Intelligent adaptive networking positioning method and system for large-scale Beidou base station

Abstract

The invention provides an intelligent self-adaptive networking positioning method and system for a large-scale Beidou base station, relates to the technical field of base station networking positioning, and can solve the technical problem that in related technologies, after the base station is offline, the base station lacks an automatic rapid recombination capability. The method comprises the steps that a reference station plane is constructed, the reference station plane comprises a plurality of reference stations, the plurality of reference stations are in communication connection to form communication links, and the plurality of communication links form a mesh topology structure of the reference station plane; when the base station is disconnected from the network topology structure corresponding to the base station plane, reconstructing the mesh topology structure according to a preset strategy; wherein the preset strategy comprises selecting an alternative base station for an original communication link of the base station according to at least one of a communication error rate, a transmission delay and a logic distance between nodes, and the alternative base station is used for forming a communication link with other base stations in the original communication link.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of reference station networking positioning, and in particular, to an intelligent adaptive networking positioning method and system for large-scale Beidou reference stations. Background Art

[0002] Most of the reference station networks in the related art lack sufficient flexibility and self-healing capabilities. When the communication link is interrupted due to signal interference, hardware failure, or environmental factors, there is a lack of an intelligent reconstruction mechanism based on dynamic factors, resulting in insufficient network robustness and a decline in the reliability of positioning services. Moreover, in the related art, manual intervention is relied on to reconnect or adjust the network structure, and the topology structure cannot be optimized in real time. At the same time, a single reference station plane is difficult to adapt to the differentiated requirements of multiple scenarios (such as agriculture, transportation, unmanned driving, mining, etc.), resulting in rigid resource allocation and frequency band conflicts. Summary of the Invention

[0003] The embodiments of the present application provide an intelligent adaptive networking positioning method and system for large-scale Beidou reference stations, which are used to improve the technical problem that there is a lack of an automated rapid reorganization ability after a reference station drops offline in the related art.

[0004] To achieve the above object, the embodiments of the present application adopt the following technical solutions:

[0005] In a first aspect, the embodiments of the present application provide an intelligent adaptive networking positioning method for large-scale Beidou reference stations, which is applied to a GNSS reference station network. The method includes: constructing a reference station plane, where the reference station plane includes multiple reference stations, and communication connections are formed between the multiple reference stations to form communication links, and the multiple communication links constitute a mesh topology structure of the reference station plane.

[0006] In this way, communication connections are formed between multiple reference stations to form a mesh topology, which can enhance the network reliability and fault tolerance ability, and the interruption of any link does not affect the overall connectivity.

[0007] When the reference station is disconnected from the network topology structure corresponding to the reference station plane, reconstruct the mesh topology structure according to a preset strategy; where the preset strategy includes selecting an alternative reference station for the original communication link of the reference station according to at least one of the communication error rate, transmission delay, and logical distance between nodes, and the alternative reference station is used to form a communication link with other reference stations in the original communication link.

[0008] In this way, when a reference station drops offline, an alternative reference station can be selected according to the communication error rate, transmission delay, and logical distance, quickly restoring network connectivity, reducing service interruption, and improving the technical problem that there is a lack of an automated rapid reorganization ability after a reference station drops offline.

[0009] In a possible implementation of the first aspect, the reference station plane includes a first reference station plane and a second reference station plane. Building the reference station plane includes: building the first reference station plane, which is used to serve the positioning requirements of the first scenario. When the requirements of the second scenario are received, multiple reference stations are selected from the first reference station plane to build the second reference station plane, and the second reference station plane is used to serve the positioning requirements of the second scenario.

[0010] In this way, by building the first reference station plane and the second reference station plane to serve different positioning requirement scenarios respectively, resources can be flexibly allocated, and the system adaptability, service independence and accuracy can be improved.

[0011] In a possible implementation of the first aspect, the number of reference stations selected from the first reference station plane for building the second reference station plane is not greater than a first threshold.

[0012] In a possible implementation of the first aspect, the method further includes: during the process of reconstructing the mesh topology structure according to a preset strategy, using the historical data or real-time observations of the neighboring stations of the reference stations disconnected from the network topology structure to dynamically generate virtual observations to replace the reference stations.

[0013] In this way, by generating virtual observations using the data of neighboring stations, the data continuity can be ensured, the influence of disconnection on the positioning accuracy can be reduced, and the service absence during the reconstruction process can be avoided.

[0014] In a possible implementation of the first aspect, the network topology structure of the second reference station plane runs in parallel with the network topology structure of the first reference station plane.

[0015] In a possible implementation of the first aspect, the reference stations in the first reference station plane use a first frequency band, and the reference stations in the second reference station plane use a second frequency band, and the first frequency band and the second frequency band are different.

[0016] In this way, by using different frequency bands for different reference station planes, signal conflicts can be avoided and service stability can be guaranteed.

[0017] Second aspect, embodiments of the present application further provide an intelligent adaptive networking positioning system for large-scale Beidou reference stations, which is used to execute the positioning method described in the first aspect. The system includes: a network status monitoring module, coupled to the reference stations, for collecting in real time communication error rates, transmission delays, and logical distances between nodes of each reference station; a topology dynamic management module, coupled to the network status monitoring module, for constructing a first reference station plane, and when a reference station in the first reference station plane is disconnected, reconstructing a communication link for the remaining reference stations in the first reference station plane according to the preset strategy.

[0018] In a possible implementation manner of the second aspect, the network monitoring module includes a data fusion processing module, and the data fusion processing module is used to dynamically generate virtual observation values according to historical data and real-time observation values of the reference stations.

[0019] In a possible implementation manner of the second aspect, the topology dynamic management module is further used to respond to the second scenario requirement and select the reference stations from the first reference station plane to construct a second reference station plane.

[0020] In a possible implementation manner of the second aspect, the network topology structure of the second reference station plane runs in parallel with the network topology structure of the first reference station plane, and the reference stations in the first reference station plane use a first frequency band, and the reference stations in the second reference station plane use a second frequency band, and the first frequency band and the second frequency band are different.

[0021] Beneficial effects: In the present application, communication links are formed through communication connections between multiple reference stations, and multiple communication links form a mesh topology structure to construct a reference station plane. When a reference station is disconnected from the network topology structure corresponding to the reference station plane, the mesh topology structure is reconstructed according to the preset strategy. The preset strategy includes selecting an alternative reference station for the original communication link of the reference station according to at least one of the communication error rate, transmission delay, and logical distance between nodes. The alternative reference station and other reference stations in the original communication link form a communication link, thereby improving the related technology and the technical problem that the reference station lacks the ability of automatic and rapid reorganization after going offline. Description of the Drawings

[0022] Figure 1 is a schematic flowchart provided for some embodiments of the present application;

[0023] Figure 2 is Figure 1 a schematic flowchart of step S600 in Detailed Embodiments

[0024] Next, the technical solutions in the embodiments of the present application will be described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0025] Hereinafter, terms such as "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first", "second", etc. may explicitly or implicitly include one or more of such features. In the description of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0026] In addition, in the present application, orientation terms such as "upper", "lower", "left", "right", etc. may include but are not limited to being defined relative to the schematic placement of components in the accompanying drawings. It should be understood that these directional terms may be relative concepts, which are used for relative description and clarification, and may change accordingly with the change of the orientation of the components in the accompanying drawings.

[0027] In the present application, unless otherwise clearly specified and defined, the term "connection" should be understood in a broad sense. For example, "connection" may be a fixed connection, a detachable connection, or an integral one; it may be directly connected or indirectly connected through an intermediate medium. In addition, the term "coupling" may be a way of realizing electrical connection for signal transmission.

[0028] As used herein, "about", "substantially" or "approximately" includes the stated value and a reference value within an acceptable deviation range of the specific value, where the acceptable deviation range is determined by those of ordinary skill in the art considering the measurement being discussed and the errors associated with the measurement of the specific quantity (i.e., the limitations of the measurement system).

[0029] In the embodiments of the present application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplarily" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or designs. Rather, the use of words such as "exemplarily" or "for example" is intended to present relevant concepts in a specific manner.

[0030] Most of the reference station networks in the related art lack sufficient flexibility and self-healing capabilities. When the communication link is interrupted due to signal interference, hardware failure, or environmental factors, there is a lack of an intelligent reconstruction mechanism based on dynamic factors, resulting in insufficient network robustness and a decline in the reliability of positioning services. Moreover, in the related art, relying on manual intervention to reconnect or adjust the network structure cannot optimize the topological structure in real time. At the same time, a single reference station plane is difficult to adapt to the differentiated requirements of multiple scenarios (such as agriculture, transportation, unmanned driving, mining, etc.), resulting in rigid resource allocation and frequency band conflicts.

[0031] The embodiments of the present application provide an intelligent adaptive networking positioning method and system for large-scale Beidou reference stations, which are applied to the GNSS reference station network to solve the technical problem in the related art that there is a lack of an automated rapid reorganization ability after the reference station goes offline.

[0032] As Figure 1 、 Figure 2 shown, the method includes:

[0033] S100. Construct a reference station plane, where the reference station plane includes multiple reference stations, and communication connections are formed between the multiple reference stations to form a communication link, and the multiple communication links constitute a mesh topology structure of the reference station plane.

[0034] Exemplarily, the constructing of the reference station plane includes:

[0035] Construct the first reference station plane, which is used to serve the positioning requirements of the first scenario. The first scenario can be a scenario with low-precision positioning requirements, such as vehicle navigation and meteorological monitoring, etc.

[0036] Exemplarily, deploy a number of dual-frequency Beidou reference stations within a set area, with a spacing of 50 - 100 km between each base station. The base stations adopt a mesh topology structure, and the central station can be located at the data center of the corresponding area. The reference stations can be connected through 4G / 5G, and the data synchronization period can be set to 30 seconds, and the positioning accuracy is less than or equal to 5 meters.

[0037] When the requirements of the second scenario are received, select multiple reference stations from the first reference station plane to construct the second reference station plane, which is used to serve the positioning requirements of the second scenario. The second scenario can be a high-precision positioning requirement, such as autonomous driving, etc.

[0038] Exemplarily, when a certain city launches an autonomous driving demonstration area, select a number of reference stations within 20 km around the demonstration area (such as 20% of the total number of nodes) from the first reference station plane, and achieve centimeter-level positioning through real-time kinematic positioning technology, such as the positioning accuracy is less than or equal to 1 cm.

[0039] Exemplarily, in order to ensure the service quality of the first reference station plane, when selecting reference stations from the first reference station plane for constructing the second reference station plane, it is not allowed to make the first reference station plane unable to meet the requirements of the first scenario. Therefore, a maximum threshold for the number of extracted reference stations can be set, such as a first threshold. It can be understood that the first threshold can be determined by those skilled in the art according to the requirements of the first scenario, and this application will not elaborate here.

[0040] In order to ensure that the services of the first scenario and the second scenario do not conflict, the network topology structure of the second reference station plane can be set to run in parallel with the network topology structure of the first reference station plane. For example, through frequency band isolation, independent topological paths, resource allocation strategies, etc., to ensure that the data streams, communication links, and computing resources of the two planes do not interfere with each other.

[0041] Exemplarily, the reference stations in the first reference station plane use a first frequency band, and the reference stations in the second reference station plane use a second frequency band, and the first frequency band and the second frequency band are different. For example, the first reference station plane uses the B1I frequency band (1561.098 MHz) with a bandwidth of 4.092 MHz, which is dedicated to low-precision services (such as vehicle navigation, meteorological monitoring). The second reference station plane uses the B2b frequency band (1207.14 MHz) with a bandwidth of 10.23 MHz, which is dedicated to high-precision services (such as autonomous driving, precise surveying and mapping). In the above example, the center frequency interval between the first frequency band and the second frequency band is ≥ 354 MHz, and co-frequency interference can be avoided through physical isolation.

[0042] Exemplarily, the first reference station plane adopts a first mesh topology structure, and all nodes establish multi-hop connections through 4G / 5G public networks or proprietary wireless links (such as LoRaWANMesh). The second reference station plane adopts a second mesh topology structure, and nodes can establish multi-hop connections through 4G / 5G public networks or proprietary wireless links (such as LoRaWANMesh), and the paths are redundant to improve the anti-interruption ability.

[0043] S200. When the reference station is disconnected from the network topology structure corresponding to the reference station plane, reconstruct the mesh topology structure according to a preset policy.

[0044] For example, when a reference station drops out or is extracted to form the second reference station plane, the mesh topology structure can be reconstructed according to a preset policy. The preset policy includes selecting an alternative reference station for the original communication link of the reference station according to at least one of the communication error rate, transmission delay, and logical distance between nodes, and the alternative reference station is used to form a communication link with other reference stations in the original communication link.

[0045] Exemplarily, in a certain reference station network, the communication link of reference station A (the original communication link node) is interrupted due to a landslide. The packet loss rate (communication error rate) of reference station A is detected to be 18%, and the link delay of reference station A is 120 ms. The shortest path hop count between reference station A and other nodes is calculated based on the Dijkstra algorithm (the physical distance is converted to a logical distance, and the weight factor is 1 hop ≈ 10 km). Candidate nodes B, C, D, and E within 100 km (logical distance ≤ 10 hops) around reference station A are screened out.

[0046] Among them, the communication error rate, transmission delay, logical distance (hops), and physical distance (km) of node B are 2%, 8 ms, 1, and 8.5 respectively. The communication error rate, transmission delay, logical distance (hops), and physical distance (km) of node C are 4%, 15 ms, 2, and 18.2 respectively. The communication error rate, transmission delay, logical distance (hops), and physical distance (km) of node D are 10%, 45 ms, 3, and 28.7 respectively. The communication error rate, transmission delay, logical distance (hops), and physical distance (km) of node D are 8%, 25 ms, 2, and 22.1 respectively.

[0047] Weights are assigned to each parameter factor, and then weighted calculations are performed. The comprehensive score of each node can be calculated, and thus the most suitable alternative reference station can be selected. For example, if the score of reference station B is the highest in the finally calculated scores, then B is selected as the alternative reference station. It can be understood that when performing weighted calculations, each factor needs to be normalized. This is a common technical means for those skilled in the art and will not be elaborated in this application.

[0048] Exemplarily, during the process of reconstructing the mesh topology structure according to a preset strategy, historical data or real-time observations of neighboring stations of the disconnected reference station are used to dynamically generate virtual observations to replace the reference station. For example, calculations are performed according to the virtual observation value calculation formula:

[0049] VRS A =w B ·Obs B +w C ·Obs C +w F ·Obs F

[0050] Among them, VRS A is the virtual observation value of reference station A, used to replace the missing data of the failed node A. Obs B , Obs C , Obs F are the real-time observation data or historical data (such as carrier phase, pseudorange, etc.) of neighboring reference stations B, C, and F respectively. w B 、wC and w F is the weight coefficient, representing the contribution degree of each neighboring station to the virtual observation value.

[0051] Among them, the weight coefficient can be determined by the distance between the reference station A and the neighboring stations. For example where Dist(A,i) 2 is the distance between the reference station A and the neighboring station i.

[0052] Since physically the GNSS signal errors (such as ionospheric delay, tropospheric delay) are positively correlated with the propagation distance, the closer the station is, the stronger the spatial correlation between its observation data and the failed node A. Therefore, a higher weight is assigned. It can be understood that the weight value needs to be normalized before being substituted into the formula for calculation.

[0053] In this way, the weight can be dynamically adjusted according to the positions of neighboring stations, which can be applied to scenarios of node failure, addition or movement. Moreover, the inverse square distance weighting can effectively suppress the observation noise (such as multipath effect) of distant stations. At the same time, multiple types of observation data (pseudorange, carrier phase, multi-frequency signal) can be fused in the calculation to improve the reliability of the virtual observation value.

[0054] In order to better provide high-precision positioning services, the demand for reference stations on the second reference station plane can be predicted. In related technologies, reference station prediction can provide reference for high-precision positioning services to a certain extent, but there are generally deficiencies. For example, the prediction model often makes simple extrapolation based on historical data, resulting in a large deviation between the prediction result and the actual demand, or the prediction model highly depends on the accumulation of a large amount of historical data. For areas with incomplete data collection or lack of historical data, the applicability and accuracy of the model are limited. In addition, with the diversification and complexity of application scenarios increasing, the dynamic change of the reference station demand may change rapidly, and the model update speed is slow, making it difficult to achieve real-time or near-real-time prediction. Therefore, the prediction methods in related technologies have poor prediction effects on the reference station demand. To improve the above problems, this method further includes:

[0055] S300. Obtain the key factors affecting the growth of the second scenario demand. Exemplarily, in this application, the key factors include the first key factor (p) and the second key factor (q).

[0056] The first key factor (p) represents the impact of users who independently adopt the high-precision positioning service of the second scenario without being affected by other users on the growth of the demand for the second scenario. These users or regions may take the lead in adopting high-precision positioning services due to their own urgent need for high-precision positioning, technological advancement or sensitivity to new technologies, etc., without being affected by whether other users adopt the service.

[0057] The second key factor (q) represents the impact of the growth in demand for the second scenario's high-precision positioning service by users who are influenced by other users or regions. When other users or regions start to adopt the high-precision positioning service, these users or regions will be affected to a certain extent and thus be more inclined to adopt the service. This impact may come from factors such as social demonstration effects, word-of-mouth dissemination, and technical exchanges.

[0058] S400. Build a prediction model based on the key factors.

[0059] Based on the above first key factor (p) and second key factor (q), build a prediction model for predicting the number of reference stations required for the second reference station plane. The expression of this prediction model is:

[0060]

[0061] where I(t) is the number of reference stations required for the second reference station plane at time t, T is the maximum number of nodes that can be separated in the first reference station plane, p is the first key factor, and q is the second key factor.

[0062] This prediction model describes the dynamic change of reference station demand over time, regards the diffusion process of reference stations as a dynamic system jointly affected by independent users and influenced users, and describes the demand change of reference stations in a specific region within a certain time. Among them, the first key factor (p) reflects the impact of the growth in demand for the second scenario by users who independently adopt the high-precision positioning service without being influenced by other users, while the second key factor (q) reflects the impact of the growth in demand for the second scenario by users who adopt the high-precision positioning service under the influence of other users or regions. In addition, the factors of the prediction model are designed to be estimable and can be quantified through historical data, ensuring the feasibility and effectiveness of the model in practical applications.

[0063] S500. Obtain the first prediction data according to the historical demand data. For example, use the historical data of the number of reference stations required in the second reference station plane in adjacent regions as the first prediction data.

[0064] S600. Obtain the intermediate value of the key factor based on the first prediction data, the current actual reference station demand data, and the prediction model.

[0065] In a possible implementation, S600 includes the following steps:

[0066] S610. Build an intermediate model according to the first prediction data.

[0067] Exemplarily, the intermediate model is composed of multiple prediction models. For example, the intermediate prediction model can be weighted and composed of a BP neural network model, a fuzzy neural network model, an adaptive probabilistic neural network model, etc. The weights of each model can be determined by the accuracy of its prediction results. The higher the accuracy, the greater the weight of the model when constructing the intermediate prediction model.

[0068] S620. Obtain the second prediction data according to the intermediate model.

[0069] Use the trained intermediate model to predict the reference station requirements to obtain the second prediction data. For example, predict the data from January 2025 to December 2025.

[0070] S630. Obtain the third prediction data according to the current actual reference station requirement data and the second prediction data.

[0071] The S630 includes:

[0072] S631. Obtain the average deviation between the first data in the second prediction data and the current actual reference station requirement data.

[0073] Exemplarily, the current actual reference station requirement data includes the data from January 2025 to April 2025. The data to be predicted is the data from May 2025 to December 2025. Take out the data in the second prediction data corresponding to the current actual reference station requirement data, that is, the data from January 2025 to April 2025 in the second prediction data, and analyze it with the actual reference station requirement data from January 2025 to April 2025 to obtain the average deviation between the two. For example, calculate the deviation value of each month between the second prediction data and the current actual reference station requirement data from January 2025 to April 2025, sum up all the deviation values and divide by the number of months to obtain the average deviation during this period.

[0074] S632. Correct the second prediction data according to the average deviation to obtain the third prediction data.

[0075] Exemplarily, add the average deviation to the predicted data of each month in the second prediction data to correct the second prediction data to obtain the third prediction data. It can be understood that the third prediction data includes the data from January 2025 to December 2025, that is, it includes the reference station requirement data that has occurred and the reference station requirement data that has not occurred / to be predicted. In this way, through the intermediate prediction model, a large amount of reference station requirement data can be obtained in this solution, so that the training of the model can be completed without a large amount of historical data.

[0076] S640. Obtain the intermediate value of the key factor according to the data of the trend node in the third prediction data and the prediction model.

[0077] Exemplarily, the second reference station plane was first constructed in August 2023, and the number of reference stations required in the first month in the second reference station plane was 10. In the following two months, the demand in the second scenario continued to increase, and the increase rate of reference stations in the second reference station plane continued to rise. The increase rate of reference stations from August 2023 to September 2023 was 20, the increase rate of reference stations from September 2023 to October 2023 reached 25. From October 2023 to November 2023, the increase rate of reference stations reached 30. The increase rate of reference stations from November 2023 to January 2024 was 28, and the increase rate of deployed reference stations from January 2024 to February 2024 was 29. The increase rate of deployed reference stations from February 2024 to March 2024 was 24, the increase rate of deployed reference stations from March 2024 to April 2024 was 18, and the increase rate of deployed reference stations from April 2024 to May 2024 was 12. After May 2024, the increase rate of deployed reference stations remained at a low level for a long time.

[0078] It can be seen that August 2023 was the first deployment time. October 2023 was the turning point between the initial diffusion stage and the stable stage. February 2024 was the turning point between the stable stage and the decline stage. May 2024 was the turning point between the decline stage and the residual stage. Therefore, during the entire cycle of the diffusion of the number of reference stations required in the second reference station plane, there are several trend nodes: the first construction time point, the turning time point from the initial diffusion stage to the stable stage, the turning time point from the stable stage to the decline stage, and the turning time point from the decline stage to the residual stage.

[0079] Obtain the number of reference stations corresponding to each trend node in the third prediction data, substitute it into the prediction model, and solve the prediction model to obtain the intermediate value of the corresponding key factor.

[0080] S700. Predict the reference station demand according to the prediction model corresponding to the intermediate value of the key factor.

[0081] Substitute the obtained intermediate value of the key factor into the prediction model, that is, substitute the determined first key factor (p) and the second key factor (q) into the prediction model, and predict the data to be predicted according to this prediction model, that is, use this model to predict the number of reference station demands from May 2025 to December 2025.

[0082] In this way, the present application first constructs a prediction model through key factors and obtains first prediction data based on historical data. Subsequently, through the first prediction data, current actual data, intermediate model, and the prediction model, the intermediate value of the key factor is obtained, making the intermediate value of the key factor more reasonable. Finally, the prediction model corresponding to the intermediate value of the key factor is used to predict the number of reference stations, enabling the prediction model to be more accurate during prediction, improving the related technology, and solving the technical problem of poor prediction effect.

[0083] Through the above steps, the present solution can combine multiple influencing factors to better predict the demand for reference stations, thereby providing strong support for the optimization of the Beidou reference station network and the improvement of high-precision positioning services.

[0084] In a second aspect, an embodiment of the present application further provides an intelligent adaptive networking positioning system for large-scale Beidou reference stations, which is used to execute the positioning method described in the first aspect. The system includes: a network status monitoring module, coupled to the reference stations, for real-time collecting communication error rates, transmission delays, and logical distances between nodes of each reference station; a topology dynamic management module, coupled to the network status monitoring module, for constructing a first reference station plane, and when a reference station in the first reference station plane is disconnected, reconstructing a communication link for the remaining reference stations in the first reference station plane according to the preset strategy.

[0085] In a possible implementation manner of the second aspect, the network monitoring module includes a data fusion processing module, and the data fusion processing module is used to dynamically generate virtual observation values according to historical data and real-time observation values of the reference stations.

[0086] In a possible implementation manner of the second aspect, the topology dynamic management module is further used to respond to the second scenario requirement and select the reference stations from the first reference station plane to construct a second reference station plane.

[0087] In a possible implementation manner of the second aspect, the network topology structure of the second reference station plane runs in parallel with the network topology structure of the first reference station plane, and the reference stations in the first reference station plane use a first frequency band, and the reference stations in the second reference station plane use a second frequency band, and the first frequency band and the second frequency band are different.

[0088] Through the description of the above embodiments, those skilled in the art can clearly understand that the prediction method in the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to enable a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0089] The above content is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. An intelligent adaptive networking positioning method for large-scale Beidou reference stations, which is applied to a GNSS reference station network, and is characterized in that, The method includes: Constructing a reference station plane, which includes a plurality of reference stations. Communication connections are formed between the plurality of reference stations to form communication links, and the plurality of communication links constitute a mesh topology structure of the reference station plane; When the reference station is disconnected from the network topology structure corresponding to the reference station plane, reconstructing the mesh topology structure according to a preset policy; Wherein, the preset policy includes selecting an alternative reference station for the original communication link of the reference station according to at least one of the communication error rate, transmission delay, and logical distance between nodes. The alternative reference station is used to form a communication link with other reference stations in the original communication link.

2. The networking positioning method according to claim 1, wherein The reference station plane includes a first reference station plane and a second reference station plane. The constructing of the reference station plane includes: Constructing the first reference station plane, which is used to serve the positioning requirements of the first scenario. When receiving the requirements of the second scenario, select a plurality of the reference stations from the first reference station plane to construct the second reference station plane, and the second reference station plane is used to serve the positioning requirements of the second scenario.

3. The networking positioning method according to claim 2, wherein The number of reference stations selected from the first reference station plane for constructing the second reference station plane is not greater than a first threshold.

4. The networking positioning method according to claim 3, wherein The method further includes: during the process of reconstructing the mesh topology structure according to the preset policy, using the historical data or real-time observation values of the neighboring stations of the reference station disconnected from the network topology structure to dynamically generate virtual observation values to replace the reference station.

5. The networking positioning method according to claim 4, wherein, The network topology structure of the second reference station plane runs in parallel with the network topology structure of the first reference station plane.

6. The networking positioning method according to claim 5, characterized in that, The reference stations in the first reference station plane use a first frequency band, the reference stations in the second reference station plane use a second frequency band, and the first frequency band and the second frequency band are different.

7. An intelligent adaptive networking positioning system for large-scale Beidou reference stations, characterized in that, For implementing the network positioning method as claimed in claim 1, the system includes: A network status monitoring module, coupled to the reference station, for real-time collecting the communication error rate, transmission delay, and logical distance index between nodes of each reference station; A topology dynamic management module, coupled to the network status monitoring module, for constructing the first reference station plane. When the reference stations in the first reference station plane are disconnected, reconstructing the communication links for the remaining reference stations in the first reference station plane according to the preset policy.

8. The networking positioning system according to claim 6, wherein The network monitoring module includes a data fusion processing module, and the data fusion processing module is used to dynamically generate virtual observation values according to the historical data and real-time observation values of the reference station.

9. The networking positioning system according to claim 6, wherein The topology dynamic management module is further used to respond to the requirements of the second scenario and select the reference stations from the first reference station plane to construct the second reference station plane.

10. The networking positioning system according to claim 9, wherein, The network topology structure of the second reference station plane runs in parallel with the network topology structure of the first reference station plane, and the reference stations in the first reference station plane use a first frequency band, the reference stations in the second reference station plane use a second frequency band, and the first frequency band and the second frequency band are different.

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