Ionized layer electron density profile abnormal fluctuation detection method, system and equipment

Abstract

The invention discloses an ionized layer electron density profile abnormal fluctuation detection method, system and equipment. The method comprises the following steps: calculating a relative electron density change rate between adjacent height points of each effective electron density profile; the height axis is divided into a plurality of intervals, for each effective electron density profile, the robust percentile index of the relative electron density change rate is counted in each height interval, and a noise upper limit threshold value, a sawtooth amplitude threshold value and an extreme sudden change threshold value are generated; formulating a three-level progressive judgment process comprising a high noise background criterion, an extreme single-point jump criterion and an effective sawtooth structure criterion based on a noise upper limit threshold, an extreme sudden change threshold and a sawtooth amplitude threshold; and if any criterion is triggered, determining that the electron density profile is invalid. According to the method, non-physical sawtooth oscillation and extreme jump can be efficiently and accurately identified, and the quality control capability of data before ionosphere modeling is remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of space environment monitoring and data quality control technology, and in particular relates to a method, system and equipment for detecting abnormal fluctuations in ionospheric electron density profile. Background Technology

[0002] The ionosphere, as a region in Earth's atmosphere with significant electromagnetic properties, has a crucial impact on applications such as radio wave propagation, satellite navigation, and space weather monitoring due to its electron density distribution. Therefore, accurate ionospheric electron density modeling has always been a key aspect of space environment research and applications. Currently, ionospheric modeling mainly relies on the fusion processing of multiple data sources, including ground-based observations (such as ionospheric altimeters and GNSS inversion) and space-based observations (such as radio occultation and in-situ satellite measurements).

[0003] Theoretically, the electron density profile of the real ionosphere exhibits a smooth, layered structure (such as the E layer, F1 layer, and F2 layer). Although a significant gradient exists near the peak region of the F2 layer, the overall change is continuous and without high-frequency oscillations. However, in actual observations or inversion processes, due to factors such as instrument noise, signal multipath effects, and numerical instability of inversion algorithms, non-physical "sawtooth" anomalies often appear—that is, the electron density rapidly and alternately rises and falls with altitude, forming a pseudo-structure resembling sawtooths. Such anomalies do not reflect the true state of the ionosphere. If they are not effectively eliminated and are directly used for modeling, it will lead to distortion of local ionospheric parameters, significantly reducing navigation and positioning accuracy and the reliability of space weather products.

[0004] Existing quality control methods mostly employ global fixed thresholds, smoothing filters, or consistency checks, primarily targeting obvious outliers or overall trend deviations, lacking a dedicated mechanism for identifying the specific morphological anomaly of "sawtooth" patterns. Especially in the high gradient region of layer F, fixed thresholds can easily misjudge normal physical structures as anomalies; while in low-density, high-layer regions, they may miss weak but systematic oscillation noise.

[0005] Currently, no publicly available technical solutions offer a systematic method for identifying and removing sawtooth-shaped electron density profiles. Therefore, a dynamic discrimination technique capable of adapting to the physical characteristics of different altitude regions is urgently needed to improve the intelligence level and robustness of ionospheric data preprocessing and modeling. Summary of the Invention

[0006] The purpose of this invention is to overcome the above-mentioned defects and problems in the prior art and provide a method, system and equipment for detecting abnormal fluctuations in ionospheric electron density profiles, which can efficiently and accurately identify non-physical sawtooth oscillations and extreme jumps, and significantly improve the quality control capability of data before ionospheric modeling.

[0007] To achieve the above objectives, the technical solution of the present invention is:

[0008] In a first aspect, the present invention provides a method for detecting abnormal fluctuations in the electron density profile of the ionosphere, comprising:

[0009] Calculate the relative rate of change of electron density between adjacent height points for each effective electron density profile;

[0010] The height axis is divided into several intervals. For each effective electron density profile, a robust percentile index of the relative electron density change rate is statistically analyzed in each height interval to generate noise upper limit threshold, sawtooth amplitude threshold and extreme mutation threshold.

[0011] Based on the noise upper limit threshold, extreme mutation threshold, and sawtooth amplitude threshold, a three-level progressive judgment process is established, including high noise background criteria, extreme single-point jump criteria, and valid sawtooth structure criteria; if any criterion is triggered, the electron density profile is determined to be invalid.

[0012] Preferably, the method for calculating the relative electron density change rate is as follows:

[0013] For each valid electron density profile, calculate the absolute value of electron density between adjacent height points:

[0014] ;

[0015] In the formula, This represents the absolute difference in electron density. For height point electron density; For height point electron density;

[0016] Normalizing based on the smaller electron density value between two adjacent points, we obtain the relative rate of change of electron density:

[0017] ;

[0018] In the formula, This represents the rate of change in relative electron density.

[0019] Preferably, the step of generating a robust percentile index for the rate of change of relative electron density within each height interval, and generating a noise upper limit threshold, a sawtooth amplitude threshold, and an extreme abrupt change threshold, includes:

[0020] Within each height interval, the 75th percentile, 95th percentile, and maximum value of the relative electron density change rate are statistically analyzed to obtain the first, second, and third statistics, respectively. The first, second, and third statistics of all electron density profiles within the same height interval are summarized. Then, the 75th percentile of the first statistic is taken to obtain the sawtooth amplitude threshold, the 75th percentile of the second statistic is taken to obtain the noise upper limit threshold, and the 75th percentile of the third statistic is taken and multiplied by 1.2 to obtain the extreme mutation threshold.

[0021] Preferably, the high-noise background criterion is:

[0022] The proportion of the relative electron density change rate exceeding the upper limit threshold of the noise level. :

[0023] ;

[0024] In the formula, It represents the rate of change of relative electron density; For height point The upper limit threshold for noise; This represents the total number of data points in the electron density profile data.

[0025] If the proportion exceeds the preset threshold, it indicates that the electron density profile is contaminated by high-frequency noise, and the electron density profile is deemed invalid.

[0026] Preferably, the extreme single-point jump criterion is:

[0027] If there are two or more height points within the valid height range that satisfy:

[0028] ;

[0029] In the formula, It represents the rate of change of relative electron density; For height point The extreme mutation threshold;

[0030] This indicates that the electron density profile contains a non-physical abrupt change, and the electron density profile is deemed invalid.

[0031] Preferably, the criterion for the effective serrated structure is:

[0032] Calculate the sign change of the electron density difference sequence to locate potential sawtooth vertices;

[0033] For each sign flip point, if at least one of the two adjacent relative electron density change rates is not lower than the sawtooth amplitude threshold, then it is a valid sawtooth vertex.

[0034] The maximum continuous length of the effective sawtooth vertices is counted. If the maximum continuous length is greater than or equal to the set threshold, it indicates that there is a systematic, non-physical periodic oscillation in the electron density profile, and the electron density profile is judged to have sawtooth anomalies.

[0035] Preferably, the method for determining the potential sawtooth vertices is as follows:

[0036] Given electron density profile and corresponding height The calculation of the first-order difference between adjacent height points is divided into:

[0037] ;

[0038] In the formula, This represents the change in electron density between two adjacent altitude points; For height point electron density; For height point electron density;

[0039] Extracting the differential symbol sequence :

[0040] ;

[0041] Calculate the difference of symbol sequences :

[0042] ;

[0043] like This indicates that at the altitude point At this point, the trend of electron density change reverses:

[0044] If the difference sign sequence changes from +1 to -1, it indicates the height point. It is a local maximum point;

[0045] If the difference sign sequence changes from -1 to +1, it indicates the height point. This is a local minimum point;

[0046] The locations of local maxima and local minima are the potential zigzag vertices.

[0047] Secondly, the present invention provides a system for detecting anomalous fluctuations in ionospheric electron density profiles. The system is used to implement the aforementioned method for detecting anomalous fluctuations in ionospheric electron density profiles. The system includes:

[0048] The relative electron density change rate acquisition module is used to calculate the relative electron density change rate between adjacent height points of each effective electron density profile;

[0049] The dynamic threshold acquisition module is used to divide the height axis into several intervals. For each effective electron density profile, it calculates the robust percentile index of the relative electron density change rate in each height interval and generates the noise upper limit threshold, sawtooth amplitude threshold and extreme mutation threshold.

[0050] The profile anomaly determination module is used to formulate a three-level progressive determination process based on the noise upper limit threshold, extreme mutation threshold, and sawtooth amplitude threshold, including high noise background criteria, extreme single-point jump criteria, and valid sawtooth structure criteria; if any criterion is triggered, the electron density profile is determined to be invalid.

[0051] Thirdly, the present invention provides an ionospheric electron density profile abnormal fluctuation detection device, including a memory and a processor;

[0052] The memory is used to store computer program code and transmit the computer program code to the processor;

[0053] The processor is configured to execute, according to instructions in the computer program code, a method for detecting abnormal fluctuations in ionospheric electron density profiles as described above.

[0054] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the method for detecting abnormal fluctuations in ionospheric electron density profiles as described above.

[0055] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0056] The present invention discloses a method, system, and device for detecting anomalous fluctuations in ionospheric electron density profiles. By fusing segmented dynamic thresholds with multi-level criteria, high-precision identification of sawtooth-shaped anomalous profiles is achieved, offering the following advantages:

[0057] (1) Strong adaptability: The threshold is dynamically adjusted with height, taking into account both the stability of the E layer and the high gradient characteristics of the F layer;

[0058] (2) Low false alarm rate: Through structural features (sign flip) and amplitude dual verification, the true gradient is avoided from being misjudged as an anomaly;

[0059] (3) High detection rate: It covers a variety of anomaly types such as high noise, extreme jumps, and periodic oscillations;

[0060] (4) Strong engineering practicality: The algorithm is simple and easy to integrate into the existing ionospheric data processing flow. Attached Figure Description

[0061] Figure 1This is a flowchart of a method for detecting abnormal fluctuations in ionospheric electron density profiles according to an embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram illustrating the analysis of Case 1, which shows the qualified electron density profile proposed in the embodiments of the present invention.

[0063] Figure 3 This is a schematic diagram illustrating the analysis of Case 2, which shows the qualified electron density profile proposed in the embodiments of the present invention.

[0064] Figure 4 This is a schematic diagram illustrating the analysis of Case 1, a non-compliant electron density profile proposed in an embodiment of the present invention.

[0065] Figure 5 This is a schematic diagram illustrating the analysis of Case 2, which shows an electron density profile of substandard quality, as presented in an embodiment of the present invention.

[0066] Figure 6 This is a schematic diagram illustrating the analysis of Case 3, a non-compliant electron density profile proposed in an embodiment of the present invention.

[0067] Figure 7 This is a structural block diagram of an ionospheric electron density profile abnormal fluctuation detection system proposed in an embodiment of the present invention.

[0068] Figure 8 This is a structural block diagram of an ionospheric electron density profile abnormal fluctuation detection device proposed in an embodiment of the present invention. Detailed Implementation

[0069] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0070] See Figure 1 This invention provides a method for detecting anomalous fluctuations in ionospheric electron density profiles, comprising:

[0071] S1. Calculate the relative rate of change of electron density between adjacent height points of each effective electron density profile;

[0072] S2. Divide the height axis into several intervals. For each effective electron density profile, calculate the robust percentile index of the relative electron density change rate in each height interval to generate the noise upper limit threshold, sawtooth amplitude threshold and extreme mutation threshold.

[0073] S3. Based on the noise upper limit threshold, extreme mutation threshold, and sawtooth amplitude threshold, a three-level progressive judgment process is formulated, including high noise background criteria, extreme single-point jump criteria, and effective sawtooth structure criteria; if any criterion is triggered, the electron density profile is determined to be invalid.

[0074] To overcome the problem that a globally fixed threshold cannot adapt to the non-uniformity of ionospheric height, this invention constructs a height-stratified dynamic threshold system based on historical high-quality electron profile data (such as long-term observations from COSMIC, Tianmu-1, Fengyun-3, the Yunyao constellation, and ionospheric altimeters). This invention is applicable to the automatic quality screening of multi-source observation data such as radio occultation (RO), ionospheric altimeter, and GNSS inversion, and is particularly useful for identifying non-physical structural anomalies such as high-frequency sawtooth oscillations and abrupt changes.

[0075] The core idea of ​​this invention is that local changes in the real ionospheric profile exhibit high dependence and statistical regularity, while non-physical anomalies manifest as random or periodic, drastic fluctuations unrelated to altitude. Based on this, this invention proposes a detection strategy combining segmented dynamic thresholds and multiple criteria fusion. Specifically, it includes: constructing a highly adaptive dynamic threshold system: utilizing a large amount of historical "normal" profile data, calculating robust percentile indices of relative electron density change rates for each altitude range to generate three types of thresholds—noise upper limit threshold, sawtooth amplitude threshold, and extreme abrupt change threshold; designing a three-level progressive discrimination process: sequentially screening for high-noise background, extreme single-point jumps, and valid sawtooth structures; triggering any condition results in an invalid profile. Compared to traditional methods, this invention has the advantages of high adaptability, low false alarm rate, and high detection rate, making it particularly suitable for quality control of multi-source ionospheric data in complex space environments.

[0076] Furthermore, the original electron density profile data is first processed using conventional quality control procedures to remove obviously invalid data. Then, through manual visual screening, abnormal profiles with non-physical oscillation characteristics such as "sawtooth" are further removed, ultimately forming a pure "normal profile" training set, i.e., an effective electron density profile dataset.

[0077] Furthermore, the method for calculating the relative electron density change rate is as follows:

[0078] For each valid electron density profile, calculate the absolute value of electron density between adjacent height points:

[0079] ;

[0080] In the formula, This represents the absolute difference in electron density. For height point electron density; For height point electron density;

[0081] Normalizing based on the smaller electron density value between two adjacent points, we obtain the relative rate of change of electron density:

[0082] ;

[0083] In the formula, It represents the rate of change of relative electron density; This indicates the smaller electron density value between two adjacent points.

[0084] Furthermore, the robust percentile index of the relative electron density change rate within each height interval is used to generate a noise upper limit threshold, a sawtooth amplitude threshold, and an extreme abrupt change threshold, including:

[0085] The rate of change of relative electron density was statistically analyzed within each altitude interval (e.g., altitude intervals were divided into intervals of 50 km along the altitude axis). The 75th percentile (P75), 95th percentile (P95), and maximum value (Max) are used to obtain the first, second, and third statistics, respectively. These statistics are then combined across all electron density profiles within the same height range. The 75th percentile of the first statistic is used to obtain the sawtooth amplitude threshold (P75_of_P75), the 75th percentile of the second statistic is used to obtain the noise upper limit threshold (P75_of_P95), and the 75th percentile of the third statistic is multiplied by 1.2 to obtain the extreme mutation threshold (1.2×P75_of_Max). This results in a dynamic threshold table that varies with height, used for anomaly detection in subsequent profiles.

[0086] This invention, through hierarchical statistics and a robust percentile strategy, accurately identifies non-physical jagged anomalies while preserving normal physical fluctuations, significantly improving the adaptability and reliability of quality control.

[0087] Based on the aforementioned dynamic thresholds, this invention designs a three-level progressive criterion, whereby the profile is deemed invalid if any criterion is triggered. First, the upper limit threshold for noise is used to assess whether the profile is in an overall high-noise state. Then, the extreme mutation threshold is used to check whether there are drastic single-point jumps in the profile. Finally, the sawtooth amplitude threshold is used to identify more deceptive "regular sawtooth" anomalies (manifested as alternating rising / falling oscillations).

[0088] Furthermore, the high-noise background criterion (overall fluctuation intensity screening) is as follows:

[0089] The proportion of the relative electron density change rate exceeding the upper limit threshold of the noise level. :

[0090] ;

[0091] In the formula, It represents the rate of change of relative electron density; For height point The upper limit threshold for noise; This represents the total number of data points in the electron density profile data.

[0092] If the proportion exceeds a preset threshold (e.g., 20%), it indicates that the electron density profile is contaminated by high-frequency noise and lacks physical reliability, thus the electron density profile is deemed invalid.

[0093] Furthermore, the extreme single-point transition criterion (non-physical transition screening) is as follows:

[0094] Extreme mutation thresholds are used to identify points with abnormally high rates of change in relative electron density. If two or more height points exist within the effective height range that satisfy the following conditions:

[0095] ;

[0096] In the formula, It represents the rate of change of relative electron density; For height point The extreme mutation threshold;

[0097] This indicates that the electron density profile contains a non-physical abrupt change, and the electron density profile is deemed invalid.

[0098] Furthermore, the effective sawtooth structure criterion (periodic oscillation identification) adopts a joint discrimination strategy of structure + amplitude, specifically as follows:

[0099] (1) Detect sign reversal. Calculate the sign change of the electron density difference sequence to locate potential sawtooth vertices (i.e., locate potential local maxima / minimum points);

[0100] (2) Verify that the amplitude meets the standard. For each sign flip point, if at least one of the two adjacent relative electron density change rates is not lower than the sawtooth amplitude threshold zigzagThrVec, then it is a valid sawtooth vertex;

[0101] (3) Continuity assessment. The maximum continuous length of the effective sawtooth vertices is statistically analyzed. If the maximum continuous length is greater than or equal to the set threshold (the set threshold is 5), it indicates that there is a systematic, non-physical periodic oscillation in the electron density profile, and the electron density profile is judged to have sawtooth anomalies.

[0102] Furthermore, the essence of sawtooth anomalies is that electron density alternately rises and falls with height, forming a continuous sequence of local extrema. This invention accurately locates potential sawtooth vertices through the following steps; the method for determining the potential sawtooth vertices is as follows:

[0103] (1) Calculate the first-order difference (reflecting the local trend of change):

[0104] Given electron density profile and corresponding height The first-order difference (i.e., change) between adjacent height points is calculated as follows:

[0105] ;

[0106] In the formula, This represents the change in electron density between two adjacent altitude points; For height point electron density; For height point electron density;

[0107] (2) Extract the difference symbol sequence :

[0108] ;

[0109] (3) Check if the symbol has changed:

[0110] Calculate the difference of symbol sequences :

[0111] ;

[0112] like This indicates that at the altitude point At this point, the trend of electron density change reverses:

[0113] If the difference sign sequence changes from +1 to -1, it indicates the height point. This is a local maximum (peak);

[0114] If the difference sign sequence changes from -1 to +1, it indicates the height point. This is a local minimum point (valley);

[0115] The locations of local maxima and minima are potential sawtooth vertices. By combining amplitude and continuity constraints, the true ionospheric structure and pseudo-oscillations can be effectively distinguished.

[0116] The conventional electron density profile quality control method uses the following four quality control indicators to screen the raw electron density profile data.

[0117] (1) Average relative deviation:

[0118] ;

[0119] In the formula, The average relative deviation; This represents the total number of electron density samples in a single profile; For the first Electron density inversion value at each sampling point; This is the background value obtained by filtering with a 7-point moving average.

[0120] If a certain cross-section If the value is greater than 0.25, it is judged as an abnormal profile and is removed.

[0121] (2) Noise factor:

[0122] ;

[0123] In the formula, Noise factor; This refers to the number of sampling points within an altitude range above 300km.

[0124] like If the profile is deemed to have excessive fluctuations in the height region, it is marked as a problem profile.

[0125] (3) Top electron density gradient:

[0126] ;

[0127] In the formula, The top electron density gradient; and The values ​​are electron density at altitudes of 490 km and 420 km, respectively, with an altitude interval of 70 km.

[0128] like If the density at the top decreases too slowly, it may reflect an inversion bias, and the profile is marked as a problem profile.

[0129] (4) and Reasonable range:

[0130] Based on existing research experience It is usually located between 180 and 450 km, while The reasonable range is to If any parameter exceeds the above range, the entire profile will be considered abnormal and discarded.

[0131] Based on the above four criteria, any electron density profile that meets any of the rejection conditions is marked as a problem profile.

[0132] Figure 2 and Figure 3 The application effect of the abnormal profile quality discrimination method proposed in this invention is demonstrated by taking the profiles ionPrf_TM01.2024.215.10.48.E24_0001.0001.nc (quality qualified electron density profile case 1) and ionPrf_TM01.2024.215.00.01.G03_0001.0001.nc (quality qualified electron density profile case 2) as examples. Figure 2 (a) and Figure 3(a) is a schematic diagram of electron density distribution with height, from Figure 2 (a) and Figure 3 (a) It can be seen that both electron density profiles exhibit smooth and continuous variation characteristics, without obvious sawtooth oscillations; Figure 2 (b) and Figure 3 (b) is a schematic diagram showing the distribution of electron density change and dynamic threshold with height. Figure 2 (b) and Figure 3 (b) It can be seen that the corresponding relative electron density change rate does not exceed the three dynamic thresholds (sawtooth amplitude threshold, noise upper limit threshold, and extreme mutation threshold) set by the present invention throughout the entire height range. The results show that the present invention can accurately identify and retain qualified profiles that conform to physical properties.

[0133] Figure 4 Taking the typical sawtooth-shaped abnormal electron density profile ionPrf_TM01.2024.215.00.57.R24_0001.0001.nc (Case 1 of substandard electron density profile) as an example, Figure 5 Taking the typical sawtooth-shaped abnormal electron density profile ionPrf_TM02.2024.215.00.57.R20_0001.0001.nc (Case 2 of substandard electron density profile) as an example, Figure 6 Taking the typical sawtooth-shaped abnormal electron density profile ionPrf_TM01.2024.215.00.35.G05_0001.0001.nc (Case 3 of unqualified electron density profile) as an example, the application effect of the abnormal profile quality discrimination method proposed in this invention is demonstrated. Figure 4 (a) Figure 5 (a) and Figure 6 (a) is a schematic diagram of electron density distribution with height. Figure 4 (b) Figure 5 (b) and Figure 6 (b) is a schematic diagram showing the distribution of electron density change and dynamic threshold with height. Figure 4 (a) Figure 5 (a) and Figure 6 (a) It can be seen that all three profiles exhibit obvious non-physical sawtooth-like undulation characteristics, among which Figure 4 (a) and Figure 5 The oscillations in the cross-section shown in (a) are particularly violent; by Figure 4 (b) Figure 5 (b) and Figure 6(b) It is evident that the corresponding relative electron density change rate significantly exceeds the three dynamic thresholds set by this invention in multiple height ranges—including the sawtooth amplitude threshold (P75_of_P75), the noise upper limit threshold (P75_of_P95), and the extreme mutation threshold (1.2×P75_of_Max). The results show that this invention can effectively identify such high-frequency oscillation anomalies and mark them as unqualified profiles.

[0134] Table 1 lists the traditional quality control indicators corresponding to the three typical sawtooth-shaped anomalous electron density profiles mentioned above. Among them, the R24 profile, with a maximum height of only 489 km, does not fully cover the 420-490 km interval, therefore the top gradient cannot be calculated; the remaining indicators are as follows: the mean relative deviation (MRD) are 0.07, 0.08, and 0.03, respectively, all far below the rejection threshold of 0.25; the upper-level noise factor Δ is respectively... , and Both are less than 0.01; the top gradients of R20 and G05 are respectively and Both meet the "rapid descent" requirement; hmF2 (294.95-350.00 km) and NmF2 ( All three sections are within the physically reasonable range. Therefore, under existing industry standards, these three sections, which clearly exhibit jagged anomalies, did not trigger any traditional quality control criteria and were incorrectly judged as "qualified" sections.

[0135] Table 1. Statistics of quality discrimination indicators for traditional methods

[0136]

[0137] This comparison clearly demonstrates that traditional methods are ineffective in identifying jagged structural anomalies, while the present invention significantly improves the detection capability of such hidden anomalies by fusing segmented dynamic thresholds with structural features.

[0138] See Figure 7 An ionospheric electron density profile anomaly fluctuation detection system is provided. The system is used to implement the aforementioned ionospheric electron density profile anomaly fluctuation detection method. The system includes:

[0139] The relative electron density change rate acquisition module is used to calculate the relative electron density change rate between adjacent height points of each effective electron density profile;

[0140] The dynamic threshold acquisition module is used to divide the height axis into several intervals. For each effective electron density profile, it calculates the robust percentile index of the relative electron density change rate in each height interval and generates the noise upper limit threshold, sawtooth amplitude threshold and extreme mutation threshold.

[0141] The profile anomaly determination module is used to formulate a three-level progressive determination process based on the noise upper limit threshold, extreme mutation threshold, and sawtooth amplitude threshold, including high noise background criteria, extreme single-point jump criteria, and valid sawtooth structure criteria; if any criterion is triggered, the electron density profile is determined to be invalid.

[0142] See Figure 8 The present invention also provides an ionospheric electron density profile abnormal fluctuation detection device, including a memory and a processor;

[0143] The memory is used to store computer program code and transmit the computer program code to the processor;

[0144] The processor is configured to execute, according to instructions in the computer program code, a method for detecting abnormal fluctuations in ionospheric electron density profiles as described above.

[0145] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described method for detecting abnormal fluctuations in ionospheric electron density profiles.

[0146] Generally, the computer instructions for implementing the method of the present invention can be carried on any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable medium except for the signal itself, which is temporarily propagating.

[0147] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EKROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0148] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, and platform frameworks such as TensorFlow and PyTorch can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer or to an external computer (e.g., via the Internet using an Internet service provider) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0149] The aforementioned equipment and non-transitory computer-readable storage media can be found in the detailed description of a method for detecting abnormal fluctuations in ionospheric electron density profiles and its beneficial effects, which will not be repeated here.

[0150] Although embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for detecting anomalous fluctuations in ionospheric electron density profiles, characterized in that, include: Calculate the relative rate of change of electron density between adjacent height points for each effective electron density profile; The height axis is divided into several intervals. For each effective electron density profile, a robust percentile index of the relative electron density change rate is statistically analyzed in each height interval to generate noise upper limit threshold, sawtooth amplitude threshold and extreme mutation threshold. Based on the noise upper limit threshold, extreme mutation threshold, and sawtooth amplitude threshold, a three-level progressive judgment process is established, including high noise background criteria, extreme single-point jump criteria, and valid sawtooth structure criteria; if any criterion is triggered, the electron density profile is determined to be invalid.

2. The method for detecting abnormal fluctuations in ionospheric electron density profiles according to claim 1, characterized in that, The method for calculating the relative electron density change rate is as follows: For each valid electron density profile, calculate the absolute value of electron density between adjacent height points: ； In the formula, This represents the absolute difference in electron density. For height point electron density; For height point electron density; Normalizing based on the smaller electron density value between two adjacent points, we obtain the relative rate of change of electron density: ； In the formula, This represents the rate of change in relative electron density.

3. The method for detecting abnormal fluctuations in ionospheric electron density profiles according to claim 1, characterized in that, The robust percentile index of the relative electron density change rate within each height interval is used to generate noise upper limit threshold, sawtooth amplitude threshold, and extreme abrupt change threshold, including: Within each height interval, the 75th percentile, 95th percentile, and maximum value of the relative electron density change rate are statistically analyzed to obtain the first, second, and third statistics, respectively. The first, second, and third statistics of all electron density profiles within the same height interval are summarized. Then, the 75th percentile of the first statistic is taken to obtain the sawtooth amplitude threshold, the 75th percentile of the second statistic is taken to obtain the noise upper limit threshold, and the 75th percentile of the third statistic is taken and multiplied by 1.2 to obtain the extreme mutation threshold.

4. The method for detecting abnormal fluctuations in ionospheric electron density profiles according to claim 1, characterized in that, The high-noise background criterion is: The proportion of the relative electron density change rate exceeding the upper limit threshold of the noise level. : ； In the formula, It represents the rate of change of relative electron density; For height point The upper limit threshold for noise; This represents the total number of data points in the electron density profile data. If the proportion exceeds the preset threshold, it indicates that the electron density profile is contaminated by high-frequency noise, and the electron density profile is deemed invalid.

5. The method for detecting abnormal fluctuations in ionospheric electron density profiles according to claim 1, characterized in that, The extreme single-point jump criterion is: If there are two or more height points within the valid height range that satisfy: ； In the formula, It represents the rate of change of relative electron density; For height point The extreme mutation threshold; This indicates that the electron density profile contains a non-physical abrupt change, and the electron density profile is deemed invalid.

6. The method for detecting abnormal fluctuations in ionospheric electron density profiles according to claim 1, characterized in that, The criterion for an effective sawtooth structure is: Calculate the sign change of the electron density difference sequence to locate potential sawtooth vertices; For each sign flip point, if at least one of the two adjacent relative electron density change rates is not lower than the sawtooth amplitude threshold, then it is a valid sawtooth vertex. The maximum continuous length of the effective sawtooth vertices is counted. If the maximum continuous length is greater than or equal to the set threshold, it indicates that there is a systematic, non-physical periodic oscillation in the electron density profile, and the electron density profile is judged to have sawtooth anomalies.

7. The method for detecting abnormal fluctuations in ionospheric electron density profiles according to claim 6, characterized in that, The method for determining the potential sawtooth vertices is as follows: Given electron density profile and corresponding height The calculation of the first-order difference between adjacent height points is divided into: ； In the formula, This represents the change in electron density between two adjacent altitude points; For height point electron density; For height point electron density; Extracting the differential symbol sequence : ； Calculate the difference of symbol sequences : ； like This indicates that at the altitude point At this point, the trend of electron density change reverses: If the difference sign sequence changes from +1 to -1, it indicates the height point. It is a local maximum point; If the difference sign sequence changes from -1 to +1, it indicates the height point. This is a local minimum point; The locations of local maxima and local minima are the potential zigzag vertices.

8. A system for detecting anomalous fluctuations in ionospheric electron density profiles, characterized in that, The system is used to implement the method according to any one of claims 1 to 7, the system comprising: The relative electron density change rate acquisition module is used to calculate the relative electron density change rate between adjacent height points of each effective electron density profile; The dynamic threshold acquisition module is used to divide the height axis into several intervals. For each effective electron density profile, it calculates the robust percentile index of the relative electron density change rate in each height interval and generates the noise upper limit threshold, sawtooth amplitude threshold and extreme mutation threshold. The profile anomaly determination module is used to formulate a three-level progressive determination process based on the noise upper limit threshold, extreme mutation threshold, and sawtooth amplitude threshold, including high noise background criteria, extreme single-point jump criteria, and valid sawtooth structure criteria; if any criterion is triggered, the electron density profile is determined to be invalid.

9. A device for detecting abnormal fluctuations in ionospheric electron density profiles, characterized in that, Including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method as described in any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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