Ocean ionized layer data enhancement method, device and equipment and medium

Abstract

The invention discloses an ocean sky ionized layer data enhancement method, device and equipment and a medium, and belongs to the technical field of ionized layer modeling. The method comprises the following steps: acquiring vertical total electron content data of a plurality of positions of an ionized layer over the ocean through observation of a low-orbit satellite; combining the vertical total electron content data of a plurality of positions of the ionosphere over the ocean and the longitude and latitude, observation time and space weather state data of the corresponding ionosphere puncture point as input feature data; inputting the input feature data into a first learning device to obtain enhanced feature data; and inputting the input feature data and the enhanced feature data into a second learning device to obtain vertical total electron content data of the ionized layer above the ocean. According to the invention, data enhancement is carried out on the ionosphere over the ocean through the integrated machine learning model, virtual observation data of the ionosphere in an unobserved area over the ocean can be obtained, and the observation coverage condition of the ionosphere over the ocean is effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of ionospheric modeling, and relates to a method, device, equipment and medium for enhancing low-orbit ionospheric data over the ocean. Background Art

[0002] The ionosphere usually refers to the upper atmosphere of the Earth from about 60 km above the ground to the top of the magnetosphere. It is named as the ionosphere because there are a large number of free electrons and ions formed by ionization by X-rays, ultraviolet rays and high-energy particles in this area. Its activity level is gradually increasing as the current solar activity enters a new round of active cycle. At present, ionospheric delay is one of the important error sources in navigation and positioning. At the same time, the intense ionospheric activity has brought serious impacts on the services and applications of satellite navigation systems. In this context, accurately constructing and predicting the spatiotemporal distribution and changing state of the ionospheric total electron content (TEC) is of great significance to mitigate its impact on the performance of high-precision navigation and positioning services.

[0003] In recent years, thanks to the rapid development of multi-system GNSS and low earth orbit (LEO) satellites, the amount of raw ionospheric observation data has increased significantly, providing a rich data source for global ionospheric TEC modeling. However, existing studies have the problem of missing ocean data in the ionospheric modeling part. Since most ionospheric observation data are obtained by ground-based GNSS, the distribution of ionospheric puncture points (IPPs) in the ocean area is basically blank, which seriously distorts the accuracy of ionospheric modeling in some areas. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method, device, equipment and medium for enhancing low-orbit ionospheric data over the ocean, which can improve the observation coverage of ionospheric data over the ocean and solve the problem of missing data over the ocean in ionospheric modeling.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions: In a first aspect, the present invention provides a method for enhancing ionospheric data over the ocean, comprising: Obtain vertical total electron content data at several locations in the ionosphere over the ocean through low-orbit satellite observations; The vertical total electron content data of several locations in the ionosphere above the ocean, as well as the latitude and longitude of the corresponding ionospheric puncture points, observation time and space weather status data are combined as input feature data; Inputting the input feature data into a first learner to obtain enhanced feature data; Inputting the input feature data and the enhanced feature data into a second learner to obtain vertical total electron content data of the ionosphere above the ocean; The first learner and the second learner are pre-trained machine learning models.

[0006] Furthermore, obtaining the vertical total electron content data of the ionosphere above the ocean includes: gridding the ionosphere above the ocean; and obtaining the vertical total electron content data of each grid of the ionosphere above the ocean.

[0007] Further, the space weather status data includes a solar activity index and a geomagnetic activity index; Wherein, the solar activity index includes the F10.7 index; the geomagnetic activity index includes the Dst index.

[0008] Furthermore, the observation time includes the year, annual accumulated day and local time when the low-orbit satellite observation data is obtained.

[0009] Furthermore, the first learner is a random forest model; and the second learner is an XGBoost model.

[0010] Furthermore, when the first learner and the second learner are trained, historical data of the vertical total electron content of the ionosphere above the ocean are obtained through low-orbit satellite observations as a data set, and the data set is divided into a training set, a validation set and a test set; The test set includes data from one day randomly selected from each month in the dataset and data from N days before and after the geomagnetic storm event within the time range of the dataset; The training set and validation set are divided using the K-fold cross-validation method.

[0011] Furthermore, it also includes: performing statistics on residuals, deviations, mean absolute deviations, root mean square errors and correlation coefficients on the fitting results and true values ​​obtained by the first learner and the second learner through the test set to evaluate the fitting capabilities of the first learner and the second learner.

[0012] In a second aspect, the present invention further provides an ionospheric data enhancement device over the ocean, the device comprising: A vertical total electron content data acquisition module is used to obtain vertical total electron content data at several locations in the ionosphere above the ocean through low-orbit satellite observations; An input feature data merging module is used to merge the vertical total electron content data of several positions in the ionosphere above the ocean, as well as the latitude and longitude, observation time and space weather status data of the corresponding ionosphere puncture points as input feature data; An enhanced feature data acquisition module, used for inputting the input feature data into the first learner to acquire enhanced feature data; A vertical total electron content data enhancement module, used for inputting the input feature data and the enhanced feature data into a second learner to obtain the vertical total electron content data of the ionosphere above the ocean; The first learner and the second learner are pre-trained machine learning models.

[0013] In a third aspect, the present invention further provides a computer device, comprising: Memory for storing computer programs; A processor is used to execute the computer program to implement the steps of the above-mentioned method for enhancing the ionospheric data over the ocean.

[0014] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the steps of the above-mentioned method for enhancing ionospheric data over the ocean.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The method for enhancing ionospheric data over the ocean provided by the present invention performs data enhancement on the ionosphere over the ocean through an integrated learning model, and can obtain virtual ionosphere observation data of unobserved areas over the ocean, effectively improving the ionosphere observation coverage over the ocean, providing more uniform ionosphere observation data for global high-precision and high-resolution ionosphere modeling, and solving the problem of low accuracy of global ionosphere modeling over the ocean to a certain extent; the present invention selects the F10.7 index representing the solar activity index and the Dst index representing the geomagnetic activity index to construct a data set, fully considering the influence of solar activity and geomagnetic activity on the ionosphere, and inserts magnetic storm event data when dividing the data set, thereby improving the accuracy of virtual ionosphere observation data over the ocean during ionospheric disturbance or activity; adopts an integrated learning method, and independently trains the first learner and the second learner to form a stable and effective combined model to obtain a result with high credibility; selects multiple parameter indicators such as residual error, deviation, mean absolute deviation, root mean square error and correlation coefficient to comprehensively evaluate the accuracy and reliability of virtual observation data generation of the model, so as to improve the effect of enhancing ionosphere data over the ocean. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of a flow chart of a method for enhancing ionospheric data over the ocean provided by an embodiment of the present invention; Figure 2 A schematic diagram of a process for constructing an integrated learning model in an embodiment of the present invention; Figure 3 Schematic diagram of input and output of the random forest model and the XGBoost model in an embodiment of the present invention; Figure 4It is a residual statistical distribution diagram of the integrated learning model in the embodiment of the present invention on the test set; Figure 5 It is a distribution diagram of absolute deviations between predicted values ​​and reference true values ​​obtained by the ensemble learning model in an embodiment of the present invention using data of different solar activity conditions as a test set; Figure 6 A schematic diagram of the correlation between the predicted value and the reference true value obtained by the ensemble learning model in an embodiment of the present invention on the test set; Figure 7 It is a statistical time series diagram of the mean absolute deviation and root mean square error of the predicted value and the reference true value obtained by the ensemble learning model in the embodiment of the present invention using the data during two magnetic storms as the test set; Figure 8 is a residual statistical distribution diagram of the ensemble learning model in an embodiment of the present invention on another test set; Fig. 9 A schematic diagram of the correlation between the predicted value and the reference true value obtained by the ensemble learning model in an embodiment of the present invention on another test set; Fig.10 A schematic diagram of the structure of an ionospheric data enhancement device over the ocean provided by an embodiment of the present invention; Fig.11 An internal structure diagram of a computer device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. The same reference numerals in the accompanying drawings indicate the same or similar components or parts. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. The embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0018] The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0019] Example 1

[0020] like Figures 1 to 9 As shown, an embodiment of the present invention provides a method for enhancing ionospheric data over the ocean. Figure 1The flowchart is a schematic diagram of the method for enhancing the ionospheric data over the ocean. This flowchart only shows the logical sequence of the method described in this embodiment. Under the premise of no conflict, in other possible embodiments of the present invention, different methods may be used. Figure 1 The steps shown or described are accomplished in the order shown.

[0021] The method for enhancing ionospheric data over the ocean provided in this embodiment can be applied to a terminal and can be executed by an ionospheric data enhancement device over the ocean. The device can be implemented by software and / or hardware, and the device can be integrated in the terminal.

[0022] See also Figure 1 The method of the embodiment of the present invention specifically includes the following steps: Step 1: Obtain vertical total electron content data at several locations in the ionosphere over the ocean through low-orbit satellite observations.

[0023] The sparse low-orbit satellite data of the ionosphere in the observable ocean area are selected, and the ionospheric observation quantity, namely the vertical total electron content (VTEC) data, is extracted according to the observation methods of different satellites.

[0024] Taking Jason-2 satellite as an example, the satellite uses dual-frequency signals for radar altitude measurement. The main frequency is Ku band (13.575 GHz) and the auxiliary frequency is C band (5.3 GHz). The calculation formula for obtaining vertical electron content data is as follows: , in, for Band ionospheric range correction number; for Band frequency, in GHz.

[0025] Step 2: Combine the vertical total electron content data of several locations in the ionosphere above the ocean, as well as the latitude and longitude, observation time and space weather status data of the corresponding ionospheric puncture points as input feature data.

[0026] Among them, the vertical total electron content data at several locations in the ionosphere above the ocean need to be preprocessed before being used as input features, including removing noise and outliers, performing coordinate transformation to unify the coordinate system, and smoothing or interpolating the data according to the sampling frequency required for analysis.

[0027] Taking the Jason-2 satellite as an example, when selecting Jason-2 observation data with a certain time span and obtaining the vertical total electron content observation value according to the above calculation formula, it is necessary to perform median smoothing on the vertical total electron content data within 10 seconds in the data preprocessing stage to obtain the vertical total electron content observation data over the ocean with a 10s adoption rate.

[0028] The input characteristic data also includes the observation time, location and space weather status data corresponding to when the low-orbit satellite obtains the vertical total electron content data of several positions in the ionosphere above the ocean.

[0029] The observation time includes the year, annual accumulated day and local time when the vertical total electron content data of each location is obtained by low-orbit satellite observation; the location is the longitude and latitude of the ionospheric piercing point corresponding to the time when the vertical total electron content data of each location is obtained; the space weather status data includes the solar activity index and the geomagnetic activity index, among which the solar activity index includes the F10.7 index and the geomagnetic activity index includes the Dst index.

[0030] Therefore, in an embodiment of the present invention, the preprocessed vertical total electron content observation value, data observation time, longitude and latitude of the ionospheric puncture point, F10.7 index and Dst index are combined to form a feature vector, which serves as the input feature data of the first learner and the first learner of the present invention.

[0031] Step 3: Input the input feature data into the first learner to obtain enhanced feature data.

[0032] Step 4: Input the input feature data and enhanced feature data into a second learner to obtain the vertical total electron content data of the ionosphere above the ocean.

[0033] Wherein, obtaining the vertical total electron content data of the ionosphere above the ocean includes: gridding the ionosphere above the ocean; obtaining the vertical total electron content data of each grid of the ionosphere above the ocean. The purpose of the present invention is to use the vertical total electron content data of some grids of the ionosphere above the ocean to complete the vertical total electron content data of the remaining grids based on an integrated learning model, so as to enhance the ionosphere data above the ocean.

[0034] The first learner and the second learner form an integrated learning model. The idea of ​​constructing the integrated learning model in the present invention is as follows: Figure 2 As shown in the figure, it mainly includes three parts: data set construction and division, model training and construction, and result evaluation and verification.

[0035] (1) Construction and division of data sets The present invention obtains the historical data of the vertical total electron content of the ionosphere above the ocean area as a data set through low-orbit satellite observation. The specific acquisition and preprocessing methods of the historical data of the vertical total electron content are as shown in the above steps 1 and 2, which will not be repeated here.

[0036] The constructed data set is divided into three parts: training set, validation set and test set. The training set is used to train the constructed ensemble learning model, the validation set evaluates the performance of the model during the training process and provides feedback to adjust the structure and parameters of the model, and the test set is used to predict the final experimental results.

[0037] The data set division rules are as follows: For the test set, it includes two parts: one part is composed of data randomly selected from one day of the data set every month, and the other part is the data of N days before and after the geomagnetic storm event within the observation time range of the data set. For the training set and validation set, the K-fold cross-validation method is used for division.

[0038] In the embodiment of the present invention, N is set to 5 and K is selected to be 30.

[0039] (2) Model training and construction In the embodiment of the present invention, the first learner uses a random forest model, and the second learner uses an XGBoost model.

[0040] The training set and validation set in the data set are input into the integrated learning model, and the relevant parameters of the model are adjusted according to the output prediction value to obtain a relatively stable ocean observation data enhancement model. The idea of ​​building an integrated model is as follows Figure 3 As shown, the specific implementation steps are as follows: The random forest model is used as the first learner. The year, annual cumulative day, local time, longitude, latitude of the ionospheric vertical total electron content puncture point at each moment, as well as the solar activity index and geomagnetic activity index of the day are input into the random forest model as feature vectors to obtain the predicted value output by the random forest algorithm.

[0041] The XGBoost model is used as the second learner. The predicted value output by the random forest model at each moment in the previous step is used as the enhanced feature data and input into the XGBoost model together with the aforementioned features as the feature vector. The result output after the XGBoost model is the final prediction result. During the training process, the label value is the vertical total electron content calculated by the ocean altimetry satellite data at each moment. The model uses back propagation to adjust the internal parameters of the model according to the residual of the prediction result and the label value.

[0042] Regarding the adjustment and setting of hyperparameters, a grid search algorithm was used in the experiment to find the optimal value for each hyperparameter, and the optimal value of each parameter was determined in turn while fixing other parameters. The final hyperparameters were determined as follows: the parameters of the random forest model were learning_rate 0.02, n_estimators 1000, max_features Auto, and random_state 42. The parameters of the XGBoost model were finally selected as learning_rate 0.05, n_estimators 2000, max_depth 8, and min_child_weight 5.

[0043] After determining the appropriate parameters, a relatively stable integrated learning model is obtained through training. The random forest model has strong generalization ability and noise resistance, and the XGBoost model has fast training speed, high prediction accuracy, and can prevent overfitting. Using these two models can better realize the ionosphere data enhancement over the ocean.

[0044] (3) Result evaluation and verification The trained ensemble learning model is evaluated and verified through the test set, and the data during the magnetic storm period is analyzed.

[0045] In order to analyze the relationship between the data generated by the ensemble learning model fitting of the present invention and the reference true value (vertical total electron content obtained by low-orbit satellite), statistical indicators such as residual, bias, mean absolute deviation (MAD), root mean square error (RMSE) and correlation coefficient (R) of the ensemble learning model on the test set were statistically evaluated.

[0046] Using Jason-2 satellite observations as the test set, the distribution of the statistical residuals between the results generated by model fitting on all test sets and the reference true values ​​is shown in the following figure. Figure 4 As shown in . It can be found that the residual results basically obey the normal distribution and are relatively concentrated, indicating that the enhanced data over the ocean generated by model fitting can reduce the observation error in the ocean area. Further analysis of the absolute deviation of the enhanced results of the ionosphere data over the ocean under different solar activity intensities, the results are as follows Figure 5 As shown, the distribution of the number of absolute deviations is highly correlated with the degree of solar activity, and the distribution of the number size is consistent with the solar activity cycle, which once again shows that the results generated by the model fitting are reliable.

[0047] The Jason-2 satellite observations were used as the test set to evaluate the correlation between the results generated by the ensemble learning model fitting and the reference truth. Figure 6 As shown, it can be seen that the correlation between the model fitting results and the reference true values ​​is high, which further illustrates the stability of the integrated learning model.

[0048] During a magnetic storm, the data generated by the integrated learning model of the present invention requires targeted processing, such as the introduction of an amplification factor. Using Jason-2 satellite observations as the test set, Figure 7 The statistical time series diagram of the mean absolute deviation and root mean square error of the results generated by the integrated learning model fitting during the two magnetic storms is shown. It can be seen that compared with the missing data in the ocean area, the error is controlled within a limited range, and the enhanced data generated by the model is acceptable.

[0049] In order to verify the generalization ability of the integrated model of the present invention, the observation data of the Jason-3 satellite of the same series as the Jason-2 satellite is used as a test set for verification using the integrated learning model. The results are as follows: Figure 8 and Fig. 9 As shown, it can be seen that the residual results also basically obey the normal distribution, and the correlation between the data enhancement results generated by the integrated model and the reference true value is high, indicating that the data enhancement model of the present invention has a strong generalization ability.

[0050] Example 2

[0051] Based on the same inventive concept as Example 1, the embodiment of the present invention further provides an ionospheric data enhancement device for implementing the above-mentioned ionospheric data enhancement method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific definition in the embodiment of the ionospheric data enhancement device above the ocean provided below can refer to the definition of the ionospheric data enhancement method above the ocean, and will not be repeated here.

[0052] like Fig.10 As shown, an embodiment of the present invention provides an ionospheric data enhancement device over the ocean, comprising: A vertical total electron content data acquisition module is used to obtain vertical total electron content data at several locations in the ionosphere above the ocean through low-orbit satellite observations; An input feature data merging module is used to merge the vertical total electron content data of several positions in the ionosphere above the ocean, as well as the latitude and longitude, observation time and space weather status data of the corresponding ionosphere puncture points as input feature data; An enhanced feature data acquisition module, used for inputting the input feature data into the first learner to acquire enhanced feature data; A vertical total electron content data enhancement module, used for inputting the input feature data and the enhanced feature data into a second learner to obtain the vertical total electron content data of the ionosphere above the ocean; The first learner and the second learner are pre-trained machine learning models.

[0053] Example 3

[0054] The embodiment of the present invention further provides a computer device, which may be a server, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the steps of the method for enhancing ionospheric data over the ocean in the aforementioned embodiment are implemented.

[0055] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0056] Example 4

[0057] The embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the following method are implemented: Obtain vertical total electron content data at several locations in the ionosphere over the ocean through low-orbit satellite observations; The vertical total electron content data of several locations in the ionosphere above the ocean, as well as the latitude and longitude of the corresponding ionospheric puncture points, observation time and space weather status data are combined as input feature data; Inputting the input feature data into a first learner to obtain enhanced feature data; Inputting the input feature data and the enhanced feature data into a second learner to obtain vertical total electron content data of the ionosphere above the ocean; The first learner and the second learner are pre-trained machine learning models.

[0058] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems, or computer program products, and therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0062] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A method for enhancing ionospheric data over the ocean, characterized in that: include: Obtain vertical total electron content data at several locations in the ionosphere over the ocean through low-orbit satellite observations; The vertical total electron content data of several locations in the ionosphere above the ocean, as well as the latitude and longitude of the corresponding ionospheric puncture points, observation time and space weather status data are combined as input feature data; Inputting the input feature data into a first learner to obtain enhanced feature data; Inputting the input feature data and the enhanced feature data into a second learner to obtain vertical total electron content data of the ionosphere above the ocean; The first learner and the second learner are pre-trained machine learning models.

2. The method for enhancing ionospheric data over the ocean according to claim 1, characterized in that: Obtaining the vertical total electron content data of the ionosphere above the ocean, including: gridding the ionosphere above the ocean; and obtaining the vertical total electron content data of each grid of the ionosphere above the ocean.

3. The method for enhancing ionospheric data over the ocean according to claim 1, characterized in that: The space weather status data includes a solar activity index and a geomagnetic activity index; Wherein, the solar activity index includes the F10.7 index; the geomagnetic activity index includes the Dst index.

4. The method for enhancing ionospheric data over the ocean according to claim 1, characterized in that: The observation time includes the year, annual accumulated day and local time when the low-orbit satellite observation data is obtained.

5. The method for enhancing ionospheric data over the ocean according to claim 1, characterized in that: The first learner is a random forest model; the second learner is an XGBoost model.

6. The method for enhancing ionospheric data over the ocean according to claim 1 or 5, characterized in that: When the first learner and the second learner are trained, historical data of vertical total electron content of the ionosphere above the ocean are obtained through low-orbit satellite observations as a data set, and the data set is divided into a training set, a validation set and a test set; The test set includes data from one day randomly selected from each month in the dataset and data from N days before and after the geomagnetic storm event within the time range of the dataset; The training set and validation set are divided using the K-fold cross-validation method.

7. The method for enhancing ionospheric data over the ocean according to claim 6, characterized in that: Also includes: The fitting results and true values ​​obtained by the first learner and the second learner through the test set are statistically analyzed for residuals, deviations, mean absolute deviations, root mean square errors and correlation coefficients to evaluate the fitting capabilities of the first learner and the second learner.

8. A low-orbit ionospheric data enhancement device over the ocean, characterized in that: include: A vertical total electron content data acquisition module is used to obtain vertical total electron content data at several locations in the ionosphere above the ocean through low-orbit satellite observations; An input feature data merging module is used to merge the vertical total electron content data of several positions in the ionosphere above the ocean, as well as the latitude and longitude, observation time and space weather status data of the corresponding ionosphere puncture points as input feature data; An enhanced feature data acquisition module, used for inputting the input feature data into the first learner to acquire enhanced feature data; A vertical total electron content data enhancement module, used for inputting the input feature data and the enhanced feature data into a second learner to obtain the vertical total electron content data of the ionosphere above the ocean; The first learner and the second learner are pre-trained machine learning models.

9. A computer device, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the computer program to implement the steps of the method for enhancing low-orbit ionospheric data over the ocean as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method for enhancing low-orbit ionospheric data over the ocean described in any one of claims 1 to 7 are implemented.

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