Beidou positioning service correction method and device for ground-based augmentation system

Abstract

The invention discloses a Beidou positioning service correction method and device for a ground-based augmentation system, and belongs to the technical field of positioning and navigation, and the method comprises a terminal space distribution dynamic analysis mechanism and a self-adaptive user screening strategy. The method comprises the following steps: collecting terminal GNSS observation data and CORS station data in real time, extracting double-difference ionospheric delay based on geometric irrelevant combination, and executing multi-dimensional quality screening; a DBSCAN clustering model is adopted to dynamically identify a high-density accumulation area and a sparse area of a terminal, and then a differential grid division strategy is implemented: for each grid unit, outliers are eliminated, and weighted averaging is performed on residual data to generate a representative value; finally, the screened crowdsourcing data and the reference station data are fused to construct a regional atmospheric error model, and VRS correction services are generated and broadcasted, so that the service reliability and the positioning precision in a complex environment are remarkably improved, and the optimal balance between the service quality and the data resource utilization efficiency is finally realized.

Description

TECHNICAL FIELD

[0001] The application relates to a Beidou positioning service correction method and device for a ground-based augmentation system and belongs to the technical field of positioning and navigation. BACKGROUND

[0002] With the in-depth development of high-precision positioning technology in key application scenarios such as surveying and exploring, intelligent driving and precision agriculture, the real-time performance, reliability and precision guarantee of positioning services have become the core requirement to support the stable operation of the system. As the infrastructure to achieve this goal, the ground-based augmentation system (GBAS) builds a wide-area distributed reference station network, continuously solves error parameters such as atmospheric delay, and broadcasts virtual observations to terminal users in real time with the help of a gridded differential model. This method effectively reduces the computing power bottleneck problem faced by the traditional virtual reference station scheme in the scenario of a large number of users, greatly improves the overall resource utilization efficiency and positioning accuracy of the system, and thus becomes the mainstream high-precision positioning service architecture.

[0003] Especially under the background of Beidou-3 global networking, the enhancement modeling technology based on crowdsourcing terminal data gradually becomes an important means to break through the limitation of the deployment density of traditional reference stations, expand the coverage range of positioning services and improve the modeling accuracy. By dynamically collecting global navigation satellite system (GNSS) observation information reported by terminal devices, effective supplement can be formed in the original sparse area of reference stations, thereby improving the spatial balance of atmospheric delay parameter modeling and further improving the positioning performance in weak coverage areas. This user-participating modeling mechanism provides new possibilities for building a high-precision positioning service system with wide-area adaptive capability, and shows great development potential and application prospect.

[0004] However, the crowdsourcing modeling method still faces a series of key problems that restrict the quality of positioning services in actual deployment. First of all, the spatial distribution of terminal devices is highly non-uniform. In real application scenarios, user devices are often concentrated in specific areas such as urban trunk roads, construction sites and industrial parks, while in vast areas such as farmland, mountains and oceans, the terminal devices are sparse or even blank. This spatial heterogeneity directly leads to significant differences in modeling accuracy in different areas, thereby affecting the reliability and consistency of positioning services. In the terminal intensive area, data redundancy leads to resource waste, while in the terminal sparse area, insufficient observation data makes the modeling capability insufficient, which cannot meet the basic requirements of high-precision services.

[0005] Secondly, the existing modeling scheme generally adopts a static modeling method of fixed grid, which is difficult to adapt to the real-time dynamic changes of terminal spatial distribution. In the terminal dense area, the grid cannot effectively utilize the local precision advantage brought by observation redundancy, but is easy to cause modeling distortion due to excessive concentration of weights; and in the terminal sparse area, due to the scarcity of data points, the modeling residual increases, and the error cannot be effectively controlled. Such static modeling mechanism without self-adaptive ability makes it difficult for the model to maintain balanced positioning accuracy in the entire service area, which seriously restricts the promotion and application of positioning service in a large range and high dynamic environment.

[0006] In addition, in actual operation, due to the lack of terminal distribution feature-oriented perception and scheduling mechanism, the crowdsourcing modeling system also has obvious shortcomings in resource utilization efficiency. A large amount of data returned by terminals is highly repetitive, which not only occupies the communication link, but also increases the computing burden of the center node. However, these redundant data cannot effectively improve the modeling capability in sparse areas, resulting in unbalanced overall resource allocation, increased system operation cost, and dual limitations of scalability and real-time performance. Such low resource scheduling efficiency has become a key bottleneck restricting large-scale deployment of positioning service.

[0007] The root cause is that the existing crowdsourcing modeling technology generally lacks effective perception and response ability to the dynamic distribution characteristics of terminals, and cannot flexibly adjust the modeling strategy and resource allocation mode according to the real-time changes of user situation. With the increasing influence of non-ideal environment such as frequent ionospheric activity, the spatial variation characteristics of atmospheric delay field become more complex, and the error control ability of static modeling method in terminal sparse area is further weakened, which may even lead to positioning error exceeding the service tolerance, seriously affecting the usability and reliability of the system. SUMMARY

[0008] The purpose of the present application is to overcome the deficiencies in the prior art, and to provide a Beidou positioning service correction method and device for ground-based augmentation system, which can optimize resource allocation according to terminal distribution changes, improve overall system efficiency while ensuring positioning accuracy, and thus effectively break through the technical bottleneck faced by current positioning service in practical application.

[0009] To achieve the above purpose, the present application adopts the following technical scheme: In a first aspect, the present application provides a Beidou positioning service correction method for a ground-based augmentation system, comprising: combining the real-time collected terminal GNSS observation data and reference station observation data to obtain geometrically independent combined observation values, and extracting terminal ionospheric delay information based on the geometrically independent combined observation values; The pre-acquired terminal latitude and longitude data is mapped to a plane coordinate system to form a distribution point set, and the distribution point set is input to a pre-constructed DBSCAN clustering model to dynamically identify high-density aggregation areas and sparse areas, and output a terminal spatial distribution state result. Based on the terminal spatial distribution state result, the target area is divided into grid cells using a differentiated grid division strategy. The terminal ionospheric delay information in the divided grid cells is subjected to outlier rejection and weighted average processing, and the filtered terminal ionospheric delay representative value is output. The terminal ionospheric delay representative value is input into a pre-constructed regional atmospheric error model to generate an ionospheric delay modeling result. Based on the ionospheric delay modeling result, GNSS observation data, and pre-acquired terminal user data, the observation data of a virtual reference station is generated. The observation data of the virtual reference station is broadcast to the terminal user to implement positioning correction services between the terminal user and the virtual reference station.

[0010] Further, the distribution point set is input to a pre-constructed DBSCAN clustering model to dynamically identify high-density aggregation areas and sparse areas, and output a terminal spatial distribution state result, including: Set the neighborhood search radius ε and the minimum point threshold MinPts; Divide the plane coordinate system into fixed-size subgrids, and build a local KD-Tree structure for each terminal point in each subgrid; For each terminal point in the distribution point set, calculate the number of samples in its ε neighborhood, and if it meets the threshold MinPts, mark it as a core point and recursively expand the density-reachable samples to form a clustering cluster, where the clustering cluster represents an area where terminal point aggregation occurs; Extract the density gradient, boundary expansion coefficient, and spatial connectivity index of the clustering cluster to obtain the terminal spatial distribution state result.

[0011] Further, the target area is divided into grid cells using a differentiated grid division strategy, including: Based on the terminal spatial distribution state result, large-scale grid cells are constructed in areas where the number of terminals is greater than a set threshold, and small-scale grid cells are divided in areas where the number of terminals is less than a set threshold.

[0012] Further, the terminal ionospheric delay information in the divided grid cells is subjected to outlier rejection and weighted average processing, and the filtered terminal ionospheric delay representative value is output, including: If the number of terminals is greater than a set threshold, the mean and standard deviation of ionospheric delay information of all terminals in the divided grid unit are calculated, outliers are removed based on the 3σ criterion, and the remaining data is weighted and averaged to generate a representative value of terminal ionospheric delay; If the number of terminals is less than a set threshold, the mean of the terminal ionospheric delay information is directly calculated as the representative value of the terminal ionospheric delay.

[0013] Further, the regional atmospheric error model includes at least one of the LIM model, the DIM model, the LCM model and the KRG model.

[0014] Further, the method further comprises multi-dimensional quality screening of the collected terminal GNSS observation data and reference station observation data, comprising: According to the signal-to-noise ratio threshold, the effective satellite number threshold and the single point positioning residual threshold, the observation values that do not meet the quality requirements are removed.

[0015] In a second aspect, the application provides a Beidou positioning service correction device for a ground-based augmentation system, comprising: The extraction module is configured to combine the real-time collected terminal GNSS observation data and reference station observation data to obtain geometrically independent combined observation values, and extract terminal ionospheric delay information based on the geometrically independent combined observation values. The first processing module is configured to map the pre-acquired terminal latitude and longitude data to a plane coordinate system to form a distribution point set, input the distribution point set into a pre-constructed DBSCAN clustering model, dynamically identify high-density aggregation areas and sparse areas, and output a terminal spatial distribution state result. The division module is configured to divide the target area into grid units using a differentiated grid division strategy based on the terminal spatial distribution state result. The second processing module is configured to perform outlier removal and weighted average processing on the terminal ionospheric delay information in the divided grid unit, and output a filtered terminal ionospheric delay representative value. The third processing module is configured to input the terminal ionospheric delay representative value into a pre-constructed regional atmospheric error model to generate an ionospheric delay modeling result. The generation module is configured to generate observation data of a virtual reference station based on the ionospheric delay modeling result, GNSS observation data and pre-acquired terminal user data. The augmentation service module is configured to broadcast the observation data of the virtual reference station to terminal users to implement positioning correction services between the terminal users and the virtual reference station.

[0016] In a third aspect, the application provides a computer-readable storage medium having a computer program stored thereon, the program being executed by a processor to implement the steps of the method of any one of the preceding aspects.

[0017] In a fourth aspect, the present application provides a computer device comprising: a memory for storing computer programs / instructions; a processor for executing the computer programs / instructions to implement the steps of the method of any one of the preceding aspects.

[0018] In a fifth aspect, the present application provides a computer program product comprising computer programs / instructions which, when executed by a processor, implement the steps of the method of any one of the preceding aspects.

[0019] Compared with the prior art, the present application has the following beneficial effects: The present application provides a Beidou positioning service correction method and device for a ground enhancement system, which dynamically identifies high-density aggregation areas and sparse areas through a DBSCAN clustering model and outputs terminal spatial distribution state results; based on the terminal spatial distribution state results, a target area is divided into grid units using a differentiated grid division strategy, thereby realizing the dynamic filtering of terminal user data and the collaborative optimization of grid division, automatically adjusting the grid scale in user-intensive areas to avoid excessive modeling deviation in sparse user areas, optimizing the grid in sparse areas to improve data utilization, and ensuring positioning accuracy by involving high-quality terminal users in modeling, thereby significantly improving service reliability and positioning accuracy in complex environments and ultimately achieving an optimal balance between service quality and data resource utilization efficiency. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is a flowchart of a Beidou positioning service correction method for a ground enhancement system provided by an embodiment of the present application; Figure 2 is a comparison chart of ionospheric delay results before and after filtering using an LIM model provided by an embodiment of the present application; Figure 3 is a comparison chart of ionospheric delay results before and after filtering using a DIM model provided by an embodiment of the present application; Figure 4 is a comparison chart of ionospheric delay results before and after filtering using an LCM model provided by an embodiment of the present application; Figure 5 is a comparison chart of ionospheric delay results before and after filtering using a KRG model provided by an embodiment of the present application. DETAILED DESCRIPTION

[0021] The present application will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solutions of the present application and cannot be used to limit the protection scope of the present application.

[0022] Embodiment 1, this embodiment introduces a Beidou positioning service correction method for a ground-based reinforcement system, comprising: The terminal GNSS (Global Navigation Satellite System) observation data collected in real time is combined with the reference station observation data to obtain geometrically independent combined observation values, and the terminal ionospheric delay information is extracted based on the geometrically independent combined observation values; The terminal latitude and longitude data obtained in advance is mapped to a plane coordinate system to form a distribution point set, the distribution point set is input into a DBSCAN (Density-Based Clustering Algorithm) clustering model constructed in advance, the high-density aggregation area and the sparse area are dynamically identified, and the terminal spatial distribution state result is output; Based on the terminal spatial distribution state result, the target area is divided into grid cells using a differentiated grid division strategy; The terminal ionospheric delay information in the divided grid cells is subjected to outlier rejection and weighted average processing, and the filtered terminal ionospheric delay representative value is output; The terminal ionospheric delay representative value is input into a pre-constructed regional atmospheric error model to generate ionospheric delay modeling results; Based on the ionospheric delay modeling results, GNSS observation data, and pre-acquired terminal user data, the observation data of a virtual reference station is generated; The observation data of the virtual reference station is broadcast to the terminal user, and the positioning correction service between the terminal user and the virtual reference station is realized.

[0023] As Figure 1 shown, the Beidou positioning service correction method for the ground-based reinforcement system provided by the embodiment specifically involves the following steps in the application process: Step 1. Real-time collection and preprocessing of crowd-sourced data.

[0024] The cloud platform inputs the GNSS raw observation data stream returned by the terminal device and the formatted observation data of the Continuous Operational Reference Station (CORS) through an asynchronous message queue, first performs multi-dimensional data quality inspection on the GNSS raw observation data stream, filters the terminal data based on the preset signal-to-noise ratio threshold, effective satellite number threshold, and single-point positioning residual threshold, and removes the observation values that do not meet the quality requirements, to ensure that the input data meet the accuracy basis for atmospheric error modeling; then the filtered GNSS raw observation data stream is combined to eliminate the influence of the geometric distance from the satellite to the terminal device, which is called geometrically independent combined observation values, and the terminal ionospheric delay information is extracted based on the combined observation values to eliminate the receiver clock error, satellite clock error, and troposphere error components; finally, the terminal ionospheric delay information and the GNSS observation data stream with part of the observation error eliminated are output.

[0025] Step 2. Dynamic analysis of terminal spatial distribution.

[0026] After the real-time collection and preliminary processing of crowdsourcing terminal data are completed, the present application further dynamically analyzes the spatial distribution characteristics of the terminal to accurately identify the terminal aggregation trend and the sparse distribution area in the region, thereby providing the spatial structure basis for subsequent grid construction and atmospheric error modeling. The cloud platform inputs the longitude and latitude data returned by the terminal equipment in real time, maps it to a plane coordinate system by using a unified projection method, and forms a terminal distribution point set that can be used for spatial analysis. In order to identify the change characteristics of the terminal density in the local area, a density-based clustering method DBSCAN is introduced to further analyze the terminal distribution point set. First, the neighborhood search radius ε and the minimum point threshold MinPts are set. Then, the plane coordinate system is divided into fixed-size subgrids, and a local KD-Tree (tree data structure) structure is established for the terminal points in each subgrid to realize fast neighborhood query. For each terminal point, the number of samples in its ε neighborhood is calculated. If the threshold MinPts is met, it is marked as a core point, and the density-reachable samples are recursively expanded to form a clustering cluster. Finally, the spatial feature of the clustering result is extracted, and the density gradient, boundary expansion coefficient and spatial connectivity index of each cluster are calculated to obtain the clustering result. The clustering result divides the spatial distribution state into three categories: high-density clustering cluster, boundary point and noise point. Among them, the clustering cluster represents the area where there is a significant terminal aggregation phenomenon; and the noise points outside the clustering usually correspond to sparse areas with limited number of terminals and low update frequency. Therefore, on the basis of identifying the spatial density distribution of the terminal, this step dynamically labels the distribution attributes of different areas, and periodically refreshes the terminal spatial clustering state, so that the method has adaptability to the change of spatial distribution state, and finally outputs the spatial distribution state of the terminal equipment.

[0027] Step 3. Adaptive user screening and terminal screening.

[0028] The cloud platform inputs the terminal ionospheric delay information obtained in step 1 and the terminal device spatial distribution state obtained in step 2, implements a differentiated grid division strategy on the target area based on the input data, dynamically adjusts the spatial resolution of grid division, constructs a large-scale grid unit in an area where the number of terminals is greater than a set threshold, and reduces the number of terminal users participating in modeling to avoid excessive modeling deviation in a sparse user area; small-scale grid units are divided in an area where the number of terminals is less than a set threshold, and more terminal user data is retained as much as possible to improve modeling accuracy. After completing grid construction, the system processes terminal observation data one by one according to the grid unit. For terminal ionospheric delay information in each grid unit, if the number of terminals is greater than a set threshold, the mean and standard deviation of the ionospheric delay of all terminals in the grid unit are calculated, combined with the 3σ criterion to identify and eliminate data points significantly deviating from the mean, which are considered as outliers; then the remaining normal observation values are introduced into the weighted average processing to improve the robustness and spatial representativeness of the representative value. For the grid unit where the number of terminals is less than the set threshold, all terminals are retained by default, and the mean of the observation values is calculated as the representative value of the grid. Finally, the terminal ionospheric delay information after screening and calculation of each grid unit is output.

[0029] Step 4. Regional atmospheric error modeling by fusing crowd-sourced data.

[0030] After completing adaptive user screening and terminal screening, the cloud platform inputs the terminal ionospheric delay information after screening and calculation of each grid unit obtained in step 3, constructs a regional atmospheric error model based on the terminal ionospheric delay information, and models the atmosphere of the region by four models, including a linear interpolation model (LIM), a distance-based linear interpolation model (DIM), a linear combination model (LCM), and a Kriging model (KRG), to output the regional ionospheric delay modeling results. Please refer to Figures 2-5 , Figures 2-5 is a comparison chart of ionospheric delay results before and after adaptive user screening and terminal screening disclosed in the embodiments of the present application. As shown in Figure 2 , from the experimental results, the LIM model improves by an average of 76.2%, as shown in Figure 3 , the DIM model improves by an average of 89.0%, as shown in Figure 4 , the LCM model improves by an average of only 16.9%, as shown in Figure 5 , the KRG model improves by an average of 91.8%, and the performance improvement of different atmospheric error modeling methods under the adaptive user screening and terminal screening strategy is significantly different in terms of improvement effect.

[0031] Step 5. Virtual reference station (VRS) service generation and broadcasting.

[0032] After completing the regional atmospheric error modeling of the fused crowd-sourced data, the cloud platform inputs the GNSS observation data stream obtained in step 1 after eliminating part of the observation errors and the regional ionospheric delay modeling result obtained in step 4, generates the observation data of the virtual reference station (VRS) based on the input data and the terminal user position data, and broadcasts the VRS data to the terminal user through the network, so that the ultra-short baseline solution between the terminal user and the virtual reference station can be implemented to support the high-precision positioning service of the terminal user.

[0033] Embodiment 2 provides a Beidou positioning service correction device for a ground-based augmentation system, comprising: The extraction module is configured to combine the terminal GNSS observation data collected in real time and the reference station observation data to obtain geometrically independent combined observation values, and extract terminal ionospheric delay information based on the geometrically independent combined observation values. The first processing module is configured to map the terminal latitude and longitude data obtained in advance to a plane coordinate system to form a distribution point set, input the distribution point set to a pre-constructed DBSCAN clustering model, dynamically identify high-density aggregation areas and sparse areas, and output a terminal spatial distribution state result. The division module is configured to divide the target area into grid cells using a differentiated grid division strategy based on the terminal spatial distribution state result. The second processing module is configured to perform outlier rejection and weighted average processing on the terminal ionospheric delay information in the divided grid cells, and output filtered terminal ionospheric delay representative values. The third processing module is configured to input the terminal ionospheric delay representative values into a pre-constructed regional atmospheric error model to generate ionospheric delay modeling results. The generation module is configured to generate observation data of a virtual reference station based on the ionospheric delay modeling results, GNSS observation data, and terminal user data obtained in advance. The augmentation service module is configured to broadcast the observation data of the virtual reference station to the terminal user to implement positioning correction services between the terminal user and the virtual reference station.

[0034] The specific functions of the above modules are described in the related content in the method of Embodiment 1, and will not be described here.

[0035] Embodiment 3 provides a computer readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the steps of the method of any one of Embodiment 1.

[0036] Embodiment 4 provides a computer device, comprising: The memory is configured to store computer programs / instructions. a processor to execute the computer programs / instructions to implement the steps of the method of any of Embodiment 1.

[0037] Embodiment 5 provides a computer program product comprising computer programs / instructions which, when executed by a processor, implement the steps of the method of any of Embodiment 1.

[0038] The above only describes the preferred embodiments of the present application. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present application, and these improvements and modifications should also be considered as falling within the protection scope of the present application.

[0039] Those skilled in the art will understand that the embodiments of the present disclosure can be provided as methods, systems or computer program products. Therefore, the present disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the present disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer usable program code.

[0040] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows 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 apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device that implements the flow Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows or blocks.

[0041] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction apparatus, which implements the flow Figure 1 one or more flows and / or blocks Figure 1 an apparatus that performs the functions specified in one or more flows or blocks.

[0042] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are generated to realize the computer-implemented processes, and the instructions executed on the computer or other programmable devices provide a process for implementing the functions specified in the flowchart Figure 1 one flow or multiple flows and / or the functions specified in the block Figure 1 one flow or multiple flows and / or the functions specified in the block

[0043] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present disclosure, but not to limit the scope of protection, although the present disclosure has been described in detail with reference to the above examples, those skilled in the art should understand: after reading the present disclosure, the skilled in the art can make various changes, modifications or equivalent replacements to the specific embodiments of the present disclosure, but these changes, modifications or equivalent replacements are all within the protection scope of the disclosed claims.

Claims

1. A method for correcting BeiDou positioning service for a ground reinforcement system, characterized in that, The method comprises the following steps: combining terminal GNSS observation data collected in real time with reference station observation data to obtain geometrically independent combined observation values, and extracting terminal ionospheric delay information based on the geometrically independent combined observation values; mapping terminal latitude and longitude data obtained in advance to a plane coordinate system to form a distribution point set, inputting the distribution point set into a pre-constructed DBSCAN clustering model, dynamically identifying high-density aggregation areas and sparse areas, and outputting terminal spatial distribution state results; dividing a target area into grid cells using a differentiated grid division strategy based on the terminal spatial distribution state results; performing outlier rejection and weighted average processing on terminal ionospheric delay information in the divided grid cells, and outputting filtered terminal ionospheric delay representative values; inputting the terminal ionospheric delay representative values into a pre-constructed regional atmospheric error model to generate ionospheric delay modeling results; generating observation data of a virtual reference station based on the ionospheric delay modeling results, GNSS observation data, and pre-obtained terminal user data; broadcasting the observation data of the virtual reference station to terminal users to implement positioning correction services between the terminal users and the virtual reference station.

2. The method for Beidou positioning service correction for ground reinforcement system according to claim 1, characterized in that, The method further comprises the following steps: setting a neighborhood search radius ε and a minimum point threshold MinPts; dividing the plane coordinate system into fixed-size subgrids, and establishing a local KD-Tree structure for terminal points in each subgrid; for each terminal point in the distribution point set, calculating the number of samples in its ε neighborhood, marking it as a core point if the number of samples meets the threshold MinPts, and recursively expanding density-reachable samples to form a clustering cluster, wherein the clustering cluster represents an area where terminal point aggregation occurs; extracting the density gradient, boundary expansion coefficient, and spatial connectivity index of the clustering cluster to obtain terminal spatial distribution state results.

3. The method for Beidou positioning service correction of foundation reinforcement system according to claim 1, characterized in that, The method further comprises the following steps: based on the terminal spatial distribution state results, constructing large-scale grid cells in areas where the number of terminals is greater than a set threshold, and dividing small-scale grid cells in areas where the number of terminals is less than the set threshold.

4. The method for Beidou positioning service correction of foundation reinforcement system according to claim 1, characterized in that, The method further comprises the following steps: if the number of terminals is greater than the set threshold, calculating the mean and standard deviation of all terminal ionospheric delay information in the divided grid cells, rejecting outliers based on the 3σ criterion, and generating terminal ionospheric delay representative values by weighted average processing on the remaining data; if the number of terminals is less than the set threshold, directly calculating the mean of the terminal ionospheric delay information as the terminal ionospheric delay representative value.

5. The method for Beidou positioning service correction of foundation reinforcement system according to claim 1, characterized in that, The regional atmospheric error model comprises at least one of an LIM model, a DIM model, an LCM model, and a KRG model.

6. The method for Beidou positioning service correction of ground foundation reinforcement system according to claim 1, characterized in that, The method further comprises the following steps: performing multi-dimensional quality screening on the collected terminal GNSS observation data and reference station observation data, comprising: The observation values not meeting the quality requirements are removed according to a signal-to-noise ratio threshold, an effective satellite number threshold and a single-point positioning residual threshold.

7. A device for correcting a Beidou positioning service for a ground reinforcement system, characterized in that The method comprises the following steps: The extraction module is configured to combine the GNSS observation data of the terminal and the observation data of the reference station to obtain geometrically independent combined observation values, and extract ionospheric delay information of the terminal based on the geometrically independent combined observation values; The first processing module is configured to map the longitude and latitude data of the terminal to a plane coordinate system to form a distribution point set, and input the distribution point set into a DBSCAN clustering model constructed in advance to dynamically identify high-density aggregation areas and sparse areas, and output a terminal spatial distribution state result; The division module is configured to divide the target area into grid cells using a differentiated grid division strategy based on the terminal spatial distribution state result; The second processing module is configured to perform outlier removal and weighted average processing on the ionospheric delay information of the terminal in the divided grid cells, and output filtered ionospheric delay representative values of the terminal; The third processing module is configured to input the ionospheric delay representative values of the terminal into a regional atmospheric error model constructed in advance to generate ionospheric delay modeling results; The generation module is configured to generate observation data of a virtual reference station based on the ionospheric delay modeling results, GNSS observation data and terminal user data obtained in advance; The enhanced service module is configured to broadcast the observation data of the virtual reference station to the terminal user to implement positioning correction services between the terminal user and the virtual reference station.

8. An electronic device, comprising: The method comprises the following steps: The memory is configured to store computer programs / instructions; The processor is configured to execute the computer programs / instructions to implement the steps of the method in any one of claims 1-6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that: The program is executed by the processor to implement the steps of the method in any one of claims 1-6.

10. A computer program product comprising computer programs / instructions, characterized in that, The computer programs / instructions are executed by the processor to implement the steps of the method in any one of claims 1-6.

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