Ionized layer disturbance area generation method and device, equipment and storage medium

Abstract

The embodiment of the invention discloses an ionosphere disturbance area generation method and device, equipment and a storage medium. The method comprises the steps of collecting first observation data of an ionosphere observed by high-frequency equipment within a preset time period and second observation data of the ionosphere observed by a common GNSS (Global Navigation Satellite System) receiver; using an ROTI index determination method to extract first target observation data of an ionosphere disturbance area in a target time period when the ionosphere is disturbed from first observation data observed by high-frequency equipment; and performing model training according to the first target observation data and the second observation data of the ionized layer disturbance region in the target time period to generate a high-frequency data generation model. The high-frequency observation data and the low-frequency observation data of the same ionosphere disturbance area are used for model training, the generated high-frequency data generation model can generate the high-frequency observation data of the ionosphere disturbance area according to the low-frequency observation data, and the high-frequency observation data are obtained while the arrangement cost of a receiver is reduced.

Description

Technical Field

[0001] The present invention relates to the field of ionospheric disturbance technology, and in particular to a method and apparatus for generating an ionospheric disturbance region, a device, equipment and a storage medium. Background Art

[0002] The ionosphere, a critical region of the Earth's atmosphere, lies at an altitude of approximately 60 to 1000 kilometers and has a profound impact on human life. By reflecting and refracting radio waves, it plays a vital role in communications, navigation, and positioning. In the Global Navigation Satellite System (GNSS), in particular, the total electron content (TEC) of the ionosphere is one of the main factors affecting positioning accuracy, often contributing more than 50% of the total GNSS error. Therefore, the stability of the ionosphere plays a crucial role in GNSS positioning accuracy.

[0003] Ionospheric disturbances, including traveling ionospheric disturbances (TIDs) and equatorial plasma bubbles (EPBs), are common irregularities in the ionosphere. These disturbances occur frequently and have serious impacts on satellite navigation and positioning, radio communications, and space operations. These anomalous disturbances can cause rapid changes in the amplitude and phase of radio signals, impacting the safety and functionality of GNSS systems and, in extreme cases, potentially disrupting satellite communications.

[0004] In current research, existing ionospheric disturbance monitoring and early warning methods are limited by the low detection frequency, sparse distribution of high-frequency receivers and high cost, which leads to limitations in existing monitoring methods and low accuracy of monitoring results. Summary of the Invention

[0005] Based on this, it is necessary to propose a method and device, equipment and storage medium for generating ionospheric disturbance areas to address the above problems in order to reduce monitoring costs and improve monitoring accuracy.

[0006] To achieve the above-mentioned objectives, the present application provides, in a first aspect, a method for generating an ionospheric disturbance region, the method comprising:

[0007] Collecting first observation data observed by a high-frequency device and second observation data observed by a common GNSS receiver within a preset time period;

[0008] extracting first target observation data of an ionospheric disturbance region within a target time period in which the ionospheric disturbance occurs from the first observation data observed by the high-frequency device using a ROTI index determination method;

[0009] Model training is performed based on the first target observation data and the second observation data of the ionospheric disturbance area within the target time period to generate a high-frequency data generation model, so that the low-frequency observation data observed by the ordinary GNSS receiver is input into the high-frequency data generation model to obtain the high-frequency observation data of the ionospheric disturbance area output by the high-frequency data generation model.

[0010] Furthermore, performing model training based on the first target observation data and the second observation data of the ionospheric disturbance region within the target time period to generate a high-frequency data generation model specifically includes:

[0011] Using the target time period and the ionospheric disturbance region within the target time period as screening conditions;

[0012] Filtering second target observation data that meets the filtering condition from the second observation data observed by the ordinary GNSS receiver;

[0013] Model training is performed based on the first target observation data and the second target observation data to generate the high-frequency data generation model.

[0014] Furthermore, performing model training based on the first target observation data and the second target observation data to generate the high-frequency data generation model specifically includes:

[0015] resampling observation data of the ionospheric disturbance region within the target time period based on the first target observation data to obtain a first two-dimensional image set, wherein the first two-dimensional image set includes two-dimensional images of the ionospheric disturbance region corresponding to different times within the target time period, and the two-dimensional images show the degree of disturbance of the ionospheric disturbance region;

[0016] resampling the observation data of the ionospheric disturbance region within the target time period based on the second target observation data to obtain a second two-dimensional image set, wherein the second two-dimensional image set includes two-dimensional images of the ionospheric disturbance region corresponding to different times within the target time period;

[0017] Using the two-dimensional images in the first two-dimensional image set to annotate the two-dimensional images in the second two-dimensional image set to generate a training sample set;

[0018] The training sample set is input into a preset generative adversarial network for training to obtain the high-frequency data generation model.

[0019] Furthermore, resampling the observation data of the ionospheric disturbance region within the target time period based on the first target observation data to obtain a first two-dimensional picture set specifically includes:

[0020] Divide the ionosphere into grids according to a preset resolution to obtain a grid distribution of the ionosphere, where each grid corresponds to a pixel in the two-dimensional image;

[0021] Acquire a plurality of target grids corresponding to the ionospheric disturbance region at a target time, wherein the target time is any time within the target time period;

[0022] Calculating the total electron content of each target grid according to the first target observation data;

[0023] A first two-dimensional target image corresponding to the target moment is generated based on the total electron content of each target grid, and the first two-dimensional target images at different moments constitute a first two-dimensional image set.

[0024] Furthermore, calculating the total electron content of each target grid according to the first target observation data specifically includes:

[0025] Calculating the total electron content of each observation point in the ionosphere according to the first target observation data;

[0026] Acquire a number of target observation points adjacent to the target grid;

[0027] Calculate the weight of each target observation point based on the position information of the target observation point based on the Kriging method;

[0028] The total electron content of the target grid is calculated according to the weights of the target observation points and the total electron content.

[0029] Furthermore, the extracting of first target observation data of the ionospheric disturbance region within the target time period in which the ionospheric disturbance occurs from the first observation data observed by the high-frequency device using the ROTI index determination method specifically includes:

[0030] Obtaining a basic observation equation of pseudorange and phase of the first observation data;

[0031] Using the precise satellite clock error products released by IGS, the basic observation equation is delayed corrected to obtain the corrected target observation equation;

[0032] Solving the target observation equation based on a non-differenced non-combined PPP model to obtain a first actual observation value of the ionosphere observed by the high-frequency device;

[0033] The ROTI index determination method is used to extract first target observation data of the ionospheric disturbance area within the target time period in which the ionospheric disturbance occurs from the first actual observation value observed by the high-frequency device.

[0034] Furthermore, the first observation data includes observation values ​​formed when different high-frequency receivers receive signals sent by different satellites;

[0035] The modified target observation equation is expressed by the following formula:

[0036]

[0037] in, They represent the pseudorange and phase observation values ​​of satellite s observed by receiver r at the i-th frequency; represents the distance from the receiver r to the satellite s; c represents the speed of light in a vacuum, and represents the corrected clock difference between the receiver r and the satellite s; represents the ionospheric delay on the observation path from the receiver r to the satellite s, including the hardware bias of the receiver r and the satellite s; represents the observation noise; They represent floating-point ambiguities including satellite and receiver phase hardware delay biases respectively; represent the floating point ambiguities without satellite and receiver phase hardware delay bias respectively; represents the unmodeled error; γ is the proportionality coefficient.

[0038] To achieve the above-mentioned purpose, the second aspect of the present application provides a device for generating an ionospheric disturbance region, the device comprising: a data acquisition module, a data processing module and a data generation module;

[0039] The data acquisition module is configured to collect first observation data observed by the high-frequency device and second observation data observed by the common GNSS receiver within a preset time period;

[0040] The data processing module is configured to extract first target observation data of an ionospheric disturbance region within a target time period in which the ionospheric disturbance occurs from the first observation data observed by the high-frequency device using a ROTI index determination method;

[0041] The data generation module is configured to perform model training based on the first target observation data and the second observation data of the ionospheric disturbance region within the target time period to generate a high-frequency data generation model, so as to utilize the low-frequency observation data observed by the ordinary GNSS receiver to input into the high-frequency data generation model to obtain the high-frequency observation data of the ionospheric disturbance region output by the high-frequency data generation model.

[0042] To achieve the above-mentioned objectives, the third aspect of the present application provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor executes the steps of the method described in the first aspect.

[0043] To achieve the above-mentioned objectives, the fourth aspect of the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method described in the first aspect.

[0044] The embodiments of the present invention have the following beneficial effects:

[0045] An embodiment of the present invention provides a method for generating an ionospheric disturbance region, the method comprising: collecting first ionospheric observation data observed by a high-frequency device within a preset time period and second ionospheric observation data observed by a conventional GNSS receiver; extracting first target ionospheric disturbance region observation data within a target time period in which the ionospheric disturbance occurs from the first observation data observed by the high-frequency device using a ROTI index determination method; performing model training based on the first target ionospheric disturbance region observation data and the second observation data within the target time period to generate a high-frequency data generation model, wherein low-frequency observation data observed by the conventional GNSS receiver is input into the high-frequency data generation model to obtain high-frequency observation data of the ionospheric disturbance region output by the high-frequency data generation model. The present invention utilizes high-frequency observation data and low-frequency observation data of the same ionospheric disturbance region for model training, and the generated high-frequency data generation model can generate high-frequency observation data of the ionospheric disturbance region based on the low-frequency observation data. This can reduce the cost of receiver deployment, allowing high-frequency observation data to be obtained even when deploying inexpensive low-frequency receivers. Furthermore, obtaining high-frequency observation data can make the observation results of the ionospheric disturbance region more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0047] in:

[0048] Figure 1 Schematic diagram of a flow chart of a method for generating an ionospheric disturbance region according to an embodiment of the present invention;

[0049] Figure 2 This is a structural block diagram of a device for generating an ionospheric disturbance region according to an embodiment of the present invention;

[0050] Figure 3 2 is a diagram showing the internal structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0052] The embodiment of the present invention proposes a method for generating an ionospheric disturbance region, which can reduce the cost while improving the accuracy of detecting ionospheric disturbances. Figure 1 , Figure 1 This is a flow chart of a method for generating an ionospheric disturbance region according to an embodiment of the present invention, the method comprising:

[0053] Step 100: Collect first observation data observed by a high-frequency device within a preset time period and second observation data observed by a common GNSS receiver.

[0054] In an embodiment of the present invention, the preset time period can be a long time series, such as obtaining first observation data from a high-frequency device and second observation data from a standard GNSS receiver from 2010 to 2023. The model trained based on this long time series of observation data can effectively reflect the differences and connections between the observation data from the high-frequency device and the standard GNSS receiver.

[0055] Both high-frequency devices and standard GNSS receivers generate observation data based on signals transmitted by satellites. The difference lies in the higher temporal resolution of high-frequency devices. Standard GNSS receivers mostly operate in the L-band (1-2 GHz), while high-frequency devices cover a wider range of high frequencies, such as HF (3-30 MHz), VHF / UHF (above 30 MHz), and even microwaves. Therefore, data observed by high-frequency devices is more resistant to interference and has a higher data transmission rate.

[0056] This embodiment obtains observation data observed by a high-frequency device and a common GNSS receiver in the same time period to analyze the first observation data and the second observation data to determine the difference and connection between the two observation data.

[0057] Step 200: Extract first target observation data of an ionospheric disturbance region within a target time period in which the ionospheric disturbance occurs from first observation data observed by a high-frequency device using a ROTI index determination method.

[0058] In an embodiment of the present invention, when a high-frequency device or a conventional GNSS receiver receives observation data from a satellite signal and generates observation data, ionospheric observation data is extracted from the observation data, thereby detecting ionospheric disturbance regions based on the ionospheric observation data. Specifically, a non-differential, non-combined PPP method can be used to extract ionospheric observation values ​​observed by the high-frequency device from first observation data of the high-frequency device, and to extract ionospheric observation data observed by the conventional GNSS receiver from second observation data of the conventional GNSS receiver.

[0059] Since high-frequency equipment has stronger interference observation capabilities and higher data transmission rates, the first observation data observed by high-frequency equipment is of higher quality. Therefore, the time and disturbance area of ​​the ionosphere disturbance determined based on the first observation data will be more accurate.

[0060] Based on this, this embodiment selects the first observation data observed by the high-frequency device to determine the time and disturbance area of ​​the ionospheric disturbance. First, the non-differenced, non-combined PPP method is used to determine the ionospheric observation value observed by the high-frequency device. Second, the ROTI index determination method is used to extract the time period of the ionospheric disturbance and the ionospheric disturbance area corresponding to this time period from the ionospheric observation data observed by the high-frequency device, as well as the first target observation data corresponding to the disturbance area during the time period of the ionospheric disturbance.

[0061] In one embodiment of the present invention, the observation data generated by the high-frequency device and the ordinary GNSS receiver may include signal strength and frequency, pseudorange, phase, Doppler shift, receiver data, satellite data, and the like.

[0062] This embodiment determines the time and area of ​​ionospheric disturbance based on accurate and abundant ionospheric observation data observed by high-frequency equipment, so that subsequent analysis based on the time, area and observation data of the ionospheric disturbance can obtain more accurate ionospheric disturbance observation results.

[0063] Step 300: Perform model training based on the first target observation data and the second observation data of the ionospheric disturbance region within the target time period to generate a high-frequency data generation model, so as to input the low-frequency observation data observed by an ordinary GNSS receiver into the high-frequency data generation model to obtain high-frequency observation data of the ionospheric disturbance region output by the high-frequency data generation model.

[0064] In this embodiment of the present invention, second target observation data of the disturbance area observed by a conventional GNSS receiver at the time the ionospheric disturbance occurs is determined based on the time and disturbance area of ​​the ionospheric disturbance occurring within the preset time period determined in step 200. It will be understood that the first target observation data and the second target observation data are data of the disturbance area observed by the high-frequency device and the conventional GNSS receiver, respectively, at the time the ionospheric disturbance occurs.

[0065] Specifically, a training sample set is generated based on the first and second target observation data. Model training is performed using this training sample set. A preset deep learning network is used to learn the relationship between data observed by high-frequency equipment and conventional GNSS equipment in the same ionospheric disturbance region during the same time period. This generates a high-frequency data generation model, which is then used to predict higher-quality high-frequency observation data from the low-frequency observation data observed by conventional GNSS receivers. This eliminates the need for extensive high-frequency equipment deployment, reducing receiver deployment costs, while also enabling the acquisition of high-frequency observation data, resulting in more accurate observations of ionospheric disturbance regions.

[0066] In one embodiment of the present invention, step 200, extracting first target observation data of an ionospheric disturbance region within a target time period during which the ionospheric disturbance occurs from first observation data observed by a high-frequency device using a ROTI index determination method, specifically includes:

[0067] Step 210: Obtain the basic observation equations of the pseudorange and phase of the first observation data.

[0068] In the embodiment of the present invention, the first observation data includes at least pseudorange and phase. The ionospheric parameters are taken as unknown parameters, and errors such as tropospheric error, relativistic effect, and multipath error are combined to construct the basic observation equation, which can be referred to formula (1):

[0069]

[0070] In formula (1), They represent the pseudorange and phase observations of satellite s at the ith frequency observed by receiver r, respectively; represents the distance from the receiver r to the satellite s, including orbit error, tropospheric error, relativistic effect, multipath error, etc.; c represents the speed of light in a vacuum; dt r 、dt s denote the clock error of receiver r and satellite s respectively; represents the ionospheric delay on the observation path from receiver r to satellite s excluding the hardware bias of receiver r and satellite s; B r,i 、 denote the pseudorange hardware delays of receiver r and satellite s at the i-th frequency respectively; denote the observation noise and the unmodeled error respectively; Represents a floating point ambiguity excluding satellite and receiver phase hardware delay bias.

[0071] Step 220: Use the precise satellite clock error product released by IGS to perform delay correction on the basic observation equation to obtain the corrected target observation equation.

[0072] When solving the non-differential non-combined PPP model, the basic observation equations are corrected using the precise satellite clock error products released by IGS.

[0073] Since the IGS published values ​​are obtained based on the ionospheric-free pseudorange and phase observation values, the satellite clock error includes the satellite hardware delay bias, which can be expressed as formula (2):

[0074]

[0075] in, represents the true value of the satellite s clock error, that is, the corrected satellite s clock error; dt s is the IGS published value of satellite s clock error; are the frequency values ​​of two signals with different frequencies; The hardware delays of satellites for the two frequencies are shown respectively.

[0076] In the non-combined PPP model, the satellite hardware delay bias cannot be offset in the pseudorange observation value, but can be absorbed by the ionospheric parameters; the phase observation value can be absorbed by the ionospheric parameters and the ambiguity parameters. Therefore, by substituting formula (2) into formula (1) and reorganizing the ionospheric parameters, the corrected target observation equation can be obtained, as shown in formula (3):

[0077]

[0078] in, They represent the pseudorange and phase observations of satellite s at the ith frequency observed by receiver r, respectively; represents the distance from the receiver r to the satellite s; c represents the speed of light in a vacuum, and represents the corrected clock difference between the receiver r and the satellite s; It represents the ionospheric delay on the observation path from receiver r to satellite s, which includes the hardware bias of receiver r and satellite s. represents the observation noise; They represent floating-point ambiguities including satellite and receiver phase hardware delay biases respectively; represent the floating point ambiguities without satellite and receiver phase hardware delay bias respectively; represents the unmodeled error; γ is the proportionality coefficient.

[0079] The specific expressions of each symbol in formula (3) are shown in formula (4):

[0080]

[0081] in, represents the corrected clock error of the receiver r; are the frequency values ​​of two signals with different frequencies; dt r represents the clock error of the receiver r; are the satellite hardware delays for the two frequencies respectively; It represents the ionospheric delay on the observation path from receiver r to satellite s, which includes the hardware bias of receiver r and satellite s. The DCB represents the ionospheric delay on the observation path from receiver r to satellite s without the hardware bias of receiver r and satellite s. r,i and Represents the differential code deviation of the receiver r and satellite s at different frequencies; They represent floating-point ambiguities including satellite and receiver phase hardware delay biases respectively; denote the floating-point ambiguity excluding the satellite and receiver phase hardware delay biases, respectively; γ is the scaling factor.

[0082] Step 230: Solve the target observation equation based on the non-difference non-combined PPP model to obtain the first actual observation value of the ionosphere observed by the high-frequency equipment.

[0083] In the embodiment of the present invention, the ionospheric delay on the observation path from receiver r to satellite s without the hardware bias of receiver r and satellite s is calculated as By estimating the parameters, the ionospheric delay deviation on the slant path can be solved, and then the first measured observation value of the ionosphere can be obtained.

[0084] Based on the thin ionosphere assumption, the MSLM (Modified Single Layer Model) projection function is used to calculate the ionospheric mapping function, thereby converting the slant path ionospheric delay to the vertical ionospheric delay. The calculation formula is as follows:

[0085]

[0086] Where h is the height at which the ionosphere is concentrated, R is the radius of the Earth, and z is the satellite altitude angle.

[0087] Step 240: Extract first target observation data of the ionospheric disturbance region within the target time period during which the ionospheric disturbance occurs from the first actual observation value observed by the high-frequency device using the ROTI index determination method.

[0088] The ROTI index determination method is a method used to monitor the irregular structure activity of the ionosphere. Its core is to evaluate the dynamic characteristics of the ionosphere by calculating the rate of change of the total electron content (TEC).

[0089] Specifically, the TEC rate of change (ROT) is calculated based on the first actual ionospheric observation value observed by the high-frequency device extracted in Step 230. This primarily involves calculating the rate of change of TEC at each observation point within adjacent time intervals. The standard deviation of the TEC rate of change is then calculated within a certain time interval to obtain the ROTI value. The ROTI reflects the degree of fluctuation in the TEC rate of change; a higher ROTI value typically indicates the activity of ionospheric irregular structures. Therefore, the ROTI value can be used to determine the disturbance region and duration of the ionospheric disturbance.

[0090] In one embodiment of the present invention, after the ionospheric disturbance region is determined, the range of the disturbance region can be appropriately expanded to ensure that the range of the ionospheric observation value can completely cover the entire ionospheric disturbance region. Optionally, it can be expanded to 150% of the diameter of the disturbance region.

[0091] In an embodiment of the present invention, the TEC change rate can be calculated by the following formula:

[0092]

[0093] Among them, TEC t and TEC t-1 are the TEC values ​​at the current moment and the previous moment respectively, and Δt is the time interval.

[0094] In one embodiment of the present invention, step 300, performing model training based on first target observation data and second observation data of the ionospheric disturbance region within the target time period to generate a high-frequency data generation model, specifically includes:

[0095] Step 310: Use the target time period and the ionospheric disturbance area within the target time period as screening conditions.

[0096] In an embodiment of the present invention, the target time period is a time period during which ionospheric disturbance occurs within a preset time period. The second observation data observed by the common GNSS receiver is filtered by setting a filtering condition.

[0097] Step 320: Filter out the second target observation data that meets the screening conditions from the second observation data observed by the common GNSS receiver.

[0098] In the embodiment of the present invention, meeting the screening condition can be understood as observation data belonging to the ionospheric disturbance region within the target time period.

[0099] Specifically, the method first uses a non-differenced, non-combined PPP method to extract ionospheric observation values ​​observed by a common GNSS receiver from second observation data observed by a common GNSS receiver. Secondly, second target observation data that meets a screening condition is filtered from the ionospheric observation values ​​observed by the common GNSS receiver. The second observation data is filtered using the screening condition to obtain second observation data related to the first target observation data.

[0100] Step 330: Perform model training based on the first target observation data and the second target observation data to generate a high-frequency data generation model.

[0101] In an embodiment of the present invention, the first target observation data is used as label data to annotate the second target observation data to generate training samples. The training samples are used to train a model to generate a high-frequency data generation model, which can output high-frequency data after inputting low-frequency data.

[0102] In one embodiment of the present invention, Step 330, performing model training based on the first target observation data and the second target observation data to generate a high-frequency data generation model, specifically includes:

[0103] Step 331. Resample the observation data of the ionospheric disturbance region within the target time period based on the first target observation data to obtain a first two-dimensional image set, wherein the first two-dimensional image set includes two-dimensional images of the ionospheric disturbance region corresponding to different times within the target time period, and the two-dimensional images show the degree of disturbance of the ionospheric disturbance region.

[0104] In an embodiment, the ionospheric scattered observation values ​​are resampled into an image, and the disturbance degree of the ionospheric disturbance area is presented through a two-dimensional image. It can be understood that a two-dimensional image represents a two-dimensional image of the ionospheric disturbance area corresponding to a certain moment.

[0105] In one embodiment of the present invention, Step 331, based on the first target observation data, resampling the observation data of the ionospheric disturbance region within the target time period to obtain a first two-dimensional image set, specifically includes:

[0106] Step 331A: Divide the ionosphere into grids according to a preset resolution to obtain a grid distribution of the ionosphere, where each grid corresponds to a pixel in the two-dimensional image.

[0107] Specifically, a regular latitude and longitude grid is generated based on the latitude and longitude range of the ionospheric region to be studied and the set resolution, thereby obtaining the ionospheric grid distribution. For example, the latitude and longitude range of the ionospheric region to be studied is 20° to 30° north latitude and 80° to 90° east longitude, and the set resolution is 1°×1°. Based on this, a regular latitude and longitude grid with a resolution of 1°×1° is generated for the ionospheric region, with each grid point corresponding to a pixel in the image. Step 331B: Obtain several target grids corresponding to the ionospheric disturbance region at the target time, where the target time is any moment within the target time period.

[0108] Step 331C: Calculate the total electron content of each target grid based on the first target observation data.

[0109] In an embodiment of the present invention, Kriging interpolation is used to determine the total electron content of each target grid. Kriging can assign different weights according to the spatial correlation between observation points, and use a weighted method to improve the accuracy and reliability of interpolation, so that the calculated total electron content of each target grid is more accurate.

[0110] In this embodiment, Step 331C, calculating the total electron content of each target grid according to the first target observation data, specifically includes:

[0111] C1. Calculate the total electron content of each observation point in the ionosphere based on the first target observation data.

[0112] C2. Obtain several target observation points adjacent to the target grid.

[0113] In this embodiment, the total electron content of each pixel is calculated using a weighted average method based on the geographic location and total electron content of surrounding scattered observations. Therefore, several observation points closest to each grid can be used as target observation points. For example, four target observation points can be obtained. The total electron content and geographic location of the target observation points are used to determine the total electron content of each grid.

[0114] C3. Calculate the weight of each target observation point based on the location information of the target observation point based on the Kriging method.

[0115] In this embodiment, the Kriging method uses the semivariogram function to measure the spatial correlation between target observation points and assigns a weight to each target observation point accordingly. Specifically, observation values ​​closer to the pixel point are given a higher weight, and vice versa. In this way, the spatial variation of the ionosphere can be more accurately reflected.

[0116] C4. Calculate the total electron content of the target grid based on the weights of each target observation point and the total electron content.

[0117] In this embodiment, the total electron content of the target grid is calculated by the following formula:

[0118]

[0119] Where, TEC grid is the total electron content of the target grid, is the total electron content of the i-th target observation point, n is the total number of target observation points corresponding to each target grid, λ i is the weight of the i-th target observation point.

[0120] Step 331D: Generate a first two-dimensional image of the target corresponding to the target moment based on the total electron content of each target grid. The first two-dimensional images of the target at different moments constitute a first two-dimensional image set.

[0121] In this embodiment, the TEC values ​​corresponding to the target grid at each moment are stored in a two-dimensional array and converted into an image format to generate a first two-dimensional image. The first two-dimensional images corresponding to each moment in the target time period constitute a first two-dimensional image set.

[0122] Step 332: resample the observation data of the ionospheric disturbance region within the target time period based on the second target observation data to obtain a second two-dimensional image set, wherein the second two-dimensional image set includes two-dimensional images of the ionospheric disturbance region corresponding to different times within the target time period.

[0123] Similarly, the second two-dimensional picture set can be obtained according to the generation method of the first two-dimensional picture set, which will not be described in detail here.

[0124] Step 333: Use the two-dimensional images in the first two-dimensional image set to annotate the two-dimensional images in the second two-dimensional image set to generate a training sample set.

[0125] Specifically, the first two-dimensional image at the same time is annotated with the second two-dimensional image, the first two-dimensional image is used as label data, and the second two-dimensional image with the label data is used as a training sample, thereby obtaining a training sample set.

[0126] Step 334: Input the training sample set into the preset generative adversarial network for training to obtain a high-frequency data generation model.

[0127] Specifically, the training sample set is input into the generative adversarial network for training, and the optimal model is selected as the high-frequency data generation model.

[0128] In one embodiment of the present invention, a model with the smallest upper RMSE may be selected as the optimal model based on a k-fold crossover experiment (k=5).

[0129] When applying the high-frequency data generation model, the ionospheric observation values ​​observed by ordinary GNSS receivers are first obtained; secondly, the ROTI index method is used to determine the ionospheric disturbance area and disturbance period observed by ordinary GNSS receivers; finally, the observation data of the disturbance period and disturbance area are input into the high-frequency data generation model to obtain high-resolution data of the ionospheric disturbance area, and then a comprehensive picture of the ionospheric disturbance area is obtained.

[0130] Obtaining a high-resolution, panoramic view of ionospheric disturbance areas can provide richer details and depth for ionospheric research and the improvement of GNSS positioning accuracy, enhancing the understanding and prediction capabilities of ionospheric changes.

[0131] In one embodiment of the present invention, a device for generating an ionospheric disturbance region is proposed, which can be referred to as Figure 2 , Figure 2 2 is a structural block diagram of a device for generating an ionospheric disturbance region in an embodiment of the present invention. The device includes: a data acquisition module 201, a data processing module 202 and a data generation module 203.

[0132] The data collection module 201 is configured to collect first observation data observed by a high-frequency device and second observation data observed by a common GNSS receiver within a preset time period.

[0133] The data processing module 202 is configured to extract first target observation data of an ionospheric disturbance region within a target time period in which the ionospheric disturbance occurs from the first observation data observed by the high-frequency device using the ROTI index determination method.

[0134] The data generation module 203 is used to perform model training based on the first target observation data and the second observation data of the ionospheric disturbance area within the target time period to generate a high-frequency data generation model, so as to use the low-frequency observation data observed by an ordinary GNSS receiver to input the high-frequency data generation model to obtain high-frequency observation data of the ionospheric disturbance area output by the high-frequency data generation model.

[0135] The ionospheric disturbance region generation device proposed in the present invention can, on the one hand, reduce the receiver deployment cost and obtain high-frequency observation data even when deploying inexpensive low-frequency receivers. On the other hand, by obtaining high-frequency observation data, the observation results of the ionospheric disturbance region can be made more accurate.

[0136] Figure 3 FIG1 shows the internal structure of a computer device in one embodiment of the present invention. The computer device can be a terminal or a system. Figure 3As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement the various steps in the above method embodiment. It will be understood by those skilled in the art that Figure 3 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 shown in the figure, or combine certain components, or have a different component arrangement.

[0137] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the processor executes each step in the above method embodiment.

[0138] In one embodiment, a computer-readable storage medium is provided, which stores a computer program. When the computer program is executed by a processor, the processor executes the steps in the above method embodiment.

[0139] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0140] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for generating an ionospheric disturbance region, characterized in that: The method comprises: Collecting first observation data observed by a high-frequency device and second observation data observed by a common GNSS receiver within a preset time period; Extracting first target observation data of an ionospheric disturbance region within a target time period in which the ionospheric disturbance occurs from the first observation data observed by the high-frequency device using a ROTI index determination method; Model training is performed based on the first target observation data and the second observation data of the ionospheric disturbance area within the target time period to generate a high-frequency data generation model, so that the low-frequency observation data observed by the ordinary GNSS receiver is input into the high-frequency data generation model to obtain the high-frequency observation data of the ionospheric disturbance area output by the high-frequency data generation model.

2. The method according to claim 1, wherein The performing model training based on the first target observation data and the second observation data of the ionospheric disturbance region within the target time period to generate a high-frequency data generation model specifically includes: Using the target time period and the ionospheric disturbance region within the target time period as screening conditions; Filtering second target observation data that meets the filtering condition from the second observation data observed by the ordinary GNSS receiver; Model training is performed based on the first target observation data and the second target observation data to generate the high-frequency data generation model.

3. The method according to claim 2, wherein The performing model training according to the first target observation data and the second target observation data to generate the high-frequency data generation model specifically includes: resampling observation data of the ionospheric disturbance region within the target time period based on the first target observation data to obtain a first two-dimensional image set, wherein the first two-dimensional image set includes two-dimensional images of the ionospheric disturbance region corresponding to different times within the target time period, and the two-dimensional images show the degree of disturbance of the ionospheric disturbance region; resampling the observation data of the ionospheric disturbance region within the target time period based on the second target observation data to obtain a second two-dimensional image set, wherein the second two-dimensional image set includes two-dimensional images of the ionospheric disturbance region corresponding to different times within the target time period; Using the two-dimensional images in the first two-dimensional image set to annotate the two-dimensional images in the second two-dimensional image set to generate a training sample set; The training sample set is input into a preset generative adversarial network for training to obtain the high-frequency data generation model.

4. The method according to claim 3, wherein Resampling the observation data of the ionospheric disturbance region within the target time period based on the first target observation data to obtain a first two-dimensional picture set specifically includes: Divide the ionosphere into grids according to a preset resolution to obtain a grid distribution of the ionosphere, where each grid corresponds to a pixel in the two-dimensional image; Acquire a plurality of target grids corresponding to the ionospheric disturbance region at a target time, wherein the target time is any time within the target time period; Calculating the total electron content of each target grid according to the first target observation data; A first two-dimensional target image corresponding to the target moment is generated based on the total electron content of each target grid, and the first two-dimensional target images at different moments constitute a first two-dimensional image set.

5. The method according to claim 4, wherein Calculating the total electron content of each target grid according to the first target observation data specifically includes: Calculating the total electron content of each observation point in the ionosphere according to the first target observation data; Acquire a number of target observation points adjacent to the target grid; Calculate the weight of each target observation point based on the position information of the target observation point based on the Kriging method; The total electron content of the target grid is calculated according to the weights of the target observation points and the total electron content.

6. The method according to claim 1, wherein The step of extracting first target observation data of an ionospheric disturbance region within a target time period in which the ionospheric disturbance occurs from the first observation data observed by the high-frequency device using the ROTI index determination method specifically includes: Obtaining a basic observation equation of pseudorange and phase of the first observation data; Using the precise satellite clock error products released by IGS, the basic observation equation is delayed corrected to obtain the corrected target observation equation; Solving the target observation equation based on a non-differenced non-combined PPP model to obtain a first actual observation value of the ionosphere observed by the high-frequency device; The ROTI index determination method is used to extract first target observation data of the ionospheric disturbance area within the target time period in which the ionospheric disturbance occurs from the first actual observation value observed by the high-frequency device.

7. The method according to claim 6, wherein The first observation data includes observation values ​​formed when different high-frequency receivers receive signals sent by different satellites; The modified target observation equation is expressed by the following formula: in, They represent the pseudorange and phase observation values ​​of satellite s observed by receiver r at the i-th frequency; represents the distance from the receiver r to the satellite s; c represents the speed of light in a vacuum, and represents the corrected clock difference between the receiver r and the satellite s; represents the ionospheric delay on the observation path from the receiver r to the satellite s, including the hardware bias of the receiver r and the satellite s; represents the observation noise; They represent floating-point ambiguities including satellite and receiver phase hardware delay biases respectively; represent the floating-point ambiguities excluding satellite and receiver phase hardware delay biases, respectively; represents the unmodeled error; γ is the proportionality coefficient.

8. A device for generating an ionospheric disturbance region, characterized in that: The device includes: a data acquisition module, a data processing module and a data generation module; The data acquisition module is configured to collect first observation data observed by the high-frequency device and second observation data observed by the common GNSS receiver within a preset time period; The data processing module is configured to extract first target observation data of an ionospheric disturbance region within a target time period in which the ionospheric disturbance occurs from the first observation data observed by the high-frequency device using a ROTI index determination method; The data generation module is configured to perform model training based on the first target observation data and the second observation data of the ionospheric disturbance region within the target time period to generate a high-frequency data generation model, so as to utilize the low-frequency observation data observed by the ordinary GNSS receiver to input into the high-frequency data generation model to obtain the high-frequency observation data of the ionospheric disturbance region output by the high-frequency data generation model.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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