A method and system for constructing a global ionospheric model through multi-source data fusion

Abstract

This invention discloses a method for constructing a global ionospheric model based on multi-source data fusion, comprising: acquiring GNSS data and TEC data from an ocean altimeter satellite and the Doris system, respectively; constructing a first global ionospheric model based on the GNSS data; dividing the first global ionospheric model into regions based on a K-means clustering algorithm, and determining the systematic deviation between the GNSS system and the ocean altimeter satellite in each region; integrating the systematic deviation and the TEC data from the ocean altimeter satellite into the first global ionospheric model to construct a second global ionospheric model; correcting the TEC data from the Doris system using the second global ionospheric model, and integrating the corrected TEC data from the Doris system into the second global ionospheric model to construct a third global ionospheric model; weighting each data source based on its data volume and accuracy, and introducing the weighted weights into the third global ionospheric model to construct a global ionospheric model based on multi-source data fusion.

Description

Technical Field

[0001] This invention relates to the field of ionospheric modeling, and in particular to a method for constructing a global ionospheric model based on multi-source data fusion using a novel system bias estimation and weighting approach. Background Technology

[0002] The ionosphere is a crucial component of space weather, and its irregular disturbances can severely impact communication systems, positioning accuracy, and power infrastructure. As a vital parameter characterizing ionospheric dynamics and various space weather activities, accurate determination of the ionospheric TEC (Temperature Temperature Coefficient) is essential for space weather monitoring and various scientific applications.

[0003] The rapid development of GNSS technology has made it possible to acquire TEC data with wider coverage and longer observation times. However, the distribution of GNSS receivers globally is uneven. In the Southern Hemisphere and most ocean regions, the coverage of GNSS receivers is very limited, resulting in poor accuracy and reliability of traditional global ionospheric models based solely on GNSS data in areas with sparse tracking stations, such as the ocean. To improve the accuracy of ionospheric models in areas lacking GNSS data, such as the ocean, it is necessary to construct a multi-source data fusion global ionospheric model by combining multiple ionospheric observation methods to enhance its accuracy and reliability in ocean regions.

[0004] Ocean altimeter satellites cover most of the ocean region, and their onboard dual-frequency radar altimeters can acquire ionospheric TEC information along their nadir points. Furthermore, compared to GNSS receivers, Doris system beacon stations are easier to deploy on islands. Therefore, constructing a multi-source data fusion global ionospheric model by combining ocean altimeter satellite ionospheric data, Doris system ionospheric data, and GNSS ionospheric data can effectively improve the model's accuracy and reliability in ocean regions.

[0005] Due to differences in satellite orbital altitudes, there is often a systematic bias between the ionospheric TEC (Electro-Conductivity Rate) obtained by ocean altimeter satellites and the relative TEC obtained by the Doris system and the TEC obtained by GNSS. The latter can be corrected using global ionospheric maps, while the former is typically used as an unknown parameter in the ionospheric modeling process. Previous studies have often treated the systematic bias between ocean altimeter satellite TEC and GNSS TEC globally as a constant within a given calculation period. However, ionospheric electron density is not uniformly distributed globally, and this bias estimation method increases estimation errors, weakens the usability of ocean altimeter data, and causes a decrease in accuracy. Furthermore, when constructing a global ionospheric model through multi-source data fusion, variance component estimation is commonly used to weight data from different data sources. However, there are often significant differences in the amount of data from different data sources. For example, GNSS generates tens of millions of data points per day, while ocean altimeter satellites and the Doris system typically only generate around hundreds of thousands. The variance component weighting method only considers the accuracy of each data source while ignoring the huge differences in data volume between them, failing to fully utilize the data from ocean altimeters and the Doris system, resulting in limited accuracy improvement in ocean regions.

[0006] The terms used in this invention specification have the following meanings:

[0007] GNSS; Global Navigation Satellite System.

[0008] TEC: Total Electron Content;

[0009] Doris: Doppler Orbitography and Radio Positioning Integrated by Satellite. Summary of the Invention

[0010] To address the issue of limited model accuracy due to systematic bias when constructing a global ionospheric model using multiple ionospheric observation methods and fused data from various sources, this invention provides a method and system for constructing a global ionospheric model using fused data from multiple sources. By employing a novel systematic bias estimation and weighting method to fuse GNSS, ocean altimeter satellite, and Doris data, the accuracy of the global ionospheric model constructed from fused data from multiple sources is improved.

[0011] According to one aspect of the present invention, a method for constructing a global ionospheric model based on multi-source data fusion is provided, comprising:

[0012] Acquire GNSS data, as well as TEC data from marine altimetry satellites and the Doris system;

[0013] Based on the GNSS data, a first global ionospheric model is constructed;

[0014] The first global ionospheric model is divided into regions based on the K-means clustering algorithm, and the systematic deviation between the GNSS system and the marine altimetry satellite is determined in each region.

[0015] The systematic bias and TEC data from ocean altimetry satellites are incorporated into the first global ionospheric model to construct the second global ionospheric model;

[0016] The TEC data of the Doris system were corrected using the second global ionospheric model, and the corrected TEC data of the Doris system were integrated into the second global ionospheric model to construct a third global ionospheric model.

[0017] By combining the data volume and accuracy of each data source, weights are assigned to each data source, and the weighted weights are then introduced into the third global ionospheric model to construct a global ionospheric model that integrates multi-source data.

[0018] As a further technical solution, based on the aforementioned GNSS data, a first global ionospheric model is constructed, including:

[0019] A global ionospheric model with a preset time resolution is constructed based on GNSS data and spherical harmonic expansion, serving as the first global ionospheric model.

[0020] As a further technical solution, the first global ionospheric model is divided into regions based on the K-means clustering algorithm, and the systematic deviations between the GNSS system and marine altimetry satellites are determined in each region, including:

[0021] The first global ionospheric model was divided into multiple regions based on the K-means clustering algorithm, and the region to which the nadir point trajectory of the ocean altimeter satellite belonged was determined.

[0022] The difference between TEC data and GNSS data from marine altimetry satellites belonging to the same area within a preset time period will be treated as a systematic bias unknown.

[0023] As a further technical solution, weighting of each data source is performed based on the data volume and accuracy of each data source, including:

[0024] Based on the third global ionosphere model, the variance component estimation is used to weight each data source, and the weighting result is used as the accuracy factor among the data sources.

[0025] The grid coverage and total data volume of each data source are statistically analyzed, and the average data volume of each grid point within the coverage area of ​​each data source is calculated. The ratio between GNSS data and the average data volume of each data source grid point is used as a quantity factor.

[0026] The product of the precision factor and the quantity factor is used as the weight of each data source.

[0027] According to one aspect of the present invention, a global ionospheric model construction system based on multi-source data fusion is provided, comprising:

[0028] The first main module is used to acquire GNSS data and TEC data from marine altimetry satellites and the Doris system, respectively.

[0029] The second main module is used to construct a first global ionospheric model based on the GNSS data;

[0030] The third main module is used to divide the first global ionospheric model into regions based on the K-means clustering algorithm, and to determine the systematic deviation between the GNSS system and the marine altimetry satellite in each region.

[0031] The fourth main module is used to integrate the systematic bias and TEC data from the ocean altimeter satellite into the first global ionospheric model to construct the second global ionospheric model;

[0032] The fifth main module is used to correct the TEC data of the Doris system using the second global ionospheric model, and to integrate the corrected TEC data of the Doris system into the second global ionospheric model to construct the third global ionospheric model.

[0033] The sixth main module is used to weight each data source based on its data volume and accuracy, and then introduce the weighted data into the third global ionospheric model to construct a global ionospheric model that integrates multi-source data.

[0034] As a further technical solution, the second main module is also used to execute the following instructions:

[0035] A global ionospheric model with a preset time resolution is constructed based on GNSS data and spherical harmonic expansion, serving as the first global ionospheric model.

[0036] As a further technical solution, the third main module is also used to execute the following instructions:

[0037] The first global ionospheric model was divided into multiple regions based on the K-means clustering algorithm, and the region to which the nadir point trajectory of the ocean altimeter satellite belonged was determined.

[0038] The difference between TEC data and GNSS data from marine altimetry satellites belonging to the same area within a preset time period will be treated as a systematic bias unknown.

[0039] As a further technical solution, the sixth main module is also used to execute the following instructions:

[0040] Based on the third global ionosphere model, the variance component estimation is used to weight each data source, and the weighting result is used as the accuracy factor among the data sources.

[0041] The grid coverage and total data volume of each data source are statistically analyzed, and the average data volume of each grid point within the coverage area of ​​each data source is calculated. The ratio between GNSS data and the average data volume of each data source grid point is used as a quantity factor.

[0042] The product of the precision factor and the quantity factor is used as the weight of each data source.

[0043] According to one aspect of the present invention, a global ionospheric model construction device based on multi-source data fusion is provided, comprising a memory and a processor, wherein the memory stores program instructions that are executed by the processor, and the processor invokes the program instructions to execute the global ionospheric model construction method based on multi-source data fusion.

[0044] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, the non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the aforementioned method for constructing a global ionospheric model based on multi-source data fusion.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] This invention integrates ionospheric observations from GNSS, ocean altimeter satellites, and the Doris system for joint modeling. First, it proposes a system bias estimation method based on k-means clustering to estimate the systematic bias between GNSS and ocean altimeter satellite data. By estimating the systematic bias by region, the accuracy of the estimation is improved, enhancing the usability of ocean altimeter satellite data. Second, this invention proposes a weighting method that comprehensively considers the data volume and accuracy of each data source, increasing the role of ocean altimeter satellite and Doris system data in the modeling process and further improving the model's accuracy in ocean regions. Finally, this invention utilizes spherical harmonic expansion to construct a global ionospheric model. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of a method for constructing a global ionospheric model based on multi-source data fusion, provided in an embodiment of the present invention.

[0049] Figure 2 This is a schematic diagram of a global ionospheric model construction system based on multi-source data fusion, provided in an embodiment of the present invention. Detailed Implementation

[0050] The terms “comprising” and “having”, and any variations thereof, in the specification, claims, and accompanying drawings of this invention are intended to cover a non-exclusive inclusion, such as a process, method, system, product, or apparatus that includes a series of steps or units, not necessarily limited to those explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. In addition, the technical features of the various embodiments or individual embodiments provided by the present invention can be arbitrarily combined to form new technical solutions. Such combinations are not bound by the order of steps and / or structural composition patterns, but must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0052] To improve the accuracy of global ionospheric models fused from multi-source data, this invention provides a novel system bias estimation and weighting method to fuse GNSS, ocean altimeter, and Doris data, thereby constructing a global ionospheric model.

[0053] Please see Figure 1 The method for constructing a global ionospheric model based on multi-source data fusion described in this invention mainly includes the following steps:

[0054] Step 1: Acquire GNSS data, marine altimeter satellite data, and Doris data. Here, marine altimeter satellite data refers to the TEC data from the marine altimeter satellite, and Doris data refers to the TEC data from the Doris system.

[0055] Step 2: Construct a first global ionospheric model with a time resolution of 1 hour based on GNSS data and spherical harmonic expansion.

[0056] It should be noted that, during the construction process of this invention, models based solely on GNSS data, models fusing GNSS data and marine altimeter satellite data, and models fusing GNSS data, marine altimeter satellite data, and Doris data were respectively developed. For ease of distinction, these can be described as a first global ionospheric model, a second global ionospheric model, and a third global ionospheric model. The terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first," "second," and "third" may explicitly or implicitly include one or more of that feature.

[0057] Step 3: Based on the k-means clustering algorithm, the first global ionospheric model constructed in Step 2 is divided into three regions, and the region to which the nadir point trajectory of the marine altimeter satellite belongs is determined. Then, the difference between the TEC data and GNSS data of the marine altimeter satellite belonging to the same region within one hour is taken as a systematic bias unknown.

[0058] It should be noted that after dividing the first global ionospheric model constructed in step 2 into 3 types of regions, it can be regarded as having 3 systematic deviations every hour. These three systematic deviations are all systematic deviations between GNSS and the marine altimeter satellite TEC. The difference is that these 3 systematic deviations may not be consistent. Therefore, it is more accurate to estimate them by dividing them into 3 unknowns. That is, the estimation accuracy of systematic deviations is improved by estimating systematic deviations by region.

[0059] Step 4: Based on Step 2, incorporate the TEC data from the ocean altimeter satellite and the systematic bias unknowns obtained in Step 3 to construct a second global ionospheric model. This second global ionospheric model is a global ionospheric model that integrates GNSS data and ocean altimeter satellite data.

[0060] Step 5: Use the second global ionospheric model obtained in Step 4 to correct the TEC data of the Doris system, and obtain the corrected TEC data of the Doris system.

[0061] Step 6: Based on Step 4, incorporate the corrected Doris system TEC data obtained in Step 5 to construct the third global ionospheric model. This third global ionospheric model is a global ionospheric model that integrates GNSS data, ocean altimeter satellite data, and Doris data.

[0062] Step 7: Based on Step 6, weights are assigned to each data source using variance component estimation, and the weighting results are used as precision factors among the data sources. The grid coverage and total data volume of each data source are statistically analyzed, and the average data volume per grid point within the coverage area of ​​each data source is calculated. The ratio between GNSS and the average data volume per grid point of each data source is then used as a quantity factor. Finally, the product of the precision factor and the quantity factor is used as the weight of each data source.

[0063] Step 8: Based on Step 7, substitute the newly obtained weights into Step 6 to determine the weights of each data source in order to construct a global ionospheric model with multi-source data fusion.

[0064] When using the method of the present invention, GNSS data, marine altimetry satellite data and Doris data from any day can be downloaded and substituted into the above process to obtain a global ionospheric model fused from multiple sources.

[0065] The implementation of the various embodiments of the present invention is based on programmed processing through a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of the present invention are encapsulated into various modules. Based on this reality, and building upon the above embodiments, the embodiments of the present invention provide a global ionospheric model construction system based on multi-source data fusion. This system is used to execute a global ionospheric model construction method based on multi-source data fusion from the above method embodiments.

[0066] See Figure 2The system comprises: a first main module for acquiring GNSS data and TEC data from the marine altimeter satellite and the Doris system, respectively; a second main module for constructing a first global ionospheric model based on the GNSS data; a third main module for dividing the first global ionospheric model into regions based on the K-means clustering algorithm, and determining the systematic deviation between the GNSS system and the marine altimeter satellite in each region; a fourth main module for integrating the systematic deviation and the TEC data from the marine altimeter satellite into the first global ionospheric model to construct a second global ionospheric model; a fifth main module for correcting the TEC data from the Doris system using the second global ionospheric model, and integrating the corrected TEC data from the Doris system into the second global ionospheric model to construct a third global ionospheric model; and a sixth main module for weighting each data source based on its data volume and accuracy, and introducing the weighted data into the third global ionospheric model to construct a multi-source data fusion global ionospheric model.

[0067] This invention provides a multi-source data fusion global ionospheric model construction system, addressing the problem of limited model accuracy due to systematic biases when constructing multi-source data fusion global ionospheric models using multiple ionospheric observation methods. Figure 2 Several modules in the model are integrated with GNSS, ocean altimeter and Doris data through new system bias estimation and weighting methods to construct a global ionospheric model, which improves the accuracy of the global ionospheric model based on multi-source data fusion.

[0068] It should be noted that the system embodiments provided by this invention, in addition to implementing the methods in the above method embodiments, are also used to implement the methods in other method embodiments provided by this invention. The difference lies only in setting corresponding functional modules, and their principles are basically the same as those of the above system embodiments provided by this invention. As long as those skilled in the art, based on the above system embodiments and referring to the specific technical solutions in other method embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and improve the modules in the above system embodiments while ensuring the practicality of the technical solutions, they can obtain corresponding system-like embodiments for implementing the methods in other method-like embodiments. For example:

[0069] Based on the above system embodiments, as a preferred embodiment, the global ionospheric model construction system for multi-source data fusion provided in this embodiment of the invention further includes a second main module configured to execute the following instructions:

[0070] A global ionospheric model with a preset time resolution is constructed based on GNSS data and spherical harmonic expansion, serving as the first global ionospheric model.

[0071] Based on the above system embodiments, as a preferred embodiment, the global ionospheric model construction system for multi-source data fusion provided in this embodiment of the invention, wherein the third main module is further configured to execute the following instructions:

[0072] The first global ionospheric model was divided into multiple regions based on the K-means clustering algorithm, and the region to which the nadir point trajectory of the ocean altimeter satellite belonged was determined.

[0073] The difference between TEC data and GNSS data from marine altimetry satellites belonging to the same area within a preset time period will be treated as a systematic bias unknown.

[0074] Based on the above system embodiments, as a preferred embodiment, the global ionospheric model construction system for multi-source data fusion provided in this embodiment of the invention, wherein the sixth main module is further configured to execute the following instructions:

[0075] Based on the third global ionosphere model, the variance component estimation is used to weight each data source, and the weighting result is used as the accuracy factor among the data sources.

[0076] The grid coverage and total data volume of each data source are statistically analyzed, and the average data volume of each grid point within the coverage area of ​​each data source is calculated. The ratio between GNSS data and the average data volume of each data source grid point is used as a quantity factor.

[0077] The product of the precision factor and the quantity factor is used as the weight of each data source.

[0078] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides a global ionospheric model construction device for multi-source data fusion, including a memory and a processor. The memory stores program instructions that are executed by the processor, and the processor calls the program instructions to execute the global ionospheric model construction method for multi-source data fusion.

[0079] In embodiments of the present invention, the memory can be non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or it can be volatile memory, such as random-access memory (RAM). Memory is any other medium capable of carrying or storing desired program code having an instruction or data structure form and accessible by a computer, but is not limited thereto. The memory in embodiments of the present invention can also be a circuit or any other device capable of implementing a storage function for storing program instructions and / or data.

[0080] In this embodiment of the invention, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in this embodiment of the invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in this embodiment of the invention can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0081] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides a non-transitory computer-readable storage medium storing computer instructions that cause the computer to execute the multi-source data fusion global ionospheric model construction method, including the following steps:

[0082] Acquire GNSS data, as well as TEC data from marine altimetry satellites and the Doris system;

[0083] Based on the GNSS data, a first global ionospheric model is constructed;

[0084] The first global ionospheric model is divided into regions based on the K-means clustering algorithm, and the systematic deviation between the GNSS system and the marine altimetry satellite is determined in each region.

[0085] The systematic bias and TEC data from ocean altimetry satellites are incorporated into the first global ionospheric model to construct the second global ionospheric model;

[0086] The TEC data of the Doris system were corrected using the second global ionospheric model, and the corrected TEC data of the Doris system were integrated into the second global ionospheric model to construct a third global ionospheric model.

[0087] By combining the data volume and accuracy of each data source, weights are assigned to each data source, and the weighted weights are then introduced into the third global ionospheric model to construct a global ionospheric model that integrates multi-source data.

[0088] In summary, this invention uses a novel system bias estimation and weighting method to fuse GNSS, ocean altimeter, and Doris data, thereby constructing a global ionospheric model and improving the accuracy of the global ionospheric model fused from multi-source data.

[0089] Unless otherwise specified, the above technologies are all well-known in the field.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a global ionospheric model through multi-source data fusion, characterized in that, include: Acquire GNSS data, as well as TEC data from marine altimetry satellites and the Doris system; Based on the GNSS data, a first global ionospheric model is constructed; The first global ionospheric model is divided into regions based on the K-means clustering algorithm, and the systematic deviation between the GNSS system and the marine altimetry satellite is determined in each region. The systematic bias and TEC data from ocean altimetry satellites are incorporated into the first global ionospheric model to construct the second global ionospheric model; The TEC data of the Doris system were corrected using the second global ionospheric model, and the corrected TEC data of the Doris system were integrated into the second global ionospheric model to construct a third global ionospheric model. By combining the data volume and accuracy of each data source, weights are assigned to each data source, and the weighted weights are then introduced into the third global ionospheric model to construct a global ionospheric model that integrates multi-source data.

2. The method for constructing a global ionospheric model based on multi-source data fusion according to claim 1, characterized in that, Based on the GNSS data, a first global ionospheric model is constructed, including: A global ionospheric model with a preset time resolution is constructed based on GNSS data and spherical harmonic expansion, serving as the first global ionospheric model.

3. The method for constructing a global ionospheric model based on multi-source data fusion according to claim 1, characterized in that, The first global ionospheric model is divided into regions based on the K-means clustering algorithm, and the systematic deviations between the GNSS system and marine altimetry satellites are determined in each region, including: The first global ionospheric model was divided into multiple regions based on the K-means clustering algorithm, and the region to which the nadir point trajectory of the ocean altimeter satellite belonged was determined. The difference between TEC data and GNSS data from marine altimetry satellites belonging to the same area within a preset time period will be treated as a systematic bias unknown.

4. The method for constructing a global ionospheric model based on multi-source data fusion according to claim 1, characterized in that, Weighting of each data source is determined based on its data volume and accuracy, including: Based on the third global ionosphere model, the variance component estimation is used to weight each data source, and the weighting result is used as the accuracy factor among the data sources. The grid coverage and total data volume of each data source are statistically analyzed, and the average data volume of each grid point within the coverage area of ​​each data source is calculated. The ratio between GNSS data and the average data volume of each data source grid point is used as a quantity factor. The product of the precision factor and the quantity factor is used as the weight of each data source.

5. A global ionospheric model construction system based on multi-source data fusion, characterized in that, include: The first main module is used to acquire GNSS data and TEC data from marine altimetry satellites and the Doris system, respectively. The second main module is used to construct a first global ionospheric model based on the GNSS data; The third main module is used to divide the first global ionospheric model into regions based on the K-means clustering algorithm, and to determine the systematic deviation between the GNSS system and the marine altimetry satellite in each region. The fourth main module is used to integrate the systematic bias and TEC data from the ocean altimeter satellite into the first global ionospheric model to construct the second global ionospheric model; The fifth main module is used to correct the TEC data of the Doris system using the second global ionospheric model, and to integrate the corrected TEC data of the Doris system into the second global ionospheric model to construct the third global ionospheric model. The sixth main module is used to weight each data source based on its data volume and accuracy, and then introduce the weighted data into the third global ionospheric model to construct a global ionospheric model that integrates multi-source data.

6. The global ionospheric model construction system based on multi-source data fusion according to claim 5, characterized in that, The second main module is also used to execute the following instructions: A global ionospheric model with a preset time resolution is constructed based on GNSS data and spherical harmonic expansion, serving as the first global ionospheric model.

7. The global ionospheric model construction system based on multi-source data fusion according to claim 5, characterized in that, The third main module is also used to execute the following instructions: The first global ionospheric model was divided into multiple regions based on the K-means clustering algorithm, and the region to which the nadir point trajectory of the ocean altimeter satellite belonged was determined. The difference between TEC data and GNSS data from marine altimetry satellites belonging to the same area within a preset time period will be treated as a systematic bias unknown.

8. The global ionospheric model construction system based on multi-source data fusion according to claim 5, characterized in that, The sixth main module is also used to execute the following instructions: Based on the third global ionosphere model, the variance component estimation is used to weight each data source, and the weighting result is used as the accuracy factor among the data sources. The grid coverage and total data volume of each data source are statistically analyzed, and the average data volume of each grid point within the coverage area of ​​each data source is calculated. The ratio between GNSS data and the average data volume of each data source grid point is used as a quantity factor. The product of the precision factor and the quantity factor is used as the weight of each data source.

9. A device for constructing a global ionospheric model through multi-source data fusion, characterized in that, The system includes a memory and a processor, the memory storing program instructions that are executed by the processor, the processor invoking the program instructions to execute the global ionospheric model construction method for multi-source data fusion as described in any one of claims 1 to 4.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions that cause the computer to execute the global ionospheric model construction method of multi-source data fusion as described in any one of claims 1 to 4.

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