A method and device for constructing an ionospheric tomography model

Abstract

This invention relates to a method and apparatus for constructing an ionospheric tomographic imaging model, belonging to the field of space science and technology. The method for constructing the ionospheric tomographic imaging model includes: determining a scale factor based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path; dividing the ionosphere into layers according to a fixed height, and constructing an electron content distribution model corresponding to each ionosphere based on a multilayer sensor; solving the electron content distribution model corresponding to each ionosphere based on the scale factor, and combining the solved electron content distribution models of each layer to obtain the ionospheric tomographic imaging model. This invention effectively improves the accuracy of ionospheric monitoring.

Description

Technical Field

[0001] This invention relates to the field of space science and technology, and in particular to a method and apparatus for constructing an ionospheric tomography model. Background Technology

[0002] Accurate acquisition and analysis of ionospheric information is of great significance in fields such as space physics, communication and navigation, and seismology. It helps in space physics research, ensures the quality of communication and navigation, and explores earthquake prediction methods.

[0003] Existing ionospheric monitoring schemes mainly rely on ionospheric altimeters and satellite signal inversion. However, due to factors such as the inherent errors of the monitoring equipment and the influence of the complex structure of the ionosphere on the inversion process, the monitoring results obtained by existing ionospheric monitoring schemes have relatively large errors and are somewhat inaccurate.

[0004] Therefore, improving the accuracy of ionospheric monitoring has become an urgent technical problem to be solved. Summary of the Invention

[0005] In view of this, it is necessary to provide a method and apparatus for constructing an ionospheric tomographic imaging model to solve the problem of insufficient accuracy of existing ionospheric monitoring schemes.

[0006] To address the aforementioned problems, in a first aspect, the present invention provides a method for constructing an ionospheric tomography model, comprising:

[0007] The scale factor is determined based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path; The ionosphere is divided into layers at a fixed height, and an electron content distribution model for each ionosphere is constructed based on a multilayer perceptron. Based on the scale factor, the electron content distribution model corresponding to each ionosphere is solved, and the solved electron content distribution models of each layer are combined to obtain the ionospheric tomographic imaging model.

[0008] In one possible implementation, determining the scale factor based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path includes: The scale factor corresponding to the fixed ionospheric height range is determined based on the ratio between the electron density integral within the fixed ionospheric height range and the total electron density integral along the GNSS ray propagation path.

[0009] In one possible implementation, the electron density integral over the fixed ionospheric height range is determined based on radiosonde data and occultation data.

[0010] In one possible implementation, the step of dividing the ionosphere into layers according to a fixed height and constructing an electron content distribution model for each ionosphere based on a multilayer perceptron includes: The ionosphere is divided into layers at 100km altitude intervals. The latitude, longitude, solar angle, local time, and seasonal information of the ionospheric puncture point are used as inputs to the electron content distribution model, and the electron content at preset spatial locations and preset times is used as the output of the electron content distribution model to construct the electron content distribution model corresponding to each ionosphere.

[0011] In one possible implementation, the electron content distribution model corresponding to each ionosphere is represented by the following formula:

[0012] in, express Electron content of the layer express The electron content distribution model corresponding to the layer, Indicates the longitude of the ionospheric puncture point. Indicates the latitude of the ionospheric puncture point. Indicates the sun angle. Indicates time-related characteristics, express The set of parameters to be trained for the electron content distribution model corresponding to the layer.

[0013] In one possible implementation, solving the electron content distribution model for each ionosphere based on the scale factor includes: Using the scale factor as prior knowledge, and with the goal of minimizing the difference between the total electron content predicted by the electron content distribution model for each ionosphere and the total electron content measured by GNSS, a loss function is constructed, and the electron content distribution model for each ionosphere is solved based on the loss function.

[0014] In one possible implementation, the loss function is expressed based on the following formula:

[0015] in, This represents the total electron content along the ray path predicted by the electron content distribution model. Represents GNSS observations. The scale factor corresponding to the layer, The electron content distribution model of the layer predicts the electron content along the ray path. These are the weight parameters.

[0016] On the other hand, the present invention also provides an apparatus for constructing an ionospheric tomography model, comprising: The determination module is used to determine the scale factor based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path. The module is used to divide the ionosphere into layers at a fixed height and to build an electron content distribution model for each ionosphere based on a multilayer perceptron. The combination module is used to solve the electron content distribution model corresponding to each ionosphere based on the scale factor, and to combine the solved electron content distribution models of each layer to obtain the ionospheric tomographic imaging model.

[0017] Secondly, the present invention also provides a modeling device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the method for constructing the ionospheric tomography model as described in any of the above implementations.

[0018] Thirdly, the present invention also provides a computer-readable storage medium for storing a computer-readable program or instruction, which, when executed by a processor, can implement the steps in the method for constructing the ionospheric tomography model described in any of the above implementations.

[0019] The beneficial effects of this invention are as follows: The method and apparatus for constructing an ionospheric tomographic imaging model provided by this invention firstly reduces the influence of systematic errors by converting the absolute TEC into a scale factor, thus providing a foundation for subsequent accurate modeling. Next, by dividing the ionosphere into layers and constructing an electron content distribution model corresponding to each ionosphere using a multilayer perceptron, the complex structure of the ionosphere is meticulously depicted, accurately describing the ionosphere. Finally, the ionospheric tomographic imaging model is obtained by solving the electron content distribution model corresponding to each ionosphere using the scale factor, ensuring the stability of the model and improving the accuracy of ionospheric monitoring. This invention effectively improves the accuracy of ionospheric monitoring. Attached Figure Description

[0020] Figure 1 A schematic flowchart of an embodiment of the method for constructing an ionospheric tomography model provided by the present invention; Figure 2 A schematic flowchart of an embodiment of the construction process of the ionospheric tomography model provided by the present invention; Figure 3 A schematic diagram of an embodiment of the apparatus for constructing an ionospheric tomography model provided by the present invention; Figure 4A schematic diagram of an embodiment of the modeling device provided by the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0023] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0024] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0025] Ionospheric tomography: Ionospheric tomography is a method that uses signals acquired by satellite or ground receivers (such as GPS, radio signals, etc.) to reconstruct the three-dimensional distribution of electron density in the ionosphere through inversion techniques. Its principle is similar to medical CT scans, using information such as propagation delays or phase changes of multipath signals to invert the spatial variations of the ionospheric structure.

[0026] This invention provides a method and apparatus for constructing an ionospheric tomography model, which will be described below.

[0027] Figure 1 A schematic flowchart of an embodiment of the method for constructing an ionospheric tomography model provided by the present invention is shown below. Figure 1 As shown, the method for constructing an ionospheric tomography model includes: S101. Determine the scale factor based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path.

[0028] It should be noted that converting absolute TEC into a scaling factor can effectively reduce the impact of systematic errors, providing a foundation for subsequent accurate modeling.

[0029] S102. The ionosphere is divided into layers according to a fixed height, and an electron content distribution model corresponding to each ionosphere is constructed based on a multilayer perceptron.

[0030] It should be noted that by dividing the ionosphere into layers and representing the electron content distribution of each layer using an electron content distribution model, the complex structure of the ionosphere can be described in detail. Compared with traditional models, the description of the ionosphere is more accurate and comprehensive.

[0031] S103. Solve the electron content distribution model corresponding to each ionosphere based on the scale factor, and combine the solved electron content distribution models of each layer to obtain the ionospheric tomographic imaging model.

[0032] It should be noted that solving the electron content distribution model corresponding to each ionosphere using a scale factor to obtain the ionospheric tomographic imaging model can ensure the stability of the model and improve the accuracy of ionospheric monitoring.

[0033] In summary, the method for constructing an ionospheric tomographic imaging model provided in this embodiment of the invention firstly reduces the influence of systematic errors by converting the absolute TEC into a scale factor, thus providing a foundation for subsequent accurate modeling. Next, it constructs an electron content distribution model for each ionosphere by dividing the ionosphere into layers and using a multilayer perceptron to meticulously depict the complex structure of the ionosphere and accurately describe it. Finally, it solves the electron content distribution model for each ionosphere using the scale factor to obtain the ionospheric tomographic imaging model, ensuring the model's stability and improving the accuracy of ionospheric monitoring. This invention effectively improves the accuracy of ionospheric monitoring.

[0034] Combination Figure 2 The ionospheric tomography model construction process provided by this invention specifically includes: 1. Data fusion and scale factor calculation.

[0035] To address the characteristics of different detection data, the absolute TEC is converted into a scale factor to reduce the impact of systematic errors, and the scale factor of GNSS rays is obtained through fitting interpolation.

[0036] First, for any GNSS ray, the relationship between the electron content within a fixed ionospheric height range and the total electron content (STEC) along the ray propagation path is as follows: , is the scaling factor, and is the parameter to be solved.

[0037] In some embodiments of the present invention, determining the scale factor based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path includes: The scale factor corresponding to the fixed ionospheric height range is determined based on the ratio between the electron density integral within the fixed ionospheric height range and the total electron density integral along the GNSS ray propagation path.

[0038] It should be noted that the scale factor corresponding to a fixed ionospheric height range can be determined by the ratio between the electron density integral within the fixed ionospheric height range and the total electron density integral along the GNSS ray propagation path.

[0039] In some embodiments of the present invention, the electron density integral within the fixed ionospheric height range is determined based on radiosonde data and occultation data.

[0040] It should be noted that precise electron density profiles at single points can be obtained using radiosonde data and occultation data. Within a certain altitude range, the electron content ratio can be expressed as:

[0041] Among them, subscript and These represent radiosonde data and occultation data, respectively.

[0042] Secondly, since GNSS rays are located at different positions than radiosondes and occultation probes, and the ionospheric data they acquire is the electron content in the vertical direction, it is necessary to convert them to the electron content in the GNSS ray path using the projection function provided by CODE.

[0043] Then, convert the absolute TEC to a scale factor, for example:

[0044] in, express Integrating the electron density over the range, after converting the absolute TEC into a scale factor, can reduce the influence of systematic errors, and the scale factor will only provide initial value constraints during tomographic imaging.

[0045] 2. Construction of electron content distribution model.

[0046] In some embodiments of the present invention, the step of dividing the ionosphere into layers according to a fixed height and constructing an electron content distribution model corresponding to each ionosphere based on a multilayer sensor includes: The ionosphere is divided into layers at 100km altitude intervals. The latitude, longitude, solar angle, local time, and seasonal information of the ionospheric puncture point are used as inputs to the electron content distribution model, and the electron content at preset spatial locations and preset times is used as the output of the electron content distribution model to construct the electron content distribution model corresponding to each ionosphere.

[0047] It should be noted that by dividing the ionosphere into layers at 100km altitude intervals, a more accurate and comprehensive description of the electrosphere can be achieved. By using the latitude, longitude, solar angle, local time, and seasonal information of the ionospheric penetration point as input to the electron content distribution model, and using the electron content at a preset spatial location and time as the output, an electron content distribution model corresponding to each ionosphere can be constructed, accurately reflecting the electron content of each ionosphere.

[0048] In some embodiments of the present invention, the electron content distribution model corresponding to each ionosphere is expressed based on the following formula:

[0049] in, express Electron content of the layer express The electron content distribution model corresponding to the layer, Indicates the longitude of the ionospheric puncture point. Indicates the latitude of the ionospheric puncture point. Indicates the sun angle. Indicates time-related characteristics, express The set of parameters to be trained for the electron content distribution model corresponding to the layer.

[0050] It should be noted that in ionospheric modeling, the analytical expressions of traditional physical or empirical models often fail to accurately capture the complex nonlinear relationships between electron density and spatial, temporal, and external driving forces. Multilayer perceptrons, through multilayer nonlinear transformations, can automatically learn and internalize these complex patterns from large amounts of data. Their mathematical essence lies in establishing a highly parameterized mapping function.

[0051] in That is, determined by network parameters The determined complex function. and These represent the longitude and latitude of the ionospheric puncture point, respectively. Indicates the sun angle. Indicates time, This describes the intensity of solar activity. index, Indicates the description of geomagnetic intensity Kp index.

[0052] The mapping process (forward propagation) of a multilayer perceptron: A system with L layers (including the input layer, L...) A multilayer perceptron (with 2 hidden layers and an output layer) has the following layer-by-layer computation process: Input layer: receives feature vectors. .

[0053] No. l Calculation of hidden layers: This layer receives the output from the layer above. After affine transformation, a nonlinear activation function is applied:

[0054]

[0055] in, This is the weight matrix of this layer. These are the bias vectors, and together they constitute the trainable parameter set of this layer.

[0056] It is a non-linear activation function, such as the ReLU function. This is the core of why networks can fit nonlinear relationships.

[0057] Output layer: The output layer typically undergoes a linear transformation or uses an activation function that guarantees a positive output.

[0058] Therefore, the mapping relationship of the entire network can be summarized as follows:

[0059] in, It is the set of all parameters to be trained in the network.

[0060] 3. Solving for model parameters.

[0061] In some embodiments of the present invention, solving the electron content distribution model corresponding to each ionosphere based on the scale factor includes: Using the scale factor as prior knowledge, and with the goal of minimizing the difference between the total electron content predicted by the electron content distribution model for each ionosphere and the total electron content measured by GNSS, a loss function is constructed, and the electron content distribution model for each ionosphere is solved based on the loss function.

[0062] It should be noted that by using the scale factor as prior knowledge and aiming to minimize the difference between the total electron content predicted by the electron content distribution model for each ionosphere and the total electron content measured by GNSS, a loss function is constructed to solve the electron content distribution model for each ionosphere. This makes the electron content distribution model closer to the actual observation value, thereby improving the accuracy of ionospheric monitoring.

[0063] In some embodiments of the present invention, the loss function is expressed based on the following formula:

[0064] in, This represents the total electron content along the ray path predicted by the electron content distribution model. Represents GNSS observations. The scale factor corresponding to the layer, The electron content distribution model of the layer predicts the electron content along the ray path. These are the weight parameters.

[0065] It should be noted that the loss function used for solving the problem can be calculated using the above formula. The first term forces the network's predicted values ​​to approximate the actual observed TEC, and the second term is a regularization term used to prevent overfitting. During the solution process, gradient descent can be used to iteratively update the parameters, allowing the network to gradually learn a mapping that best approximates the true physical laws of the ionosphere.

[0066] In terms of data processing, this invention effectively mitigates the impact of systematic errors by converting absolute TEC into a scale factor and using radiosonde and occultation data for fitting and interpolation to obtain the GNSS ray scale factor, laying a solid foundation for subsequent accurate modeling. In the model construction stage, the ionosphere is divided into layers, and the electron content distribution of each layer is represented by an electron content distribution model. This allows for a detailed depiction of the complex structure of the ionosphere, providing a more accurate and comprehensive description compared to traditional models. Overall, this invention overcomes the problems of low data processing accuracy, coarse model construction, and unstable parameter solutions in traditional ionospheric research, significantly improving the accuracy and efficiency of ionospheric monitoring. In the field of space physics research, it helps to explore the physical mechanisms of the ionosphere in depth; in communication and navigation, it provides strong support for ensuring stable signal propagation and accurate positioning; and in other related fields that rely on ionospheric data, it provides more reliable data, thereby powerfully promoting the development and progress of various fields.

[0067] To better implement the ionospheric tomography model construction method in this embodiment of the invention, based on the ionospheric tomography model construction method, correspondingly, as follows: Figure 3As shown, this embodiment of the invention also provides an apparatus for constructing an ionospheric tomographic imaging model. The apparatus 300 for constructing an ionospheric tomographic imaging model includes: Module 301 is used to determine the scale factor based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path. Module 302 is used to divide the ionosphere into layers according to a fixed height and to build an electron content distribution model for each ionosphere based on a multilayer perceptron. The combination module 303 is used to solve the electron content distribution model corresponding to each ionosphere based on the scale factor, and to combine the solved electron content distribution models of each layer to obtain an ionospheric tomographic imaging model.

[0068] The ionospheric tomography model construction device 300 provided in the above embodiments can realize the technical solutions described in the above embodiments of the ionospheric tomography model construction method. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the ionospheric tomography model construction method, and will not be repeated here.

[0069] like Figure 4 As shown, the present invention also provides a modeling device 400. The modeling device 400 includes a processor 401, a memory 402, and a display 403. Figure 4 Only some components of the modeling device 400 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0070] In some embodiments, processor 401 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 402 or process data, such as the magnetic resonance image optimization method of the present invention.

[0071] In some embodiments, processor 401 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 401 may be local or remote. In some embodiments, processor 401 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0072] In some embodiments, memory 402 may be an internal storage unit of modeling device 400, such as a hard disk or memory of modeling device 400. In other embodiments, memory 402 may also be an external storage device of modeling device 400, such as a pluggable hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided on modeling device 400.

[0073] Furthermore, the memory 402 may include both internal storage units of the modeling device 400 and external storage devices. The memory 402 is used to store application software and various types of data installed on the modeling device 400.

[0074] In some embodiments, display 403 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an organic light-emitting diode (OLED) touchscreen. Display 403 is used to display information from the modeling device 400 and to display a user interface for visualization. Components 401-403 of the modeling device 400 communicate with each other via a system bus.

[0075] In one embodiment, when processor 401 executes the program for constructing an ionospheric tomography model in memory 402, the following steps can be implemented: The scale factor is determined based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path; The ionosphere is divided into layers at a fixed height, and an electron content distribution model for each ionosphere is constructed based on a multilayer perceptron. Based on the scale factor, the electron content distribution model corresponding to each ionosphere is solved, and the solved electron content distribution models of each layer are combined to obtain the ionospheric tomographic imaging model.

[0076] It should be understood that when the processor 401 executes the program for constructing the ionospheric tomography model in the memory 402, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0077] Furthermore, this embodiment of the invention does not specifically limit the type of the modeling device 400 mentioned. The modeling device 400 can be a portable electronic device such as a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, or laptop computer. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic devices can also be other portable electronic devices, such as laptop computers with touch-sensitive surfaces (e.g., touch panels). It should also be understood that in some other embodiments of the invention, the modeling device 400 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0078] Accordingly, this application also provides a computer-readable storage medium for storing computer-readable programs or instructions. When the programs or instructions are executed by a processor, they can implement the steps or functions in the construction method of the ionospheric tomography model provided in the above-described method embodiments.

[0079] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0080] The above provides a detailed description of the method and apparatus for constructing an ionospheric tomography model provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for constructing an ionospheric tomography model, characterized in that, include: The scale factor is determined based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path; The ionosphere is divided into layers at a fixed height, and an electron content distribution model for each ionosphere is constructed based on a multilayer perceptron. Based on the scale factor, the electron content distribution model corresponding to each ionosphere is solved, and the solved electron content distribution models of each layer are combined to obtain the ionospheric tomographic imaging model.

2. The method for constructing an ionospheric tomography model according to claim 1, characterized in that, The determination of the scale factor based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path includes: The scale factor corresponding to the fixed ionospheric height range is determined based on the ratio between the electron density integral within the fixed ionospheric height range and the total electron density integral along the GNSS ray propagation path.

3. The method for constructing an ionospheric tomography model according to claim 2, characterized in that, The electron density integral within the fixed ionospheric height range is determined based on radiosonde data and occultation data.

4. The method for constructing an ionospheric tomography model according to claim 1, characterized in that, The process of dividing the ionosphere into layers of fixed height and constructing an electron content distribution model for each ionosphere based on a multilayer perceptron includes: The ionosphere is divided into layers at 100km altitude intervals. The latitude, longitude, solar angle, local time, and seasonal information of the ionospheric puncture point are used as inputs to the electron content distribution model, and the electron content at preset spatial locations and preset times is used as the output of the electron content distribution model to construct the electron content distribution model corresponding to each ionosphere.

5. The method for constructing an ionospheric tomography model according to claim 4, characterized in that, The electron content distribution model for each ionosphere is expressed based on the following formula: in, express Electron content of the layer express The electron content distribution model corresponding to the layer, Indicates the longitude of the ionospheric puncture point. Indicates the latitude of the ionospheric puncture point. Indicates the sun angle. Indicates time-related characteristics, express The set of parameters to be trained for the electron content distribution model corresponding to the layer.

6. The method for constructing an ionospheric tomography model according to claim 1, characterized in that, The process of solving the electron content distribution model for each ionosphere based on the scale factor includes: Using the scale factor as prior knowledge, and with the goal of minimizing the difference between the total electron content predicted by the electron content distribution model for each ionosphere and the total electron content measured by GNSS, a loss function is constructed, and the electron content distribution model for each ionosphere is solved based on the loss function.

7. The method for constructing an ionospheric tomography model according to claim 6, characterized in that, The loss function is expressed based on the following formula: in, This represents the total electron content along the ray path predicted by the electron content distribution model. Represents GNSS observations. The scale factor corresponding to the layer, The electron content distribution model of the layer predicts the electron content along the ray path. These are the weight parameters.

8. A device for constructing an ionospheric tomography model, characterized in that, include: The determination module is used to determine the scale factor based on the proportional relationship between the electron content within a fixed ionospheric height range and the total electron content along the GNSS ray propagation path. The module is used to divide the ionosphere into layers at a fixed height and to build an electron content distribution model for each ionosphere based on a multilayer perceptron. The combination module is used to solve the electron content distribution model corresponding to each ionosphere based on the scale factor, and to combine the solved electron content distribution models of each layer to obtain the ionospheric tomographic imaging model.

9. A modeling device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the method for constructing the ionospheric tomography model according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store computer-readable programs or instructions, which, when executed by a processor, can implement the steps in the method for constructing an ionospheric tomography model as described in any one of claims 1 to 7.

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