Intelligent adaptive network positioning method and system for large-scale compass reference station

Abstract

The application provides an intelligent adaptive networking positioning method and system for large-scale Beidou reference stations, relates to the technical field of reference station networking positioning, and can improve the technical problem that there is a lack of automatic rapid reorganization capability after the reference station is disconnected in the related art. The method comprises the following steps: constructing a reference station plane, the reference station plane comprising a plurality of reference stations, the plurality of reference stations being communicatively connected to form communication links, and the plurality of communication links constituting a mesh topology of the reference station plane; when the reference station is disconnected from the network topology corresponding to the reference station plane, the mesh topology is reconfigured according to a preset strategy; wherein the preset strategy comprises selecting a replacement reference station for the original communication link of the reference station according to at least one of a communication error rate, a transmission delay, and a logical distance between nodes, and the replacement reference station is used to form a communication link with other reference stations in the original communication link.

Description

TECHNICAL FIELD

[0001] The embodiment of the present application relates to the technical field of reference station networking positioning, and particularly relates to an intelligent adaptive networking positioning method and system for large-scale Beidou reference stations. BACKGROUND

[0002] The reference station network in the related art mostly lacks sufficient flexibility and self-repairing capability. When a communication link is interrupted due to signal interference, hardware failure or environmental factors, there is a lack of intelligent reconstruction mechanism based on dynamic factors, resulting in insufficient network robustness and decreased positioning service reliability. In the related art, manual intervention is relied on to reconnect or adjust the network structure, which cannot optimize the topology structure in real time. Meanwhile, a single reference station plane cannot adapt to differentiated requirements in multiple scenarios (such as agriculture, transportation, unmanned driving and mining), causing rigid resource allocation and frequency band conflicts. SUMMARY

[0003] The embodiment of the present application provides an intelligent adaptive networking positioning method and system for large-scale Beidou reference stations, which is used to improve the technical problem of lack of automatic rapid recombination capability after a reference station goes offline in the related art.

[0004] To achieve the above object, the embodiment of the present application adopts the following technical scheme:

[0005] In a first aspect, the embodiment of the present application provides an intelligent adaptive networking positioning method for large-scale Beidou reference stations, applied to a GNSS reference station network, and the method comprises the following steps: constructing a reference station plane, wherein the reference station plane comprises a plurality of reference stations, the plurality of reference stations are communicatively connected to form communication links, and the plurality of communication links constitute a mesh topology structure of the reference station plane.

[0006] In this way, the plurality of reference stations are communicatively connected to form a mesh topology, which can enhance network reliability and fault tolerance capability, and any link interruption does not affect the overall connectivity.

[0007] When the reference station is disconnected from the network topology structure corresponding to the reference station plane, the mesh topology structure is reconstructed according to a preset strategy; wherein the preset strategy comprises selecting a replacement reference station for the original communication link of the reference station according to at least one of a communication error rate, a transmission time delay and a logical distance between nodes, and the replacement 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 goes offline, a replacement reference station can be selected according to the communication error rate, the transmission time delay and the logical distance, the network connectivity is quickly restored, the service interruption is reduced, and the technical problem of lack of automatic rapid recombination capability after a reference station goes offline is improved.

[0009] In a possible implementation manner of the first aspect, the reference station planes include a first reference station plane and a second reference station plane, and the method of constructing the reference station plane includes: constructing the first reference station plane, the first reference station plane being used to serve positioning requirements of a first scene; and when a second scene requirement is received, selecting a plurality of reference stations from the first reference station plane to construct the second reference station plane, the second reference station plane being used to serve positioning requirements of the second scene.

[0010] In this way, by constructing the first reference station plane and the second reference station plane to respectively serve different positioning requirement scenes, resource allocation can be flexible, and system adaptability and service independence and accuracy can be improved.

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

[0012] In a possible implementation manner of the first aspect, the method further includes: in the process of reconstructing the mesh topology according to the preset strategy, dynamically generating virtual observation values of the reference stations that are disconnected from the network topology by using historical data or real-time observation values of neighboring stations of the reference stations.

[0013] In this way, by generating virtual observation values by using neighboring station data, data continuity can be ensured, the influence of disconnection on positioning accuracy can be reduced, and service loss in the reconstruction process can be avoided.

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

[0015] In a possible implementation manner of the first aspect, 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.

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

[0017] In a second aspect, embodiments of the present application also provide an intelligent adaptive networking positioning system for large-scale Beidou reference stations, configured to perform the positioning method of the first aspect. The system comprises: a network state monitoring module coupled to the reference stations, configured to collect the communication error rate, transmission delay of each reference station and the logical distance between nodes in real time; and a topology dynamic management module coupled to the network state monitoring module, configured to construct a first reference station plane. When a reference station in the first reference station plane is disconnected, the topology dynamic management module is configured to reconstruct the communication link for the remaining reference stations in the first reference station plane according to the preset strategy.

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

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

[0020] In a possible implementation 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, wherein the first frequency band and the second frequency band are different.

[0021] Beneficial effects: The present application forms a communication link between multiple reference stations, multiple communication links constitute a mesh topology structure to construct a reference station plane, and 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 a preset strategy. The preset strategy includes selecting a replacement reference station for the original communication link of the reference station according to at least one of the communication error rate, the transmission delay and the logical distance between nodes, and the replacement reference station and the other reference stations in the original communication link constitute a communication link, thereby improving the related art and solving the technical problem of lack of automatic and rapid recombination capability after the reference station goes offline. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 Flowchart for some embodiments of the present application;

[0023] Figure 2 For Figure 1 Flowchart for step S600 in the method. DETAILED DESCRIPTION

[0024] With reference to the drawings, the technical solutions in the embodiments of the present application will be described below. Obviously, the described embodiments are only a part of the embodiments of the present application, but not all the embodiments of the present application.

[0025] Hereinafter, the terms "first", "second", and the like are used only for the purpose of description, and should not be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, the features defined with "first", "second", and the like can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

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

[0027] In the present application, unless otherwise specified and limited, the term "connection" should be understood broadly, for example, "connection" can be fixed connection, or detachable connection, or integral; can be directly connected, or indirectly connected through intermediate medium. In addition, the term "coupling" can be an electrically connected manner for signal transmission.

[0028] As used herein, "about", "approximately", or "nearly" includes the stated value and a range of reference values within the acceptable deviation of the specific value, wherein the acceptable deviation range is determined by the ordinary skill in the art considering the measurement being discussed and the error related to the measurement of the specific quantity (i.e. the limitation of the measurement system).

[0029] In the embodiments of the present application, the words "exemplarily" or "for example" and the like are used to represent as an example, illustration or description. Any embodiment or design scheme described as "exemplarily" or "for example" in the embodiments of the present application should not be interpreted as more preferred or more advantageous than other embodiments or design schemes. Rather, the use of "exemplarily" or "for example" and the like is intended to present the relevant concept in a specific manner.

[0030] The reference station network in the related art mostly lacks sufficient flexibility and self-repairing capability, and lacks intelligent reconstruction mechanism based on dynamic factors when the communication link is interrupted due to signal interference, hardware failure or environmental factors, resulting in insufficient network robustness and decreased positioning service reliability. In the related art, manual intervention is relied on to reconnect or adjust the network structure, which cannot optimize the topology structure in real time, and at the same time, a single reference station plane is difficult to adapt to differentiated needs in multiple scenarios (such as agriculture, transportation, unmanned driving, mining, etc.), causing rigid resource allocation and frequency band conflict.

[0031] The embodiment of the present application provides an intelligent adaptive networking positioning method and system for large-scale Beidou reference stations, applied to a GNSS reference station network, to improve the technical problem that the reference station lacks automatic rapid reorganization capability after falling offline in the related art.

[0032] As shown in Figure 1 , Figure 2 The method comprises the following steps:

[0033] S100, a reference station plane is constructed, the reference station plane comprising a plurality of reference stations, the plurality of reference stations being communicatively connected to form communication links, and the plurality of communication links constituting a mesh topology of the reference station plane.

[0034] Exemplarily, the construction of the reference station plane comprises:

[0035] The first reference station plane is constructed, and the first reference station plane is used to serve the positioning needs of a first scenario. The first scenario can be a scenario of low-precision positioning needs, such as vehicle navigation and weather monitoring, etc.

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

[0037] When a second scenario demand is received, a plurality of reference stations are selected from the first reference station plane to construct a second reference station plane, and the second reference station plane is used to serve the positioning needs of a second scenario. The second scenario can be a high-precision positioning demand, such as autonomous driving, etc.

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

[0039] Exemplarily, in order to guarantee the service quality of the first reference station plane, when the reference stations in the first reference station plane are selected to construct the second reference station plane, the first reference station plane cannot be unable to meet the requirements of the first scene. Therefore, a maximum threshold of 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 a person skilled in the art according to the requirements of the first scene, and the present application does not repeat it here.

[0040] In order to guarantee that the services of the first scene and the second scene 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. Such as through frequency band isolation, independent topology path and resource allocation strategy, etc., to ensure that the data flow, communication link and computing resource 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, 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 B1I frequency band (1561.098MHz), with a bandwidth of 4.092MHz, which is dedicated to low-precision services (such as vehicle navigation, weather monitoring). The second reference station plane uses B2b frequency band (1207.14MHz), with a bandwidth of 10.23MHz, which is dedicated to high-precision services (such as autonomous driving, precision mapping). In the above example, the center frequency interval of the first frequency band and the second frequency band is ≥354MHz, and the same frequency interference can be avoided through physical isolation.

[0042] Exemplarily, the first reference station plane adopts a first mesh topology structure, and all nodes are connected through 4G / 5G public network or special wireless link (such as LoRaWANMesh) to establish multi-hop connection. The second reference station plane adopts a second mesh topology structure, and nodes can be connected through 4G / 5G public network or special wireless link (such as LoRaWANMesh) to establish multi-hop connection, and the path is 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, the mesh topology structure is reconstructed according to a preset strategy.

[0044] When the reference station is offline or is extracted to form the second reference station plane, the mesh topology structure can be reconstructed according to a preset strategy. The preset strategy includes selecting a replacement reference station for the original communication link of the reference station according to at least one of the communication error rate, the transmission delay and the logical distance between nodes, and the replacement 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, reference station A (original communication link node) is interrupted due to landslide. The packet loss rate (communication error rate) of reference station A is detected as 18%, and the link delay of reference station A is 120 ms. The shortest path hop count (physical distance is converted into logical distance, and the weight factor is 1 hop ≈ 10 km) between reference station A and other nodes is calculated based on Dijkstra algorithm. Candidate nodes B, C, D and E within 100 km range (logical distance ≤ 10 hops) around reference station A are screened.

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

[0047] Each parameter factor is assigned a weight, and then weighted calculation is performed to calculate the comprehensive score of each node, so that the most suitable replacement reference station is selected. For example, if the score calculated finally is that the score of reference station B is the highest, then B is selected as the replacement reference station. It can be understood that normalization needs to be performed on each factor when performing weighted calculation. This is a common technical means for those skilled in the art, and will not be described herein.

[0048] Exemplarily, in the process of reconstructing the mesh topology according to the preset strategy, the historical data or real-time observation values of the adjacent stations of the disconnected reference station are used to dynamically generate virtual observation values for replacing the reference station. For example, the calculation is 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] Wherein, VRS A is the virtual observation value of reference station A, which is used to replace the missing data of failed node A, Obs B , Obs C , and Obs F are real-time observation data or historical data (such as carrier phase, pseudo-range, etc.) of adjacent reference stations B, C, and F, w B , wC , w F is a weight coefficient, representing the contribution degree of each adjacent station to the virtual observation value.

[0051] wherein the weight coefficient can be determined by the distance between the reference station A and the adjacent station. For example, wherein Dist(A, i) 2 is the distance between the reference station A and the adjacent station i.

[0052] Since the physical GNSS signal error (such as ionospheric delay, tropospheric delay) is positively correlated with the propagation distance, the closer the distance between the stations, the stronger the spatial correlation between the observation data and the failed node A, and therefore a higher weight is given. 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 position of the adjacent station, and can be applied to the scenarios of node failure, addition or movement, and the inverse square distance weighting can effectively suppress the observation noise (such as multipath effect) of the distant station, while multiple types of observation data (pseudo-range, 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 in the second reference station plane can be predicted. In related technologies, reference station prediction can provide a reference for high-precision positioning services to a certain extent, but there are generally deficiencies. For example, the prediction model is often based on simple extrapolation of historical data, resulting in a large deviation between the prediction result and the actual demand, or the prediction model is highly dependent on the accumulation of a large amount of historical data, and the applicability and accuracy of the model are limited in areas where data collection is not comprehensive or historical data is lacking. In addition, with the diversification and increasing complexity of application scenarios, the dynamic changes in reference station demand can quickly change, and the model update speed is slow, making it difficult to achieve real-time or near-real-time prediction. Therefore, the prediction effect of the reference station demand in the related technology is poor, and in order to improve the above problems, the method further comprises:

[0055] S300, acquiring a key factor affecting the growth of the demand of the second scene. Illustratively, in the present application, the key factor includes a first key factor (p) and a second key factor (q).

[0056] The first key factor (p) represents the influence of the demand growth of the second scene by the users who independently use the high-precision positioning service of the second scene without the influence of other users. These users or regions may be the first to use high-precision positioning services due to their own urgent demand for high-precision positioning, technical advancement, or sensitivity to new technologies, etc., without being affected by whether other users use the service.

[0057] The second key factor (q) represents the influence of the increase in the demand of the second scenario by the users who adopt the high-precision positioning service of the second scenario due to the influence of other users or regions. When other users or regions start to adopt the high-precision positioning service, these users or regions will be influenced to a certain extent, and thus will be more inclined to adopt the service. This influence can come from factors such as social demonstration effect, word-of-mouth, technology exchange, and the like.

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

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

[0060]

[0061] wherein 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] The prediction model describes the dynamic change of the demand for reference stations over time, and regards the diffusion process of the reference stations as a dynamic system influenced by both independent users and influenced users, and describes the change of the demand for the reference stations in a certain region within a certain time. The first key factor (p) reflects the influence of the increase in the demand of the second scenario by the users who independently adopt the high-precision positioning service without the influence of other users, and the second key factor (q) reflects the influence of the increase in the demand of the second scenario by the users who adopt the high-precision positioning service due to 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, thereby ensuring the feasibility and effectiveness of the model in actual application.

[0063] S500, obtaining first prediction data according to historical demand data. For example, the historical data of the number of reference stations required in the second reference station plane of a similar region is taken as the first prediction data.

[0064] S600, obtaining intermediate values of the key factors according to the first prediction data, current actual reference station demand data, and the prediction model.

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

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

[0067] Exemplarily, the intermediate model is composed of a plurality of prediction models. The intermediate prediction model can be composed of a BP neural network model, a fuzzy neural network model, and a self-adaptive probabilistic neural network model, etc. The weight of each model can be determined by the accuracy of the prediction result, and the higher the accuracy, the greater the weight in the composition of the intermediate prediction model.

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

[0069] The trained intermediate model is used to predict the demand of the reference station, and the second prediction data is obtained, such as predicting the data from January 2025 to December 2025.

[0070] S630, obtaining third prediction data according to the current actual reference station demand data and the second prediction data.

[0071] The S630 includes:

[0072] S631, obtaining the average deviation of the first data in the second prediction data and the current actual reference station demand data.

[0073] Exemplarily, the current actual reference station demand data includes data from January 2025 to April 2025. The data to be predicted is from May 2025 to December 2025. The data corresponding to the current actual reference station demand data in the second prediction data is taken out, that is, the data from January 2025 to April 2025 in the second prediction data is analyzed with the actual reference station demand data from January 2025 to April 2025, and the average deviation of the two is obtained. For example, the deviation value of the second prediction data and the current actual reference station demand data in each month from January 2025 to April 2025 is calculated, and the sum of all deviation values is divided by the number of months to obtain the average deviation of this period of time.

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

[0075] Exemplarily, the prediction data of each month in the second prediction data is added to the average deviation, so as to correct the second prediction data to obtain the third prediction data. It can be understood that the third prediction data includes data from January 2025 to December 2025, that is, it includes the occurred reference station demand data and the unoccurred / predicted reference station demand data. In this way, the intermediate prediction model can obtain a large amount of reference station demand data, so as to complete the training of the model without a large amount of historical data.

[0076] S640, obtaining 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] For example, the second reference station plane is first constructed in August 2023, and the number of reference stations required in the second reference station plane in the first month is 10. In the following two months, the demand of the second scenario continues to increase, and the increase of the reference stations in the second reference station plane continues to rise. The increase of the reference stations from August 2023 to September 2023 is 20, the increase of the reference stations from September 2023 to October 2023 reaches 25. From October 2023 to November 2023, the increase of the reference stations reaches 30. From November 2023 to January 2024, the increase of the reference stations is 28, from January 2024 to February 2024, the increase of the reference stations deployed is 29. From February 2024 to March 2024, the increase of the reference stations deployed is 24, from March 2024 to April 2024, the increase of the reference stations deployed is 18, and from April 2024 to May 2024, the increase of the reference stations deployed is 12. After May 2024, the increase of the reference stations deployed continues at a low level for a long time.

[0078] It can be seen that August 2023 is the first deployment time. October 2023 is the turning point from the initial stage of diffusion to the stable stage. February 2024 is the turning point from the stable stage to the recession stage. May 2024 is the turning point from the recession stage to the residual stage. Therefore, in 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 stage of diffusion to the stable stage, the turning time point from the stable stage to the recession stage, and the turning time point from the recession stage to the residual stage.

[0079] The number of reference stations corresponding to each trend node in the third prediction data is obtained, which is substituted into the prediction model, and the prediction model is solved to obtain the intermediate value of the corresponding key factor.

[0080] S700, predicting 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 to-be-predicted data according to the prediction model, that is, use the model to predict the number of reference stations required from May 2025 to December 2025.

[0082] Thus, the application first constructs a prediction model through a key factor, and obtains first prediction data according to historical data, and then obtains an intermediate value of the key factor through the first prediction data, current actual data, an intermediate model and the prediction model, so that the intermediate value of the key factor is more reasonable, and finally the number of reference stations is predicted by using the prediction model corresponding to the intermediate value of the key factor, so that the prediction model can be more accurate when predicting, and the related art is improved, and the poor prediction effect is improved.

[0083] Through the above steps, the present application can better predict the demand for reference stations by combining various influencing factors, 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, the embodiments of the application also provide an intelligent adaptive networking positioning system for large-scale Beidou reference stations, which is used to execute the positioning method as described in the first aspect, and the system comprises: a network state monitoring module coupled with the reference stations, configured to collect the communication error rate, transmission delay and logical distance index between nodes of each reference station in real time; and a topology dynamic management module coupled with the network state monitoring module, configured to construct a first reference station plane, and when a reference station in the first reference station plane is disconnected, reconstruct 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 comprises a data fusion processing module, which is configured 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 configured to select the reference stations from the first reference station plane to construct a second reference station plane in response to a second scene demand.

[0087] In a possible implementation manner of the second aspect, the network topology structure of the second reference station plane and the network topology structure of the first reference station plane run in parallel, 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.

[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 realized by means of software and a necessary general hardware platform, and of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, an optical disk), and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the method of each embodiment of the present application.

[0089] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any change or replacement within the technical scope disclosed in the present application should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A large-scale Beidou reference station-oriented intelligent adaptive networking positioning method applied to a GNSS reference station network, characterized in that, The method comprises: constructing a reference station plane, the reference station plane comprising a plurality of reference stations, the plurality of reference stations being communicatively connected to form a plurality of communication links, the plurality of communication links constituting a mesh topology of the reference station plane; collecting, in real time, a communication quality indicator of each reference station, the communication quality indicator comprising a parameter selected from a group consisting of a communication error rate, a transmission delay, and a logical distance between nodes; when a reference station is disconnected from a network topology corresponding to the reference station plane, selecting, according to a weighted sum of the parameters of the communication quality indicator, a replacement reference station for an original communication link of the reference station, the replacement reference station being used to form a communication link with other reference stations in the original communication link; wherein the reference station plane comprises a first reference station plane and a second reference station plane, and the constructing a reference station plane comprises: constructing the first reference station plane, the first reference station plane being used to serve a positioning requirement of a first scenario, and when a second scenario requirement is received, selecting a plurality of the reference stations from the first reference station plane to construct the second reference station plane, the second reference station plane being used to serve a positioning requirement of the second scenario; wherein the constructing the second reference station plane does not cause the first reference station plane to be unable to meet the requirement of the first scenario; wherein the disconnection comprises a disconnection of the reference station or a disconnection of the reference station for constructing the second reference station plane.

2. The networked positioning method of claim 1, wherein, The number of the reference stations selected from the first reference station plane for constructing the second reference station plane is not greater than a first threshold.

3. The networked positioning method of claim 2, wherein, The method further comprises: in a process of reconstructing the mesh topology according to a preset strategy, dynamically generating a virtual observation value for replacing the reference station by using historical data or real-time observation values of neighboring stations of the reference station disconnected from the network topology.

4. The networked positioning method of claim 3, wherein, The network topology of the second reference station plane runs in parallel with the network topology of the first reference station plane.

5. The networked positioning method of claim 4, wherein, 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, the first frequency band being different from the second frequency band.

6. A large-scale Beidou reference station-oriented intelligent adaptive networking positioning system, characterized in that, The system is used to perform the network positioning method of claim 1, and the system comprises: a network state monitoring module coupled to the reference stations and configured to collect, in real time, a communication error rate, a transmission delay, and a logical distance between nodes of each reference station; a topology dynamic management module coupled to the network state monitoring module and configured to construct a first reference station plane, and when a reference station in the first reference station plane is disconnected, reconstruct a communication link for the remaining reference stations in the first reference station plane according to a preset strategy.

7. The networked positioning system of claim 6, wherein, The network state monitoring module comprises a data fusion processing module configured to dynamically generate a virtual observation value according to historical data and real-time observation values of the reference stations.

8. The networked positioning system of claim 6, wherein, The topology dynamic management module is further configured to select the reference stations from the first reference station plane to construct a second reference station plane in response to a second scenario requirement.

9. The networked positioning system of claim 8, wherein, The network topology of the second reference station plane operates in parallel with the network topology 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, the first frequency band and the second frequency band being different.

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