Continuous operation reference station system resolving method and server

Abstract

The invention provides a continuous operation reference station system resolving method and a server. The method comprises: receiving entity observation data from a plurality of entity reference stations in a continuous operation reference station system (CORS) by calling a first thread, each entity observation data having a corresponding receiving epoch; and by calling a second thread, forecasting the entity observation data corresponding to the receiving epoch closest to the preset calculation moment to the preset calculation moment, predicting target observation data corresponding to the preset calculation moment according to the forecasted entity observation data, determining a main reference station corresponding to the virtual reference station when the preset calculation moment is reached, and determining the target observation data corresponding to the virtual reference station when the preset calculation moment is reached. Resolving according to the target observation data to obtain an atmospheric error of the virtual reference station relative to the main reference station; and by calling a third thread, correcting the target observation data of the main reference station according to the atmospheric error to obtain virtual observation data at a preset resolving moment. According to the invention, the resolving efficiency can be improved, and the age of receiving the virtual observation data by the user can be shortened.

Description

Technical Field

[0001] The present application relates to the field of navigation and positioning technology, and in particular to a continuously operating reference station system solution method and server. Background Art

[0002] A Continuous Operating Reference Station (CORS) system refers to a network of multiple continuously operating Global Navigation Satellite System (GNSS) reference stations that can capture satellite signals in real time and convert them into observation data through a network composed of modern computers, data communications and the Internet (LAN / WAN) technology. These observation data can then be transmitted to the data processing center, and after a series of complex solution processes, various types of GNSS service information required by users are generated. Due to the large number of physical reference stations in CORS, in order to improve the solution efficiency, it is often necessary to subnet CORS by deploying a server cluster. This division method decomposes the huge CORS network into multiple smaller subnets. Each subnet consists of a group of physical reference stations. A specific server is responsible for receiving the observation data sent by the physical reference station of the corresponding subnet and performing CORS solution.

[0003] However, there may be delays in the transmission of observation data, resulting in unstable collection time of physical reference station observation data. The server will perform CORS solution only after collecting the observation data of the physical reference station of the corresponding subnet, which will lead to poor solution efficiency and cause users to receive virtual observation data for a long time. Summary of the invention

[0004] In view of the above, it is necessary to provide a continuously operating reference station system solution method and server, which can solve the technical problems that the collection time of physical observation data is unstable due to the network delay of physical observation data, resulting in poor solution efficiency and the age of virtual observation data received by users is too long.

[0005] On the one hand, the present application provides a continuously operating reference station system solution method, which is applied to a server, and the server communicates with a terminal device. The method includes: by calling a first thread, receiving physical observation data from multiple physical reference stations in a continuously operating reference station system CORS, each physical observation data has a corresponding receiving epoch, by calling a second thread, forecasting the physical observation data corresponding to the receiving epoch closest to a preset solution time to the preset solution time, predicting the target observation data corresponding to the preset solution time based on the forecasted physical observation data, when arriving at the preset solution time, determining the physical reference station corresponding to the virtual reference station as the main reference station, and performing baseline ambiguity resolution based on the target observation data to obtain the atmospheric error of the virtual reference station relative to the main reference station, and by calling a third thread, correcting the target observation data of the main reference station according to the atmospheric error to obtain the virtual observation data at the preset solution time.

[0006] In some embodiments of the present application, the method also includes: storing the received entity observation data in a data queue, the received entity observation data being arranged in a preset order in the data queue, and forecasting the entity observation data corresponding to the received epoch closest to the preset solution time to the preset solution time by calling the second thread, including: extracting the entity observation data at the tail of the data queue, and forecasting the extracted entity observation data to the preset solution time.

[0007] In some embodiments of the present application, the predicted entity observation data includes a first pseudorange observation value and a first carrier phase observation value, and the first pseudorange observation value and the first carrier phase observation value both include a first distance between a target reference station corresponding to the predicted entity observation data and a corresponding satellite and a first satellite clock error of the satellite. The calling of a second thread to predict the target observation data corresponding to the preset solution time according to the predicted entity observation data includes: obtaining a second distance between the target reference station and the satellite and a second satellite clock error of the satellite corresponding to the preset solution time, calculating a second pseudorange observation value according to the first pseudorange observation value, the first distance, the first satellite clock error, the second distance and the second satellite clock error, calculating a second carrier phase observation value according to the first carrier phase observation value, the first distance, the first satellite clock error, the second distance and the second satellite clock error, and determining the second pseudorange observation value and the second carrier phase observation value as the target observation data.

[0008] In some embodiments of the present application, if the receiving epoch of the predicted entity observation data is t, and the preset solution time is t+1, the calculation formula of the second pseudorange observation value is expressed as:

[0009]

[0010] The calculation formula of the second carrier phase observation value is expressed as:

[0011]

[0012] in, represents the second pseudorange observation value, i represents the identifier of the satellite, a represents the identifier of the target reference station, represents the first pseudorange observation value, represents the second distance, represents the first distance, dt i (t+1) represents the second satellite clock error, dt i (t) represents the first satellite clock error, C represents the speed of light, represents the second carrier phase observation value, Represents the first carrier phase observation value.

[0013] In some embodiments of the present application, receiving entity observation data from multiple physical reference stations in the continuously running reference station system by calling the first thread includes: using mobile communication technology to receive the entity observation data sent from the multiple physical reference stations.

[0014] In some embodiments of the present application, the first thread, the second thread, and the third thread are threads executed in parallel.

[0015] In some embodiments of the present application, determining the physical reference station corresponding to the virtual reference station as the main reference station includes: constructing a Delaunay triangulation based on the reference coordinates of the multiple physical reference stations, determining the triangular area corresponding to the virtual reference station in the Delaunay triangulation based on the virtual station coordinates of the virtual reference station, and determining the main reference station corresponding to the virtual reference station from the physical reference stations in the triangular area based on the virtual station coordinates and the reference coordinates of each physical reference station in the triangular area.

[0016] In some embodiments of the present application, multiple reference stations corresponding to the vertices of the triangular area form multiple baselines connected end to end, and the baseline ambiguity resolution is performed based on the target observation data to obtain the atmospheric error of the virtual reference station relative to the main reference station, including: based on the target observation data of multiple physical reference stations in the triangular area, using the Kalaman filter algorithm to estimate the double-difference ionospheric-free ambiguity of each baseline, using the multi-epoch smoothing method to perform integer ambiguity fixation on the moving wide-lane combined observation values ​​corresponding to the target observation data of multiple physical reference stations in the triangular area, and obtaining each baseline. The method comprises the steps of: calculating a double difference wide term ambiguity of a virtual reference station relative to a master reference station according to the virtual station coordinates and the double difference tropospheric delay and the double difference ionospheric delay of the plurality of baselines, calculating a double difference narrow term ambiguity of each baseline according to a linear relationship between the double difference ionospheric-free ambiguity and the double difference wide term ambiguity, calculating a double difference tropospheric delay and a double difference ionospheric delay of each baseline according to the double difference narrow term ambiguity, interpolating the double difference tropospheric delay and the double difference ionospheric delay of the virtual reference station relative to the master reference station according to the virtual station coordinates and the double difference tropospheric delay and the double difference ionospheric delay of the plurality of baselines, and determining the double difference tropospheric delay and the double difference ionospheric delay of the virtual reference station relative to the master reference station as the atmospheric error.

[0017] In some embodiments of the present application, the method also includes: receiving a data acquisition request sent by a terminal device, obtaining a requested time point by parsing the data acquisition request, and searching for and sending virtual observation data of a preset solution moment corresponding to the requested time point to the terminal device.

[0018] On the other hand, the present application provides a server, comprising: a memory storing at least one instruction; and a processor executing the at least one instruction to implement the continuously running reference station system solution method.

[0019] In the above technical solution, the closer the receiving epoch of the entity observation data is to the preset solution time, the higher the freshness and real-time performance of the entity observation data. Therefore, the entity observation data corresponding to the receiving epoch closest to the preset solution time is forecasted to the preset solution time, which can ensure that the latest received entity observation data is forecasted to the preset solution time. By forecasting the latest received entity observation data to the preset solution time, the preset solution time is no longer dependent on the collection time of the entity observation data, thereby avoiding the problem of unstable collection time of the entity observation data caused by network delay of the entity observation data. Solving the forecasted latest received entity observation data and generating virtual observation data based on the atmospheric error obtained by the solution can reduce the solution error, improve the solution efficiency and the timeliness of the virtual observation data, thereby shortening the age of the user receiving the virtual observation data and improving the positioning efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is an application environment diagram of a continuously operating reference station system solution method provided in an embodiment of the present application.

[0021] Figure 2 It is a flow chart of a continuously operating reference station system solution method provided in one embodiment of the present application.

[0022] Figure 3 It is a schematic diagram of a flow chart of a continuously operating reference station system solution method provided in another embodiment of the present application.

[0023] Figure 4 It is a flowchart of a method for determining a virtual reference station and a main reference station provided in one embodiment of the present application.

[0024] Figure 5 It is a flowchart of a method for performing baseline ambiguity resolution on target observation data provided by one embodiment of the present application.

[0025] Figure 6 It is a functional module diagram of a continuously operating reference station system solving device provided in one embodiment of the present application.

[0026] Figure 7 It is a schematic diagram of the structure of a server provided in one embodiment of the present application. DETAILED DESCRIPTION

[0027] It should be noted that in this application, "at least one" means one or more, and "more than one" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims and drawings of this application are used to distinguish similar objects, rather than to describe a specific order or sequence.

[0028] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way. The following embodiments and features in the embodiments may be combined with each other without conflict.

[0029] The continuously operating reference station system solution method provided in the embodiment of the present application can be applied to a server in a continuously operating reference station data center or a server group consisting of multiple servers. The continuously operating reference station system solution method will be described below in combination with application scenarios and flow charts. The server can be a cloud server, and this application does not limit this.

[0030] like Figure 1 , is an application environment diagram of the continuous operation reference station system solution method provided by an embodiment of the present application. Figure 1 In the embodiment, the server 10 communicates with the terminal device 20 and multiple physical reference stations 30. The multiple physical reference stations 30 receive signals from the satellite 40, convert the received signals into observation data, and send the observation data to the server 10. The terminal device 20 can send a data acquisition request to the server 10 and receive virtual observation data sent by the server 10 in response to the data acquisition request, thereby achieving precise positioning. The server 10 and the terminal device 20, as well as the server 10 and the multiple physical reference stations 30 can communicate through a wireless network (such as Wi-Fi, 4G / 5G mobile data network).

[0031] like Figure 2 As shown, it is a flow chart of the method for solving the continuous operation reference station system provided by an embodiment of the present application. According to different requirements, the order of each step in the flow chart can be adjusted according to actual requirements, and some steps can be omitted. The method is applied to the server, for example Figure 1 The server 10 is shown.

[0032] S11, receiving entity observation data from multiple entity reference stations in a continuously operating reference station system CORS by calling a first thread, each entity observation data having a corresponding receiving epoch.

[0033] In some embodiments of the present application, the continuously operating reference station system includes a plurality of continuously operating global navigation satellite system (GNSS) reference stations. In order to facilitate the distinction between the GNSS reference station and the virtual reference station described below, in the plurality of embodiments of the present application, the GNSS reference station is referred to as a physical reference station. The physical reference station is capable of capturing satellite signals and converting the satellite signals into observation data. In order to distinguish the observation data obtained by the physical reference station based on the captured satellite signals from the virtual observation data described below, the embodiments of the present application refer to the observation data obtained by the physical reference station based on the captured satellite signals as physical observation data. Since the data types of the physical observation data of each physical reference station are substantially the same, the data content of the physical observation data can refer to the description of the physical observation data of the target reference station described below.

[0034] In some embodiments of the present application, the server receives entity observation data from multiple entity reference stations in a continuously operating reference station system CORS by calling a first thread, including: the server receives entity observation data sent from multiple entity reference stations by using mobile communication technology by calling the first thread. The received epoch is a receiving timestamp or time mark of the entity observation data.

[0035] The mobile communication technology may be customized, and this application does not limit this. For example, the mobile communication technology may be the fourth generation mobile communication technology (4G) and the fifth generation mobile communication technology (5G).

[0036] In related technologies, servers often need to use a specific network to receive entity observation data sent by a physical reference station through the Radio Technical Commission for Maritime Services (RTCM) protocol. A specific network is a private network specially established for data transmission, or it can be a virtual private network (VPN) with specific security policies and protocols. The construction of this specific network requires a lot of resources and costs. In the embodiment of the present application, mobile communication technology is used to receive entity observation data, and there is no need to build a dedicated network for transmission, which can not only simplify the transmission process of entity observation data, but also reduce hardware costs.

[0037] In some embodiments of the present application, the server can call the first thread to store the received entity observation data in the first data queue. Since the data queue structure follows the first-in-first-out principle, the entity observation data received first is often located at the head of the first data queue, and the latest received entity observation data is located at the tail of the first data queue. This characteristic of the data queue structure can effectively maintain the order of the entity observation data.

[0038] In some embodiments of the present application, the server may receive entity observation data in real time or periodically.

[0039] S12, by calling the second thread, forecasting the entity observation data corresponding to the receiving epoch closest to the preset solution time to the preset solution time, and predicting the target observation data corresponding to the preset solution time based on the forecasted entity observation data.

[0040] In some embodiments of the present application, the preset calculation time may be a time point for performing CORS data calculation, and there may be multiple preset calculation times. The preset calculation time may be customized, and the present application does not limit this. For example, the preset calculation time may be configured according to a preset time period. For example, if the preset time period is 1 second, the multiple preset calculation times may be 9:00, 9:01, 9:02…

[0041] In some embodiments of the present application, entity observation data are arranged in a first data queue in a preset order, and the most recently received entity observation data is located at the tail of the first data queue (the entity observation data corresponding to the receiving epoch closest to the preset solution time is located at the tail of the first data queue), and the server calls the second thread to forecast the entity observation data corresponding to the receiving epoch closest to the preset solution time to the preset solution time, including: by calling the second thread, the server extracts the entity observation data at the tail of the first data queue, and forecasts the extracted entity observation data to the preset solution time.

[0042] In some embodiments of the present application, the server determines the entity reference station corresponding to the predicted entity observation data as the target reference station, and the entity observation data corresponding to the target reference station may include the first pseudo-range observation value of the satellite corresponding to the target reference station, the first carrier phase observation value, and other parameters related to satellite signal propagation. The first pseudo-range observation value is a value consisting of the first distance (geometric distance) between the reference station and the corresponding satellite, the speed of light, the first satellite clock error of the satellite, the receiver clock error corresponding to the receiver of the target entity station, tropospheric delay, ionospheric delay, and pseudo-range noise. The second carrier phase observation value is a value consisting of the first pseudo-range observation value of the satellite corresponding to the target reference station, the first carrier phase observation value, and other parameters related to satellite signal propagation. The first pseudo-range observation value is a value consisting of the first distance between the reference station and the corresponding satellite, the speed of light, the first satellite clock error of the satellite, the receiver clock error corresponding to the receiver of the target entity station, tropospheric delay, ionospheric delay, the wavelength of the carrier signal generated by the receiver of the target entity station, the carrier phase ambiguity of the satellite signal, and the carrier phase noise.

[0043] For example, if the receiving epoch of the predicted entity observation data is t, the first pseudorange observation value can be expressed as:

[0044]

[0045] The first carrier phase observation can be expressed as:

[0046]

[0047] in, represents the first pseudorange observation value, i represents the satellite identifier, a represents the target reference station identifier, represents the first distance, C represents the speed of light, dt i (t) represents the first satellite clock error, dt a (t) represents the receiver clock error, represents the tropospheric delay, represents the ionospheric delay, represents the pseudorange noise, represents the first carrier phase observation value, λ represents the wavelength of the carrier signal, represents the carrier phase ambiguity, represents the carrier phase noise.

[0048] In some embodiments of the present application, the server may obtain target observation data by respectively deriving the first pseudorange observation value and the first carrier phase observation value. The target observation data includes a second pseudorange observation value corresponding to the first pseudorange observation value, and a second carrier phase observation value corresponding to the first carrier phase observation value.

[0049] In some embodiments of the present application, by calling the second thread, the server predicts the target observation data corresponding to the preset solution time based on the predicted entity observation data, including: obtaining the second distance between the target reference station and the satellite and the second satellite clock error of the satellite corresponding to the preset solution time, calculating the second pseudorange observation value based on the first pseudorange observation value, the first distance, the first satellite clock error, the second distance and the second satellite clock error, calculating the second carrier phase observation value based on the first carrier phase observation value, the first distance, the first satellite clock error, the second distance and the second satellite clock error, and determining the second pseudorange observation value and the second carrier phase observation value as the target observation data.

[0050] Among them, the server can determine the second distance between the target reference station and the satellite and the second satellite clock error of the satellite corresponding to the preset solution time based on the ephemeris message sent by the satellite.

[0051] For example, if the preset solution time is the next epoch t+1 of the receiving epoch t of the predicted entity observation data, the expression for the server to derive the first pseudorange observation value can refer to formula (3), and the expression for the server to derive the first carrier phase observation value can refer to formula (4):

[0052]

[0053]

[0054] The position of the target reference station a is fixed and does not change with time. However, since satellite i can predict the position of satellite i at time t+1 based on the satellite ephemeris, it is necessary to compensation; since satellite i can predict the clock error of satellite i at time t+1 based on the satellite ephemeris, it is necessary to to compensate; It cannot be modeled and there is no need to consider compensating for this; and Indicates the change of atmospheric error. Since the atmospheric error changes slowly in time series, the difference between the reception time t of the forecast entity observation data and the preset solution time t+1 is one second. The difference of one second can be ignored, so there is no need to consider and Since both pseudorange noise and carrier phase noise present normal distribution, there is no need to consider and After the receiver locks onto the satellite, the carrier phase ambiguity remains unchanged, so there is no need to consider Therefore, by respectively adjusting the as well as By performing compensation, a second pseudorange observation value and a second carrier phase observation value can be obtained. The calculation formula of the second pseudorange observation value is expressed as:

[0055]

[0056] The calculation formula of the second carrier phase observation value is expressed as:

[0057]

[0058] in, represents the second pseudorange observation value, i represents the satellite identifier, a represents the target reference station identifier, t+1 represents the preset solution time (the next epoch of the reception epoch of the predicted entity observation data), represents the first pseudorange observation value, represents the second distance, Indicates the first distance, dt i (t+1) represents the second satellite clock error, dt i (t) represents the first satellite clock error, C represents the speed of light, represents the second carrier phase observation value, Indicates the first carrier phase observation value. In this embodiment, since the data queue structure follows the first-in-first-out principle, the entity observation data in the first data queue are arranged in order, thereby ensuring that the entity observation data most recently received by the first data queue is forecasted to the preset solution time. as well as Compensation can reduce errors, thereby making the calculated second pseudorange observation value and second carrier phase observation value more accurate. By forecasting the latest received entity observation data to the preset solution time, the preset solution time is no longer dependent on the collection time of the entity observation data, thereby avoiding the problem of unstable collection time of the entity observation data caused by network delay of the entity observation data.

[0059] S13, when the server time reaches the preset solution time, by calling the second thread, the physical reference station corresponding to the virtual reference station is determined as the main reference station, and the baseline ambiguity is resolved according to the target observation data to obtain the atmospheric error of the virtual reference station relative to the main reference station.

[0060] In some embodiments of the present application, after obtaining the atmospheric error between the virtual reference station and the main reference station, the server may store the atmospheric error in a constructed second data queue through a second thread, so that the second data queue has a corresponding queue length.

[0061] In some embodiments of the present application, the virtual reference station has corresponding virtual station coordinates, and the virtual station coordinates can be calculated through the data of the CORS network and a pre-established mathematical model. The generation process of the virtual station coordinates can refer to the relevant technology, and this application will not describe it in detail. The main reference station can be the physical reference station closest to the virtual reference station. The atmospheric error between the virtual reference station and the main reference station can be obtained by performing baseline ambiguity resolution on the second pseudorange observation value and the second carrier phase observation value corresponding to the main reference station. The atmospheric error includes the double difference tropospheric delay and the double difference ionospheric delay of the virtual reference station relative to the main reference station.

[0062] Since the second thread is used to perform baseline ambiguity resolution, the second thread can also be called a CORS algorithm resolution thread. In this embodiment, the latest received entity observation data of the forecast is resolved, which can reduce the resolution error. When the server time reaches the preset resolution time, the resolution is performed, thereby improving the resolution efficiency.

[0063] S14, by calling the third thread, the target observation data of the main reference station is corrected according to the atmospheric error to obtain the virtual observation data at the preset solution time.

[0064] In some embodiments of the present application, the virtual observation data includes a third pseudorange observation value and a third carrier phase observation value. The server monitors the second data queue by calling the third thread. When the queue length of the second data queue is not zero, the server extracts the atmospheric error in the second data queue and corrects the target observation data of the main reference station according to the atmospheric error to obtain the virtual observation data at the preset solution time.

[0065] In some embodiments of the present application, the server corrects the target observation data of the main reference station according to the atmospheric error by calling the third thread, and obtains the virtual observation data of the virtual reference station, including: the server calls the third thread, constructs an atmospheric correction model, inputs the atmospheric error and the target observation data of the main reference station as parameters into the corresponding parameters in the atmospheric correction model, and obtains the virtual observation data at a preset solution time.

[0066] Among them, the atmospheric correction model can be custom constructed and will not be described in detail in this application.

[0067] In other embodiments of the present application, the server can calculate the satellite-to-earth distance between the satellite and the main reference station, and obtain virtual observation data by inputting the atmospheric error, the satellite-to-earth distance and the target observation data of the main reference station into the corresponding parameters in the atmospheric correction model.

[0068] In some embodiments of the present application, the first thread, the second thread, and the third thread execute corresponding tasks and steps in parallel. The first thread, the second thread, and the third thread execute steps S11, S12, and S13 in parallel, so that the execution of steps S11, S12, and S13 is independent and does not affect each other.

[0069] In some embodiments of the present application, the method also includes: the server calls a third thread, receives a data acquisition request sent by a terminal device, obtains the requested time point by parsing the data acquisition request, searches for and sends virtual observation data of a preset solution time corresponding to the requested time point to the terminal device.

[0070] Among them, the request time point can be the time point (preset solution time) corresponding to the virtual observation data requested by the terminal device, or the request time point can be the time point when the terminal device sends a data acquisition request, and the preset solution time corresponding to the request time point can be the same preset solution time as the request time point. The server can send the virtual observation data to the terminal device by calling the third thread and using network technologies such as kafka or redis, thereby realizing the positioning function. The process of propagating virtual observation data using network technologies such as kafka or redis can also be called the production process of virtual observation data, so the third thread can also be called the virtual observation station production thread, and the process of obtaining virtual observation data based on modifying the target observation data of the main reference station can be called the generation process of virtual observation data.

[0071] In some embodiments of the present application, in order to more intuitively illustrate the continuous operation reference station system solution method of the present application embodiment, as shown in FIG. Figure 3 , which is a schematic diagram of a flow chart of a continuously operating reference station system solution method provided in another embodiment of the present application.

[0072] exist Figure 3 In the example, the physical station consumption thread is equivalent to the first thread mentioned above, the CORS algorithm solution thread is equivalent to the second thread mentioned above, and the virtual observation station production thread is equivalent to the third thread mentioned above. If the preset solution time is t+1, t+2..., after the physical station consumption thread collects the physical station data at time t (physical observation data of the physical reference station), the CORS algorithm solution thread can predict the physical station data (physical observation data) at time t+1. When the server time reaches time t+1, the CORS algorithm solution thread can solve the baseline ambiguity at time t+1 based on the predicted physical station data. The virtual observation station production thread generates the virtual station data at time t+1 (virtual observation data of the virtual reference station) based on the baseline ambiguity at time t+1, and produces the virtual station data at time t+1. By analogy, after the physical station consumption thread collects the physical station data at time t+1 (physical observation data of the physical reference station), the CORS algorithm solving thread can predict the physical station data at time t+2 (physical observation data). When the server time reaches time t+2, the CORS algorithm solving thread can solve the baseline ambiguity at time t+2 based on the predicted physical station data. The virtual observation station production thread generates the virtual station data at time t+2 (virtual observation data of the virtual reference station) based on the baseline ambiguity at time t+1, and produces the virtual station data at time t+2.

[0073] In the above technical solution, the closer the receiving epoch of the entity observation data is to the preset solution time, the higher the freshness and real-time performance of the entity observation data. Therefore, the entity observation data corresponding to the receiving epoch closest to the preset solution time is forecasted to the preset solution time, which can ensure that the latest received entity observation data is forecasted to the preset solution time. By forecasting the latest received entity observation data to the preset solution time, the preset solution time is no longer dependent on the collection time of the entity observation data, thereby avoiding the problem of unstable collection time of the entity observation data caused by network delay of the entity observation data. Solving the forecasted latest received entity observation data and generating virtual observation data based on the atmospheric error obtained by the solution can reduce the solution error, improve the solution efficiency and the timeliness of the virtual observation data, thereby shortening the age of the user receiving the virtual observation data and improving the positioning efficiency.

[0074] In some embodiments of the present application, Figure 4 FIG. 1 is a flowchart of a method for determining a virtual reference station and a master reference station provided in an embodiment of the present application, comprising the following steps:

[0075] S131, constructing a Delaunay triangulation network according to the reference coordinates of multiple physical reference stations.

[0076] In some embodiments of the present application, the server can triangulate the reference coordinates of multiple physical reference stations into a series of interconnected triangles based on the Delaunay triangulation algorithm. During the triangulation process, it is ensured that the circumscribed circle of each triangle does not contain any reference coordinates other than the reference coordinates corresponding to the vertices of the triangle, so as to satisfy the properties of the Delaunay triangulation network and obtain a Delaunay triangulation network.

[0077] S132, determining a triangular area corresponding to the virtual reference station in the Delaunay triangulation network according to the virtual station coordinates of the virtual reference station.

[0078] In some embodiments of the present application, the server may perform spatial search and matching on the Delaunay triangulation network through the virtual station coordinates, and determine the triangular area corresponding to the virtual reference station in the Delaunay triangulation network.

[0079] S133, determining a main reference station corresponding to the virtual reference station from the physical reference stations in the triangular area according to the virtual station coordinates and the reference coordinates of each physical reference station in the triangular area.

[0080] In some embodiments of the present application, the server may calculate the distance between the virtual reference station and each physical reference station in the triangular area according to the virtual station coordinates and the reference coordinates of each physical reference station in the triangular area, and determine the main reference station from the physical reference stations in the triangular area according to the distance between the virtual reference station and each physical reference station in the triangular area. For example, the server may use the physical reference station closest to the virtual reference station as the main reference station.

[0081] In some embodiments of the present application, multiple reference stations corresponding to the vertices of the triangular area corresponding to the terminal device form multiple (3) baselines connected end to end, such as Figure 5 FIG. 1 is a flowchart of a method for performing baseline ambiguity resolution on target observation data provided by an embodiment of the present application, comprising the following steps:

[0082] S134, based on the target observation data of multiple physical reference stations in the triangular area, the double-difference ionospheric-free ambiguity of each baseline is estimated using the Kalaman filter algorithm.

[0083] In some embodiments of the present application, the server uses a differential method to generate double-difference observation data corresponding to each baseline in the triangular area based on the target observation data between multiple physical reference stations corresponding to each baseline, and uses the Kalman filtering algorithm to estimate the double-difference ionospheric-free ambiguity of each baseline according to the double-difference observation data corresponding to each baseline. The process can refer to the Kalman filtering algorithm in the related art to set the initial state vector and covariance matrix, and use the Kalman gain to update the initial state vector and covariance matrix. The iterative process will not be repeated in this application. The double-difference observation data corresponding to each baseline includes pseudorange observation values ​​and carrier phase observation values.

[0084] S135, using a multi-epoch smoothing method, integer ambiguity fixation is performed on the moving wide-lane combined observation values ​​corresponding to the target observation data of multiple physical reference stations in the triangular area to obtain the double-difference wide-term ambiguity of each baseline.

[0085] In some embodiments of the present application, the mobile wide lane (MW) combined observation value includes carrier phase observation values ​​of multiple frequencies and pseudorange observation values ​​of multiple frequencies. The server can extract the pseudorange observation values ​​of the L1 frequency band (L1 Frequency Band) and the L2 frequency band (L2FrequencyBand) from the pseudorange observation values ​​in the double difference observation data corresponding to each baseline, and extract the carrier phase observation values ​​of the L1 frequency band and the L2 frequency band from the carrier phase observation values ​​in the double difference observation data corresponding to each baseline, and process the pseudorange observation values ​​of the L1 frequency band, the pseudorange observation values ​​of the L2 frequency band, the carrier phase observation values ​​of the L1 frequency band, and the carrier phase observation values ​​of the L2 frequency band according to the preset algorithm to obtain the double difference wide term ambiguity of each baseline. Among them, the preset algorithm includes but is not limited to: least squares algorithm and Kalman filter algorithm.

[0086] In some embodiments of the present application, the server processes the pseudorange observation values ​​of the L1 frequency band, the pseudorange observation values ​​of the L2 frequency band, the carrier phase observation values ​​of the L1 frequency band, and the carrier phase observation values ​​of the L2 frequency band according to the least squares algorithm or the Kalman filter algorithm to obtain the double difference wide term ambiguity method of each baseline. The method can refer to the description of the least squares algorithm or the Kalman filter algorithm in the relevant technology, and the present application will not describe it in detail.

[0087] S136, calculating the double difference narrow term ambiguity of each baseline according to the linear relationship between the double difference ionospheric-free ambiguity and the double difference wide term ambiguity.

[0088] In some embodiments of the present application, the linear relationship between the double-difference ionospheric-free ambiguity of each baseline and the double-difference wide-term ambiguity of each baseline is obtained by analyzing and deriving the signal characteristics of the global navigation satellite system and the data processing method, and the linear relationship between the double-difference ionospheric-free ambiguity and the double-difference wide-lane ambiguity can be expressed as a mathematical formula or equation, which describes the proportional relationship or difference relationship between the double-difference ionospheric-free ambiguity and the double-difference wide-lane ambiguity. The server operates the double-difference ionospheric-free ambiguity and the double-difference wide-term ambiguity based on the linear relationship to obtain the double-difference narrow-term ambiguity of each baseline.

[0089] Among them, the server calculates the double-difference ionospheric-free ambiguity and the double-difference wide-term ambiguity based on a linear relationship, and the method for obtaining the double-difference narrow-term ambiguity can refer to the relevant technology, and this application will not describe it in detail.

[0090] S137, calculating the double difference tropospheric delay and the double difference ionospheric delay of each baseline according to the double difference narrow term ambiguity.

[0091] In some embodiments of the present application, the server may establish a tropospheric delay model and an ionospheric delay model, input the double-difference narrow term ambiguity of each baseline into the tropospheric delay model to obtain the double-difference tropospheric delay of each baseline, and input the double-difference narrow term ambiguity of each baseline into the ionospheric delay model to obtain the double-difference ionospheric delay of each baseline.

[0092] The tropospheric delay model can be constructed based on atmospheric profile data (such as temperature, pressure, water vapor content, etc.) and geometric characteristics of the signal propagation path. The model expression of the tropospheric delay model can include the mathematical relationship between the tropospheric delay and the altitude angle, signal frequency, atmospheric parameters, etc.

[0093] The ionospheric delay model can be constructed based on the electron density distribution of the ionosphere and the integral on the signal propagation path. The model expression of the ionospheric delay model can include the mathematical relationship between the ionospheric delay and the signal frequency, the electron density distribution and the signal propagation path.

[0094] S138, interpolating the double difference tropospheric delay and the double difference ionospheric delay of the virtual reference station relative to the main reference station according to the virtual station coordinates and the double difference tropospheric delay and the double difference ionospheric delay of the multiple baselines, and determining the double difference tropospheric delay and the double difference ionospheric delay of the virtual reference station relative to the main reference station as the atmospheric error.

[0095] In some embodiments of the present application, the process of the server interpolating the double difference tropospheric delay and double difference ionospheric delay of the virtual reference station relative to the main reference station according to the virtual station coordinates and the double difference tropospheric delay and double difference ionospheric delay of multiple baselines can refer to the process of estimating the double difference tropospheric delay and double difference ionospheric delay of the virtual reference station relative to the main reference station using the spatial interpolation method in the related art, and the present application will not repeat the description. Among them, the spatial interpolation method can be inverse distance weighted interpolation, Kriging interpolation, polynomial interpolation, spline interpolation, etc.

[0096] like Figure 6 , which is a functional module diagram of a continuously operating reference station system solution device provided by an embodiment of the present application. The continuously operating reference station system solution device 11 includes a receiving unit 110, a forecasting unit 111, a prediction unit 112, a determination unit 113, a solution unit 114 and a correction unit 115. The module / unit referred to in the present application refers to a type of system that can be Figure 7 A series of computer readable instruction segments acquired by the processor 103 in the embodiment and capable of completing fixed functions, which are stored in Figure 7 In the memory 102 in the present embodiment, the functions of each module / unit will be described in detail in the subsequent embodiments.

[0097] The receiving unit 110 is used to receive entity observation data from multiple entity reference stations in a continuously operating reference station system CORS by calling a first thread, and each entity observation data has a corresponding receiving epoch.

[0098] The forecasting unit 111 is used to forecast the entity observation data corresponding to the receiving epoch closest to the preset solution time to the preset solution time by calling the second thread.

[0099] The prediction unit 112 is used to predict the target observation data corresponding to the preset solution time according to the predicted entity observation data by calling the second thread.

[0100] The determining unit 113 is used to determine the physical reference station corresponding to the virtual reference station as the main reference station when the preset solution time is reached by calling the second thread.

[0101] The solving unit 114 is used to perform baseline ambiguity solution according to the target observation data by calling the second thread to obtain the atmospheric error of the virtual reference station relative to the main reference station.

[0102] The correction unit 115 is used to correct the target observation data of the main reference station according to the atmospheric error by calling the third thread to obtain the virtual observation data at the preset solution time.

[0103] like Figure 7 FIG. 1 is a schematic diagram of the structure of a server provided by an embodiment of the present application. Figure 7 In the embodiment, the server 10 may include a communication module 101, a memory 102, a processor 103, an input / output (I / O) interface 104 and a bus 105. The processor 103 is coupled to the communication module 101, the memory 102 and the input / output interface 104 through the bus 105.

[0104] The communication module 101 may include a wired communication module and / or a wireless communication module. The wired communication module may provide one or more wired communication solutions such as universal serial bus (USB), controller area network bus (CAN, Controller Area Network). The wireless communication module may provide one or more wireless communication solutions such as wireless fidelity (wireless fidelity, Wi-Fi), Bluetooth (Bluetooth, BT), mobile communication network, frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR), etc.

[0105] The memory 102 may include one or more random access memories (RAM) and one or more non-volatile memories (NVM). The random access memory can be directly read and written by the processor 103, and can be used to store executable programs (such as machine instructions) of other running programs, and can also be used to store user and application data. The random access memory may include static random-access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), etc.

[0106] The non-volatile memory may also store executable programs and user and application data, etc., and may be loaded into the random access memory in advance for direct reading and writing by the processor 103. The non-volatile memory may include a disk storage device and a flash memory. For example, the flash memory may be a flash memory (Nand Flash).

[0107] The memory 102 is used to store one or more computer programs. The one or more computer programs are configured to be executed by the processor 103. The one or more computer programs include a plurality of instructions, and when the plurality of instructions are executed by the processor 103, the continuously running reference station system solution method executed on the server 10 can be implemented.

[0108] In other embodiments, Figure 7 The server 10 shown also includes an external memory interface for connecting to an external memory to expand the storage capacity of the server 10 .

[0109] The processor 103 may include one or more processing units, for example, the processor 103 may include an application processor (AP), a modem processor, a graphics processor (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), and / or a neural-network processing unit (NPU), etc. Different processing units may be independent devices or integrated into one or more processors.

[0110] The processor 103 provides computing and control capabilities. For example, the processor 103 is used to execute a computer program stored in the memory 102 to implement the above-mentioned continuously running reference station system solution method.

[0111] The input / output interface 104 is used to provide a channel for user input or output. For example, the input / output interface 104 can be used to connect various input and output devices, such as a mouse, keyboard, touch device, display screen, etc., so that the user can enter information or visualize information.

[0112] The bus 105 is at least used to provide a channel for mutual communication among the communication module 101 , the memory 102 , the processor 103 , and the input / output interface 104 in the server 10 .

[0113] It is to be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the server 10. In other embodiments of the present application, the server 10 may include more or fewer components than shown in the figure, or combine some components, or split some components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0114] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the methods in the above-mentioned embodiments of the present application.

[0115] The computer-readable storage medium may be the internal memory of the server described in the above embodiment, such as the hard disk or memory of the server. The computer-readable storage medium may also be an external storage device of the server, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the server.

[0116] In some embodiments, the computer-readable storage medium may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function, etc.; the data storage area may store data created according to the use of the server, etc.

[0117] It is to be understood that the structure illustrated in the embodiments of the present application does not constitute a specific limitation on the server. In other embodiments of the present application, the server may include more or fewer components than shown in the figure, or combine certain components, or split certain components, or arrange the components differently. The components shown in the figure may be implemented in hardware, software, or a combination of software and hardware.

[0118] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0119] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional module in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0121] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present application is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present application. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0122] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in this application can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0123] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present application.

Claims

1. A continuously running reference station system solution method, applied to a server, characterized in that: The server communicates with the terminal device, and the method includes: By calling the first thread, receiving entity observation data from multiple entity reference stations in a continuously operating reference station system CORS, each entity observation data having a corresponding receiving epoch; By calling the second thread, the entity observation data corresponding to the receiving epoch closest to the preset solution time is forecasted to the preset solution time, and the target observation data corresponding to the preset solution time is predicted according to the forecasted entity observation data. When the preset solution time is reached, the entity reference station corresponding to the virtual reference station is determined as the main reference station, and the baseline ambiguity is resolved according to the target observation data to obtain the atmospheric error of the virtual reference station relative to the main reference station; By calling the third thread, the target observation data of the main reference station is corrected according to the atmospheric error to obtain the virtual observation data at the preset solution time.

2. The method for solving the continuously operating reference station system according to claim 1, characterized in that: The method further includes: storing the received entity observation data in a data queue, wherein the received entity observation data is arranged in a preset order in the data queue, and forecasting the entity observation data corresponding to the receiving epoch closest to the preset solution time to the preset solution time by calling the second thread includes: The entity observation data at the tail of the data queue is extracted, and the extracted entity observation data is forecasted to the preset solution time.

3. The method for solving the continuously operating reference station system according to claim 1, characterized in that: The predicted entity observation data includes a first pseudorange observation value and a first carrier phase observation value, and the first pseudorange observation value and the first carrier phase observation value both include a first distance between a target reference station corresponding to the predicted entity observation data and a corresponding satellite and a first satellite clock error of the satellite, and the calling of the second thread to predict the target observation data corresponding to the preset solution time according to the predicted entity observation data includes: Acquire a second distance between the target reference station and the satellite and a second satellite clock error of the satellite corresponding to the preset solution time; A second pseudorange observation value is calculated based on the first pseudorange observation value, the first distance, the first satellite clock error, the second distance and the second satellite clock error; a second carrier phase observation value is calculated based on the first carrier phase observation value, the first distance, the first satellite clock error, the second distance and the second satellite clock error; and the second pseudorange observation value and the second carrier phase observation value are determined as the target observation data.

4. The method for solving the continuously operating reference station system according to claim 3, characterized in that: If the receiving epoch of the forecasted entity observation data is t, and the preset solution time is t+1, The calculation formula of the second pseudorange observation value is expressed as: The calculation formula of the second carrier phase observation value is expressed as: in, represents the second pseudorange observation value, i represents the identifier of the satellite, a represents the identifier of the target reference station, represents the first pseudorange observation value, represents the second distance, represents the first distance, dt i (t+1) represents the second satellite clock error, dt i (t) represents the first satellite clock error, C represents the speed of light, represents the second carrier phase observation value, Represents the first carrier phase observation value.

5. The method for solving the continuously operating reference station system according to claim 1, characterized in that: The receiving of entity observation data from a plurality of entity reference stations in the continuously running reference station system by calling the first thread comprises: The physical observation data sent from the multiple physical reference stations are received by using mobile communication technology.

6. The method for solving the continuously operating reference station system according to claim 1, characterized in that: The first thread, the second thread, and the third thread are threads executed in parallel.

7. The method for solving the continuously operating reference station system according to claim 1, characterized in that: The determining of the physical reference station corresponding to the virtual reference station as the main reference station comprises: Constructing a Delaunay triangulation network according to the reference coordinates of the plurality of physical reference stations; Determine a triangular area corresponding to the virtual reference station in the Delaunay triangulation network according to the virtual station coordinates of the virtual reference station; According to the virtual station coordinates and the reference coordinates of each physical reference station in the triangular area, a main reference station corresponding to the virtual reference station is determined from the physical reference stations in the triangular area.

8. The continuously operating reference station system solution method according to claim 7, characterized in that: Multiple reference stations corresponding to the vertices of the triangular area form multiple baselines connected end to end, and the baseline ambiguity resolution is performed based on the target observation data to obtain the atmospheric error of the virtual reference station relative to the main reference station, including: Based on the target observation data of multiple physical reference stations in the triangular area, the double-difference ionospheric-free ambiguity of each baseline is estimated using the Kalaman filter algorithm; Using a multi-epoch smoothing method, integer ambiguity fixation is performed on the moving wide lane combined observation values ​​corresponding to the target observation data of multiple physical reference stations in the triangular area to obtain the double difference wide term ambiguity of each baseline; Calculating the double difference narrow term ambiguity of each baseline according to the linear relationship between the double difference ionospheric-free ambiguity and the double difference wide term ambiguity; Calculating a double-difference tropospheric delay and a double-difference ionospheric delay of each baseline according to the double-difference narrow term ambiguity; The double difference tropospheric delay and the double difference ionospheric delay of the virtual reference station relative to the main reference station are interpolated according to the virtual station coordinates and the double difference tropospheric delay and the double difference ionospheric delay of the multiple baselines, and the double difference tropospheric delay and the double difference ionospheric delay of the virtual reference station relative to the main reference station are determined as the atmospheric error.

9. The continuously operating reference station system solution method according to claim 1, characterized in that: The method further comprises: Receiving a data acquisition request sent by a terminal device; Obtaining the request time point by parsing the data acquisition request; Find and send to the terminal device the virtual observation data of the preset solution time corresponding to the requested time point.

10. A server, characterized in that: The server comprises: a memory storing at least one instruction; and A processor executes the at least one instruction to implement the continuously operating reference station system solution method as described in any one of claims 1 to 9.

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