Equivalent global average TEC model construction method and device and electronic equipment
By obtaining observation data from global ionosphere maps and sparse GNSS sites, and using neural network models to calculate equivalent global average TECs, the complex data requirements in the existing technology are solved and efficient and accurate modeling is achieved.
Patent Information
- Application Number
- CN202510242389.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-03
- Publication Date
- 2025-07-25
AI Technical Summary
The prior art requires obtaining a large amount of ionosphere observation data of GNSS stations, and the modeling process is complex.
By obtaining the global average TEC in the global ionosphere map, using observation data files of sparse GNSS sites in multiple uniformly distributed around the world, the TEC characteristic parameters of the ionosphere puncture point are calculated, the data set is constructed, and the neural network model is used for training to calculate the equivalent global average TEC.
Reduces the number of sites for data collection, reduces data dependence, and improves computing efficiency and accuracy.
Smart Images
Figure CN120370341A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of ionospheric key parameter monitoring, and particularly relates to a method, device, and electronic device for constructing an equivalent global average TEC model. Background Art
[0002] The global average TEC (Total Electron Content) is an important indicator for studying the characteristics of the global ionosphere. The global average TEC can suppress the local characteristics of TEC, thereby highlighting the overall characteristics of the global ionosphere. Therefore, it is more suitable for studying the morphological changes of the global ionosphere than TEC.
[0003] In the related art, the global average TEC is calculated by weighted averaging of the TEC on the grid cells of the GIM (Global Ionospheric Map) according to the area. However, GIM products need to obtain ionospheric observation data of hundreds of GNSS (Global Navigation Satellite System) stations around the world and go through a relatively complex modeling process to obtain. Summary of the Invention
[0004] This application provides a method, device, and electronic device for constructing an equivalent global average TEC model to solve the problem that a large amount of ionospheric observation data of GNSS stations needs to be obtained in the related art and the modeling process is complex.
[0005] In the first aspect of the embodiments of this application, a method for constructing an equivalent global average TEC model is provided, including the following steps: obtaining the global average TEC in the global ionospheric map; obtaining the observation data files of multiple uniformly distributed sparse GNSS stations around the world; calculating the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time according to the observation data files, and constructing a data set based on the TEC characteristic parameters; matching the TEC characteristic parameters in the data set with the global average TEC, training the ionospheric model by using the data set and the global average TEC, and calculating the equivalent global average TEC by using the trained ionospheric model.
[0006] Optionally, obtaining the global average TEC in the global ionospheric map includes: performing grid processing on the global ionospheric map; calculating the global average TEC at a target interval by weighted averaging according to the grid area.
[0007] Optionally, calculate the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time according to the observation data file, including: identifying the slant path between each station and each satellite at the observation time; calculating the TEC on the slant path according to the observation data file; projecting the TEC on the slant path onto the vertical direction to obtain the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time.
[0008] Optionally, determine the ionospheric piercing points according to the receivers of each station and the observed satellites.
[0009] Optionally, the samples in the dataset include the observation time, the longitude of the piercing point, the dimension of the piercing point, and the TEC characteristic parameters.
[0010] Optionally, the input layer of the ionospheric model is the samples in the dataset, and the output layer of the neural network model is the global average TEC.
[0011] Optionally, the ionospheric model is a BP neural network model including multiple hidden layers.
[0012] Optionally, the sparse GNSS stations are GNSS stations with the number of stations less than the sparse threshold.
[0013] The second aspect of the embodiments of the present application provides an equivalent global average TEC model construction device, including: a first acquisition module for acquiring the global average TEC in the global ionospheric map; a second acquisition module for acquiring the observation data files of multiple uniformly distributed sparse GNSS stations globally; a first calculation module for calculating the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time according to the observation data file, and constructing a dataset based on the TEC characteristic parameters; a second calculation module for matching the TEC characteristic parameters in the dataset with the global average TEC, training the ionospheric model using the dataset and the global average TEC, and calculating the equivalent global average TEC using the trained ionospheric model.
[0014] The third aspect of the embodiments of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the equivalent global average TEC model construction method as in the above embodiments.
[0015] Thus, the present application has the following beneficial effects:
[0016] In the embodiments of the present application, by obtaining the global average TEC in the global ionosphere map and the observation data files of multiple evenly distributed sparse GNSS stations globally, calculating the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation moment, constructing a data set based on the TEC characteristic parameters, matching the TEC characteristic parameters in the data set with the global average TEC, and using the data set and the global average TEC to train the ionosphere model, and finally using the trained ionosphere model to calculate the equivalent global average TEC, the number of stations for data collection is reduced, the data dependence is reduced, and the calculation efficiency and accuracy are improved. Thus, the problem in the related art that a large amount of ionospheric observation data of GNSS stations needs to be obtained and the modeling process is complex is solved.
[0017] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:
[0019] Figure 1 is a flowchart of a method for constructing an equivalent global average TEC model according to an embodiment of the present application;
[0020] Figure 2 is a schematic flowchart of a method for calculating the global average TEC based on a backpropagation neural network according to an embodiment of the present application.
[0021] Figure 3 is an example diagram of an apparatus for constructing an equivalent global average TEC model according to an embodiment of the present application;
[0022] Figure 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS
[0023] Embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application and should not be construed as limiting the present application.
[0024] The method, apparatus, and electronic device for constructing an equivalent global mean TEC model according to the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the problem in the related art mentioned in the above background art that a large amount of ionospheric observation data of GNSS stations needs to be obtained and the modeling process is complex, the present application provides a method for constructing an equivalent global mean TEC model. In this method, by obtaining the global mean TEC in the global ionospheric map and the observation data files of a plurality of uniformly distributed sparse GNSS stations globally, calculating the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation moment, constructing a data set based on the TEC characteristic parameters, matching the TEC characteristic parameters in the data set with the global mean TEC, and training the ionospheric model using the data set and the global mean TEC, and finally calculating the equivalent global mean TEC using the trained ionospheric model, the number of stations for data collection is reduced, the data dependence is reduced, and the calculation efficiency and accuracy are improved. Thus, the problem in the related art that a large amount of ionospheric observation data of GNSS stations needs to be obtained and the modeling process is complex is solved.
[0025] Specifically, Figure 1 FIG. is a schematic flowchart of a method for constructing an equivalent global mean TEC model provided by an embodiment of the present application.
[0026] As Figure 1 shown, the method for constructing the equivalent global mean TEC model includes the following steps:
[0027] In step S101, obtain the global mean TEC in the global ionospheric map.
[0028] Among them, the global ionospheric map is a map representing the spatial distribution of the total electron content of the Earth's ionosphere; TEC is an index for measuring the total number of free electrons along a certain path, with the unit of electrons per square meter; the global mean TEC is obtained by calculation, and the calculation method will be described in detail below and will not be elaborated here.
[0029] In the embodiment of the present application, obtaining the global mean TEC in the global ionospheric map includes: performing grid processing on the global ionospheric map; calculating the global mean TEC at the target interval by weighted average according to the grid area.
[0030] Among them, grid processing refers to dividing the global ionospheric map into multiple grid cells; the target interval is specifically defined according to the actual situation and will not be specifically defined here.
[0031] It can be understood that in the embodiment of the present application, the global ionospheric map is first subjected to grid processing, the map is divided into multiple grid cells, and the global mean TEC at the target interval is calculated by weighted average according to the area of each grid cell.
[0032] In step S102, obtain the observation data files of multiple globally and evenly distributed sparse GNSS stations.
[0033] It can be understood that in the embodiments of the present application, multiple GNSS stations are obtained globally and evenly distributed, so as to ensure that data in different regions of the world can be collected. Although the number of stations is small, their distribution is wide enough to provide representative samples.
[0034] In the embodiments of the present application, a sparse GNSS station is a GNSS station with a number of stations less than a sparse threshold.
[0035] Among them, the sparse threshold refers to the number of stations required to achieve the research goal, generally no more than twenty, and is specifically set according to the actual situation and will not be specifically limited here.
[0036] It can be understood that in the embodiments of the present application, GNSS stations with a number less than the sparse threshold are selected globally, ensuring uniform geographical distribution, achieving a small number of GNSS stations while ensuring as wide a coverage range as possible, and collecting data files of observed satellites from the GNSS stations.
[0037] In step S103, calculate the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time according to the observation data file, and construct a data set based on the TEC characteristic parameters.
[0038] Among them, the ionospheric piercing point refers to the point where the signal path from the GNSS receiver to the satellite intersects the ionosphere; calculating the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time according to the observation data file, and the calculation method will be described in detail below and will not be elaborated here.
[0039] It can be understood that in the embodiments of the present application, the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time are calculated according to the data files of observed satellites collected from the GNSS stations, and a data set is constructed based on these parameters.
[0040] In the embodiments of the present application, calculating the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time according to the observation data file includes: identifying the slant path between each station and each satellite at the observation time; calculating the TEC on the slant path according to the observation data file; projecting the TEC on the slant path to the vertical direction to obtain the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time.
[0041] Among them, the slant path refers to the actual path of satellite signal propagation from the GNSS receiver, and this path is not vertical but has a certain angle; the TEC on the slant path refers to the total electron content calculated along this slant path.
[0042] It can be understood that in the embodiment of the present application, to calculate the TEC characteristic parameters of the ionospheric piercing points between each site and each satellite at the observation time according to the observation data file, it is first necessary to identify the slant paths formed between each site and each satellite at the observation time, and based on the GNSS observation data file of this site, calculate the TEC on the slant path. Then, project the calculated TEC on the slant path onto the vertical direction, so as to obtain the TEC characteristic parameters at the ionospheric piercing points between each site and each satellite at the observation time.
[0043] In the embodiment of the present application, the ionospheric piercing points are determined according to the receiver of each site and the observed satellite.
[0044] It can be understood that the embodiment of the present application can use the data collected by the receivers of the selected sparse GNSS sites, combined with the satellite orbit information at the observation time, to calculate the exact signal paths between the receivers and each observed satellite. Based on this information, the intersection points of the signal paths and the ionosphere, that is, the positions of the ionospheric piercing points, can be determined.
[0045] In the embodiment of the present application, the samples in the dataset include the observation time, the longitude of the piercing point, the latitude of the piercing point, and the TEC characteristic parameters.
[0046] Among them, the observation time refers to the specific time point when GNSS observations are carried out; the longitude of the piercing point and the latitude of the piercing point jointly define the geographical location of the ionospheric piercing point.
[0047] It can be understood that the dataset in the embodiment of the present application includes the observation time, the longitude of the piercing point, the latitude of the piercing point, and the TEC characteristic parameters, which are very valuable for understanding the changing trend of the global ionosphere, establishing models, etc.
[0048] In step S104, match the TEC characteristic parameters in the dataset with the global average TEC, use the dataset and the global average TEC to train the ionospheric model, and use the trained ionospheric model to calculate the equivalent global average TEC.
[0049] Among them, the ionospheric model is used to establish the relationship between the TEC characteristic parameters of the GNSS sites and the global average TEC, which will be described in detail below and will not be elaborated here.
[0050] It can be understood that in the embodiments of the present application, the dataset of TEC characteristic parameters collected and calculated by sparse GNSS stations is matched with the global average TEC at the corresponding moment. Then, these data are used to train the ionospheric model. After the model training is completed, the equivalent global average TEC value can be calculated using this model, thus simplifying the large amount of GNSS station data and complex modeling processes required by traditional methods and improving the efficiency and accuracy.
[0051] In the embodiments of the present application, the input layer of the ionospheric model is the samples in the dataset, and the output layer of the neural network model is the global average TEC.
[0052] Among them, the input layer refers to the part of the neural network model that receives data, and the data in the input layer comes from the samples in the dataset; the output layer refers to the part of the neural network model that generates results.
[0053] It can be understood that in the embodiments of the present application, the samples in the dataset are used as the input layer of the neural network model, and the global average TEC is obtained through the output layer of the neural network model.
[0054] In the embodiments of the present application, the ionospheric model is a BP neural network model including multiple hidden layers.
[0055] Among them, the multiple hidden layers refer to the layers in the neural network located between the input layer and the output layer. The multiple hidden layers can enable the neural network to learn the complex non-linear relationship between the input data and the output result; the BP neural network model is a multi-layer feedforward neural network trained based on the error backpropagation algorithm. The input data is propagated forward to calculate the output, and the weights in the network are adjusted through backpropagation according to the output error to minimize the error between the predicted value and the actual value.
[0056] It can be understood that the ionospheric model in the embodiments of the present application is a BP neural network model including multiple hidden layers, which can enable the neural network to learn the complex non-linear relationship between the input data and the output result and obtain more accurate results.
[0057] According to the method for constructing an equivalent global average TEC model proposed in the embodiments of the present application, by obtaining the global average TEC in the global ionospheric map and the observation data files of multiple uniformly distributed sparse GNSS stations globally, calculating the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation moment, constructing a dataset based on the TEC characteristic parameters, matching the TEC characteristic parameters in the dataset with the global average TEC, and training the ionospheric model using the dataset and the global average TEC, and finally calculating the equivalent global average TEC using the trained ionospheric model, the number of stations for data collection is reduced, the data dependence is lowered, and the calculation efficiency and accuracy are improved.
[0058] The following further describes the method for constructing an equivalent global mean TEC model through a specific embodiment. This embodiment uses a backpropagation neural network to calculate the global mean TEC based on the TEC products and CODE GIM products observed by GNSS stations, as Figure 2 shown, including the following steps:
[0059] Step S201: Obtain the global ionospheric map grid products at each moment with an interval of 1 hour from the Center for Orbit Determination in Europe (CODE), and calculate the global mean TEC at each moment by area-weighted average, taking it as the parameter y.
[0060] Step S202: Select 20 evenly distributed GNSS stations globally and collect the observation data corresponding to CODE GIM at an interval of 1 h.
[0061] Step S203: The sampling rate is 30 seconds. Calculate the TEC of the piercing point at a height of 450 km for each satellite at the observation moment. Determine the ionospheric piercing point through the station receiver and the observed satellite, calculate the TEC on the slant path based on the GNSS observations, that is, STEC (Slant Total Electron Content), and project it to the vertical direction to convert it into the TEC characteristic parameter of the piercing point.
[0062] Step S204: Repeat Step S203 to obtain the global mean TEC characteristic parameters of each station in turn, forming a data set including features such as time, piercing point longitude, piercing point latitude, and TEC in a long time range, taking it as the parameter x.
[0063] Step S205: Construct a BP neural network to establish the relationship between x and y, and randomly select the training set and the validation set (here, the data set ratio is set to 8:2).
[0064] Step S206: Train the BP neural network. Construct a backpropagation neural network (BPNN) model with 3 hidden layers. The input layer is the time series, piercing point longitude and latitude, and TEC characteristic parameters of the GNSS stations, and the output layer is the global mean TEC at the corresponding moment. Input the training set and validation set data into the BPNN, train the model until convergence, and save the model parameters.
[0065] Step S207: Calculate the equivalent global mean TEC using the trained ionospheric model. Obtain the TEC characteristic parameters (time, piercing point longitude, piercing point latitude, TEC) calculated at the reference moment to be calculated, input them into the trained neural network model, and obtain the equivalent global mean TEC at the corresponding moment.
[0066] Next, a device for constructing an equivalent global average TEC model according to an embodiment of the present application will be described with reference to the accompanying drawings.
[0067] Figure 3 It is a block diagram of a device for constructing an equivalent global average TEC model according to an embodiment of the present application.
[0068] As Figure 3 shown, the device 10 for constructing an equivalent global average TEC model includes: a first acquisition module 301, a second acquisition module 302, a first calculation module 303, and a second calculation module 304.
[0069] Among them, the first acquisition module is used to acquire the global average TEC in the global ionosphere map; the second acquisition module is used to acquire the observation data files of multiple uniformly distributed sparse GNSS stations globally; the first calculation module is used to calculate the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time according to the observation data files, and construct a data set based on the TEC characteristic parameters; the second calculation module is used to match the TEC characteristic parameters in the data set with the global average TEC, train the ionospheric model using the data set and the global average TEC, and calculate the equivalent global average TEC using the trained ionospheric model.
[0070] In an embodiment of the present application, the first acquisition module 301 is further configured to: perform grid processing on the global ionosphere map; calculate the global average TEC at a target interval by weighted averaging according to the grid area.
[0071] In an embodiment of the present application, the first calculation module 303 is further configured to: identify the slant path between each station and each satellite at the observation time; calculate the TEC on the slant path according to the observation data files; project the TEC on the slant path to the vertical direction to obtain the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time.
[0072] In an embodiment of the present application, the first calculation module 303 is further configured to: determine the ionospheric piercing point according to the receiver of each station and the observed satellite.
[0073] In an embodiment of the present application, the samples in the data set include the observation time, the longitude of the piercing point, the latitude of the piercing point, and the TEC characteristic parameters.
[0074] In an embodiment of the present application, the input layer of the ionospheric model is the samples in the data set, and the output layer of the neural network model is the global average TEC.
[0075] In an embodiment of the present application, the ionospheric model is a BP neural network model including multiple hidden layers.
[0076] In the embodiments of the present application, a sparse GNSS station is a GNSS station with the number of stations less than a sparse threshold.
[0077] It should be noted that the foregoing explanation of the embodiments of the method for constructing an equivalent global average TEC model is also applicable to the device for constructing an equivalent global average TEC model in this embodiment, and will not be elaborated here.
[0078] The device for constructing an equivalent global average TEC model proposed according to the embodiments of the present application obtains the global average TEC in the global ionospheric map and the observation data files of multiple uniformly distributed sparse GNSS stations globally, calculates the TEC characteristic parameters of the ionospheric piercing points between each station and each satellite at the observation time, constructs a data set based on the TEC characteristic parameters, matches the TEC characteristic parameters in the data set with the global average TEC, and uses the data set and the global average TEC to train the ionospheric model. Finally, the trained ionospheric model is used to calculate the equivalent global average TEC, reducing the number of stations for data collection, reducing data dependence, and improving calculation efficiency and accuracy.
[0079] Figure 4 It is a schematic structural diagram of an electronic device provided by the embodiments of the present application. The electronic device may include:
[0080] A memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.
[0081] When the processor 402 executes the program, it implements the method for constructing an equivalent global average TEC model provided in the foregoing embodiments.
[0082] Further, the electronic device further includes:
[0083] A communication interface 404 for communication between the memory 401 and the processor 402.
[0084] The memory 401 is used to store a computer program executable on the processor 402.
[0085] The memory 401 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.
[0086] If the memory 401, the processor 402, and the communication interface 404 are implemented independently, the communication interface 404, the memory 401, and the processor 402 can be interconnected via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 only a thick line is used in Figure 4 , but it does not mean that there is only one bus or one type of bus.
[0087] Optionally, in a specific implementation, if the memory 401, the processor 402, and the communication interface 404 are integrated on a single chip, the memory 401, the processor 402, and the communication interface 404 can communicate with each other through an internal interface.
[0088] The processor 402 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.
[0089] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms are not necessarily directed to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.
[0090] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.
[0091] Any process or method description depicted in the flowchart or otherwise described herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of the present application includes additional implementations, where the functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed. This should be understood by those skilled in the art to which the embodiments of the present application pertain.
[0092] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays, field programmable gate arrays, and the like.
[0093] Those of ordinary skill in the art can understand that all or part of the steps carried by the methods of implementing the above embodiments can be completed by instructing relevant hardware through a program. The above program can be stored in a computer-readable storage medium, and when executed, includes one or a combination of the steps of the method embodiments.
[0094] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.
Claims
1. A method for constructing an equivalent global average TEC model, characterized in that Including the following steps: Obtain the global average TEC in the global ionosphere map; Obtain the observation data files of multiple uniformly distributed sparse GNSS stations globally; Calculate the TEC characteristic parameters of the ionospheric pierce points between each station and each satellite at the observation time according to the observation data files, and construct a data set based on the TEC characteristic parameters; Match the TEC characteristic parameters in the data set with the global average TEC, train the ionosphere model using the data set and the global average TEC, and calculate the equivalent global average TEC using the trained ionosphere model.
2. The equivalent global average TEC model construction method according to claim 1, characterized in that The obtaining of the global average TEC in the global ionosphere map includes: Perform grid processing on the global ionosphere map; Calculate the global average TEC at the target interval by weighted average according to the grid area.
3. The equivalent global average TEC model construction method according to claim 1, wherein The calculating of the TEC characteristic parameters of the ionospheric pierce points between each station and each satellite at the observation time according to the observation data files includes: Identify the slant paths between each station and each satellite at the observation time; Calculate the TEC on the slant paths according to the observation data files; Project the TEC on the slant paths to the vertical direction to obtain the TEC characteristic parameters of the ionospheric pierce points between each station and each satellite at the observation time.
4. The method for constructing an equivalent global average TEC model according to claim 1 or 3, characterized in that, Determine the ionospheric pierce points according to the receiver of each station and the observed satellites.
5. The method for constructing an equivalent global average TEC model according to claim 1, wherein The samples in the data set include the observation time, pierce point longitude, pierce point latitude, and TEC characteristic parameters.
6. The equivalent global average TEC model construction method according to claim 1, wherein The input layer of the ionosphere model is the samples in the data set, and the output layer of the neural network model is the global average TEC.
7. The method for constructing an equivalent global average TEC model according to claim 1 or 6, characterized in that, The ionosphere model is a BP neural network model including multiple hidden layers.
8. The method for constructing an equivalent global average TEC model according to claim 1, wherein, The sparse GNSS stations are GNSS stations with the number of stations less than the sparse threshold.
9. An equivalent global average TEC model construction device, characterized in that Including: The first obtaining module is used to obtain the global average TEC in the global ionosphere map; The second obtaining module is used to obtain the observation data files of multiple uniformly distributed sparse GNSS stations globally; The first calculating module is used to calculate the TEC characteristic parameters of the ionospheric pierce points between each station and each satellite at the observation time according to the observation data files, and construct a data set based on the TEC characteristic parameters; The second calculating module is used to match the TEC characteristic parameters in the data set with the global average TEC, train the ionosphere model using the data set and the global average TEC, and calculate the equivalent global average TEC using the trained ionosphere model.
10. An electronic device, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the method for constructing the equivalent global average TEC model according to any one of claims 1-8.