A method and device for calculating equivalent global average TEC based on occultation observation

Abstract

The present invention discloses a method and device for calculating an equivalent global average TEC based on occultation observations, the method comprising: obtaining VTEC characteristic parameters calculated by occultation events in each preset time period before and after a reference time to be calculated; inputting the VTEC characteristic parameters into a trained neural network model, and outputting the equivalent global average TEC corresponding to the reference time. The present invention uses global occultation observations to calculate parameters equivalent to the global average TEC, namely, equivalent global average TEC parameters, thereby simplifying the calculation process of the global average TEC.

Description

Technical Field

[0001] The invention relates to ionospheric key parameter monitoring, and in particular to an equivalent global average TEC calculation method and device based on occultation observation. Background Art

[0002] The global average TEC (Total electronic concentration) 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. The traditional method calculates the global average TEC based on the area-weighted average of TEC on the grid cells of the Global Ionosphere Map (GIM). However, the Global Ionosphere Map (GIM) product requires the acquisition of ionospheric observation data from hundreds of GNSS (Global Navigation Satellite System) stations around the world, and a more complex modeling process is required to obtain it. Summary of the invention

[0003] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a method and device for calculating the equivalent global average TEC based on occultation observation, which utilizes occultation observation to invert the ionospheric characteristic parameters, and then inverts the equivalent global average TEC.

[0004] According to one aspect of the present invention, there is provided a method for calculating an equivalent global average TEC based on occultation observations, comprising:

[0005] Obtain VTEC characteristic parameters calculated from occultation events in each preset time period before and after the reference time to be calculated;

[0006] The VTEC characteristic parameters are input into the trained neural network model, and the equivalent global average TEC corresponding to the reference time is output; wherein the training of the neural network model includes:

[0007] Obtaining ionospheric profile products obtained by inverting long-term occultation events, and calculating VTEC characteristic parameters based on the ionospheric profile products;

[0008] Based on the global ionospheric map product, the global average TEC in the same long time series is calculated;

[0009] Matching the VTEC characteristic parameters with the global average TEC in time to form a training data set;

[0010] Constructing a neural network model, wherein the input layer of the neural network model is the VTEC characteristic parameters in different time periods, and the output layer is the global average TEC at a reference time in the corresponding time period;

[0011] The constructed neural network model is trained using the training data set to obtain a trained neural network model.

[0012] As a further technical solution, the VTEC characteristic parameters include the time, longitude, latitude and vertical total electron content of the ionosphere when the occultation occurs. The acquisition process of each characteristic parameter includes:

[0013] The ionospheric profile products obtained by inverting long-term occultation events include the time, longitude, latitude, profile height and electron density sequence of the occultation.

[0014] The vertical total electron content of the ionosphere is calculated based on the profile height and electron density series.

[0015] As a further technical solution, the calculation of the global average TEC includes:

[0016] Obtain the grid products of the global ionosphere map at preset time intervals and calculate the global average TEC by area-weighted average;

[0017] Repeat the previous step to calculate the global average TEC for each time interval in the same long time series from the gridded product of the global ionosphere map.

[0018] As a further technical solution, the preset time interval is N hours.

[0019] As a further technical solution, the VTEC characteristic parameters are matched with the global average TEC in time, including:

[0020] The global average TEC every N hours is matched with the VTEC characteristic parameters in the preset time periods before and after the reference time, wherein the reference time is within the N-hour time period, and the sum of the preset time period before and after the reference time is N hours.

[0021] As a further technical solution, the neural network model is constructed based on a multi-layer back-propagation neural network.

[0022] According to one aspect of the present invention, there is provided an equivalent global average TEC calculation device based on occultation observation, comprising:

[0023] A data acquisition module, used to obtain VTEC characteristic parameters calculated from occultation events in each preset time period before and after the reference time to be calculated;

[0024] A model calculation module is used to input the VTEC characteristic parameters into the trained neural network model and output the equivalent global average TEC corresponding to the reference time; wherein the training of the neural network model includes:

[0025] Obtaining ionospheric profile products obtained by inverting long-term occultation events, and calculating VTEC characteristic parameters based on the ionospheric profile products;

[0026] Based on the global ionospheric map product, the global average TEC in the same long time series is calculated;

[0027] Matching the VTEC characteristic parameters with the global average TEC in time to form a training data set;

[0028] Constructing a neural network model, wherein the input layer of the neural network model is the VTEC characteristic parameters in different time periods, and the output layer is the global average TEC at a reference time in the corresponding time period;

[0029] The constructed neural network model is trained using the training data set to obtain a trained neural network model.

[0030] According to one aspect of the present invention, there is provided an electronic device, comprising a processor and a memory, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of a method for calculating an equivalent global average TEC based on occultation observations.

[0031] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the steps of the equivalent global average TEC calculation method based on occultation observation.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] (1) The present invention uses a GNSS receiver carried on a low-orbit satellite to perform occultation observations. When the occultation observation signal passes through the atmosphere, it must contain information about the electron activity in the ionosphere. Based on this, the present invention proposes a method for inverting the ionospheric characteristic parameters using occultation observations, and then inverting the equivalent global average TEC. In order to express the nonlinear relationship between the ionospheric characteristic parameters calculated from these discrete occultation events and the global average TEC, the present invention uses a neural network (NN) to construct a model and train it, and uses the trained model to achieve rapid calculation of the equivalent global average TEC.

[0034] (2) Compared with the existing method of calculating the global average TEC, the present invention only needs to obtain the occultation observation data of the GNSS receiver carried on the low-orbit satellite, and use the global occultation observation data to calculate the parameters equivalent to the global average TEC. There is no need to obtain the ionospheric observation data of hundreds of GNSS stations around the world, and there is no need for a complicated modeling process. It can be achieved using a simple neural network model, which simplifies the calculation process of the global average TEC. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction is given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A schematic flow chart of a method for calculating an equivalent global average TEC based on occultation observations provided in an embodiment of the present invention.

[0037] Figure 2 A schematic diagram of the training process of a neural network model provided in an embodiment of the present invention.

[0038] Figure 3 A schematic diagram of the structure of an equivalent global average TEC calculation device based on occultation observation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In view of the complicated current situation of the existing global average TEC calculation and modeling process, the present invention uses global occultation observations to calculate parameters equivalent to the global average TEC, namely, equivalent global average TEC parameters, thereby simplifying the calculation process of the global average TEC.

[0040] It should be noted that unless otherwise specified, the technologies mentioned in the specification of the present invention are all well-known technologies.

[0041] The terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or the structural composition mode, but must be based on the ability of ordinary technicians in this field to achieve. When the combination of technical solutions is contradictory or cannot be achieved, it should be considered that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0043] The embodiment of the present invention provides a method for calculating the equivalent global average TEC based on occultation observations, such as Figure 1 As shown, including:

[0044] Obtain VTEC characteristic parameters calculated from occultation events in each preset time period before and after the reference time to be calculated;

[0045] The VTEC characteristic parameters are input into the trained neural network model, and the equivalent global average TEC corresponding to the reference time is output.

[0046] Wherein, the training of the neural network model is as follows: Figure 2 As shown, including:

[0047] Step 1, obtaining an ionospheric profile product obtained by inverting a long series of occultation events, and calculating VTEC characteristic parameters based on the ionospheric profile product, wherein the VTEC characteristic parameters include the time, longitude, latitude and vertical total electron content of the ionosphere when the occultation occurs.

[0048] Specifically, the time, longitude, latitude, profile height and electron density sequence of the occultation can be obtained through the ionospheric profile product, and the ionospheric vertical total electron content (VTEC) is calculated according to the profile height and the electron density sequence.

[0049] Step 2: Based on the global ionospheric map product, the global average TEC in the same long time series is calculated.

[0050] Specifically include:

[0051] Step 2.1, obtain the 1-hour gridded Global Ionospheric Map (GIM) product from the European Orbit Determination Center (CODE) and calculate the global average TEC by area-weighted average.

[0052] Step 2.2: Repeat step 2.1 to calculate the global average TEC for each hour in the above long time series from the gridded product of the Global Ionospheric Map (GIM).

[0053] Step 3, matching the VTEC characteristic parameters with the global average TEC by time to form a training data set.

[0054] The VTEC characteristic parameters calculated from the above multiple discrete occultation events are matched with the global average TEC at the corresponding time, that is, the global average TEC per hour is matched with the occultation VTEC characteristic parameters in the 30 minutes before and after the reference time to obtain the training data set.

[0055] Furthermore, the training data set is divided into a training set and a validation set, which are used for model training and validation, respectively.

[0056] Step 4, constructing a neural network model, wherein the input layer of the neural network model is the VTEC characteristic parameters (time, longitude, latitude, VTEC) in different time periods, and the output layer is the global average TEC at the reference time in the corresponding time period.

[0057] Step 5: Use the training data set to train the constructed neural network model to obtain the trained neural network model and save the model parameters.

[0058] As a preferred embodiment, the present invention introduces an embodiment of calculating the global average TEC based on a back-propagation neural network using TEC products and CODE GIM products obtained from occultation observations, wherein the training process of the neural network model includes the following steps:

[0059] (1) Download the electron density profile products consisting of discrete occultation events of the COSMIC satellite for a total of 11 years (one solar activity cycle) from 2010 to 2020, and calculate the characteristic parameters of the occultation event, including time, longitude, latitude, and VTEC.

[0060] (2) The 1-hour Global Ionospheric Map (GIM) grid product is obtained from the European Orbit Determination Center (CODE) and the global average TEC is calculated by area-weighted average.

[0061] (3) Repeat step (2) to calculate the global average TEC for each hour in the above long time series (2010-2020) from the Global Ionospheric Map (GIM) grid product.

[0062] (4) Align the VTEC characteristic parameters calculated from the above multiple discrete occultation events with the global average TEC calculated from the GIM grid product at the corresponding time. That is, align the VTEC characteristic parameter data of the global average TEC per hour with the VTEC characteristic parameter data in the 30 minutes before and after the reference time, and randomly select the training set and the validation set (here the data set ratio is set to 8:2).

[0063] (5) A back propagation neural network (BPNN) model with three hidden layers was constructed. The input layer is the VTEC characteristic parameters (time, longitude, latitude, VTEC) calculated from discrete occultation events in the above different time periods, and the output layer is the global average TEC at the corresponding time.

[0064] (6) Input the training set and validation set data into BPNN, train the model until convergence, and save the model parameters.

[0065] After getting the trained model, further steps include:

[0066] (7) Calculate the equivalent global average TEC using the trained model. Obtain the VTEC characteristic parameters (time, longitude, latitude, VTEC) calculated from the discrete occultation events of the COSMIC satellite in the 30-minute period before and after the reference time to be calculated, and input them into the trained neural network model to obtain the equivalent global average TEC at the corresponding time.

[0067] The implementation basis of each embodiment of the present invention is to implement programmed processing through a device with a processor function. Therefore, in engineering practice, the technical solutions and functions of each embodiment of the present invention are encapsulated into various modules. Based on this reality, on the basis of the above embodiments, an embodiment of the present invention provides an equivalent global average TEC calculation device based on occultation observation, which is used to execute an equivalent global average TEC calculation method based on occultation observation in the above method embodiment.

[0068] See also Figure 3The device comprises: a data acquisition module, used for acquiring VTEC characteristic parameters calculated by occultation events in each preset time period before and after the reference time to be calculated; a model calculation module, used for inputting the VTEC characteristic parameters into the trained neural network model, and outputting the equivalent global average TEC corresponding to the reference time; wherein the training of the neural network model comprises: acquiring the ionospheric profile product obtained by inverting the occultation event of a long time series, and calculating the VTEC characteristic parameters according to the ionospheric profile product; calculating the global average TEC in the same long time series according to the global ionospheric map product; matching the VTEC characteristic parameters with the global average TEC according to time to form a training data set; constructing a neural network model, wherein the input layer of the neural network model is the VTEC characteristic parameters in different time periods, and the output layer is the global average TEC at the reference time in the corresponding time period; and training the constructed neural network model using the training data set to obtain a trained neural network model.

[0069] An equivalent global average TEC calculation device based on occultation observation is provided in an embodiment of the present invention, using Figure 3 Several modules in the , use global occultation observations to calculate parameters equivalent to the global average TEC, namely, the equivalent global average TEC parameters, which simplifies the calculation process of the global average TEC.

[0070] It should be noted that the device embodiments provided by the present invention are used to implement the methods in the above method embodiments as well as the methods in other method embodiments provided by the present invention. The only difference is that the corresponding functional modules are set, and the principle is basically the same as the principle of the above device embodiments provided by the present invention. As long as the technical personnel in the field refer to the specific technical solutions in other method embodiments on the basis of the above device embodiments, obtain the corresponding technical means and the technical solutions composed of these technical means by combining the technical features, and on the premise of ensuring the practicality of the technical solutions, improve the modules in the above device embodiments to obtain the corresponding device class embodiments, which are used to implement the methods in other method class embodiments. For example:

[0071] Based on the content of the above device embodiment, as a preferred embodiment, in an equivalent global average TEC calculation device based on occultation observation provided in the embodiment of the present invention, the VTEC characteristic parameters include the time, longitude, latitude and vertical total electron content of the ionosphere when the occultation occurs, and the data acquisition module is further used to execute the following instructions:

[0072] The ionospheric profile products obtained by inverting long-term occultation events include the time, longitude, latitude, profile height and electron density sequence of the occultation.

[0073] The vertical total electron content of the ionosphere is calculated based on the profile height and electron density series.

[0074] Based on the contents of the above device embodiments, as a preferred embodiment, in an equivalent global average TEC calculation device based on occultation observation provided in an embodiment of the present invention, the calculation of the global average TEC includes:

[0075] Obtain the grid products of the global ionosphere map at preset time intervals and calculate the global average TEC by area-weighted average;

[0076] Repeat the previous step to calculate the global average TEC for each time interval in the same long time series from the gridded product of the global ionosphere map.

[0077] Based on the content of the above device embodiment, as a preferred embodiment, in an equivalent global average TEC calculation device based on occultation observation provided in an embodiment of the present invention, the preset time interval is N hours.

[0078] Based on the contents of the above device embodiments, as a preferred embodiment, in an equivalent global average TEC calculation device based on occultation observation provided in an embodiment of the present invention, the VTEC characteristic parameters are matched with the global average TEC in time, including:

[0079] The global average TEC every N hours is matched with the VTEC characteristic parameters in the preset time periods before and after the reference time, wherein the reference time is within the N-hour time period, and the sum of the preset time period before and after the reference time is N hours.

[0080] Based on the contents of the above device embodiments, as a preferred embodiment, in an equivalent global average TEC calculation device based on occultation observation provided in an embodiment of the present invention, the neural network model is constructed based on a multi-layer back-propagation neural network.

[0081] Based on the same inventive concept as the above-mentioned embodiment, an embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of the equivalent global average TEC calculation method based on occultation observation, as shown below:

[0082] Obtain VTEC characteristic parameters calculated from occultation events in each preset time period before and after the reference time to be calculated;

[0083] The VTEC characteristic parameters are input into the trained neural network model, and the equivalent global average TEC corresponding to the reference time is output; wherein the training of the neural network model includes:

[0084] Obtaining ionospheric profile products obtained by inverting long-term occultation events, and calculating VTEC characteristic parameters based on the ionospheric profile products;

[0085] Based on the global ionospheric map product, the global average TEC in the same long time series is calculated;

[0086] Matching the VTEC characteristic parameters with the global average TEC in time to form a training data set;

[0087] Constructing a neural network model, wherein the input layer of the neural network model is the VTEC characteristic parameters in different time periods, and the output layer is the global average TEC at a reference time in the corresponding time period;

[0088] The constructed neural network model is trained using the training data set to obtain a trained neural network model.

[0089] Based on the same inventive concept as the above-mentioned embodiment, the embodiment of the present invention further provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions enable the computer to execute the steps of the equivalent global average TEC calculation method based on occultation observation, as shown below:

[0090] Obtain VTEC characteristic parameters calculated from occultation events in each preset time period before and after the reference time to be calculated;

[0091] The VTEC characteristic parameters are input into the trained neural network model, and the equivalent global average TEC corresponding to the reference time is output; wherein the training of the neural network model includes:

[0092] Obtaining ionospheric profile products obtained by inverting long-term occultation events, and calculating VTEC characteristic parameters based on the ionospheric profile products;

[0093] Based on the global ionospheric map product, the global average TEC in the same long time series is calculated;

[0094] Matching the VTEC characteristic parameters with the global average TEC in time to form a training data set;

[0095] Constructing a neural network model, wherein the input layer of the neural network model is the VTEC characteristic parameters in different time periods, and the output layer is the global average TEC at a reference time in the corresponding time period;

[0096] The constructed neural network model is trained using the training data set to obtain a trained neural network model.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A method for calculating equivalent global average TEC based on occultation observations, characterized in that: include: Obtain VTEC characteristic parameters calculated from occultation events in each preset time period before and after the reference time to be calculated; The VTEC characteristic parameters are input into the trained neural network model, and the equivalent global average TEC corresponding to the reference time is output; wherein the training of the neural network model includes: An ionospheric profile product obtained by inverting a long-time series of occultation events is obtained, and VTEC characteristic parameters are calculated based on the ionospheric profile product; the VTEC characteristic parameters include the time, longitude, latitude, and vertical total electron content of the ionosphere when the occultation occurs, and the process of obtaining each characteristic parameter includes: obtaining an ionospheric profile product obtained by inverting a long-time series of occultation events, including the time, longitude, latitude, profile height, and electron density sequence when the occultation occurs; and calculating the vertical total electron content of the ionosphere based on the profile height and the electron density sequence; Based on the global ionospheric map product, the global average TEC in the same long time series is calculated; Matching the VTEC characteristic parameters with the global average TEC in time to form a training data set; Constructing a neural network model, wherein the input layer of the neural network model is the VTEC characteristic parameters in different time periods, and the output layer is the global average TEC at a reference time in the corresponding time period; The constructed neural network model is trained using the training data set to obtain a trained neural network model.

2. The method for calculating equivalent global average TEC based on occultation observation according to claim 1, characterized in that: The calculation of the global average TEC includes: Obtain the grid products of the global ionosphere map at preset time intervals and calculate the global average TEC by area-weighted average; Repeat the previous step to calculate the global average TEC for each time interval in the same long time series from the gridded product of the global ionosphere map.

3. The equivalent global average TEC calculation method based on occultation observation according to claim 2, characterized in that: The preset time interval is N hours.

4. The method for calculating the equivalent global average TEC based on occultation observation according to claim 3, characterized in that: Matching the VTEC characteristic parameters to the global average TEC over time includes: The global average TEC every N hours is matched with the VTEC characteristic parameters in the preset time periods before and after the reference time, wherein the reference time is within the N-hour time period, and the sum of the preset time period before and after the reference time is N hours.

5. The method for calculating equivalent global average TEC based on occultation observation according to claim 1, characterized in that: The neural network model is constructed based on a multi-layer back-propagation neural network.

6. An equivalent global average TEC calculation device based on occultation observation, characterized in that: include: A data acquisition module, used to obtain VTEC characteristic parameters calculated from occultation events in each preset time period before and after the reference time to be calculated; A model calculation module is used to input the VTEC characteristic parameters into the trained neural network model and output the equivalent global average TEC corresponding to the reference time; wherein the training of the neural network model includes: An ionospheric profile product obtained by inverting a long-time series of occultation events is obtained, and VTEC characteristic parameters are calculated based on the ionospheric profile product; the VTEC characteristic parameters include the time, longitude, latitude, and vertical total electron content of the ionosphere when the occultation occurs, and the process of obtaining each characteristic parameter includes: obtaining an ionospheric profile product obtained by inverting a long-time series of occultation events, including the time, longitude, latitude, profile height, and electron density sequence when the occultation occurs; and calculating the vertical total electron content of the ionosphere based on the profile height and the electron density sequence; Based on the global ionospheric map product, the global average TEC in the same long time series is calculated; Matching the VTEC characteristic parameters with the global average TEC in time to form a training data set; Constructing a neural network model, wherein the input layer of the neural network model is the VTEC characteristic parameters in different time periods, and the output layer is the global average TEC at a reference time in the corresponding time period; The constructed neural network model is trained using the training data set to obtain a trained neural network model.

7. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the steps of a method for calculating an equivalent global average TEC based on occultation observation as claimed in any one of claims 1 to 5.

8. A non-transitory computer readable storage medium, characterized in that: The non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the steps of the equivalent global average TEC calculation method based on occultation observations as claimed in any one of claims 1 to 5.

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