Methods, devices, computer equipment and storage media for detecting ionospheric anomalies

Abstract

This application relates to a method, apparatus, computer device, storage medium, and computer program product for ionospheric anomaly detection. The method includes: acquiring a current time series to be detected, the current time series reflecting the fluctuation characteristics of the total electron content of the ionosphere; dividing the current time series into two adjacent subsequences based on a time detection window, and calculating the variance difference value between the two adjacent subsequences; adjusting the length of the time detection window multiple times, correspondingly acquiring multiple variance difference values, and selecting the two adjacent subsequences corresponding to the largest variance difference value as the first detection subsequence and the second detection subsequence, the length of the first detection subsequence being the same as the length of the corresponding time detection window; obtaining the anomaly detection result of the current time series based on the anomaly points in the first and second detection subsequences; and determining whether an anomaly has occurred in the ionosphere based on the anomaly detection result. This method can achieve high-precision ionospheric anomaly detection.

Description

Technical Field

[0001] This application relates to the field of ionospheric anomaly detection technology, and in particular to an ionospheric anomaly detection method, apparatus, computer equipment, storage medium and computer program product. Background Technology

[0002] The ionosphere is an ionized region of the Earth's atmosphere, located approximately 60–1000 km above the Earth's surface, and is closely related to human life. Current research indicates 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] With the continuous development of satellite navigation and positioning technology, using Global Navigation Satellite System (GNSS) satellite data to calculate the total electronic content (TEC) in the ionosphere has become the main means of monitoring anomalous ionospheric activity.

[0004] Due to limitations such as elevation angle and physical obstruction along the propagation path, the TEC time series continuously provided by a single satellite during ionospheric anomalies is relatively short. It can only roughly estimate the trend of anomaly changes over a long period of time, making it difficult to determine the intensity of small-scale TEC fluctuations in a short period of time. Furthermore, the ionosphere is affected by various factors such as solar activity, geomagnetic storms, and seasonal changes over a long period of time, resulting in low accuracy in ionospheric anomaly detection. Summary of the Invention

[0005] Therefore, it is necessary to provide a high-precision method, apparatus, computer equipment, computer-readable storage medium, and computer program product for detecting ionospheric anomalies, addressing the aforementioned technical problems.

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

[0007] Obtain the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere;

[0008] Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window.

[0009] The length of the time detection window is adjusted multiple times to obtain multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window.

[0010] Based on the anomalies in the first and second detection subsequences, obtain the anomaly detection results for the current time series, and determine whether anomalies have occurred in the ionosphere based on the anomaly detection results.

[0011] In one embodiment, obtaining the current time series to be detected includes:

[0012] Based on the sliding window method, multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences are obtained;

[0013] Multiple fluctuation values ​​corresponding to the sequence are extracted based on multiple fluctuation features to construct the current time series.

[0014] In one embodiment, based on the sliding window method, multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences are obtained, including:

[0015] Set the size and step size of the sliding window, and perform a sliding window operation on the collected total electron content sequence according to the size and step size of the sliding window to obtain multiple fluctuation feature extraction sequences;

[0016] The middle term of each fluctuation feature extraction sequence is used as the reference value, and the average value of all non-middle terms of each fluctuation feature extraction sequence is used as the background value.

[0017] Calculate the difference between the reference value and the background value in multiple fluctuation feature extraction sequences, and use the difference as the fluctuation value.

[0018] In one embodiment, calculating the variance difference between two adjacent subsequences includes:

[0019] Obtain the variance of each of the two adjacent subsequences;

[0020] The absolute value of the variance difference between two adjacent subsequences is taken as the variance difference value.

[0021] In one embodiment, the anomaly detection result of the current time series is obtained based on the anomaly points in the first detection subsequence and the second detection subsequence, including:

[0022] Obtain the anomaly detection threshold;

[0023] If the last item in the first detection subsequence or the first item in the second detection subsequence is greater than the anomaly detection threshold, it is determined that the current time series is abnormal.

[0024] If the last item in the first detection subsequence and the first item in the second detection subsequence are both not greater than the anomaly detection threshold, it is determined that no anomaly has occurred in the current time series.

[0025] In one embodiment, obtaining the anomaly detection threshold includes:

[0026] Based on the sliding window method, multiple sub-sequences of the collected sequence are obtained, and anomaly detection background values ​​are obtained based on the multiple sub-sequences;

[0027] The anomaly detection threshold is set at twice the standard deviation of the background value.

[0028] Secondly, this application also provides an ionospheric anomaly detection device. The device includes:

[0029] The sequence acquisition module is used to acquire the current time series to be detected. The current time series is used to reflect the fluctuation characteristics of the total electron content of the ionosphere.

[0030] The outlier detection module is used to divide the current time series into two adjacent subsequences based on the time detection window, calculate the variance difference value between the two adjacent subsequences, and the length of one of the two adjacent subsequences is the same as the length of the time detection window; the length of the time detection window is adjusted multiple times to obtain multiple variance difference values, and the two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence, and the length of the first detection subsequence is the same as the length of the corresponding time detection window;

[0031] The anomaly detection module is used to obtain the anomaly detection results of the current time series based on the anomaly points in the first detection subsequence and the second detection subsequence, and to determine whether there is an anomaly in the ionosphere based on the anomaly detection results.

[0032] 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:

[0033] Obtain the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere;

[0034] Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window.

[0035] The length of the time detection window is adjusted multiple times to obtain multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window.

[0036] Based on the anomalies in the first and second detection subsequences, obtain the anomaly detection results for the current time series, and determine whether anomalies have occurred in the ionosphere based on the anomaly detection results.

[0037] 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:

[0038] Obtain the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere;

[0039] Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window.

[0040] The length of the time detection window is adjusted multiple times to obtain multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window.

[0041] Based on the anomalies in the first and second detection subsequences, obtain the anomaly detection results for the current time series, and determine whether anomalies have occurred in the ionosphere based on the anomaly detection results.

[0042] 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:

[0043] Obtain the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere;

[0044] Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window.

[0045] The length of the time detection window is adjusted multiple times to obtain multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window.

[0046] Based on the anomalies in the first and second detection subsequences, obtain the anomaly detection results for the current time series, and determine whether anomalies have occurred in the ionosphere based on the anomaly detection results.

[0047] The aforementioned ionospheric anomaly detection method, apparatus, computer equipment, storage medium, and computer program product acquire the current time series to be detected, which reflects the fluctuation characteristics of the total electron content in the ionosphere. Based on a time detection window, the current time series is divided into two adjacent subsequences, and the variance difference value between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window. The length of the time detection window is adjusted multiple times, corresponding to multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window. Based on the anomaly points in the first and second detection subsequences, the anomaly detection result of the current time series is obtained. Based on the anomaly detection result, it is determined whether an anomaly has occurred in the ionosphere. The entire ionospheric anomaly detection process acquires the current time series reflecting the fluctuation characteristics of the total electron content in the ionosphere, detects anomalies in the current time series based on the concept of variance variable point detection, and determines whether an anomaly has occurred in the ionosphere based on the anomaly detection result, thus achieving high-precision ionospheric anomaly detection. Attached Figure Description

[0048] Figure 1 This is an application environment diagram of an ionospheric anomaly detection method in one embodiment;

[0049] Figure 2 This is a flowchart illustrating an ionospheric anomaly detection method in one embodiment;

[0050] Figure 3 This is a schematic diagram of the process for obtaining the total electron content fluctuation value in one embodiment;

[0051] Figure 4 This is a flowchart illustrating the ionospheric anomaly detection method in another embodiment;

[0052] Figure 5 This is a structural block diagram of an ionospheric anomaly detection device in one embodiment;

[0053] Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0054] 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.

[0055] The ionospheric anomaly detection method provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104, or it can be located in the cloud or on another network server. Users operate on terminal 102, and terminal 102 responds to user operations, thereby achieving high-precision ionospheric anomaly detection.

[0056] Specifically, terminal 102 acquires the current time series to be detected, which reflects the fluctuation characteristics of the total electron content in the ionosphere. Based on a time detection window, the current time series is divided into two adjacent subsequences, and the variance difference value between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window. The length of the time detection window is adjusted multiple times, corresponding to multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window. Based on the anomalies in the first and second detection subsequences, the anomaly detection result of the current time series is obtained. Based on the anomaly detection result, it is determined whether an anomaly has occurred in the ionosphere. Terminal 102 can be, but is not limited to, various personal computers, laptops, and tablets. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0057] In one embodiment, such as Figure 2 As shown, an ionospheric anomaly detection method is provided, which is applied to... Figure 1 Taking terminal 102 as an example, the explanation includes the following steps:

[0058] S100: Obtain the current time series to be detected. The current time series is used to reflect the fluctuation characteristics of the total electron content of the ionosphere.

[0059] The current time series is a sequence that reflects the fluctuation characteristics of the total electron content in the ionosphere currently collected.

[0060] Optionally, the total electron content of the ionosphere can be calculated using GNSS carrier observations, as shown in equations (1) and (2) below:

[0061]

[0062]

[0063] Where TEC refers to the total electron content, and λ1 and λ2 represent the carrier wavelengths of different carrier waves. These are the carrier phase observations for different carriers, N1 and N2 represent integer ambiguity, and f is a constant that depends only on the frequency.

[0064] As shown in equations (1) and (2) above, due to the existence of integer ambiguity, the absolute magnitude of the total electron content cannot be determined by the GNSS carrier observation. However, in the absence of cycle slips, the ambiguity parameter does not change with time within a continuous arc segment. Therefore, the total electron content collected at the current epoch is further subtracted between epochs to eliminate the influence of integer ambiguity, thereby obtaining an electron content fluctuation sequence that reflects the fluctuation characteristics of the absolute magnitude of the total electron content. In this embodiment, the sliding window method is used to obtain multiple fluctuation feature extraction sequences of the total electron content time series, calculate the fluctuation value of each fluctuation feature extraction sequence, and construct the current time series based on the multiple fluctuation values ​​corresponding to the multiple fluctuation feature extraction sequences.

[0065] Specifically, the sliding window size is set to 2n+1 epochs, and the sliding window step size is set to 1 epoch. Multiple fluctuation feature extraction sequences of length 2n+1 are obtained by sliding. The middle term of each fluctuation feature extraction sequence is used as a reference value. The average of the total electron content values ​​of n epochs before and after the reference value is used as the background value to better extract the fluctuation value of the total electron content. The calculation method for extracting the fluctuation value using the sliding window method is shown in the following formula (3):

[0066] dTEC=TEC(t i )-aver(TEC(t i-n ),…,TEC(t i-1 ),TEC(t i+1 ),…,TEC(t i+n (3)

[0067] Here, `aver` is the average operator, used to average the total electron content values ​​over n epochs before and after the reference value. The total electron content fluctuation value `dTEC` can accurately extract minute fluctuations in the total electron content `TEC`, enabling subsequent high-precision ionospheric anomaly monitoring via variance. Using the sliding window method, multiple electron content fluctuation values ​​`dTEC` can be extracted, thus obtaining the current time series reflecting the characteristics of the currently acquired total electron content fluctuations.

[0068] S200: Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference value of the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window.

[0069] The size and step size of the time detection window can be adjusted and modified through software.

[0070] Optionally, the electron content fluctuation sequence of n epochs is selected as the current time series X = [x1, x2, x3, ..., x n Set a time detection window of length k (k = 2, 3, ..., n-2) to divide the current time series into two adjacent subsequences X. a =[x1,x2,...,x k ] and X b =[x k+1 ,x k+2 ,...,x n ]. Calculate the subsequence X a The variance is SubsequenceX b The variance is The formula for calculating the difference in variance between these two adjacent subsequences is shown in equation (4) below:

[0071]

[0072] Among them, D k This represents the variance difference between two adjacent subsequences.

[0073] S300: Adjust the length of the time detection window multiple times to obtain multiple variance difference values. Take the two adjacent subsequences corresponding to the largest variance difference value as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window.

[0074] The variance difference value is the absolute value of the variance difference between two adjacent subsequences.

[0075] Optionally, by adjusting the length of the time detection window, the two adjacent subsequences are continuously adjusted, using D... k The variance difference between two adjacent subsequences is represented by T = maxD. After adjusting the time detection window length multiple times, a maximum variance difference value T = maxD will appear. k (k = 2, 3, ..., n-2). Suppose that when k = t (t ∈ {2, 3, ..., n-2}), D k Reaching the maximum value indicates that the deviation between two adjacent subsequences is greatest at this point, and the two points x located next to the time detection sequence boundary line k=t are considered to be at this point. k and xk+1 These two points are most likely to show anomalies. By detecting whether these two points are anomalies, we can determine whether there are any anomalies in the current time series, and thus determine whether there are any anomalies in the ionosphere.

[0076] S400: Based on the anomaly points in the first and second detection subsequences, obtain the anomaly detection results of the current time series, and determine whether anomalies have occurred in the ionosphere based on the anomaly detection results.

[0077] Among them, the outlier is at x k and x k+1 The anomaly detection result indicates that no anomalies were found in the current time series (x). k and x k+1 (No outliers found) or an anomaly occurs in the current time series (x) k and x k+1 (Anomalies exist in the data).

[0078] Optionally, x k and x k+1 As anomaly detection points, the anomaly detection result is obtained by detecting whether there is a point among the two points that exceeds the anomaly detection threshold. The specific process of obtaining the anomaly detection threshold is as follows: Select the fluctuation feature extraction sequence of length n at the same time in the previous thirty days as the background value. Since the sample size is large, the expected value of the background value data can be approximated as 0. The formula for calculating its standard deviation σ0 is as follows (5):

[0079]

[0080] The anomaly detection threshold is set to twice the standard deviation of the background value (2σ0). ​​When the difference D between the variances of two adjacent subsequences... k When the maximum value is obtained, if the two points x next to the boundary line of the detected sequence are... k and x k+1 If the value exceeds 2σ0, the current time series is considered abnormal; otherwise, it is considered normal. An abnormal time series indicates abnormal fluctuations in total charge, thus confirming an ionospheric anomaly. A normal time series indicates normal fluctuations in total charge, thus confirming a normal ionospheric state. In this case, GNSS carrier observations can be used again to obtain the total electron content data for the next time step. The next time series reflecting the total electron content of the ionosphere at the next time step is selected as the new detection sequence, and the above ionospheric anomaly detection steps are repeated.

[0081] The aforementioned ionospheric anomaly detection method acquires the current time series to be detected, which reflects the fluctuation characteristics of the total electron content in the ionosphere. Based on a time detection window, the current time series is divided into two adjacent subsequences, and the variance difference value between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window. The length of the time detection window is adjusted multiple times, corresponding to multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are selected as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window. Based on the anomaly points in the first and second detection subsequences, the anomaly detection result of the current time series is obtained. Based on the anomaly detection result, it is determined whether an anomaly has occurred in the ionosphere. The entire ionospheric anomaly detection process acquires the current time series reflecting the fluctuation characteristics of the total electron content in the ionosphere, detects anomalies in the current time series based on the idea of ​​variance variable point detection, and determines whether an anomaly has occurred in the ionosphere based on the anomaly detection result, thus achieving high-precision ionospheric anomaly detection.

[0082] In one embodiment, obtaining the current time series to be detected includes:

[0083] Based on the sliding window method, multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences are obtained;

[0084] Multiple fluctuation values ​​corresponding to the sequence are extracted based on multiple fluctuation features to construct the current time series.

[0085] Based on the currently acquired GNSS carrier observations, the corresponding total electron content time series can be calculated, as shown in equations (1) and (2) above. Due to the existence of integer ambiguity, the absolute magnitude of the total electron content cannot be determined from the GNSS carrier observations. However, in the absence of cycle slips, the ambiguity parameter does not change with time within a continuous arc segment. Therefore, the total electron content acquired at the current epoch is further subtracted between epochs to eliminate the influence of integer ambiguity, thereby obtaining an electron content fluctuation sequence that reflects the fluctuation characteristics of the absolute magnitude of the total electron content.

[0086] Multiple subsequences (fluctuation feature extraction sequences) are extracted from the total electron content sequence using the sliding window method. The fluctuation value of each subsequence is calculated, and the current time series is constructed based on the fluctuation values ​​of multiple subsequences.

[0087] In this embodiment, the total electron content collected at the current epoch is subtracted between epochs, and multiple fluctuation feature extraction sequences are extracted from the total electron content sequence using the sliding window method. By calculating the corresponding value of each fluctuation feature extraction sequence, the current time series is constructed, thereby eliminating the influence of integer ambiguity, accurately extracting the features of the total electron content, and realizing high-precision short-term ionospheric anomaly detection.

[0088] In one embodiment, such as Figure 3 As shown, based on the sliding window method, multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences are obtained, including:

[0089] S102: Set the size and step size of the sliding window, and perform a sliding window operation on the collected total electron content sequence according to the size and step size of the sliding window to obtain multiple fluctuation feature extraction sequences;

[0090] S104: Use the middle term of each fluctuation feature extraction sequence as the reference value, and use the average value of all non-middle terms of each fluctuation feature extraction sequence as the background value;

[0091] S106: Calculate the difference between the reference value and the background value in multiple fluctuation feature extraction sequences, and use the difference as the fluctuation value.

[0092] The sliding window size is set to 2n+1 epochs, and the step size is set to 1 epoch. The sliding window is moved over the total electron content sequence to obtain multiple fluctuation feature extraction sequences of length 2n+1. The middle term of each fluctuation feature extraction sequence is used as a reference value, and the average of the total electron content values ​​before and after the reference value n epochs is used as the background value to better extract the fluctuation value of the total electron content. The calculation method for extracting the fluctuation value using the sliding window method is shown in equation (3) above.

[0093] In this embodiment, a sliding window method is used to extract multiple fluctuation feature extraction sequences from the total electron content sequence, and the fluctuation value corresponding to each fluctuation feature extraction sequence is calculated. This improves the detection accuracy of small-scale ionospheric anomalies while reducing the computational load of subsequent variance change point detection, thus achieving high-precision short-term ionospheric anomaly detection.

[0094] In one embodiment, calculating the variance difference between two adjacent subsequences includes:

[0095] Obtain the variance of each of the two adjacent subsequences;

[0096] The absolute value of the variance difference between two adjacent subsequences is taken as the variance difference value.

[0097] Select the electron content fluctuation value sequence of n epochs as the current time series X=[x1,x2,x3,...,x nSet a time detection window of size k (k = 2, 3, ..., n-2) to divide the current time series into two adjacent subsequences X. a =[x1,x2,...,x k ] and X b =[x k+1 ,x k+2 ,...,x n ], where subsequence X a The length of the detection window is the same as the length of the detection window. Calculate the length of each of the two adjacent subsequences X. a and X b The variance is and The absolute value of the variance difference between the two adjacent subsequences is taken as the variance difference value. The specific calculation method of the variance difference value is shown in the above formula (4).

[0098] In this embodiment, after dividing the current time series consisting of the total electron content fluctuation value into two adjacent subsequences through the time detection window, the variance of each of the two adjacent subsequences is calculated. The absolute value of the difference between the variances of the two adjacent subsequences is taken as the variance difference value. By finding the maximum variance difference value, variance change point detection is performed, thereby enabling rapid and high-precision detection of ionospheric anomalies.

[0099] In one embodiment, the anomaly detection result of the current time series is obtained based on the anomaly points in the first detection subsequence and the second detection subsequence, including:

[0100] Obtain the anomaly detection threshold;

[0101] If the last item in the first detection subsequence or the first item in the second detection subsequence is greater than the anomaly detection threshold, it is determined that the current time series is abnormal.

[0102] If the last item in the first detection subsequence and the first item in the second detection subsequence are both not greater than the anomaly detection threshold, it is determined that no anomaly has occurred in the current time series.

[0103] Using a time detection window of length k (k = 2, 3, ..., n-2), the current time series is divided into two adjacent subsequences X. a =[x1,x2,...,x k ] and X b =[x k+1 ,x k+2 ,...,x n The subsequence X is calculated. a The variance is SubsequenceX b The variance is Therefore, the variance difference between two adjacent subsequences By adjusting the length of the time detection window, the maximum variance difference value T = maxD can be found. k .

[0104] Suppose that when k = t (t ∈ {2, 3, ..., n-2}), D k Reaching the maximum value indicates that the deviation between two adjacent subsequences is greatest at this point, and the two points x located next to the time detection sequence boundary line k=t are considered to be at this point. k and x k+1 These two points are most likely to exhibit anomalies, and it can be determined whether the current time series shows any anomalies by detecting whether these two points are anomaly points.

[0105] In this embodiment, anomaly detection points are determined based on the maximum variance difference value. By detecting whether the anomaly detection points exceed the anomaly detection threshold, it is determined whether the current time series has anomalies, thereby determining whether the ionosphere has anomalies, thus achieving fast and high-precision ionospheric anomaly detection.

[0106] In one embodiment, obtaining the anomaly detection threshold includes:

[0107] Based on the sliding window method, multiple sub-sequences of the collected sequence are obtained, and anomaly detection background values ​​are obtained based on the multiple sub-sequences;

[0108] The anomaly detection threshold is set at twice the standard deviation of the background value.

[0109] The fluctuation feature extraction sequence with length n at the same time in the previous thirty days is selected as the background value. Since the number of samples is large, the expected value of the background value data can be approximated as 0. The formula for calculating its standard deviation σ0 is shown in the above formula (5). The standard deviation 2σ0 of the background value is taken as the anomaly detection threshold.

[0110] If the anomaly detection point in the current time series exceeds the anomaly detection threshold, the current time series is determined to be abnormal; otherwise, it is determined that the current time series is not abnormal. An anomaly in the current time series indicates abnormal fluctuations in total charge, thus confirming an ionospheric anomaly. Conversely, if the current time series is not abnormal, it indicates normal fluctuations in total charge, thus confirming a normal ionospheric state. In this case, the total electron content data collected at the next time step can be obtained through GNSS carrier observations. The next time series reflecting the total electron content of the ionosphere at the next time step is selected as the new detection sequence, and the above ionospheric anomaly detection steps are repeated.

[0111] To illustrate the technical solution for ionospheric anomaly detection in this application in detail, specific application examples will be used below, combined with... Figure 4 The entire process is explained in detail, including the following steps:

[0112] 1. The total electron content of the ionosphere is calculated using GNSS carrier wave observations. The calculation method is shown in the following two formulas:

[0113]

[0114]

[0115] Where TEC refers to the total electron content, and λ1 and λ2 represent the carrier wavelengths of different carrier waves. These are the carrier phase observations for different carriers, N1 and N2 represent integer ambiguity, and f is a constant that depends only on the frequency.

[0116] 2. Using a sliding window with a window size of 2n+1 epochs and a step size of 1 epoch, multiple fluctuation feature extraction sequences of length 2n+1 are extracted from the total electron content sequence. The middle term of each fluctuation feature extraction sequence is used as a reference value. The average of the total electron content values ​​n epochs before and after the reference value is used as the background value. The fluctuation value of each fluctuation feature extraction sequence is calculated separately. The current time series is constructed based on the multiple fluctuation values ​​corresponding to the multiple fluctuation feature extraction sequences. The calculation method for extracting the fluctuation value from the current time series using the sliding window is shown in the following formula:

[0117] dTEC=TEC(t i )-aver(TEC(t i-n ),…,TEC(t i-1 ),TEC(t i+1 ),…,TEC(t i+n ))

[0118] Here, aver is the average value operator, used to average the total electron content values ​​over n epochs before and after the reference value.

[0119] 3. Select the electron content fluctuation value sequence of n epochs as the current time series X=[x1,x2,x3,...,x n Based on a time detection window of size k (k = 2, 3, ..., n-2), the current time series X = [x1, x2, x3, ..., xn-2] is... n Divide into two adjacent subsequences X a =[x1,x2,...,x k ] and X b =[x k+1 ,x k+2 ,...,x n ]. Calculate the subsequence X a The variance is SubsequenceX b The variance is The formula for calculating the difference in variance between these two adjacent subsequences is as follows:

[0120]

[0121] Among them, D k This represents the variance difference between two adjacent subsequences.

[0122] 4. Adjust the length k of the time detection window multiple times to obtain multiple variance difference values. Assume that when k = t (t ∈ {2, 3, ..., n-2}), D... k Reaching the maximum value indicates that the deviation between two adjacent subsequences is greatest at this point. The two points x located beside the time detection sequence boundary k=t are considered as points... k and x k+1 As an anomaly detection point.

[0123] 5. Select the fluctuation feature extraction sequence of length n at the same time point in the previous thirty days as the background value. Since the sample size is large, the expected value of this background value data can be approximated as 0. The formula for calculating the double standard deviation 2σ0 of the background value is as follows:

[0124]

[0125] 6. Take twice the standard deviation of the background value, 2σ0, as the anomaly detection threshold. If the anomaly detection point in the current time series exceeds the anomaly detection threshold, determine that the current time series is abnormal and the ionosphere is abnormal; otherwise, determine that the current time series is not abnormal and the ionosphere is normal. After determining that the ionosphere is normal, the total electron content data collected at the next moment can be obtained again through GNSS carrier observations. Select the next time series that reflects the total electron content of the ionosphere at the next moment as the new detection series, and repeat the above ionospheric anomaly detection steps.

[0126] 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.

[0127] Based on the same inventive concept, such as Figure 5As shown in the embodiments of this application, an ionospheric anomaly detection device for implementing the ionospheric anomaly detection method described above is also provided. The device includes:

[0128] The sequence acquisition module 501 is used to acquire the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere.

[0129] The outlier acquisition module 502 is used to divide the current time series into two adjacent subsequences based on the time detection window, calculate the variance difference value between the two adjacent subsequences, and the length of one of the two adjacent subsequences is the same as the length of the time detection window; the length of the time detection window is adjusted multiple times to obtain multiple variance difference values, and the two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence, and the length of the first detection subsequence is the same as the length of the corresponding time detection window;

[0130] The anomaly detection module 503 is used to obtain the anomaly detection result of the current time series based on the anomaly points in the first detection subsequence and the second detection subsequence, and to determine whether an anomaly has occurred in the ionosphere based on the anomaly detection result.

[0131] The aforementioned ionospheric anomaly detection device acquires the current time series to be detected, which reflects the fluctuation characteristics of the total electron content in the ionosphere. Based on a time detection window, the current time series is divided into two adjacent subsequences, and the variance difference value between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window. The length of the time detection window is adjusted multiple times, corresponding to multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window. Based on the anomaly points in the first and second detection subsequences, the anomaly detection result of the current time series is obtained. Based on the anomaly detection result, it is determined whether an anomaly has occurred in the ionosphere. The entire ionospheric anomaly detection process acquires the current time series reflecting the fluctuation characteristics of the total electron content in the ionosphere, detects anomalies in the current time series based on the concept of variance variable point detection, and determines whether an anomaly has occurred in the ionosphere based on the anomaly detection result, thus achieving high-precision ionospheric anomaly detection.

[0132] In one embodiment, the sequence acquisition module 501 is further configured to acquire multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences based on the sliding window method; and construct the current time series based on the multiple fluctuation values ​​corresponding to the multiple fluctuation feature extraction sequences.

[0133] In one embodiment, the sequence acquisition module 501 is further configured to set the size and step size of the sliding window, perform a sliding window operation on the collected total electron content sequence according to the size and step size of the sliding window, and acquire multiple fluctuation feature extraction sequences; use the middle term of each fluctuation feature extraction sequence as a reference value, and use the average value of all non-middle terms of each fluctuation feature extraction sequence as a background value; calculate the difference between the reference value and the background value in the multiple fluctuation feature extraction sequences, and use the difference as the fluctuation value.

[0134] In one embodiment, the outlier acquisition module 502 is further configured to acquire the variance of two adjacent subsequences respectively; and use the absolute value of the difference between the variances of the two adjacent subsequences as the variance difference value.

[0135] In one embodiment, the anomaly detection module 503 is further configured to obtain an anomaly detection threshold; if the last item in the first detection subsequence or the first item in the second detection subsequence is greater than the anomaly detection threshold, it is determined that the current time series is abnormal; if neither the last item in the first detection subsequence nor the first item in the second detection subsequence is greater than the anomaly detection threshold, it is determined that the current time series is not abnormal.

[0136] In one embodiment, the anomaly detection module 503 is further configured to acquire multiple sub-sequences of the acquisition sequence based on the sliding window method, acquire anomaly detection background values ​​based on the multiple sub-sequences, and use twice the standard deviation of the anomaly detection background values ​​as anomaly detection thresholds.

[0137] 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 the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0138] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, input / output interfaces, a communication interface, a display unit, and an input device. The processor, memory, and input / output interfaces are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output 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 and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interfaces are used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements an ionospheric anomaly detection method. The display unit is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0139] Those skilled in the art will understand that Figure 6 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.

[0140] 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 perform the following steps:

[0141] Obtain the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere;

[0142] Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window.

[0143] The length of the time detection window is adjusted multiple times to obtain multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window.

[0144] Based on the anomalies in the first and second detection subsequences, obtain the anomaly detection results for the current time series, and determine whether anomalies have occurred in the ionosphere based on the anomaly detection results.

[0145] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences based on the sliding window method; and constructing the current time series based on the multiple fluctuation values ​​corresponding to the multiple fluctuation feature extraction sequences.

[0146] In one embodiment, when the processor executes the computer program, it further implements the following steps: setting the size and step size of the sliding window, performing a sliding window operation on the collected total electron content sequence according to the size and step size of the sliding window to obtain multiple fluctuation feature extraction sequences; using the middle term of each fluctuation feature extraction sequence as a reference value, and using the average value of all non-middle terms of each fluctuation feature extraction sequence as a background value; calculating the difference between the reference value and the background value in the multiple fluctuation feature extraction sequences, and using the difference as the fluctuation value.

[0147] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining the variances of two adjacent subsequences respectively; and using the absolute value of the difference between the variances of the two adjacent subsequences as the variance difference value.

[0148] In one embodiment, when the processor executes the computer program, it further performs the following steps: obtaining an anomaly detection threshold; determining that the current time series is abnormal if the last item in the first detection subsequence or the first item in the second detection subsequence is greater than the anomaly detection threshold; and determining that the current time series is not abnormal if neither the last item in the first detection subsequence nor the first item in the second detection subsequence is greater than the anomaly detection threshold.

[0149] In one embodiment, when the processor executes the computer program, it further implements the following steps: acquiring multiple subsequences of the acquisition sequence based on the sliding window method, acquiring anomaly detection background values ​​based on the multiple subsequences, and using twice the standard deviation of the anomaly detection background values ​​as anomaly detection thresholds.

[0150] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0151] Obtain the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere;

[0152] Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window.

[0153] The length of the time detection window is adjusted multiple times to obtain multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window.

[0154] Based on the anomalies in the first and second detection subsequences, obtain the anomaly detection results for the current time series, and determine whether anomalies have occurred in the ionosphere based on the anomaly detection results.

[0155] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences based on the sliding window method; and constructing the current time series based on the multiple fluctuation values ​​corresponding to the multiple fluctuation feature extraction sequences.

[0156] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: setting the size and step size of the sliding window; performing a sliding window operation on the collected total electron content sequence according to the size and step size of the sliding window to obtain multiple fluctuation feature extraction sequences; using the middle term of each fluctuation feature extraction sequence as a reference value, and using the average value of all non-middle terms of each fluctuation feature extraction sequence as a background value; calculating the difference between the reference value and the background value in the multiple fluctuation feature extraction sequences, and using the difference as the fluctuation value.

[0157] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining the variances of two adjacent subsequences respectively; and using the absolute value of the difference between the variances of the two adjacent subsequences as the variance difference value.

[0158] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining an anomaly detection threshold; determining that the current time series is abnormal if the last item in the first detection subsequence or the first item in the second detection subsequence is greater than the anomaly detection threshold; and determining that the current time series is not abnormal if neither the last item in the first detection subsequence nor the first item in the second detection subsequence is greater than the anomaly detection threshold.

[0159] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on the sliding window method, it acquires multiple sub-sequences of the acquisition sequence, and obtains anomaly detection background values ​​based on the multiple sub-sequences; and uses twice the standard deviation of the anomaly detection background values ​​as anomaly detection threshold.

[0160] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0161] Obtain the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere;

[0162] Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference between the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window.

[0163] The length of the time detection window is adjusted multiple times to obtain multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are used as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window.

[0164] Based on the anomalies in the first and second detection subsequences, obtain the anomaly detection results for the current time series, and determine whether anomalies have occurred in the ionosphere based on the anomaly detection results.

[0165] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences based on the sliding window method; and constructing the current time series based on the multiple fluctuation values ​​corresponding to the multiple fluctuation feature extraction sequences.

[0166] In one embodiment, when the computer program is executed by the processor, it further implements the following steps: setting the size and step size of the sliding window; performing a sliding window operation on the collected total electron content sequence according to the size and step size of the sliding window to obtain multiple fluctuation feature extraction sequences; using the middle term of each fluctuation feature extraction sequence as a reference value, and using the average value of all non-middle terms of each fluctuation feature extraction sequence as a background value; calculating the difference between the reference value and the background value in the multiple fluctuation feature extraction sequences, and using the difference as the fluctuation value.

[0167] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining the variances of two adjacent subsequences respectively; and using the absolute value of the difference between the variances of the two adjacent subsequences as the variance difference value.

[0168] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: obtaining an anomaly detection threshold; determining that the current time series is abnormal if the last item in the first detection subsequence or the first item in the second detection subsequence is greater than the anomaly detection threshold; and determining that the current time series is not abnormal if neither the last item in the first detection subsequence nor the first item in the second detection subsequence is greater than the anomaly detection threshold.

[0169] In one embodiment, when the computer program is executed by the processor, it further performs the following steps: based on the sliding window method, it acquires multiple sub-sequences of the acquisition sequence, and obtains anomaly detection background values ​​based on the multiple sub-sequences; and uses twice the standard deviation of the anomaly detection background values ​​as anomaly detection threshold.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 detecting ionospheric anomalies, characterized in that, The method includes: Obtain the current time series to be detected, which is used to reflect the fluctuation characteristics of the total electron content of the ionosphere; Based on the time detection window, the current time series is divided into two adjacent subsequences, and the variance difference value of the two adjacent subsequences is calculated. The length of one of the two adjacent subsequences is the same as the length of the time detection window. The length of the time detection window is adjusted multiple times to obtain multiple variance difference values. The two adjacent subsequences corresponding to the largest variance difference value are taken as the first detection subsequence and the second detection subsequence. The length of the first detection subsequence is the same as the length of the corresponding time detection window. Based on the anomalies in the first and second detection subsequences, obtain the anomaly detection results of the current time series, and determine whether the ionosphere is abnormal based on the anomaly detection results; The step of obtaining the anomaly detection result of the current time series based on the anomalies in the first detection subsequence and the second detection subsequence includes: Obtain the anomaly detection threshold; If the last item in the first detection subsequence or the first item in the second detection subsequence is greater than the anomaly detection threshold, it is determined that the current time series is abnormal. If neither the last item in the first detection subsequence nor the first item in the second detection subsequence is greater than the anomaly detection threshold, it is determined that the current time series does not exhibit any anomalies.

2. The method according to claim 1, characterized in that, The step of obtaining the current time series to be detected includes: Based on the sliding window method, multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences are obtained; Based on the multiple fluctuation features, extract multiple fluctuation values ​​corresponding to the sequence to construct the current time series.

3. The method according to claim 2, characterized in that, The method based on the sliding window approach obtains multiple fluctuation values ​​corresponding to multiple fluctuation feature extraction sequences, including: Set the size and step size of the sliding window, and perform a sliding window operation on the collected total electron content sequence according to the size and step size of the sliding window to obtain multiple fluctuation feature extraction sequences; The middle term of each fluctuation feature extraction sequence is used as a reference value, and the average value of all non-middle terms of each fluctuation feature extraction sequence is used as a background value. The difference between the reference value and the background value in the plurality of fluctuation feature extraction sequences is calculated, and the difference is used as the fluctuation value.

4. The method according to claim 1, characterized in that, The calculation of the variance difference value between the two adjacent subsequences includes: Obtain the variance of each of the two adjacent subsequences; The absolute value of the variance difference between the two adjacent subsequences is taken as the variance difference value.

5. The method according to claim 1, characterized in that, The process of obtaining the anomaly detection threshold includes: Based on the sliding window method, multiple subsequences of the acquisition sequence are obtained, and anomaly detection background values ​​are obtained based on the multiple subsequences; The anomaly detection threshold is set at twice the standard deviation of the background anomaly detection value.

6. An ionospheric anomaly detection device, characterized in that, The device includes: The sequence acquisition module is used to acquire the current time series to be detected, which reflects the fluctuation characteristics of the total electron content of the ionosphere. The outlier detection module is used to divide the current time series into two adjacent subsequences based on a time detection window, calculate the variance difference value of the two adjacent subsequences, wherein the length of one of the two adjacent subsequences is the same as the length of the time detection window; adjust the length of the time detection window multiple times to obtain multiple variance difference values, and take the two adjacent subsequences corresponding to the largest variance difference value as the first detection subsequence and the second detection subsequence, wherein the length of the first detection subsequence is the same as the length of the corresponding time detection window; An anomaly detection module is used to obtain the anomaly detection result of the current time series based on the anomaly points in the first detection subsequence and the second detection subsequence, and to determine whether an anomaly has occurred in the ionosphere based on the anomaly detection result. The anomaly detection module is further configured to obtain an anomaly detection threshold; if the last item in the first detection sub-sequence or the first item in the second detection sub-sequence is greater than the anomaly detection threshold, determine that the current time series is abnormal; if neither the last item in the first detection sub-sequence nor the first item in the second detection sub-sequence is greater than the anomaly detection threshold, determine that the current time series is not abnormal.

7. 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 5.

8. 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 5.

9. 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 5.

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