Ionospheric anomaly determination method and device, computer device and storage medium
By constructing the ionospheric sequence to be detected and sub-sequences, calculating the slope of fluctuation values and optimizing the anomaly threshold, the problem of inaccurate thresholds in traditional ionospheric anomaly judgment is solved, and the judgment accuracy is improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHENZHEN POWER SUPPLY BUREAU
- Filing Date
- 2022-12-23
- Publication Date
- 2026-06-19
Smart Images
Figure CN115963515B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of ionospheric anomaly detection technology, and in particular to a method, apparatus, computer equipment, and storage medium for determining ionospheric anomalies. Background Technology
[0002] The ionosphere is an ionized region of the Earth's atmosphere containing a considerable number of free electrons and ions. During extreme geological events such as earthquakes, tsunamis, and solar storms, the free electrons in the ionosphere will exhibit corresponding abnormal phenomena synchronously or even in advance. Therefore, detecting abnormal changes in the ionosphere can be used to predict disasters in advance.
[0003] In traditional techniques, the total electron content in the ionosphere is typically estimated using satellite systems. Based on the estimation results, the data in the ionosphere is further analyzed. Finally, the analysis results are compared with anomaly thresholds used to determine whether an anomaly has occurred in the ionosphere, and the anomaly is determined based on the comparison results.
[0004] However, traditional techniques rely excessively on anomaly thresholds to determine ionospheric anomalies. When the anomaly thresholds are not accurate enough, the accuracy of ionospheric anomaly determination will be low. Summary of the Invention
[0005] Therefore, it is necessary to provide an ionospheric anomaly determination method, apparatus, computer equipment, computer-readable storage medium, and computer program product that can improve the accuracy of ionospheric anomaly determination in response to the above-mentioned technical problems.
[0006] Firstly, this application provides a method for determining ionospheric anomalies. The method includes:
[0007] The electron wave values of the ionosphere at each epoch are obtained, and the detection sequence is constructed based on the selected electron wave values of multiple epochs.
[0008] Based on a sliding window, multiple subsequences of equal length are extracted from the sequence to be detected.
[0009] For each subsequence to be detected, the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected is obtained, and multiple fluctuation value slopes corresponding to the subsequence to be detected are obtained.
[0010] Based on the slope of multiple fluctuation values, the radius of the slope confidence interval of the subsequence to be detected is determined, and the initial anomaly threshold is optimized based on the slope of multiple fluctuation values to obtain the target anomaly threshold;
[0011] When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has anomalies in multiple epochs corresponding to the subsequence to be detected.
[0012] In one embodiment, extracting multiple subsequences of equal length from the sequence to be detected based on a sliding window includes:
[0013] A sliding window of fixed size is controlled to slide on the sequence to be detected with a fixed step size, and the electronic fluctuation value covered by the sliding window after each slide is determined.
[0014] Based on the electron fluctuation values covered by the sliding window after each sliding, multiple sub-sequences to be detected are constructed.
[0015] In one embodiment, each subsequence to be detected includes at least three epochs. For each subsequence to be detected, the fluctuation slope between two adjacent epochs in the subsequence to be detected is obtained, resulting in multiple fluctuation slopes corresponding to the subsequence to be detected, including:
[0016] For each subsequence to be detected, obtain the time interval between two adjacent epochs;
[0017] Based on the time interval and the electronic fluctuation values of adjacent epochs, the fluctuation slope between adjacent epochs is obtained, resulting in multiple fluctuation slopes corresponding to the subsequence to be detected.
[0018] In one embodiment, before determining the radius of the slope confidence interval for the subsequence to be detected based on multiple fluctuation value slopes, the following steps are included:
[0019] Based on the slope of multiple fluctuation values, the mean slope and root mean square deviation of the slope of the subsequence to be detected are obtained;
[0020] Based on the slope of multiple fluctuation values, the radius of the slope confidence interval for the subsequence to be detected is determined as follows:
[0021] Based on the mean slope and the root mean square deviation of the slope, the upper limit and lower limit of the slope confidence interval corresponding to the subsequence to be detected are determined;
[0022] Based on the upper limit and lower limit of the slope confidence interval, the radius of the slope confidence interval corresponding to the subsequence to be detected is determined.
[0023] In one embodiment, the initial anomaly threshold is optimized based on multiple fluctuation value slopes to obtain a target anomaly threshold, including:
[0024] Based on the empirical method, an initial anomaly threshold is obtained, and based on the slope of multiple fluctuation values corresponding to each subsequence to be detected, the average slope of each subsequence to be detected is obtained.
[0025] Based on the average slope of each subsequence to be detected, the initial anomaly threshold is optimized to obtain the target anomaly threshold for each subsequence to be detected.
[0026] In one embodiment, the ionospheric anomaly determination method further includes:
[0027] Traverse the slope confidence interval radius of multiple subsequences to be detected extracted from the sequence to be detected;
[0028] When the slope confidence interval radii of multiple sub-sequences extracted from the sequence to be detected do not exceed the target anomaly threshold, it is determined that the ionosphere is normal at multiple epochs in the sequence to be detected.
[0029] Secondly, this application also provides an ionospheric anomaly determination device. The device includes:
[0030] The module for constructing the sequence to be detected is used to obtain the electron wave values of the ionosphere at each epoch, and to construct the sequence to be detected based on the electron wave values of multiple selected epochs.
[0031] The module for extracting subsequences to be detected is used to extract multiple subsequences of equal length from the sequence to be detected based on a sliding window.
[0032] The fluctuation slope acquisition module is used to obtain the fluctuation slope between two adjacent epochs in each subsequence to be detected, and obtain multiple fluctuation slopes corresponding to the subsequence to be detected.
[0033] The target anomaly threshold acquisition module is used to determine the radius of the slope confidence interval of the subsequence to be detected based on multiple fluctuation value slopes, and to optimize the initial anomaly threshold based on multiple fluctuation value slopes to obtain the target anomaly threshold.
[0034] The anomaly detection module is used to determine that the ionosphere is abnormal at multiple epochs corresponding to the subsequence to be detected when the radius of the slope confidence interval exceeds the target anomaly threshold.
[0035] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform the following steps:
[0036] The electron wave values of the ionosphere at each epoch are obtained, and the detection sequence is constructed based on the selected electron wave values of multiple epochs.
[0037] Based on a sliding window, multiple subsequences of equal length are extracted from the sequence to be detected.
[0038] For each subsequence to be detected, the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected is obtained, and multiple fluctuation value slopes corresponding to the subsequence to be detected are obtained.
[0039] Based on the slope of multiple fluctuation values, the radius of the slope confidence interval of the subsequence to be detected is determined, and the initial anomaly threshold is optimized based on the slope of multiple fluctuation values to obtain the target anomaly threshold;
[0040] When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has anomalies in multiple epochs corresponding to the subsequence to be detected.
[0041] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, performs the following steps:
[0042] The electron wave values of the ionosphere at each epoch are obtained, and the detection sequence is constructed based on the selected electron wave values of multiple epochs.
[0043] Based on a sliding window, multiple subsequences of equal length are extracted from the sequence to be detected.
[0044] For each subsequence to be detected, the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected is obtained, and multiple fluctuation value slopes corresponding to the subsequence to be detected are obtained.
[0045] Based on the slope of multiple fluctuation values, the radius of the slope confidence interval of the subsequence to be detected is determined, and the initial anomaly threshold is optimized based on the slope of multiple fluctuation values to obtain the target anomaly threshold;
[0046] When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has anomalies in multiple epochs corresponding to the subsequence to be detected.
[0047] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, performs the following steps:
[0048] The electron wave values of the ionosphere at each epoch are obtained, and the detection sequence is constructed based on the selected electron wave values of multiple epochs.
[0049] Based on a sliding window, multiple subsequences of equal length are extracted from the sequence to be detected.
[0050] For each subsequence to be detected, the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected is obtained, and multiple fluctuation value slopes corresponding to the subsequence to be detected are obtained.
[0051] Based on the slope of multiple fluctuation values, the radius of the slope confidence interval of the subsequence to be detected is determined, and the initial anomaly threshold is optimized based on the slope of multiple fluctuation values to obtain the target anomaly threshold;
[0052] When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has anomalies in multiple epochs corresponding to the subsequence to be detected.
[0053] The aforementioned ionospheric anomaly determination method, apparatus, computer equipment, storage medium, and computer program product first acquire the electron fluctuation values of the ionosphere at each epoch. Then, based on the electron fluctuation values of multiple selected epochs, a sequence to be detected is constructed. Using a sliding window, multiple subsequences of equal length are extracted from the sequence to be detected. For each subsequence, the slope of the fluctuation values between two adjacent epochs is acquired, resulting in multiple fluctuation slopes corresponding to the subsequence. Then, based on the multiple fluctuation slopes, the radius of the slope confidence interval of the subsequence is determined. Based on the multiple fluctuation slopes, the initial anomaly threshold is optimized to obtain a target anomaly threshold. When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that an anomaly has occurred in the multiple epochs corresponding to the subsequence to be detected. Throughout the process, for each subsequence to be detected, the initial anomaly threshold is optimized according to the slope of the fluctuation value of each subsequence to be detected, so as to obtain the target anomaly threshold for each subsequence to be detected. This achieves adaptive optimization of the target anomaly threshold, so that each subsequence to be detected has its own corresponding more accurate anomaly judgment method, thereby improving the accuracy of ionospheric anomaly judgment. Attached Figure Description
[0054] Figure 1 This is a flowchart illustrating an ionospheric anomaly determination method in one embodiment;
[0055] Figure 2 This is a flowchart of an ionospheric anomaly determination method in one embodiment;
[0056] Figure 3 This is a flowchart illustrating the ionospheric anomaly determination method in another embodiment;
[0057] Figure 4 This is a structural block diagram of an ionospheric anomaly detection device in one embodiment;
[0058] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation
[0059] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.
[0060] In one embodiment, such as Figure 1As shown, an ionospheric anomaly determination method is provided. This embodiment illustrates the application of this method to a terminal. It is understood that this method can also be applied to a server, and further to a system including both a terminal and a server, and implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps:
[0061] Step 102: Obtain the electron fluctuation value of the ionosphere at each epoch, and construct the sequence to be detected based on the selected electron fluctuation values at multiple epochs.
[0062] Specifically, the epoch can be the sampling time at which the total electron content in the ionosphere is obtained. The electron fluctuation value can be the fluctuation value of the TEC (Total Electronic Content) in the ionosphere.
[0063] Optionally, the server can pre-obtain the total electron content of the ionosphere at each epoch, and then calculate the electron fluctuation value of the ionosphere at each epoch based on the total electron content of each epoch. Then, according to the detection requirements, multiple epochs are selected, and based on the electron fluctuation values of the selected multiple epochs, a sequence to be detected is constructed to detect whether the ionosphere is abnormal at the selected multiple epochs.
[0064] For example, the server can first determine the total electron content (TEC) of the ionosphere at each epoch, and then combine the epoch difference method with the averaging method to calculate the electron fluctuation value (dTEC) of the ionosphere at each epoch. The process of determining the total electron content of the ionosphere at each epoch can be specifically shown in formula (1):
[0065]
[0066] Where λ1 represents the carrier wavelength of carrier 1 that was collected, and λ2 represents the carrier wavelength of carrier 2 that was collected in the same epoch. This represents the carrier phase observation value of carrier 1. N1 represents the observed carrier phase of carrier 2. N2 represents the integer ambiguity of carrier 1 and N2 represents the integer ambiguity of carrier 2. f0 is a frequency constant that depends only on the frequencies of carrier 1 and carrier 2 and can be considered a known quantity.
[0067] Taking the i-th epoch as an example, the difference method and the averaging method are combined to calculate the electron wave value dTEC(t) of the ionosphere in the i-th epoch. i The process can be specifically shown in formula (2):
[0068] dTEC(t i )
[0069] =TEC(t)i )-aver{TEC(t i-p ), ...,TEC(t) i-1 ), TEC(t i+1 ), ...,TEC(t) i+p (2)
[0070] Here, aver is the average operator. TEC(t) i The total electron content of the ionosphere at epoch i is represented by formula (2). Formula (2) represents that the server divides the p epth and p epth epths before and after epth ...
[0071] Optionally, after completing the detection of the electron wave values of the selected multiple epochs, the server can continue to select multiple new epochs to construct new sequences to be detected, so as to continue to detect whether the ionosphere has anomalies in other epochs.
[0072] Step 104: Based on the sliding window, extract multiple subsequences of equal length from the sequence to be detected.
[0073] The sliding window is a fixed-size window that can cover a fixed number of electron fluctuation values for multiple consecutive epochs.
[0074] Optionally, for each subsequence to be detected, the server can extract multiple subsequences of equal length from the sequence to be detected by moving the sliding window over the sequence to be detected, thereby changing the epoch corresponding to the center of the sliding window. Here, equal length indicates that each subsequence to be detected contains an equal number of electron wave values.
[0075] Step 106: For each subsequence to be detected, obtain the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected, and obtain multiple slopes of the fluctuation value corresponding to the subsequence to be detected.
[0076] Optionally, for each subsequence to be detected, the server can obtain the rate of change of the electron fluctuation value between any two adjacent epochs, i.e., the fluctuation value slope, based on the electron fluctuation values of any two adjacent epochs in the subsequence to be detected. Furthermore, the server can iterate through multiple subsequences to be detected to obtain multiple fluctuation value slopes corresponding to each subsequence.
[0077] Step 108: Based on multiple fluctuation value slopes, determine the radius of the slope confidence interval of the subsequence to be detected, and optimize the initial anomaly threshold based on multiple fluctuation value slopes to obtain the target anomaly threshold.
[0078] The confidence interval refers to the estimated interval of the population parameter constructed from the sample statistics. The initial anomaly threshold is determined based on historical experience and is used to determine whether anomalies have occurred in the ionosphere.
[0079] Optionally, for each subsequence to be detected, the server can construct a slope confidence interval for the subsequence based on the slopes of multiple fluctuation values corresponding to the subsequence, determine the radius of the slope confidence interval, and optimize the initial anomaly threshold based on the multiple fluctuation value slopes to obtain the target anomaly threshold for the subsequence. Further, the server can iterate through multiple subsequences to be detected to obtain the target anomaly threshold corresponding to each subsequence.
[0080] Step 110: When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has anomalies in multiple epochs corresponding to the subsequence to be detected.
[0081] Optionally, for each subsequence to be detected, after determining the slope confidence interval radius and the target detection window of the subsequence to be detected, the server can compare the slope confidence interval radius of the subsequence to be detected with the target detection window. When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has anomalies at multiple epochs corresponding to the subsequence to be detected. Furthermore, the server can traverse multiple subsequences to be detected to detect whether the ionosphere has anomalies at multiple epochs corresponding to each subsequence to be detected.
[0082] In the above-mentioned method for determining ionospheric anomalies, the electron fluctuation values of the ionosphere at each epoch are first obtained. Then, based on the electron fluctuation values of the selected multiple epochs, a sequence to be detected is constructed. Based on a sliding window, multiple subsequences of equal length are extracted from the sequence to be detected. For each subsequence to be detected, the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected is obtained, resulting in multiple fluctuation value slopes corresponding to the subsequence to be detected. Then, based on the multiple fluctuation value slopes, the radius of the slope confidence interval of the subsequence to be detected is determined. Based on the multiple fluctuation value slopes, the initial anomaly threshold is optimized to obtain the target anomaly threshold. When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has an anomaly at multiple epochs corresponding to the subsequence to be detected. Throughout the process, for each subsequence to be detected, the initial anomaly threshold is optimized according to the slope of the fluctuation value of each subsequence to be detected, so as to obtain the target anomaly threshold for each subsequence to be detected. This achieves adaptive optimization of the target anomaly threshold, so that each subsequence to be detected has its own corresponding more accurate anomaly judgment method, thereby improving the accuracy of ionospheric anomaly judgment.
[0083] In one embodiment, extracting multiple subsequences of equal length from the sequence to be detected based on a sliding window includes:
[0084] A sliding window of fixed size is controlled to slide on the sequence to be detected with a fixed step size, and the electronic fluctuation value covered by the sliding window after each slide is determined.
[0085] Based on the electron fluctuation values covered by the sliding window after each sliding, multiple sub-sequences to be detected are constructed.
[0086] Optionally, the server can control a sliding window of fixed size to slide across the sequence to be detected, starting from the first electron wave value of the multiple electron wave values corresponding to the sequence to be detected, according to a fixed step size, until the sliding window covers the last electron wave value of the sequence to be detected, and determine the multiple electron wave values covered by the sliding window after each slide. Based on the multiple electron wave values covered by the sliding window after each slide, the server can construct the subsequence to be detected corresponding to the sliding window after each slide, thus obtaining multiple subsequences to be detected.
[0087] For example, a detection sequence X = [x1, x2, ..., xn] including electron wave values of n epochs is used. n Taking [example] as an example, when the size of the sliding window is m (the sliding window can cover the electron fluctuation values corresponding to m consecutive epochs), and the fixed step size is the electron fluctuation values of 1 epoch, the server can extract multiple subsequences X to be detected according to the fixed step size. j =[x j x j+1, ..., x j+m ],[j=1,2,...,nm],X j This refers to the j-th extracted subsequence to be detected.
[0088] In this embodiment, multiple sub-sequences to be detected are extracted from the sequence to be detected by a sliding window, so that the ionosphere can be accurately detected at which epoch an anomaly occurs by detecting multiple sub-sequences one by one.
[0089] In one embodiment, each subsequence to be detected includes at least three epochs. For each subsequence to be detected, the fluctuation slope between two adjacent epochs in the subsequence to be detected is obtained, resulting in multiple fluctuation slopes corresponding to the subsequence to be detected, including:
[0090] For each subsequence to be detected, obtain the time interval between two adjacent epochs;
[0091] Based on the time interval and the electronic fluctuation values of adjacent epochs, the fluctuation slope between adjacent epochs is obtained, resulting in multiple fluctuation slopes corresponding to the subsequence to be detected.
[0092] Optionally, for each two adjacent epochs in the subsequence to be detected, the server can obtain the time interval between the adjacent epochs, and based on the electron fluctuation values of each adjacent epoch, obtain the difference between the electron fluctuation values between the adjacent epochs, and then based on the time interval between the adjacent epochs and the difference between the electron fluctuation values, obtain the fluctuation value slope between the adjacent epochs, thereby obtaining multiple fluctuation value slopes corresponding to the subsequence to be detected.
[0093] For example, taking a sliding window of size m, where each subsequence to be detected includes m consecutive epochs corresponding to electron fluctuation values, the server can obtain m-1 fluctuation value slopes. Specifically, the server can obtain the fluctuation value slopes between adjacent epochs using formula (3):
[0094]
[0095] Where, k r Let x be the electron wave value at the r-th epoch in the subsequence to be detected. r The electron wave value x at the (r+1)th epoch r+1 The slope of the fluctuation value between two adjacent epochs, where Δt is the time interval between two adjacent epochs.
[0096] In this embodiment, by calculating the slope of multiple fluctuation values corresponding to the subsequence to be detected, a slope confidence interval can be constructed using the multiple fluctuation value slopes, so as to determine whether the ionosphere has an anomaly based on the properties of the slope confidence interval.
[0097] In one embodiment, before determining the radius of the slope confidence interval for the subsequence to be detected based on multiple fluctuation value slopes, the following steps are included:
[0098] Based on the slope of multiple fluctuation values, the mean slope and root mean square deviation of the slope of the subsequence to be detected are obtained;
[0099] Based on the slope of multiple fluctuation values, the radius of the slope confidence interval for the subsequence to be detected is determined as follows:
[0100] Based on the mean slope and the root mean square deviation of the slope, the upper limit and lower limit of the slope confidence interval corresponding to the subsequence to be detected are determined;
[0101] Based on the upper limit and lower limit of the slope confidence interval, the radius of the slope confidence interval corresponding to the subsequence to be detected is determined.
[0102] Optionally, for each subsequence to be detected, the server can obtain the mean slope and root mean square deviation of the slope of the subsequence based on the slopes of multiple fluctuation values corresponding to the subsequence. Then, based on the mean slope and root mean square deviation, the server can determine the upper and lower limits of the slope confidence interval corresponding to the subsequence. Finally, according to the formula for calculating the radius of the confidence interval, the server can determine the radius of the slope confidence interval corresponding to the subsequence. Furthermore, the server can obtain the slope confidence interval radii for each of the multiple subsequences to be detected in a similar manner.
[0103] For example, taking a sliding window of size m as an example, the process of determining the upper limit and lower limit of the slope confidence interval corresponding to the subsequence to be detected based on the mean slope and the root mean square error of the slope can be specifically shown in formulas (4) and (5):
[0104]
[0105]
[0106] Where, μ s Let σ be the mean slope of the s-th subsequence to be detected. s Let be the root mean square error of the slope of the s-th subsequence to be detected, Z be a random variable following a normal distribution, and α be the significance level. In this embodiment, α is set to 0.05. Let be the upper limit of the slope confidence interval for the s-th subsequence to be detected. This is the lower limit of the slope confidence interval for the s-th subsequence to be detected.
[0107] Taking a sliding window of size m as an example, the process of determining the radius of the slope confidence interval corresponding to the subsequence to be detected based on the upper limit and lower limit of the slope confidence interval can be specifically shown in formula (6):
[0108]
[0109] Among them, R s The radius of the slope confidence interval for the s-th subsequence to be detected can reflect the characteristic size of the confidence interval of the normal distribution to which the slope of the fluctuation value of the s-th subsequence to be detected follows when the confidence level is 95%. It can characterize the structural features of the s-th subsequence to be detected. In this embodiment, abnormal data can be detected based on the structural features of the s-th subsequence to be detected.
[0110] Optionally, for each subsequence to be detected, in the process of obtaining the mean slope and root mean square error of the slope of the subsequence to be detected based on the slopes of multiple fluctuation values of each subsequence to be detected, a sliding window of size m is used as an example for illustration. When the Lth subsequence to be detected X L =[x L x L+1 , ..., x L+m The (L+1)th subsequence to be detected, X L+1 =[x L+1 x L+2 , ..., x L+1+m The server can obtain X using formulas (7) and (8). L+1 The mean slope μ L+1 With the root mean square of the slope σ L+1 .
[0111]
[0112]
[0113] Where, μ L For X L The mean slope, σ L For X L The root mean square error of the slope, k L For x L x L+1 The slope of the fluctuation values between, k L+m For x L+m x L+m+1 The slope of the fluctuation value between them.
[0114] Based on formula (8), it can be seen that the server can determine the mean slope μ of the previous subsequence to be detected. L , and the root mean square error of the slope σ LBased on this, only the slope k of the first fluctuation value of the previous subsequence to be detected is needed. L The slope k of the last fluctuation value of the next subsequence to be detected L+m The mean slope μ of the next subsequence to be detected L+1 This allows for the rapid calculation of the root mean square error of the slope σ of the next target to be detected. L+1 .
[0115] In this embodiment, by determining the slope confidence interval radius of the subsequence to be detected, it is possible to determine whether an anomaly has occurred in the ionosphere based on the slope confidence interval radius of the subsequence to be detected.
[0116] In one embodiment, the initial anomaly threshold is optimized based on multiple fluctuation value slopes to obtain a target anomaly threshold, including:
[0117] Based on the empirical method, an initial anomaly threshold is obtained, and based on the slope of multiple fluctuation values corresponding to each subsequence to be detected, the average slope of each subsequence to be detected is obtained.
[0118] Based on the average slope of each subsequence to be detected, the initial anomaly threshold is optimized to obtain the target anomaly threshold for each subsequence to be detected.
[0119] Optionally, the server can use an empirical method to obtain an initial anomaly threshold for judging whether the ionosphere has sent anomalies, based on historical data and the standards used to judge whether the ionosphere has sent anomalies. Based on the slope of multiple fluctuation values corresponding to each subsequence to be detected, the server can obtain the average slope of multiple fluctuation values corresponding to each subsequence to be detected. Then, based on the average slope of each subsequence to be detected, the server can adaptively optimize the initial anomaly threshold to obtain the target anomaly threshold corresponding to each subsequence to be detected.
[0120] For example, the process of optimizing the initial anomaly threshold based on the mean slope of each subsequence to be detected can be specifically shown in formula (9):
[0121]
[0122] Where H is the initial anomaly threshold, which in this embodiment can be 0.1, μ s Let be the mean slope of the s-th subsequence to be detected. is the target anomaly threshold for the s-th subsequence to be detected.
[0123] In this embodiment, since electrons in the ionosphere are affected by factors such as seasonal changes, in order to avoid the anomaly judgment criteria being limited to the initial anomaly threshold (which is not accurate enough), the initial anomaly threshold can be adaptively optimized by the average slope of each subsequence to be detected, so as to obtain the target anomaly threshold for each subsequence to be detected, thereby improving the detection accuracy of ionospheric anomalies.
[0124] In one embodiment, the ionospheric anomaly determination method further includes:
[0125] Traverse the slope confidence interval radius of multiple subsequences to be detected extracted from the sequence to be detected;
[0126] When the slope confidence interval radii of multiple sub-sequences extracted from the sequence to be detected do not exceed the target anomaly threshold, it is determined that the ionosphere is normal at multiple epochs in the sequence to be detected.
[0127] Optionally, the server can iterate through the slope confidence interval radii of multiple subsequences to be detected extracted from the sequence to be detected, and compare the slope confidence interval radius of each subsequence to be detected with the target anomaly threshold of each subsequence to be detected one by one. When the slope confidence interval radii of multiple subsequences to be detected extracted from the sequence to be detected do not exceed the target anomaly threshold, it is determined that the ionosphere is normal in multiple epochs of the sequence to be detected.
[0128] For example, when the slope confidence interval radius of the s-th subsequence to be detected exceeds the target anomaly threshold, it indicates that the structure of the s-th subsequence to be detected is abnormal, that is, the ionosphere is abnormal in multiple epochs corresponding to the s-th subsequence to be detected.
[0129] For example, taking a sliding window of size m as an example, when the Lth subsequence to be detected X... L =[x L x L+1 , ..., x L+m The radius of the slope confidence interval exceeds X. L When the target anomaly threshold is reached, the server can determine that an anomaly has occurred in the ionosphere within multiple epochs corresponding to the Lth detected subsequence, while when X... L-1 =[x L-1 x L , ..., x L-1+m ]、X L+1 =[x L+1 x L+2 , ..., x L+1+m When the slope confidence interval radii of each subsequence do not exceed their respective target anomaly thresholds, the server can determine the x in the Lth subsequence to be detected. L This is an abnormal electronic fluctuation value.
[0130] In this embodiment, by comparing the slope confidence interval radius of each sequence to be detected with the target anomaly threshold, the effect of accurately determining ionospheric anomalies can be achieved.
[0131] In one embodiment, such as Figure 2 The diagram shows a flowchart of a method for determining ionospheric anomalies. The main steps include:
[0132] The process involves acquiring a sequence to be detected, inputting a fixed size for a sliding window, and extracting multiple sub-sequences by sliding the window across the sequence. For each sub-sequence, the slope of the fluctuation values between adjacent epochs is obtained. The mean and root mean square deviation of the slopes for each sub-sequence are calculated. Based on these slopes, the radius of the slope confidence interval for each sub-sequence is calculated. The initial anomaly threshold is then optimized based on the mean slope, yielding a target anomaly threshold for each sub-sequence. Further, for each sub-sequence, the ionosphere is assessed for anomalies within multiple epochs by determining whether the slope confidence interval radius exceeds the target anomaly threshold. The slope confidence interval radii of the extracted sub-sequences are iterated. If none of the slope confidence interval radii exceed the target anomaly threshold, the ionosphere is considered normal within the corresponding epochs of the sequence.
[0133] In another embodiment, such as Figure 3 As shown, a flowchart illustrating another method for determining ionospheric anomalies is provided. The main steps include:
[0134] Step 302: Obtain the electron wave values of the ionosphere at each epoch, and construct the detection sequence based on the selected electron wave values of multiple epochs.
[0135] Step 304: Slide a fixed-size sliding window over the sequence to be detected according to a fixed step size, determine the electronic fluctuation value covered by the sliding window after each slide, and construct multiple sub-sequences to be detected based on the electronic fluctuation value covered by the sliding window after each slide.
[0136] Step 306: For each two adjacent epochs in the subsequence to be detected, obtain the time interval between the adjacent epochs. Based on the time interval and the electronic fluctuation value of each adjacent epoch, obtain the fluctuation value slope between the adjacent epochs, and obtain multiple fluctuation value slopes corresponding to the subsequence to be detected.
[0137] Step 308: Based on the slope of multiple fluctuation values, obtain the mean slope and root mean square deviation of the slope of the subsequence to be detected;
[0138] Step 310: Based on the mean slope and the root mean square deviation of the slope, determine the upper limit and lower limit of the slope confidence interval corresponding to the subsequence to be detected; based on the upper limit and lower limit of the slope confidence interval, determine the radius of the slope confidence interval corresponding to the subsequence to be detected.
[0139] Step 312: Based on the empirical method, obtain the initial anomaly threshold. Based on the average slope of each subsequence to be detected, optimize the initial anomaly threshold to obtain the target anomaly threshold for each subsequence to be detected.
[0140] Step 314: When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has anomalies in multiple epochs corresponding to the subsequence to be detected;
[0141] Step 316: Traverse the slope confidence interval radii of multiple sub-sequences extracted from the sequence to be detected. When the slope confidence interval radii of multiple sub-sequences extracted from the sequence to be detected do not exceed the target anomaly threshold, it is determined that the ionosphere is normal in multiple epochs of the sequence to be detected.
[0142] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.
[0143] Based on the same inventive concept, this application also provides an ionospheric anomaly determination device for implementing the ionospheric anomaly determination method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the ionospheric anomaly determination device provided below can be found in the limitations of the ionospheric anomaly determination method described above, and will not be repeated here.
[0144] In one embodiment, such as Figure 4 As shown, an ionospheric anomaly determination device is provided, comprising: a sequence construction module 402, a subsequence extraction module 404, a fluctuation value slope acquisition module 406, a target anomaly threshold acquisition module 408, and an anomaly determination module 410, wherein:
[0145] The sequence to be detected module 402 is used to obtain the electron wave value of the ionosphere at each epoch, and construct the sequence to be detected based on the selected electron wave values at multiple epochs.
[0146] The subsequence extraction module 404 is used to extract multiple subsequences of equal length from the sequence to be detected based on a sliding window.
[0147] The fluctuation slope acquisition module 406 is used to obtain the fluctuation slope between two adjacent epochs in each subsequence to be detected, and obtain multiple fluctuation slopes corresponding to the subsequence to be detected.
[0148] The target anomaly threshold acquisition module 408 is used to determine the radius of the slope confidence interval of the subsequence to be detected based on multiple fluctuation value slopes, and to optimize the initial anomaly threshold based on multiple fluctuation value slopes to obtain the target anomaly threshold.
[0149] The anomaly determination module 410 is used to determine that the ionosphere is abnormal at multiple epochs corresponding to the subsequence to be detected when the radius of the slope confidence interval exceeds the target anomaly threshold.
[0150] In the aforementioned ionospheric anomaly determination device, the electron fluctuation values of the ionosphere at each epoch are first obtained. Then, based on the electron fluctuation values of the selected multiple epochs, a sequence to be detected is constructed. Based on a sliding window, multiple subsequences of equal length are extracted from the sequence to be detected. For each subsequence to be detected, the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected is obtained, resulting in multiple fluctuation value slopes corresponding to the subsequence to be detected. Then, based on the multiple fluctuation value slopes, the radius of the slope confidence interval of the subsequence to be detected is determined. Based on the multiple fluctuation value slopes, the initial anomaly threshold is optimized to obtain the target anomaly threshold. When the slope confidence interval radius exceeds the target anomaly threshold, it is determined that the ionosphere has an anomaly at multiple epochs corresponding to the subsequence to be detected. Throughout the process, for each subsequence to be detected, the initial anomaly threshold is optimized according to the slope of the fluctuation value of each subsequence to be detected, so as to obtain the target anomaly threshold for each subsequence to be detected. This achieves adaptive optimization of the target anomaly threshold, so that each subsequence to be detected has its own corresponding more accurate anomaly judgment method, thereby improving the accuracy of ionospheric anomaly judgment.
[0151] In one embodiment, the subsequence extraction module is further configured to control a sliding window of fixed size to slide on the subsequence to be detected according to a fixed step size, determine the electronic fluctuation value covered by the sliding window after each slide, and then construct multiple subsequences to be detected based on the electronic fluctuation value covered by the sliding window after each slide.
[0152] In one embodiment, each subsequence to be detected includes at least three epochs. The fluctuation value slope acquisition module is also used to obtain the time interval between two adjacent epochs in each subsequence to be detected, and then obtain the fluctuation value slope between adjacent epochs based on the time interval and the electron fluctuation value of each adjacent epoch, thereby obtaining multiple fluctuation value slopes corresponding to the subsequence to be detected.
[0153] In one embodiment, the ionospheric anomaly determination device further includes a confidence interval radius determination module. The confidence interval radius determination module is used to obtain the mean slope and root mean square deviation of the slope of the subsequence to be detected based on the slope of multiple fluctuation values, and then determine the upper limit and lower limit of the slope confidence interval corresponding to the subsequence to be detected based on the mean slope and root mean square deviation of the slope, and then determine the slope confidence interval radius corresponding to the subsequence to be detected based on the upper limit and lower limit of the slope confidence interval.
[0154] In one embodiment, the target anomaly threshold acquisition module is further configured to obtain an initial anomaly threshold based on an empirical method, and to obtain the average slope of each subsequence to be detected based on the slope of multiple fluctuation values corresponding to each subsequence to be detected, and then to optimize the initial anomaly threshold based on the average slope of each subsequence to be detected, so as to obtain the target anomaly threshold corresponding to each subsequence to be detected.
[0155] In one embodiment, the ionospheric anomaly determination device further includes a traversal module, which is used to traverse the slope confidence interval radius of multiple sub-sequences extracted from the sequence to be detected. When the slope confidence interval radius of multiple sub-sequences extracted from the sequence to be detected does not exceed the target anomaly threshold, it is determined that the ionosphere is normal in multiple epochs of the sequence to be detected.
[0156] Each module in the aforementioned ionospheric anomaly detection device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.
[0157] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores ionospheric anomaly detection data. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an ionospheric anomaly detection method.
[0158] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0159] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.
[0160] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above method embodiments.
[0161] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.
[0162] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant countries and regions.
[0163] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0164] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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.
[0165] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.
Claims
1. A method for determining ionospheric anomalies, characterized in that, The method includes: The electron wave values of the ionosphere at each epoch are obtained, and the detection sequence is constructed based on the selected electron wave values of multiple epochs. Based on a sliding window, multiple subsequences of equal length are extracted from the sequence to be detected. For each subsequence to be detected, the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected is obtained, and multiple fluctuation value slopes corresponding to the subsequence to be detected are obtained. Based on the slopes of multiple fluctuation values, the mean slope and root mean square deviation of the slope of the subsequence to be detected are obtained; Based on the mean slope and the root mean square deviation of the slope, the upper limit of the slope confidence interval and the lower limit of the slope confidence interval corresponding to the subsequence to be detected are determined; Based on the upper limit of the slope confidence interval and the lower limit of the slope confidence interval, the radius of the slope confidence interval corresponding to the subsequence to be detected is determined; Based on the empirical method, an initial anomaly threshold is obtained, and based on the slope of the multiple fluctuation values corresponding to each of the subsequences to be detected, the average slope value corresponding to each subsequence to be detected is obtained. Based on the average slope of each of the subsequences to be detected, the initial anomaly threshold is optimized to obtain the target anomaly threshold for each of the subsequences to be detected. When the radius of the slope confidence interval exceeds the target anomaly threshold, it is determined that the ionosphere is abnormal at multiple epochs corresponding to the subsequence to be detected.
2. The method of claim 1, wherein, The step of extracting multiple subsequences of equal length from the sequence to be detected based on a sliding window includes: A sliding window of fixed size is controlled to slide on the sequence to be detected according to a fixed step size, and the electronic fluctuation value covered by the sliding window after each slide is determined. Based on the electronic fluctuation values covered by the sliding window after each sliding, multiple sub-sequences to be detected are constructed.
3. The method of claim 1, wherein, Each of the subsequences to be detected includes at least three epochs; For each subsequence to be detected, the slope of the fluctuation value between two adjacent epochs in the subsequence to be detected is obtained, and the multiple fluctuation value slopes corresponding to the subsequence to be detected are obtained as follows: For each of the two adjacent epochs in the subsequence to be detected, the time interval between the adjacent epochs is obtained; Based on the time interval and the electron fluctuation values of each of the adjacent epochs, the fluctuation slope between the adjacent epochs is obtained, and multiple fluctuation slopes corresponding to the subsequence to be detected are obtained.
4. The method of claim 1, wherein, The method further includes: The slope confidence interval radius of multiple subsequences extracted from the sequence to be detected is traversed. When the slope confidence interval radius of multiple sub-sequences extracted from the sequence to be detected does not exceed the target anomaly threshold, it is determined that the ionosphere is normal in multiple epochs of the sequence to be detected.
5. An ionospheric anomaly determination device characterized by comprising: The device includes: The module for constructing the sequence to be detected is used to obtain the electron wave values of the ionosphere at each epoch, and to construct the sequence to be detected based on the electron wave values of multiple selected epochs. The subsequence extraction module is used to extract multiple subsequences of equal length from the sequence to be detected based on a sliding window. The fluctuation slope acquisition module is used to obtain the fluctuation slope between two adjacent epochs in each of the subsequences to be detected, and to obtain multiple fluctuation slopes corresponding to the subsequence to be detected. The confidence interval radius determination module is used to obtain the mean slope and root mean square deviation of the slope of the subsequence to be detected based on the slopes of multiple fluctuation values; determine the upper limit and lower limit of the slope confidence interval corresponding to the subsequence to be detected based on the mean slope and the root mean square deviation of the slope; and determine the radius of the slope confidence interval corresponding to the subsequence to be detected based on the upper limit and the lower limit of the slope confidence interval. The target anomaly threshold acquisition module is used to obtain an initial anomaly threshold based on an empirical method, and to obtain the average slope of each of the multiple fluctuation values corresponding to each of the subsequences to be detected; and to optimize the initial anomaly threshold based on the average slope of each of the subsequences to be detected to obtain the target anomaly threshold corresponding to each of the subsequences to be detected. An anomaly detection module is used to determine that the ionosphere is abnormal at multiple epochs corresponding to the subsequence to be detected when the radius of the slope confidence interval exceeds the target anomaly threshold.
6. The apparatus of claim 5, wherein, The subsequence extraction module is specifically used for: A sliding window of fixed size is controlled to slide on the sequence to be detected according to a fixed step size, and the electronic fluctuation value covered by the sliding window after each slide is determined. Based on the electronic fluctuation values covered by the sliding window after each sliding, multiple sub-sequences to be detected are constructed.
7. The apparatus of claim 5, wherein, Each of the subsequences to be detected includes at least three epochs; The fluctuation value slope acquisition module is specifically used for: For each of the two adjacent epochs in the subsequence to be detected, the time interval between the adjacent epochs is obtained; Based on the time interval and the electron fluctuation values of each of the adjacent epochs, the fluctuation slope between the adjacent epochs is obtained, and multiple fluctuation slopes corresponding to the subsequence to be detected are obtained.
8. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.
9. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.
10. A computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.