPU occupancy rate of a single baseline and the memory occupancy rate are respectively:

[0130]

[0131]

[0132] Step 2.2: Baseline solution and relative error extraction of a single baseline

[0133] Step 2.2.1: Coordinate and observation value acquisition. The baseline solution server obtains the coordinate and observation value information of the baseline to be solved from the message bus.

[0134] Step 2.2.2: Form double differences and fix double differences of the reference stations. The baseline solution method first determines the BeiDou ultra-wide-lane ambiguity based on the HMW combination method, then determines the wide-lane ambiguity based on the TCAR method of the ionosphere-free combination, and finally determines the original ambiguities of B1I and B3I of all satellites based on the wide-lane and dual-frequency ionosphere-free combined ambiguities.

[0135] Step 2.2.3: Extract baseline errors

[0136] According to the fixed ambiguities, reference station coordinates and other information, extract the error information related to and independent of the baseline geometry. According to the double-difference observation equations of L1 and L2 carriers:

[0137]

[0138]

[0139] where , , 、 are the frequencies of carriers L1 and L2 respectively, 、 are the wavelengths of carriers L1 and L2 respectively, is the double-difference phase observation value on carriers L1 and L2, is the double-difference geometric distance, are the L1 and L2 double-difference ambiguities, is the double-difference tropospheric delay, is the double-difference ionospheric delay. After fixing the integer ambiguities between reference stations, the magnitude of the double-difference carrier phase observation noise is very small, so the observation noise can be ignored, and the double-difference ionospheric delay and double-difference tropospheric delay with centimeter-level accuracy at the reference station can be obtained. Their estimation formulas are as follows:

[0140]

[0141]

[0142] Step 3: Establish the main station relative error model and evaluate the server resource occupancy

[0143] Step 3.1: Evaluate the real-time resource occupancy of the processing server

[0144] Let the number of current sites for the error model establishment server to process relative error calculation be , and assume that the CPU and memory resources occupied by the relative error calculation of each site are equal. Let the CPU occupancy rate occupied by the main station error model establishment process in the current server, and the memory occupancy rate , the CPU occupancy rate for establishing 1 master station error model and the memory occupancy rate are respectively:

[0145]

[0146]

[0147] Step 3.2: Calculation of the relative error of the master station

[0148] Step 3.2.1: Obtaining the relative error value of the baseline. The solution server obtains the relative error information of the baselines related to the station to be solved and 10 surrounding stations from the message bus.

[0149] Step 3.2.2: Overall adjustment and solution of the relative error. The overall adjustment algorithm for the baseline error correction number is carried out in three steps: 1) Use the triangle network closure difference test to judge whether there are gross errors; 2) Use the error correction numbers of each baseline to carry out the overall adjustment solution of the relative error correction number; 3) Based on the adjustment result, locate and repair the gross error and carry out the adjustment again.

[0150] In the first step, the closure difference of the error correction numbers of each side baseline in the same triangle must theoretically be 0. Considering the existence of observation errors, its closure difference should be within a certain extremely small threshold range. Therefore, if the closure difference of the triangle network exceeds the limit, there must be at least one error correction number of the baseline in the triangle network that is incorrect. In the second step, according to the basic principle of indirect adjustment, the baseline vector adjustment method is used to solve the correction number for a specified master station. According to the relationship of double differences and baseline transmission, the following error equations describing the relationship of each baseline vector can be listed:

[0151] (2)

[0152] Where are the error correction numbers of each baseline in the reference station triangle network, is the adjusted estimate of the double-difference relative error correction number from the master station to other reference stations, is the coefficient matrix describing the linear relationship between the error correction numbers of each baseline in the triangle network and the relative error correction number of the master station, is the residual of the error correction number.

[0153] Step 3.2.3: Establishment of the regional error model. Integrate the correction numbers of all master stations to construct a regional error model, provide high-precision error correction numbers for the GNSS network RTK rover, improve the positioning accuracy of the rover and shorten the initialization time, and is expressed by the interpolation method as:

[0154]

[0155] Where is the total number of slave stations, is the serial number of the slave station, is the slave station correction, is the slave station interpolation coefficient, is the slave station coordinate value relative to the interpolation position to be interpolated, is the correction of the interpolation position to be interpolated.

[0156] Step 4: The core server calculates the corresponding remaining computing power according to the real-time resource occupancy evaluation of the baseline solution server and the error model establishment server respectively, and dynamically allocates the baseline solution task and the error modeling task according to the corresponding remaining computing power.

[0157] Let the memory and CUP occupancy rates of all the baseline processing servers be and , considering the problem of server processing redundancy, the maximum memory and CUP occupancy of the server are both designed to be 80% (which can be set to other values according to actual needs), then the remaining available number of baselines to be processed (i.e., the remaining computing power of the baseline processing server) is:

[0158]

[0159] Let the memory and CUP occupancy rates of all the error model establishment servers be and , considering the problem of server processing redundancy, the maximum memory and CUP occupancy of the server are both designed to be 80%, then the remaining available number of baselines to be processed (i.e., the remaining computing power of the error model establishment server) is:

[0160]

[0161] and satisfy the following conditions:

[0162] ,

[0163] The core server dynamically allocates the baseline solution task and the error modeling task according to and , that is, give priority to the task with a large number of baselines to be processed.

[0164] In specific implementation, the above process can be automatically run by computer software technology, and the system device running the method process of the present invention should also be within the protection scope of the present invention.

[0165] On the other hand, the embodiment of the present invention further provides GNSS network RTK reference station distributed data processing, including:

[0166] A processor and a memory, the memory is used to store program instructions, and the processor is used to call the stored instructions in the memory to execute the GNSS network RTK reference station distributed data processing method as described in the above technical solution.

[0167] The specific embodiments described herein are merely illustrative of the spirit of the present invention. Those skilled in the art of the present invention can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, but will not deviate from the spirit of the present invention or exceed the scope defined by the appended claims.

Claims

1. A distributed data processing method for GNSS network RTK base stations, characterized in that: The steps include: Step 1: The core server receives real-time observation data from all reference stations through the message bus and determines the available reference stations. Based on the available reference stations, the core server dynamically groups baselines using the Denauney triangle, optimizes the number and geometric conditions of baselines, and obtains a baseline set. The baseline set is used as the task to be solved and assigned to the baseline solving server; The set of available reference stations is used as the error modeling task and assigned to the error model building server; Step 2: The baseline solution server first calculates the baseline solution server real-time resource occupancy assessment, and then performs baseline solution to obtain the double-difference ionospheric delay and the double-difference tropospheric delay. Step 3: The error model establishment server calculates the real-time resource usage of the error model establishment server, then solves all baselines in parallel, extracts baseline error corrections, and calculates the relative corrections of other sites relative to this site to establish the master site error model. In step 4, the core server calculates the corresponding remaining computing capacity based on the baseline solution server real-time resource occupancy assessment and the error model establishment server real-time resource occupancy assessment, and dynamically allocates the baseline solution task and the error modeling task based on the corresponding remaining computing capacity.

2. The distributed data processing method for a GNSS network RTK reference station according to claim 1, wherein: In step 1, the specific implementation method of obtaining the baseline set is as follows: Step 1.2.1: Input all base station coordinates to form a point set , refers to the horizontal and vertical coordinates of the i-th point; Step 1.2.2, initialize the triangle from the point set Select the super triangle that contains all the points , covering the entire point set; Step 1.2.3, click Insert the current triangulation into the result of initializing the triangle, checkpoint Is it within the circumcircle of a certain triangle? If so, reconstruct the relevant triangle; Step 1.2.4: If an edge does not satisfy the Delaunay condition, that is, the circumcircle contains other points, swap the edges and update the triangle structure; Step 1.2.5, remove the edges of the super triangle and output the remaining triangle set and baseline set.

3. The distributed data processing method for GNSS network RTK reference stations according to claim 1, wherein: In step 2, the baseline processing server real-time resource usage evaluation is calculated as follows: Set the number of baselines processed by the baseline processing server to , each baseline solution occupies equal CPU and memory resources, and the CPU usage of the baseline solution process in the current server is , memory usage , then the CPU usage of a single baseline and memory usage They are: 。 4. The distributed data processing method for GNSS network RTK reference stations according to claim 1, wherein: In step 2, the calculation process of the double-difference ionospheric delay and the double-difference tropospheric delay is as follows: Step 2.2.1: The baseline solution server obtains the coordinates and observation value information of the baseline to be calculated from the message bus; Step 2.2.2: Determine the BeiDou ultra-wide lane ambiguity based on the HMW combination method, then determine the wide lane ambiguity based on the ionosphere-free combination TCAR method, and finally determine the original satellite ambiguity based on the wide lane and dual-frequency ionosphere-free combination ambiguity. Step 2.2.3: Based on the fixed ambiguity and reference station coordinate information, extract the baseline geometry-related and geometry-independent error information, according to the L1 and L2 carrier double difference observation equations: in , , 、 are the frequencies of carriers L1 and L2 respectively, 、 are the wavelengths of carriers L1 and L2 respectively, is the double difference phase observation value on carriers L1 and L2, is the double difference geometric distance, is the L1 and L2 double difference ambiguity, is the double-difference tropospheric delay, is the double-difference ionospheric delay; finally, the double-difference ionospheric delay and double-difference tropospheric delay with centimeter-level accuracy at the reference station are obtained, and the estimation formula is as follows: 。 5. The distributed data processing method for GNSS network RTK reference stations according to claim 1, wherein: In step 3, the calculation method for the error model to establish the server real-time resource occupancy evaluation is as follows: Set the error model to establish the current number of sites processed by the server. The CPU and memory resources occupied by the relative error calculation of each site are equal. The CPU usage of the error model establishment process of the main site in the current error model establishment server is , memory occupancy rate , then the CPU occupancy rate of the error model of a master station is and memory usage They are: 。 6. The distributed data processing method for GNSS network RTK reference stations according to claim 1, wherein: In step 3, the specific implementation method of establishing the master station error model is as follows: Step 3.2.1: The error model establishment server obtains the relative error information of the baselines of the requested site and several surrounding sites from the message bus; Step 3.2.2: Extract baseline error corrections using the baseline error correction global adjustment algorithm. This is done in three steps: 1) Use the triangulation error closure test to determine whether there are gross errors; 2) Use the error corrections of each baseline to perform a global adjustment of the relative error corrections; 3) Based on the adjustment results, locate and repair any gross errors and perform another adjustment. Step 3.2.3: Integrate the baseline error corrections of all master stations to construct the master station error model, which can be expressed as follows using interpolation: in is the total number of auxiliary stations, is the auxiliary station number, Auxiliary Station number of corrections, Auxiliary Station The interpolation coefficients, Auxiliary Station The coordinate value relative to the position to be interpolated, is the correction number for the position to be interpolated.

7. The distributed data processing method for GNSS network RTK reference stations according to claim 6, wherein: In step 3.2.2 (1), the closure error of the baseline error correction numbers of each side in the same triangle is within the minimum threshold range. Therefore, if the closure error of the triangulated network exceeds the minimum threshold range, the error correction number of at least one baseline in the triangulated network must be incorrect.

8. The distributed data processing method for GNSS network RTK reference stations according to claim 6, wherein: In step 3.2.2, 2), based on the basic principles of indirect adjustment, the baseline vector adjustment method is used to solve the correction for a specific master station. According to the relationship between double differences and baseline transfer, the error equations describing the relationship between the baseline vectors are listed as follows: （2） in is the error correction number of each baseline in the base station triangle network, It is the estimated value of the double difference relative error correction from the main station to other reference stations. is the coefficient matrix describing the linear relationship between the error correction number of each baseline in the triangulation network and the relative error correction number of the master station. is the residual of the error correction number.

9. The distributed data processing method for GNSS network RTK reference stations according to claim 1, wherein: In step 4, the remaining computing capacity of the baseline processing server and the remaining computing capacity of the error model establishment server are first calculated; Set the baseline processing server's total memory and CPU usage to and , the maximum memory and CPU usage are both P%, then the remaining computing capacity of the baseline processing server for: in, and These are the CPU usage and memory usage of a single baseline respectively; Set the error model to establish that all memory and CPU usage of the server are and , the maximum memory and CPU usage are both P%, then the error model is set to establish the remaining computing capacity of the server for: in, and The CPU usage and memory usage of the error model established for one master station respectively; and and The following conditions are met: , Core server based on and Dynamically allocate baseline solution tasks and error modeling tasks, giving priority to tasks with large remaining computing power.

10. A distributed data processing device for a GNSS network RTK base station, characterized in that: include: A processor and a memory, the memory being used to store program instructions, and the processor being used to call the stored instructions in the memory to execute the distributed data processing method for a GNSS network RTK reference station according to any one of claims 1 to 9.

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