Ionospheric anomaly determination method and apparatus, computer device, and storage medium

Abstract

The application relates to an ionosphere anomaly determination method and device, computer equipment and a storage medium. The method comprises the following steps: determining an to-be-detected sequence of an ionosphere at a current time period and a history sequence of the ionosphere at a plurality of history time periods based on electron fluctuation values of the ionosphere at a plurality of epochs in each time period; extracting a plurality of to-be-detected subsequences from the to-be-detected sequence based on a detection window with variable size, and extracting a plurality of history subsequences from each history sequence; determining the average difference between the to-be-detected subsequence and each history subsequence based on the detection window with the same size, so as to determine a target detection window; and determining that an anomaly occurs at a target epoch of the ionosphere at the current time period when the last electron fluctuation value in the target detection window exceeds a fluctuation threshold. The method can improve the detection accuracy of the ionosphere anomaly.

Description

Technical Field

[0001] This application relates to the field of ionospheric detection technology, and in particular to a method, apparatus, computer equipment, and storage medium for determining ionospheric anomalies. Background Technology

[0002] The ionosphere is the upper region of Earth's atmosphere that is ionized by high-energy solar radiation and cosmic rays. It is an important component of the Earth-Sun space environment and is closely related to human life. Current research shows that during extreme geological events such as earthquakes, tsunamis, and solar storms, the ionosphere will exhibit corresponding anomalies synchronously or even in advance. Therefore, detecting changes in ionospheric anomalies can be used to predict the occurrence of geological disasters.

[0003] In existing technologies, with the completion of the construction of the new generation of satellite navigation and positioning systems, satellite data from GNSS (Global Navigation Satellite System) are generally used to solve for the ionospheric TEC (Total Electronic Content) in order to determine whether there is any abnormal activity in the ionosphere.

[0004] However, in existing technologies, GNSS-based ionospheric anomaly detection methods can only estimate within a certain range, resulting in low accuracy in detecting ionospheric anomalies. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for determining ionospheric anomalies that can improve the detection accuracy of ionospheric anomalies, in response to the above-mentioned technical problems.

[0006] Firstly, this application provides a method for determining ionospheric anomalies. The method includes:

[0007] Based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, the detection sequence of the ionosphere in the current time period and the historical sequences in multiple historical time periods are determined.

[0008] Based on a detection window with variable size, multiple subsequences to be detected are extracted from the sequence to be detected, and multiple historical subsequences are extracted from each historical sequence.

[0009] For the target subsequence and multiple historical subsequences extracted based on the same detection window size, the average difference between the target subsequence and each historical subsequence is determined, and the average difference corresponding to each detection window size is obtained.

[0010] Based on the average differences, the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences is determined from the detection windows of each size;

[0011] When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, it is determined that the target epoch of the ionosphere is abnormal within the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value.

[0012] In one embodiment, determining the ionosphere's target sequence for the current time period and its historical sequences for each of the multiple historical time periods, based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, includes:

[0013] Obtain the electron content of the ionosphere at each epoch;

[0014] For each epoch, the epoch is used as the center point to construct the epoch interval to which the epoch belongs;

[0015] Obtain the average electron content of all epochs except the epoch itself within the epoch interval. Based on the electron content of each epoch and the average electron content, obtain the electron fluctuation value for each epoch.

[0016] In one embodiment, determining the ionosphere's detectable sequence in the current time period and its historical sequences in multiple historical time periods, based on the electron fluctuation values ​​of the ionosphere at each epoch within each time period, includes:

[0017] Based on the electron fluctuation values ​​of the ionosphere at multiple epochs in the current time period, a detection sequence of the ionosphere in the current time period is constructed.

[0018] Multiple historical time periods are selected, and for each historical time period, a historical sequence of the ionosphere is constructed based on the electron fluctuation values ​​of the ionosphere at multiple epochs in the historical time period.

[0019] In one embodiment, for a target subsequence extracted based on a detection window of the same size and multiple historical subsequences, determining the average difference between the target subsequence and each historical subsequence includes:

[0020] For the target subsequence and multiple historical subsequences extracted based on the same detection window size, the maximum mean difference between the target subsequence and each historical subsequence is obtained respectively.

[0021] The maximum mean difference is squared to obtain unbiased estimates between the target subsequence and each historical subsequence.

[0022] The mean of each unbiased estimate is calculated to obtain the average difference between the target subsequence and each historical subsequence. This average difference is then used as the average difference corresponding to the detection window.

[0023] In one embodiment, based on the average differences, determining the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences from detection windows of various sizes includes:

[0024] For the target subsequence and multiple historical subsequences extracted based on the detection window of the same size, the similarity between the target subsequence and each historical subsequence is obtained based on the average difference between the statistical method and the detection window, and the similarity is used as the similarity corresponding to the detection window.

[0025] Based on the similarity of each detection window of each size, the target detection window with the lowest average similarity is determined from the detection windows of each size.

[0026] In one embodiment, determining that the ionosphere has not experienced an anomaly in the target epoch within the current time period before the last electron fluctuation value in the target detection window exceeds the fluctuation threshold includes:

[0027] The mean and standard deviation of electron fluctuations in the ionosphere over multiple historical time periods are obtained, and the fluctuation threshold is obtained based on the mean and standard deviation.

[0028] The electron fluctuation value of the ionosphere at the target epoch within the current time period is compared with the fluctuation threshold to obtain the comparison result;

[0029] Based on the comparison results, it is determined whether the ionosphere has an anomaly at the target epoch.

[0030] Secondly, this application also provides an apparatus for determining ionospheric anomalies. The apparatus includes:

[0031] The sequence determination module is used to determine the ionosphere's detectable sequence in the current time period and its historical sequences in multiple historical time periods based on the electron wave values ​​of the ionosphere at multiple epochs within each time period.

[0032] The subsequence extraction module is used to extract multiple subsequences to be detected from the sequence to be detected based on a detection window of variable size, and to extract multiple historical subsequences from each historical sequence respectively.

[0033] The average difference acquisition module is used to determine the average difference between the target subsequence and each historical subsequence for the target subsequence extracted based on the detection window of the same size and multiple historical subsequences, and to obtain the average difference corresponding to each detection window size.

[0034] The detection window determination module is used to determine the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences from detection windows of various sizes, based on the average differences.

[0035] The anomaly determination module is used to determine that the ionosphere has an anomaly in the target epoch within the current time period when the last electron fluctuation value in the target detection window exceeds the fluctuation threshold. The target epoch is the epoch corresponding to the last electron fluctuation value.

[0036] 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:

[0037] Based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, the detection sequence of the ionosphere in the current time period and the historical sequences in multiple historical time periods are determined.

[0038] Based on a detection window with variable size, multiple subsequences to be detected are extracted from the sequence to be detected, and multiple historical subsequences are extracted from each historical sequence.

[0039] For the target subsequence and multiple historical subsequences extracted based on the same detection window size, the average difference between the target subsequence and each historical subsequence is determined, and the average difference corresponding to each detection window size is obtained.

[0040] Based on the average differences, the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences is determined from the detection windows of each size;

[0041] When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, it is determined that the target epoch of the ionosphere is abnormal within the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value.

[0042] 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:

[0043] Based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, the detection sequence of the ionosphere in the current time period and the historical sequences in multiple historical time periods are determined.

[0044] Based on a detection window with variable size, multiple subsequences to be detected are extracted from the sequence to be detected, and multiple historical subsequences are extracted from each historical sequence.

[0045] For the target subsequence and multiple historical subsequences extracted based on the same detection window size, the average difference between the target subsequence and each historical subsequence is determined, and the average difference corresponding to each detection window size is obtained.

[0046] Based on the average differences, the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences is determined from the detection windows of each size;

[0047] When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, it is determined that the target epoch of the ionosphere is abnormal within the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value.

[0048] 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:

[0049] Based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, the detection sequence of the ionosphere in the current time period and the historical sequences in multiple historical time periods are determined.

[0050] Based on a detection window with variable size, multiple subsequences to be detected are extracted from the sequence to be detected, and multiple historical subsequences are extracted from each historical sequence.

[0051] For the target subsequence and multiple historical subsequences extracted based on the same detection window size, the average difference between the target subsequence and each historical subsequence is determined, and the average difference corresponding to each detection window size is obtained.

[0052] Based on the average differences, the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences is determined from the detection windows of each size;

[0053] When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, it is determined that the target epoch of the ionosphere is abnormal within the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value.

[0054] The aforementioned method, apparatus, computer equipment, storage medium, and computer program product for determining ionospheric anomalies determine the ionospheric sequence to be detected in the current time period and the historical sequences in multiple historical time periods based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period. Using a variable-size detection window, multiple sub-sequences to be detected are extracted from the sequence to be detected, and multiple historical sub-sequences are extracted from each historical sequence. For the sub-sequences to be detected and the multiple historical sub-sequences extracted based on the same-sized detection window, the average difference between the target sub-sequence to be detected and each historical sub-sequence is determined, resulting in the average difference corresponding to each size of detection window. Based on the average difference, the target detection window with the lowest average similarity between the sub-sequence to be detected and the multiple historical sub-sequences is determined from the detection windows of each size. When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, an anomaly is determined to have occurred in the target epoch of the ionosphere within the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value. Throughout the process, the target detection window is first precisely selected based on the average difference corresponding to each size of the detection window, thus narrowing the scope of anomaly detection. When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, the epoch corresponding to the last electron fluctuation value in the target detection window is determined as the target epoch. It is then used to determine whether an anomaly occurs in the ionosphere at the target epoch, thereby achieving accurate detection of ionospheric anomalies and improving the detection accuracy of ionospheric anomalies. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a method for determining ionospheric anomalies in one embodiment;

[0056] Figure 2 This is a flowchart of an ionospheric anomaly determination method in one embodiment;

[0057] Figure 3 This is a flowchart illustrating the method for determining ionospheric anomalies in another embodiment;

[0058] Figure 4 This is a structural block diagram of an ionospheric anomaly determination device in one embodiment;

[0059] Figure 5 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0060] 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.

[0061] In one embodiment, such as Figure 1As shown, a method for determining ionospheric anomalies 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 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:

[0062] Step 102: Based on the electron fluctuation values ​​of the ionosphere at multiple epochs in each time period, determine the sequence to be detected in the ionosphere in the current time period and the historical sequences in multiple historical time periods.

[0063] Specifically, an epoch refers to the sampling time for obtaining the electron content in the ionosphere. Each time period includes multiple epochs, with a fixed time interval between adjacent epochs. The fluctuation value of the electron content in the ionosphere at each epoch is called the electron fluctuation value. The time period can be configured according to the actual application scenario.

[0064] Optionally, the server can first determine the electron fluctuation value of the ionosphere at each epoch within each time period based on the electron content of the ionosphere at each epoch within each time period, thereby obtaining the electron fluctuation value of the ionosphere at each epoch within the current time period, and obtaining the electron fluctuation value of the ionosphere at each epoch within each historical time period, so as to construct the detection sequence of the ionosphere in the current time period and the historical sequences in each of the multiple historical time periods.

[0065] For example, the server can select the electron fluctuation values ​​y1, y2, y3, ..., y4 from multiple epochs within the current time period. n To construct the detection sequence Y = [y1, y2, y3, ..., y] of the ionosphere in the current time period. n ], where y n This represents the electron fluctuation value at the nth epoch selected within the current time period. Assuming there are N historical time periods, for each historical time period, the server can select n corresponding epochs from multiple epochs within each historical time period, based on the epoch corresponding to each electron fluctuation value in Y, to construct a historical sequence of the ionosphere in each historical time period. Among them, X 1 The historical sequence representing the first historical time cycle, It represents the electron fluctuation value of the selected nth epoch in the first historical time period.

[0066] Wherein, the epoch corresponding to y1 and The corresponding epochs are the same (the same time), and the epoch corresponding to y2 is the same as... The corresponding epochs are the same...y nCorresponding epoch and The corresponding epochs are the same. (Using y) n Taking the corresponding epoch as an example, let's assume that y n The corresponding epoch is 10:00, then The corresponding epochs are all 10:00, that is With y n Corresponding to the same moment within different time periods.

[0067] Step 104: Based on a detection window of variable size, extract multiple subsequences to be detected from the sequence to be detected, and extract multiple historical subsequences from each historical sequence.

[0068] Optionally, for each size of the detection window, the server can extract a subsequence to be detected from the sequence to be detected, and extract a historical subsequence from each historical sequence, and then extract multiple subsequences to be detected from the sequence to be detected based on the variable-sized detection window, and extract multiple historical subsequences from each historical sequence.

[0069] For example, the size of the detection window can be k (k = 2, 3, ..., n). By changing the size of the detection window, the server can extract n-1 subsequences Y from the sequence Y to be detected. k =[y1,y2,y3,...,y k For each historical time period, the server can extract n-1 historical subsequences from the corresponding historical sequence. For multiple historical time periods, N×(n-1) historical subsequences can be obtained. The resulting historical subsequences can be specifically represented as follows:

[0070] Step 106: For the target subsequence and multiple historical subsequences extracted based on the detection window of the same size, determine the average difference between the target subsequence and each historical subsequence, and obtain the average difference corresponding to each detection window size.

[0071] The average difference can be used to characterize the similarity between the target subsequence and each historical subsequence. The larger the average difference, the smaller the similarity between the target subsequence and each historical subsequence.

[0072] Optionally, for the subsequence to be detected and multiple historical subsequences extracted based on the detection window of the same size, the server can first obtain the average difference between the subsequence to be detected and each historical subsequence, and use the obtained average difference as the average difference corresponding to the detection window of that size, so as to obtain the average difference corresponding to each detection window of each size.

[0073] Step 108: Based on the average differences, determine the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences from the detection windows of each size.

[0074] Optionally, the server can obtain the average similarity between the target subsequence and multiple historical subsequences for each detection window of each size based on the average differences, and determine the target detection window with the lowest average similarity between the target subsequence and multiple historical subsequences.

[0075] Step 110: When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, it is determined that the target epoch of the ionosphere is abnormal in the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value.

[0076] Optionally, after determining the target detection window, the server can first obtain the last electron fluctuation value in the subsequence to be detected corresponding to the target detection window, and determine whether the last electron fluctuation value exceeds the fluctuation threshold. When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, the server can determine that the ionosphere has an anomaly in the target epoch within the current time period.

[0077] For example, when the last electron fluctuation value does not exceed the fluctuation threshold, the server can further determine whether the traversal of the electron fluctuation values ​​corresponding to other epochs in the current time period has been completed. If not, the server collects the electron fluctuation values ​​corresponding to the next batch of multiple epochs in the current time period and repeats the above steps. If the traversal has been completed, the server directly outputs the result that the ionosphere is normal.

[0078] In the above-mentioned method for determining ionospheric anomalies, based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, the method determines the ionosphere's target sequence for the current time period and its historical sequences for multiple historical time periods. Using a variable-size detection window, multiple target sub-sequences are extracted from the target sequence, and multiple historical sub-sequences are extracted from each historical sequence. For the target sub-sequence and multiple historical sub-sequences extracted using a detection window of the same size, the average difference between the target sub-sequence and each historical sub-sequence is determined, resulting in the average difference corresponding to each size of detection window. Based on the average difference, the method identifies the target detection window with the lowest average similarity between the target sub-sequence and multiple historical sub-sequences from each size of detection window. When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, the method determines that an anomaly has occurred in the target epoch of the ionosphere within the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value. Throughout the process, the target detection window is first precisely selected based on the average difference corresponding to each size of the detection window, thus narrowing the scope of anomaly detection. When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, the epoch corresponding to the last electron fluctuation value in the target detection window is determined as the target epoch. It is then used to determine whether an anomaly occurs in the ionosphere at the target epoch, thereby achieving accurate detection of ionospheric anomalies and improving the detection accuracy of ionospheric anomalies.

[0079] In one embodiment, determining the ionosphere's target sequence for the current time period and its historical sequences for each of the multiple historical time periods, based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, includes:

[0080] Obtain the electron content of the ionosphere at each epoch;

[0081] For each epoch, the epoch is used as the center point to construct the epoch interval to which the epoch belongs;

[0082] Obtain the average electron content of all epochs except the epoch itself within the epoch interval. Based on the electron content of each epoch and the average electron content, obtain the electron fluctuation value for each epoch.

[0083] Specifically, the electron content can be defined as the total electron concentration (TEC) of the ionosphere.

[0084] Optionally, the server can first obtain the electron content of the ionosphere at each epoch. For each epoch, the server can construct an epoch interval with the epoch as the center point, then obtain the electron content of other epochs in the epoch interval, calculate the average electron content of the other epochs, and then obtain the electron fluctuation value of each epoch by calculating the difference between the electron content of the epoch and the average electron content. The size of the epoch interval can be configured according to the actual application scenario.

[0085] For example, the server can determine the electron content (TEC) of the ionosphere at each epoch based on GNSS (Global Navigation Satellite System) satellites and the acquired carrier phase observations, as shown in formula (1):

[0086]

[0087] 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.

[0088] As can be seen from formula (1), due to the uncertainty of integer ambiguity, it is impossible to obtain data that reflects the absolute magnitude of ionospheric TEC. All parameters in the absolute magnitude characterization calculation formula can be accurately obtained.

[0089] Furthermore, the server can eliminate the influence of integer ambiguity by subtracting from formula (1) to obtain the electron fluctuation value (dTEC) that reflects the absolute magnitude of TEC. The electron content TEC (t) of the ionosphere at epoch a is used as the quanta value. a ), the electron content TEC(t) of the bth epoch b Taking t as an example, the server can obtain TEC(t) using formula (2). a ) relative to TEC(t b The electronic fluctuation value dTEC(t) a ):

[0090]

[0091] Among them, t a Let t be the sampling time corresponding to the a-th epoch. bThis represents the sampling time corresponding to the b-th epoch.

[0092] Optionally, to avoid extreme values ​​(positive / negative values) when the TEC fluctuates significantly during the subtraction of two single epochs, formula (2) can be further optimized. The electron content TEC(t) of the ionosphere at the c-th epoch is used as the quantization factor. c Taking the cth epoch as an example, the server can use the cth epoch as the center point to construct the epoch interval [cm, c+m] to which the cth epoch belongs. That is, the m epochs before and the m epochs after the cth epoch are all divided into the epoch interval to which the cth epoch belongs. Then, the average electron content of the other epochs in the epoch interval is obtained. By calculating the difference between the electron content of the cth epoch and the average electron content, the electron fluctuation value of the cth epoch is obtained. The specific process is shown in formula (3):

[0093] dTEC(t c )

[0094] =TEC(t) c )-aver{TEC(t c-m ),…,TEC(t c-1 ),TEC(t c+1 ),…,TEC(t c+m )} (3)

[0095] Here, aver is the average value operator.

[0096] Furthermore, the server can combine the method shown in formula (3) with the sliding window method. With the size of the epoch interval remaining unchanged, the epoch of the center point of the epoch interval can be changed by sliding to obtain the electron fluctuation value corresponding to each epoch.

[0097] In this embodiment, by calculating the difference between epochs, the electron fluctuation value that reflects the ionosphere at each epoch can be accurately obtained, so that the ionospheric anomaly can be accurately detected based on the electron fluctuation value.

[0098] In one embodiment, determining the ionosphere's detectable sequence in the current time period and its historical sequences in multiple historical time periods, based on the electron fluctuation values ​​of the ionosphere at each epoch within each time period, includes:

[0099] Based on the electron fluctuation values ​​of the ionosphere at multiple epochs in the current time period, a detection sequence of the ionosphere in the current time period is constructed.

[0100] Multiple historical time periods are selected, and for each historical time period, a historical sequence of the ionosphere is constructed based on the electron fluctuation values ​​of the ionosphere at multiple epochs in the historical time period.

[0101] Optionally, after obtaining the electron fluctuation value of the ionosphere at each epoch, the server can first determine the multiple epochs to be detected within the current time period, and then obtain the corresponding electron fluctuation values ​​to construct the detection sequence of the ionosphere in the current time period. Then, multiple historical time periods can be selected, and for each historical time period, the corresponding electron fluctuation values ​​within each historical time period can be obtained according to the multiple epochs to be detected within the current time period to construct the historical sequence of the ionosphere corresponding to each historical time period.

[0102] In this embodiment, the sequence to be detected and multiple historical sequences are constructed separately so that anomalies in the sequence to be detected can be detected based on multiple historical sequences.

[0103] In one embodiment, for a target subsequence extracted based on a detection window of the same size and multiple historical subsequences, determining the average difference between the target subsequence and each historical subsequence includes:

[0104] For the target subsequence and multiple historical subsequences extracted based on the same detection window size, the maximum mean difference between the target subsequence and each historical subsequence is obtained respectively.

[0105] The maximum mean difference is squared to obtain unbiased estimates between the target subsequence and each historical subsequence.

[0106] The mean of each unbiased estimate is calculated to obtain the average difference between the target subsequence and each historical subsequence. This average difference is then used as the average difference corresponding to the detection window.

[0107] The Maximum Mean Discrepancy (MMD) is one of the most widely used loss functions in transfer learning. It is mainly used to measure the distance between the distributions of two different but related random variables. The larger the MMD, the smaller the similarity between the two random variables. In this embodiment, the squared result of the MMD is named the unbiased estimate.

[0108] Optionally, for the target subsequence and multiple historical subsequences extracted based on detection windows of the same size, the server can obtain the maximum mean difference between the target subsequence and each historical subsequence, then square each maximum mean difference, and obtain unbiased estimates between the target subsequence and each historical subsequence based on the regeneration property of the regeneration kernel Hilbert space. Then, the mean of each unbiased estimate is calculated to obtain the average difference between the target subsequence and each historical subsequence, and the average difference is used as the average difference corresponding to the detection window, thereby obtaining the average difference corresponding to each detection window of each size.

[0109] For example, taking the detection window size as k as an example, the server can use formula (4) to obtain the detected subsequence Y. k , and the historical subsequence within the p-th historical time period The maximum mean difference between them:

[0110]

[0111] Where F represents the reproducing kernel Hilbert space, E(·) represents the mean, and sup{·} represents the supremum, which is the maximum value in the set. Formula (4) represents: based on a certain continuous function f(·), such that Y k and The difference in means is maximized when mapped to the reproducing kernel Hilbert space F, resulting in the maximum mean difference.

[0112] The continuous function can be obtained based on the kernel function in transfer learning, and the server can further use the kernel function to represent the maximum mean difference. Since the resulting expression is not intuitive enough, a more intuitive unbiased estimate, also expressed using a kernel function, can be obtained by squaring the difference between the largest means. Specifically, it can be shown in formula (5):

[0113]

[0114] in, g(·) represents the kernel function. The larger the value of the unbiased estimator, the stronger the representation... With Y k The smaller the similarity between them, the better if and only if With Y k When they are the same, the value of the unbiased estimate is zero.

[0115] For example, based on the method shown in formula (5), with a detection window size of k, the server can obtain unbiased estimates between the target subsequence to be detected and each historical subsequence, and can calculate the mean of each unbiased estimate, i.e., the average difference, and use the obtained average difference as the average difference corresponding to the detection window of size k. Based on the same method, the server can obtain the average differences corresponding to multiple detection windows of different sizes, and the obtained multiple average differences can be expressed by formula (6):

[0116]

[0117] Among them, Z k The average difference is represented by a detection window of size k, and the properties of the average difference are similar to those of the unbiased estimate / maximum mean difference, i.e., the larger the value, the greater the difference and the smaller the similarity.

[0118] In this embodiment, the average difference corresponding to each detection window is obtained so that the target detection window can be selected based on the average difference.

[0119] In one embodiment, based on the average differences, determining the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences from detection windows of various sizes includes:

[0120] For the target subsequence and multiple historical subsequences extracted based on the detection window of the same size, the similarity between the target subsequence and each historical subsequence is obtained based on the average difference between the statistical method and the detection window, and the similarity is used as the similarity corresponding to the detection window.

[0121] Based on the similarity of each detection window of each size, the target detection window with the lowest average similarity is determined from the detection windows of each size.

[0122] Optionally, for the target subsequence and multiple historical subsequences extracted based on detection windows of the same size, the server can further obtain the average similarity between the target subsequence and each historical subsequence based on the average difference between the statistical method and the detection window, and use the obtained average similarity as the average similarity corresponding to the detection window, thereby obtaining the average similarity corresponding to each size of detection window. Then, based on the average similarity corresponding to each size of detection window, the target detection window with the lowest average similarity between the corresponding target subsequence and each historical subsequence is determined from the detection windows of each size.

[0123] For example, the server can obtain the minimum average similarity between the target subsequence and each historical subsequence based on the average difference corresponding to the detection window using the M statistic method, as shown in formula (7):

[0124]

[0125] in, The average similarity between the target subsequence and each historical subsequence when the detection window size is k. The larger the value, the smaller the average similarity between the target subsequence and each historical subsequence.

[0126] Alternatively, assume that when k = η, M k Take the maximum value, then the target subsequence Y is... η =[y1,y2,y3,...,y η The average similarity between Y and each historical subsequence is the smallest. η-1 =[y1,y2,y3,...,y η-1 ]、Y η+1 =[y1,y2,y3,...,y η ,y η+1 The average similarity between the detected subsequence corresponding to other detection windows of different sizes and each historical subsequence is higher than that of Y. η If the average similarity between Y and each historical subsequence is high, then Y... η The last electron wave value y in η The probability of an anomaly occurring is highest there, and the server can further... η It was identified as the largest outlier.

[0127] In this embodiment, the target detection window is selected by filtering out the average difference corresponding to each size of the detection window, which further narrows the scope of anomaly detection and can improve the detection accuracy of ionospheric anomalies.

[0128] In one embodiment, determining that the ionosphere has not experienced an anomaly in the target epoch within the current time period before the last electron fluctuation value in the target detection window exceeds the fluctuation threshold includes:

[0129] The mean and standard deviation of electron fluctuations in the ionosphere over multiple historical time periods are obtained, and the fluctuation threshold is obtained based on the mean and standard deviation.

[0130] The electron fluctuation value of the ionosphere at the target epoch within the current time period is compared with the fluctuation threshold to obtain the comparison result;

[0131] Based on the comparison results, it is determined whether the ionosphere has an anomaly at the target epoch.

[0132] Optionally, the server can obtain the mean and standard deviation of electron fluctuation values ​​corresponding to all epochs of the ionosphere over multiple historical time periods, calculate the fluctuation threshold based on the mean and standard deviation, determine the epoch corresponding to the last electron fluctuation value in the target detection window as the target epoch, and compare the electron fluctuation value of the ionosphere at the target epoch in the current time period with the fluctuation threshold to obtain the comparison result. Based on the comparison result, it can determine whether the ionosphere has an anomaly at the target epoch.

[0133] For example, when the amount of data is large enough, the mean of the ionospheric electron wave value can be considered approximately zero. To simplify the calculation process, the mean μ of the electron wave value corresponding to all epochs within multiple historical time periods can be approximated as zero. The server can calculate the standard deviation of the electron wave value corresponding to all epochs within multiple historical time periods using formula (8):

[0134]

[0135] in, It represents the electron fluctuation value corresponding to the j-th selected epoch within the i-th historical time period.

[0136] Optionally, the server can obtain a fluctuation threshold μ+2σ based on the mean μ and standard deviation σ. Since the mean is approximated as zero, the fluctuation threshold can be updated to 2σ. Further, when k = η, M... k Taking the minimum value, i.e., the ηth epoch as the target epoch, as an example, the server can use the electron fluctuation value y of the ionosphere in the ηth epoch within the current time period. η Identify it as the largest outlier, and set y η Compared with the fluctuation threshold 2σ, when y η When the value exceeds 2σ, determine the electron fluctuation value y of the ionosphere in the ηth epth epoch of the current time period. η An anomaly has occurred; otherwise, no anomaly has occurred.

[0137] In this embodiment, by calculating the fluctuation threshold, it is possible to accurately determine whether the ionosphere is abnormal at the target epoch within the current time period, thereby providing an accuracy rate for detecting ionospheric anomalies.

[0138] In one embodiment, such as Figure 2 As shown, taking a daily average as an example, a flowchart of a method for determining ionospheric anomalies is provided. The main steps include:

[0139] The server can first obtain multiple sequences to be detected for the day, Y = [y1, y2, y3, ..., y...]. n, and then obtain multiple historical sequences of the previous N days Then input the size k of the detection window. First, set the detection window k = 2, obtain the subsequence to be detected and multiple historical subsequences, and then calculate the unbiased estimate and then the average difference. When k < n, accumulate k and repeat the above steps until the average differences of the detection windows of each size when k = 2, 3, 4,..., n are obtained respectively. Then, based on the M statistic and multiple average differences, calculate the average similarity M corresponding to each detection window of each size k . Assume that when k = η, M k takes the maximum value, and further extract the maximum anomaly point y η , and judge y η whether it is greater than the fluctuation threshold 2σ. When y η is greater than the fluctuation threshold 2σ, it is determined that the ionosphere has an anomaly at y on that day η . When y η is not greater than the fluctuation threshold 2σ, judge whether the traversal of the electron fluctuation values corresponding to other epochs on that day has been completed. If not, collect the electron fluctuation values corresponding to the next batch of n epochs respectively and repeat the above steps. If the traversal has been completed, directly output the result that the ionosphere is normal.

[0140] In another embodiment, as Figure 3 shown, a flowchart of another method for determining ionospheric anomalies is provided. The main steps include:

[0141] Step 302, obtain the electron content of the ionosphere at each epoch. For each epoch, use the epoch as the center point to construct the epoch interval to which the epoch belongs;

[0142] Step 304, obtain the average electron content of other epochs in the epoch interval except the epoch. Based on the electron content of the epoch and the average electron content, obtain the electron fluctuation value of each epoch;

[0143] Step 306, based on the electron fluctuation values of multiple epochs in the current time period of the ionosphere, construct a sequence to be detected of the ionosphere in the current time period;

[0144] Step 308, select multiple historical time periods. For each historical time period, based on the electron fluctuation values of multiple epochs in the historical time period of the ionosphere, construct a historical sequence of the ionosphere in the historical time period. Then, based on a detection window with variable size, extract multiple subsequences to be detected from the sequence to be detected, and extract multiple historical subsequences from each historical sequence respectively;

[0145] Step 310: For the target subsequence and multiple historical subsequences extracted based on the detection window of the same size, obtain the maximum mean difference between the target subsequence and each historical subsequence, and perform a square operation on each maximum mean difference to obtain the unbiased estimate between the target subsequence and each historical subsequence.

[0146] Step 312: Calculate the mean of each unbiased estimate to obtain the average difference between the target subsequence to be detected and each historical subsequence, and use the average difference as the average difference corresponding to the detection window.

[0147] Step 314: For the subsequence to be detected and multiple historical subsequences extracted based on the detection window of the same size, the average similarity between the subsequence to be detected and each historical subsequence is obtained based on the statistical method and the average difference corresponding to the detection window. The similarity is used as the average similarity corresponding to the detection window.

[0148] Step 316: Based on the average similarity corresponding to each size of the detection window, determine the target detection window with the lowest average similarity from the detection windows of each size.

[0149] Step 318: Obtain the mean and standard deviation of electron fluctuation values ​​in the ionosphere over multiple historical time periods, and obtain the fluctuation threshold based on the mean and standard deviation;

[0150] Step 320: Compare the electron fluctuation value of the ionosphere at the target epoch within the current time period with the fluctuation threshold to obtain the comparison result. Based on the comparison result, determine whether the ionosphere has an anomaly at the target epoch.

[0151] Step 322: When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, it is determined that the target epoch of the ionosphere is abnormal in the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value.

[0152] 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.

[0153] Based on the same inventive concept, this application also provides an ionospheric anomaly determination apparatus for implementing the aforementioned ionospheric anomaly determination method. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the ionospheric anomaly determination apparatus provided below can be found in the limitations of the ionospheric anomaly determination method described above, and will not be repeated here.

[0154] In one embodiment, such as Figure 4 As shown, an ionospheric anomaly determination device is provided, comprising: a sequence determination module 402, a subsequence extraction module 404, an average difference acquisition module 406, a detection window determination module 408, and an anomaly determination module 410, wherein:

[0155] The sequence determination module 402 is used to determine the sequence to be detected in the current time period and the historical sequences in multiple historical time periods based on the electron wave values ​​of the ionosphere in multiple epochs within each time period.

[0156] The subsequence extraction module 404 is used to extract multiple subsequences to be detected from the sequence to be detected based on a detection window of variable size, and to extract multiple historical subsequences from each historical sequence respectively.

[0157] The average difference acquisition module 406 is used to determine the average difference between the target subsequence and each historical subsequence for the target subsequence extracted based on the detection window of the same size and multiple historical subsequences, and to obtain the average difference corresponding to each detection window of each size.

[0158] The detection window determination module 408 is used to determine the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences from detection windows of various sizes, based on the average differences.

[0159] The anomaly determination module 410 is used to determine that the target epoch of the ionosphere is abnormal in the current time period when the last electron fluctuation value in the target detection window exceeds the fluctuation threshold. The target epoch is the epoch corresponding to the last electron fluctuation value.

[0160] In the aforementioned ionospheric anomaly determination device, based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, the device determines the ionosphere's target sequence for the current time period and its historical sequences for multiple historical time periods. Using a variable-size detection window, it extracts multiple target sub-sequences from the target sequence and multiple historical sub-sequences from each historical sequence. For the target sub-sequences and historical sub-sequences extracted using the same-sized detection window, it determines the average difference between the target sub-sequence and each historical sub-sequence, obtaining the average difference corresponding to each size of detection window. Based on the average difference, it determines the target detection window with the lowest average similarity between the target sub-sequence and the multiple historical sub-sequences from each size of detection window. When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, it is determined that the ionosphere has an anomaly at the target epoch within the current time period. The target epoch is the epoch corresponding to the last electron fluctuation value. Throughout the process, the target detection window is first accurately selected based on the average difference corresponding to each size of the detection window. When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, the epoch corresponding to the last electron fluctuation value in the target detection window is determined as the target epoch. It is then used to determine whether the ionosphere is abnormal at the target epoch, so as to achieve accurate detection of ionospheric anomalies and improve the detection accuracy of ionospheric anomalies.

[0161] In one embodiment, the ionospheric anomaly determination device further includes a fluctuation value acquisition module, which is used to acquire the electron content of the ionosphere at each epoch. For each epoch, the epoch is used as the center point to construct the epoch interval to which the epoch belongs. Then, the average electron content of other epochs in the epoch interval is acquired. Based on the electron content of the epoch and the average electron content, the electron fluctuation value of each epoch is acquired.

[0162] In one embodiment, the sequence determination module is further configured to construct a sequence to be detected of the ionosphere in the current time period based on the electron fluctuation values ​​of the ionosphere at multiple epochs in the current time period, and then select multiple historical time periods, and for each historical time period, construct a historical sequence of the ionosphere in the historical time period based on the electron fluctuation values ​​of the ionosphere at multiple epochs in the historical time period.

[0163] In one embodiment, the average difference acquisition module is further configured to, for the target subsequence and multiple historical subsequences extracted based on the detection window of the same size, obtain the maximum mean difference between the target subsequence and each historical subsequence, respectively, perform a square operation on each maximum mean difference, obtain the unbiased estimate between the target subsequence and each historical subsequence, and then calculate the mean of each unbiased estimate to obtain the average difference between the target subsequence and each historical subsequence, and use the average difference as the average difference corresponding to the detection window.

[0164] In one embodiment, the detection window determination module is further configured to, for the target subsequence to be detected and multiple historical subsequences extracted based on detection windows of the same size, obtain the similarity between the target subsequence to be detected and each historical subsequence based on the average difference between the statistical method and the detection window, use the similarity as the similarity corresponding to the detection window, and then, based on the similarity corresponding to each size of the detection window, determine the target detection window with the lowest average similarity from the detection windows of each size.

[0165] In one embodiment, the ionospheric anomaly determination device further includes an anomaly judgment module. The anomaly judgment module is used to obtain the mean and standard deviation of the electron fluctuation values ​​of the ionosphere in multiple historical time periods, obtain a fluctuation threshold based on the mean and standard deviation, and then compare the electron fluctuation value of the ionosphere in the target epoch within the current time period with the fluctuation threshold to obtain a comparison result. Based on the comparison result, it is determined whether the ionosphere has an anomaly in the target epoch.

[0166] 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.

[0167] 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 interfaces (I / O), 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 determination data. The I / O interfaces are used for exchanging information 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 determination method.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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: Based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period, the detection sequence of the ionosphere in the current time period and the historical sequences in multiple historical time periods are determined. Based on a detection window of variable size, multiple sub-sequences to be detected are extracted from the sequence to be detected, and multiple historical sub-sequences are extracted from each of the historical sequences. For the target subsequence and multiple historical subsequences extracted based on the same detection window size, the average difference between the target subsequence and each historical subsequence is determined, and the average difference corresponding to each detection window size is obtained. Based on the average differences described above, the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences is determined from the detection windows of each size; When the last electron fluctuation value in the target detection window exceeds the fluctuation threshold, it is determined that the target epoch of the ionosphere within the current time period is abnormal, and the target epoch is the epoch corresponding to the last electron fluctuation value.

2. The method according to claim 1, characterized in that, The determination of the ionosphere's target sequence in the current time period and its historical sequences in multiple historical time periods, based on the electron fluctuation values ​​of the ionosphere at each epoch within each time period, includes: Obtain the electron content of the ionosphere at each epoch; For each epoch, the epoch interval to which the epoch belongs is constructed, with the epoch as the center point. The average electron content of all epochs other than the stated epoch within the epoch interval is obtained. Based on the electron content of the stated epoch and the average electron content, the electron fluctuation value of each stated epoch is obtained.

3. The method according to claim 1, characterized in that, The determination of the ionosphere's detectable sequence in the current time period and its historical sequences in multiple historical time periods, based on the electron fluctuation values ​​of the ionosphere at each epoch within each time period, includes: Based on the electron fluctuation values ​​of the ionosphere at multiple epochs in the current time period, a detection sequence of the ionosphere in the current time period is constructed. Multiple historical time periods are selected, and for each historical time period, a historical sequence of the ionosphere in the historical time period is constructed based on the electron fluctuation values ​​of the ionosphere in multiple epochs of the historical time period.

4. The method according to claim 1, characterized in that, The determination of the average difference between the target subsequence and each historical subsequence, based on the target subsequence extracted from a detection window of the same size, and multiple historical subsequences, includes: For the target subsequence and multiple historical subsequences extracted based on the same detection window size, the maximum mean difference between the target subsequence and each historical subsequence is obtained respectively. The maximum mean difference of each of the above is squared to obtain the unbiased estimate between the target subsequence and each historical subsequence; The mean of each unbiased estimate is calculated to obtain the average difference between the target subsequence and each historical subsequence, and the average difference is used as the average difference corresponding to the detection window.

5. The method according to claim 1, characterized in that, The process of determining the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences from detection windows of various sizes, based on the average differences mentioned above, includes: For the target subsequence and multiple historical subsequences extracted based on the detection window of the same size, the similarity between the target subsequence and each historical subsequence is obtained based on the average difference between the statistical method and the detection window, and the similarity is used as the similarity corresponding to the detection window. Based on the similarity of each detection window of each size, the target detection window with the lowest average similarity is determined from the detection windows of each size.

6. The method according to claim 1, characterized in that, The step of determining that the ionosphere has an anomaly before the target epoch in the current time period occurs when the last electron fluctuation value in the target detection window exceeds the fluctuation threshold includes: The mean and standard deviation of electron fluctuation values ​​of the ionosphere in multiple historical time periods are obtained, and a fluctuation threshold is obtained based on the mean and the standard deviation. The electron fluctuation value of the ionosphere at the target epoch within the current time period is compared with the fluctuation threshold to obtain the comparison result; Based on the comparison results, it is determined whether the ionosphere is abnormal at the target epoch.

7. An ionospheric anomaly determination device, characterized in that, The device includes: The sequence determination module is used to determine the ionosphere's detectable sequence in the current time period and its historical sequences in multiple historical time periods based on the electron fluctuation values ​​of the ionosphere at multiple epochs within each time period. The subsequence extraction module is used to extract multiple subsequences to be detected from the sequence to be detected based on a detection window of variable size, and to extract multiple historical subsequences from each of the historical sequences respectively; The average difference acquisition module is used to determine the average difference between the target subsequence and each historical subsequence for the target subsequence extracted based on the detection window of the same size and multiple historical subsequences, and to obtain the average difference corresponding to each detection window size. The detection window determination module is used to determine the target detection window with the lowest average similarity between the subsequence to be detected and multiple historical subsequences from detection windows of various sizes, based on the average differences mentioned above. An anomaly determination module is used to determine that the ionosphere has an anomaly in the current time period when the last electron fluctuation value in the target detection window exceeds the fluctuation threshold. The target epoch is the epoch corresponding to the last electron fluctuation value.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, 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 6.

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 6.

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