Regional crowdsourcing ionospheric scintillation monitoring and early warning method and system based on low-cost equipment

Abstract

The invention discloses a regional crowdsourcing ionospheric scintillation monitoring and early warning method and system based on low-cost equipment, and belongs to the field of ionospheric scintillation monitoring, and the method comprises the steps: recording reference station observation data and ISMR data as an accurate data set; recording observation data acquired by the low-cost equipment as a fuzzy data set; performing flicker intensity assignment of ROTI and GFTI by using ISMR data in the accurate data set; interpolation is carried out on the flicker index of the position where the puncture point coordinates in the fuzzy data set are located by adopting common Kriging interpolation; subtraction is carried out on the interpolation result and the index of the corresponding position in the fuzzy data set, and an abnormal difference value in the subtraction result is calculated; and dividing the obtained ionospheric scintillation detection result according to a scintillation generation range, and analyzing a possible scintillation generation position so as to carry out early warning. A large amount of low-cost equipment observation data in the area is introduced to fill the blank area of professional equipment monitoring data, and the monitoring data density and the resolution of the monitoring result are improved.

Description

Technical Field

[0001] This invention belongs to the field of ionospheric scintillation monitoring, specifically relating to a regional crowdsourced method and system for ionospheric scintillation monitoring and early warning based on low-cost equipment. Background Technology

[0002] Ionospheric disturbances, a significant phenomenon in the space environment, refer to rapid and significant changes in the physical properties of the ionosphere, such as electron density or ion composition, caused by various factors. Among the various types of disturbances, ionospheric scintillation, with its extremely small spatial scale and drastic changes, has become a major threat to the accuracy and reliability of the BeiDou Navigation Satellite System. Ionospheric scintillation is mainly caused by small-scale structures such as plasma bubbles, which typically range in size from tens of meters to tens of kilometers. These structures can cause dramatic changes in local ionospheric electron density by tens or even hundreds of times within a short period, greatly affecting signal stability. This severely interferes with the receiver's ability to stably track BeiDou satellite signals, easily triggering cycle slips and even leading to complete signal loss, ultimately resulting in meter-level errors in positioning accuracy.

[0003] To accurately and comprehensively capture and monitor ionospheric scintillation, which is characterized by its small scale, short duration, rapid changes, and dramatic impact, large-scale, high-density, and continuous observational data and monitoring networks are essential. Therefore, current monitoring methods primarily focus on constructing vast and complex monitoring networks using dedicated ionospheric scintillation monitors (ISMRs), ionospheric vertical lobe instruments, satellite-based incoherent scattering radars, and ground-based radars, and calculating the phase scintillation index. and amplitude flicker index To determine the location and intensity of scintillation, it is necessary to analyze the data. However, considering the widespread distribution and dynamic changes of scintillation globally, relying solely on expensive ISMRs is far from sufficient. Therefore, the widespread application of general-purpose Global Navigation Satellite System (GNSS) receivers has become crucial for acquiring massive amounts of observational data and conducting large-area monitoring.

[0004] Based on total electron content data obtainable from general-purpose receivers, scholars have proposed several indirect but effective scintillation monitoring indices, such as the earliest Rate of Change of Total Electron Content (TEC) Index (ROTI) and the subsequent Rate of Change of Electron Content Along Arc (AATR) Index. These indices indirectly characterize the intensity of ionospheric scintillation by analyzing the intensity of changes in ionospheric delay in GNSS signals within a specific time window. However, the above indices require expensive and precise GNSS receiver observation data as input for calculation, and the current deployment density cannot achieve more refined and accurate scintillation detection. Therefore, this method proposes to use low-cost equipment, such as low-cost GNSS receivers installed in smartphones, vehicles, and drones capable of receiving GNSS signals, to conduct regional crowdsourced ionospheric scintillation monitoring and construct a scintillation early warning system to provide services to users within the region. Summary of the Invention

[0005] The purpose of this invention is to address the problems of low data density and limited observable range of existing monitoring data sources, which increase the probability of missed detections and blurred ionospheric scintillation boundaries, and reduce the quality of scintillation monitoring. This invention provides a regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment. By proposing a new scintillation monitoring index calculated using single-frequency observations, it introduces a large amount of low-cost equipment observation data within the region to fill the gaps in professional equipment monitoring data, thereby improving the density of monitoring data and the resolution of monitoring results. This provides users within the region with high-quality, accurate, and clear ionospheric scintillation monitoring services.

[0006] According to one aspect of the present invention, a regional crowdsourced method for ionospheric scintillation monitoring and early warning based on low-cost equipment is provided, comprising:

[0007] Based on the acquired base station observation data and ISMR data, the ROTI, the geometrically uncombined ionospheric scintillation index (GFTI), and the ISMR scintillation index were calculated and recorded together with the base station observation data and ISMR data as the accurate dataset.

[0008] ROTI and GFTI are calculated based on observation data acquired using low-cost equipment, and are collectively referred to as a fuzzy dataset along with the observation data acquired using low-cost equipment.

[0009] Based on the accurate dataset, the ordinary Kriging interpolation method is used to interpolate the coordinates of the puncture point in the fuzzy dataset.

[0010] The difference is obtained by subtracting the interpolation result from the scintillation index corresponding to the location of the puncture point in the fuzzy dataset, and the threshold of abnormal differences is calculated.

[0011] For the abnormal points corresponding to the abnormal differences caused by ionospheric scintillation, the range of scintillation occurrence is divided, and the location of subsequent scintillation occurrence is analyzed and early warning is issued based on the scintillation drift speed and direction within a continuous time period.

[0012] Furthermore, based on the accurate dataset, the ordinary kriging interpolation method is used to interpolate the coordinates of the puncture points in the fuzzy dataset, including:

[0013] If the scintillation index corresponding to the location of the puncture point in the fuzzy dataset is ROTI, then the ROTI in the accurate dataset is used for interpolation.

[0014] If the scintillation index corresponding to the location of the puncture point in the fuzzy dataset is GFTI, then the GFTI in the accurate dataset is used for interpolation.

[0015] Furthermore, after calculating the threshold for outlier differences in the differences, the following steps are included:

[0016] Based on the outlier point corresponding to each outlier difference, find the point in other fuzzy datasets that is closest to it;

[0017] The causes of outliers were analyzed using the inverse distance weighting method.

[0018] The anomaly was caused by ionospheric flicker, so the data interpolation was corrected.

[0019] If the anomaly is caused by an anomaly in the original data of the fuzzy dataset, then the original data is removed, and this step is repeated until there is no anomaly data.

[0020] Furthermore, for the anomalous points corresponding to the abnormal differences caused by ionospheric scintillation, they are divided according to the range of scintillation occurrence, including:

[0021] For each outlier, determine the ROTI and GFTI indices;

[0022] Connect the boundaries of the flickering areas in the judgment results to complete the division of the flickering range.

[0023] Furthermore, based on the flicker drift speed and direction over a continuous time period, the location of subsequent flickering events is analyzed, and early warnings are issued, including:

[0024] Collect the locations of ionospheric scintillation points detected over a continuous time period, and calculate the movement direction and speed of different scintillation points based on satellite data;

[0025] Based on the calculation results, predict the location of the flashing point in the next time period and broadcast the coordinates of the flashing point location;

[0026] Calculate the distance between the satellite puncture point coordinates and the location of the flashing point, and issue an early warning based on the calculation results.

[0027] Furthermore, the low-cost devices include smartphones and low-cost vehicle-mounted GNSS modules.

[0028] Furthermore, the method also includes: assigning scintillation intensity values ​​to ROTI and GFTI using ISMR data from an accurate dataset.

[0029] According to one aspect of the present invention, a regional crowdsourced ionospheric scintillation monitoring and early warning system based on low-cost equipment is provided, comprising:

[0030] The first main module is used to calculate ROTI, GFTI and ISMR scintillation index based on the acquired base station observation data and ISMR data, and record them together with the base station observation data and ISMR data as the accurate dataset.

[0031] The second main module is used to calculate ROTI and GFTI based on the observation data obtained from low-cost equipment, and to record them together with the observation data obtained from low-cost equipment as a fuzzy dataset.

[0032] The third main module is used to interpolate the coordinates of the puncture point in the fuzzy dataset using the ordinary Kriging interpolation method based on the accurate dataset.

[0033] The fourth main module is used to calculate the difference by subtracting the interpolation result from the scintillation index corresponding to the location of the puncture point in the fuzzy dataset, and to calculate the threshold of abnormal differences in the difference.

[0034] The fifth main module is used to divide the abnormal points corresponding to the abnormal differences caused by ionospheric scintillation according to the range of scintillation occurrence, and to analyze the location of subsequent scintillation occurrences and provide early warnings based on the scintillation drift speed and direction within a continuous time period.

[0035] According to one aspect of the present invention, an electronic device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to implement the steps of the regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost devices.

[0036] According to one aspect of the present invention, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps of the regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost devices.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. This invention uses low-cost equipment for crowdsourced ionospheric scintillation monitoring, effectively compensating for the problem of missed detection of ionospheric scintillation caused by the low distribution density of professional equipment, improving the user's positioning accuracy and reliability, and providing users with more accurate and refined early warning services for the range of ionospheric scintillation occurrence.

[0039] 2. This invention proposes a new scintillation monitoring index calculated using single-frequency observations, introduces a large amount of low-cost equipment observation data in the region to fill the gaps in the monitoring data of professional equipment, and improves the density of monitoring data and the resolution of monitoring results. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 The flowchart of the regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment provided by the present invention is shown. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Existing technologies for ionospheric scintillation detection require dual-frequency observation signals or continuous signal strength information from a dedicated scintillation monitoring receiver and a general-purpose GNSS receiver. However, low-cost receivers, which have a larger market share, only output single-frequency signals and are frequently interrupted due to antenna obstruction and equipment tracking loops, making it impossible to monitor ionospheric scintillation.

[0044] like Figure 1As shown, this invention proposes a regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment, including: Step 1: Collecting observation data from a base station and data from a dedicated ionospheric scintillation monitor within the service area. The collected data is used to calculate ROTI and GFTI, and together with the base station observation data and the dedicated ionospheric scintillation monitor data, it forms an accurate dataset. Step 2: Using a smartphone and a low-cost vehicle-mounted GNSS module, observation data is collected within the service area. The ROTI index is calculated using dual-frequency observation data, and the GFTI index is calculated using single-frequency data. The ROTI index, GFTI index, and collected observation data are together formed a fuzzy dataset. The collected observation data, ROTI index, and GFTI index are anonymized before being transmitted to a server. The observation data includes time, satellite number, puncture point coordinates, etc. Step 3: Using ISMR data from the accurate dataset as a reference, ROTI and GFTI scintillation intensities are assigned. Using the ordinary Kriging interpolation method, the ionospheric scintillation index at the location of the satellite puncture point coordinates in the fuzzy dataset is calculated based on the data in the accurate dataset. If the data at the corresponding location in the fuzzy dataset is ROTI, the ROTI in the accurate dataset is used for interpolation; if the data at the corresponding location in the fuzzy dataset is GFTI, the GFTI in the accurate dataset is used for interpolation. Step 4: Calculate the threshold for abnormal differences based on the difference between the interpolation result in Step 3 and the corresponding value in the fuzzy dataset. For each abnormal difference, find the nearest point in another fuzzy dataset and determine the cause of the current abnormal data using the inverse distance weighting method. If it is determined to be caused by ionospheric scintillation, the overall data interpolation is corrected; if it is caused by abnormal original data, the data is removed. Repeat this step until there is no abnormal data. Step 5: Divide the obtained ionospheric scintillation detection results according to the scintillation occurrence range. Analyze the possible locations of subsequent scintillation based on the scintillation drift velocity and direction within a continuous time period, and provide early warning services to users within the server area.

[0045] Specifically, the present invention provides the following steps for step one:

[0046] First, collect observation data from the current time reference station within the region, broadcast ephemeris data, and the phase scintillation index output by ISMR. and amplitude flicker index The satellite position and its penetration point at an altitude of 350 km were calculated using broadcast ephemeris data. The coordinates of the penetration point were then used as the coordinates of the intersection between the satellite signal propagation direction and the ionosphere. Subsequently, the ROTI index was calculated using dual-frequency observations from the reference station. The ROTI calculation method involves combining the dual-frequency (frequency points i and j) carrier observations of satellite s from the same reference station r into a geometry-free combine (GF) combination. :

[0047] (1)

[0048] In the formula, This represents the i-th frequency signal carrier observation value obtained by the reference station r during the tracking of satellite s; Indicates carrier wavelengths without geometric combinations. This represents the observations of the reference station r without geometric combination carriers obtained during the tracking of satellite s. Indicates the carrier wavelength at frequency point i. The carrier wavelength at frequency point j is represented. , The reference frequency of the ionosphere Let i be the frequency of frequency point i. This represents the first-order ionospheric delay at the reference frequency during the tracking process of satellite s by reference station r. The impact of higher-order delays on the positioning results is ignored here. This represents the integer ambiguity of the carrier observation at time-frequency point i of satellite s tracked by reference station r. and Let r and s represent the carrier hardware delays of the reference station r and satellite s, respectively. The frequency j has the same meaning as the frequency i. The ionospheric rate of change (ROT) can be obtained from the single difference between epochs (time periods) in equation (1), as shown in equation (2).

[0049] (2)

[0050] In the formula, This indicates a single difference between epochs of observations without geometric combination carriers. and The epochal difference represents the non-integer ambiguity. This represents the time difference between two epoch observations. Within the same arc segment, the integer part of the ambiguity remains unchanged. Furthermore, it is assumed that the non-integer ambiguity changes smoothly over a short period; therefore, the single difference between epochs for the integer ambiguity part is 0. and It is approximately 0. ROTI can be calculated according to equation (3).

[0051] (3)

[0052] Where 〈 〉 indicates calculating the mean of the data sequence.

[0053] Generally, ROTI within a 60s time window is calculated using 1Hz high-frequency GNSS observations, so the unit is TECU / min.

[0054] The GFTI index is then calculated using single-frequency observations. The calculation method involves using a geometrically non-geometric combination of single-frequency carrier and pseudorange observations, and subtracting the carrier and pseudorange observation equations to obtain the result.

[0055] (4)

[0056] In the formula, This indicates single-frequency observations without geometric combinations. This represents the pseudorange observation value of the i-th frequency signal obtained by the base station r during the tracking of satellite s. and These represent the pseudorange hardware delays of the base station and the satellite, respectively. The noise of this observation is represented by the symbols, and the meanings of the other symbols are the same as in equation (1). This allows the extraction of ionospheric delay information containing ambiguity and hardware delay bias. The above observations without geometric combination are subject to abrupt changes due to cycle slips. Therefore, the Doppler-aided cycle slip detection and repair (DACS-DR) method is used for single-frequency cycle slip detection and repair. This method calculates the epoch difference of the 1Hz carrier observations. and the mean of Doppler observations of these two epochs By comparison and The difference is used to determine whether a cycle slip has occurred and to repair it. After repairing the cycle slip, the average value over a period of time (10s) is taken to reduce the impact of pseudorange observation noise.

[0057] (5)

[0058] After obtaining the ionospheric delay information, a 300-second window was used to perform linear fitting on the observation results to obtain the ionospheric variation trend within this time period, and the fitting residuals were calculated. :

[0059] (6)

[0060] In the formula, The fitted linear function, and The coefficients are linear function coefficients, and t is the epoch time. After obtaining the residuals, the ionospheric scintillation index GFTI is obtained by calculating the standard deviation of the residuals.

[0061] (7)

[0062] Specifically, the present invention provides the following steps for step two:

[0063] First, the calculation methods for ROTI and GFTI described above are installed on the crowdsourcing user terminal. Smartphone users can install an app, while low-cost vehicle-mounted GNSS modules can be upgraded remotely or have their firmware flashed. The crowdsourcing user terminal calculates the corresponding indices based on the GNSS data. For devices that can only receive single-frequency observations, only the GFTI index is calculated. Considering the poor data continuity and high noise, a 30-second window is used to calculate the moving average. For devices that can receive dual-frequency observations, both ROTI and GFTI indices are calculated. After calculation, the ROTI and GFTI indices, along with their corresponding satellite numbers, puncture point coordinates, and time, are packaged and sent anonymously to the server. The server stores the received data in its database for subsequent calculations.

[0064] Specifically, the present invention provides the following steps for step three:

[0065] First, the data from step one is processed by removing the phase flicker index output by ISMR. and amplitude flicker index Abnormal data in ROTI and GFTI will be compared with the phase scintillation index of the ISMR output of the corresponding satellite and time. and amplitude flicker index Normalization is performed as shown in equation (8).

[0066] (8)

[0067] In the formula, These represent all satellites at that moment. The maximum and minimum values ​​of the observed values, , , These represent the corresponding ROTI values ​​and their maximum and minimum values, respectively. GFTI is calculated similarly, yielding the normalized GFTI exponent. Then, ordinary kriging interpolation is used to interpolate the data from the accurate dataset to the fuzzy dataset based on the satellite puncture point coordinates. If the scintillation index of the corresponding location in the fuzzy dataset is ROTI, then the ROTI from the accurate dataset is used for interpolation; if the scintillation index of the corresponding location in the fuzzy dataset is GFTI, then the GFTI from the accurate dataset is used for interpolation.

[0068] Specifically, the present invention provides the following steps for step four:

[0069] First, the fuzzy dataset is normalized using equation (8), and the difference between the central tendency of the normalized fuzzy dataset and the interpolation result in step three is calculated. Absolute Absolute Deviation (MAD) is used to detect outlier data; the calculation method is as follows:

[0070] (9)

[0071] In the formula, X represents the difference dataset. For samples in X, This represents the inverse cumulative distribution function. The method for determining this is... 'median' represents the median, and 'a' is the corresponding coefficient, which is 4 here. After identifying outliers, find the 10 closest data points in different locations within the fuzzy dataset. Using the inverse distance weighting method, calculate the index at that point using data from the subset. Collect these 10 data points obtained through the inverse distance weighting method and combine them with the outliers to form a subset. If the MAD method reveals that the original outlier data in the subset still exceeds the threshold, it is removed; otherwise, the data is marked as missed ionospheric scintillation data (e.g., ...). Figure 1 (The missing data in the list) is used to check for the next outlier. Repeat the above process until no outliers are detected.

[0072] Specifically, the present invention provides the following steps for step five:

[0073] The obtained ionospheric scintillation detection results are divided according to the scintillation occurrence range. Based on the scintillation drift velocity and direction within a continuous time period, the potential location of subsequent scintillations is analyzed, providing early warning services to users within the server area. The specific operation method is as follows: The locations of ionospheric scintillation points detected in step four are collected from multiple consecutive epochs. The movement direction and velocity of different scintillation points are calculated according to satellite data. Then, the scintillation location for the next time period is predicted based on the calculation results. The satellite puncture point coordinates of this location are broadcast to users via the network. Users can calculate the distance between the satellite puncture point coordinates and the location to determine whether the scintillation will affect the satellite's observation data (if it affects the satellite's observation data, it will affect satellite positioning), thereby improving positioning reliability and determining whether to issue an early warning. The occurrence and absence of scintillation can be determined using the ROTI and GFTI indices. Connecting the boundaries of the scintillation-affected areas completes this range division.

[0074] The implementation of the various embodiments of the present invention is based on programmed processing through a device with processor functionality. Therefore, in practical engineering, the technical solutions and functions of the various embodiments of the present invention are encapsulated into various modules. Based on this reality, and building upon the above embodiments, the embodiments of the present invention provide a regional crowdsourced ionospheric scintillation monitoring and early warning system based on low-cost equipment. This system is used to execute a regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment from the above method embodiments.

[0075] The system comprises: a first main module, used to calculate ROTI, GFTI, and ISMR scintillation indices based on acquired base station observation data and ISMR data, and to record these indices together with the base station observation data and ISMR data as an accurate dataset; a second main module, used to calculate ROTI and GFTI based on observation data acquired by low-cost equipment, and to record these indices together with the observation data acquired by low-cost equipment as a fuzzy dataset; a third main module, used to interpolate the coordinates of the puncture points in the fuzzy dataset using ordinary kriging interpolation based on the accurate dataset; a fourth main module, used to calculate the difference by subtracting the interpolation result from the scintillation index corresponding to the coordinates of the puncture points in the fuzzy dataset, and to calculate the threshold for abnormal differences; and a fifth main module, used to divide the abnormal points corresponding to abnormal differences caused by ionospheric scintillation according to the scintillation range, and to analyze the subsequent scintillation locations and issue early warnings based on the scintillation drift velocity and direction within a continuous time period.

[0076] The regional crowdsourced ionospheric scintillation monitoring and early warning system based on low-cost equipment provided in this invention addresses the problems of low data density, limited observable range, increased probability of missed detection and blurred ionospheric scintillation boundaries, and reduced scintillation monitoring quality caused by existing monitoring data sources. It employs several modules, proposes new scintillation monitoring indicators calculated using single-frequency observations, and introduces a large amount of low-cost equipment observation data within the region to fill the gaps in professional equipment monitoring data. This improves the density of monitoring data and the resolution of monitoring results, providing users within the region with high-quality, accurate, and clear ionospheric scintillation monitoring services.

[0077] Based on the same inventive concept as the foregoing embodiments, this embodiment of the invention also provides an electronic device, including a memory and a processor. The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize a regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment as proposed in the above embodiments.

[0078] This invention also provides a computer-readable storage medium storing a computer program thereon. When executed by a processor, this program improves the density of monitoring data and the resolution of monitoring results, providing users in the region with high-quality, accurate, and clear ionospheric scintillation monitoring services.

[0079] Finally, it should be noted that the above specific embodiments are merely representative examples of the present invention. Obviously, the present invention is not limited to the above specific embodiments and many variations are possible. Any simple modifications, equivalent changes, and alterations made to the above specific embodiments based on the technical essence of the present invention should be considered within the protection scope of the present invention.

Claims

1. A regional crowdsourced method for ionospheric scintillation monitoring and early warning based on low-cost equipment, characterized in that, include: Based on the acquired base station observation data and ISMR data, ROTI, GFTI and ISMR scintillation index are calculated and recorded together with the base station observation data and ISMR data as the accurate dataset. ROTI and GFTI are calculated based on observation data obtained from low-cost equipment, and are collectively referred to as a fuzzy dataset along with the observation data obtained from low-cost equipment. Based on the accurate dataset, the ordinary Kriging interpolation method is used to interpolate the coordinates of the satellite puncture points in the fuzzy dataset. The difference is obtained by subtracting the interpolation result from the scintillation index corresponding to the location of the puncture point in the fuzzy dataset, and the threshold of abnormal differences is calculated. For the abnormal points corresponding to the abnormal differences caused by ionospheric scintillation, the range of scintillation occurrence is divided, and the location of subsequent scintillation occurrence is analyzed and early warning is issued based on the scintillation drift speed and direction within a continuous time period.

2. The regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment according to claim 1, characterized in that, Based on the accurate dataset, the ordinary kriging interpolation method is used to interpolate the coordinates of the puncture points in the fuzzy dataset, including: If the scintillation index corresponding to the location of the puncture point in the fuzzy dataset is ROTI, then the ROTI in the accurate dataset is used for interpolation. If the scintillation index corresponding to the location of the puncture point in the fuzzy dataset is GFTI, then the GFTI in the accurate dataset is used for interpolation.

3. The regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment according to claim 1, characterized in that, After calculating the threshold for outliers in the difference calculation, the following is included: Based on the outlier point corresponding to each outlier difference, find the point in other fuzzy datasets that is closest to it; The causes of outliers were analyzed using the inverse distance weighting method. The anomaly was caused by ionospheric flicker, so the data interpolation was corrected. If the anomaly is caused by an anomaly in the original data of the fuzzy dataset, then the original data is removed, and this step is repeated until there is no anomaly data.

4. The regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment according to claim 1, characterized in that, For anomalous points corresponding to abnormal differences caused by ionospheric scintillation, they are divided according to the range of scintillation occurrence, including: For each outlier, determine the ROTI and GFTI indices; Connect the boundaries of the areas where flickering occurs in the judgment results to complete the division of the flickering range.

5. The regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment according to claim 1, characterized in that, Based on the analysis of the flicker drift speed and direction over a continuous time period, the location of subsequent flickering events can be determined and warnings can be issued, including: Collect the locations of ionospheric scintillation points detected over a continuous time period, and calculate the movement direction and speed of different scintillation points based on satellite data; Based on the calculation results, predict the location of the flashing point in the next time period and broadcast the coordinates of the flashing point location; Calculate the distance between the satellite puncture point coordinates and the location of the flashing point, and issue an early warning based on the calculation results.

6. The regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment according to claim 1, characterized in that, The low-cost devices include smartphones and low-cost vehicle-mounted GNSS modules.

7. The regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment according to claim 1, characterized in that, The method further includes: assigning scintillation intensity values ​​to ROTI and GFTI using ISMR data from an accurate dataset.

8. A regional crowdsourced ionospheric scintillation monitoring and early warning system based on low-cost equipment, characterized in that, include: The first main module is used to calculate ROTI, GFTI and ISMR scintillation index based on the acquired base station observation data and ISMR data, and record them together with the base station observation data and ISMR data as the accurate dataset. The second main module is used to calculate ROTI and GFTI based on the observation data obtained from low-cost equipment, and to record them together with the observation data obtained from low-cost equipment as a fuzzy dataset. The third main module is used to interpolate the coordinates of the puncture point in the fuzzy dataset using the ordinary Kriging interpolation method based on the accurate dataset. The fourth main module is used to calculate the difference by subtracting the interpolation result from the scintillation index corresponding to the location of the puncture point in the fuzzy dataset, and to calculate the threshold of abnormal differences in the difference. The fifth main module is used to divide the abnormal points corresponding to the abnormal differences caused by ionospheric scintillation according to the range of scintillation occurrence, and to analyze the location of subsequent scintillation occurrences and provide early warnings based on the scintillation drift speed and direction within a continuous time period.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the regional crowdsourced ionospheric scintillation monitoring and early warning method based on low-cost equipment as described in any one of claims 1 to 7.

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