Geomagnetic storm event extraction method based on wavelet scale spectrum

Abstract

The invention provides a geomagnetic storm event extraction method based on a wavelet scale spectrum, and relates to the technical field of space weather event monitoring. The method comprises the steps that Dst index data and SYM-H index data are acquired and preprocessed, and a Dst index time sequence and an SYM-H index time sequence are obtained; according to the Dst index time sequence, geomagnetic storm event features defined by the Dst index are extracted; according to the geomagnetic storm event characteristics defined by the Dst index, extracting the geomagnetic storm event characteristics defined by the SYM-H index from the SYM-H index time sequence based on a wavelet scale spectrum method; and constructing a geomagnetic storm event list according to the geomagnetic storm event characteristics defined by the Dst index and the geomagnetic storm event characteristics defined by the SYM-H index. According to the method, the time node information of the initial phase, the main phase and the recovery phase of the geomagnetic storm event can be extracted more accurately and automatically, and a complete geomagnetic storm event list of nearly four solar activity cycles is constructed.

Description

Technical Field

[0001] The present application relates to the technical field of space weather event monitoring, and in particular to a method for extracting geomagnetic storm events based on wavelet scaling spectrum. Background Art

[0002] Geomagnetic storms are global, violent disturbances of the Earth's magnetic field caused by solar surface activity. They are the most representative global space weather events resulting from the arrival of solar wind plasma in Earth's space. Existing geomagnetic storm screening schemes primarily use the Dst index or SYM-H index to automatically identify candidate geomagnetic storm events based on a defined threshold, followed by manual verification to ultimately identify and confirm the event. The general steps include: first, automatically searching for the moment when the Dst index or SYM-H index falls below -80 nT to define the end of the main phase; then, automatically searching for the moment when the Dst index or SYM-H index first reaches -15 nT after the end of the main phase to define the end of the recovery phase. After this initial screening, manual verification, combining the two ring current indices with solar wind parameters and interplanetary magnetic field conditions over the corresponding time period, is used to determine the onset times of the initial and main phases. The onset of the initial phase or geomagnetic onset is determined by a sudden increase in solar wind parameters, which serves as the criterion. The start of the main phase is determined manually by searching for a sudden decrease in the Dst index or SYM-H index.

[0003] The above existing screening schemes require manual confirmation of specific times with the help of human experience, and cannot fully automatically determine the start and end times of each stage of the geomagnetic storm, resulting in large errors. In addition, the sudden onset of the geomagnetic storm requires reference to solar wind parameters and interplanetary magnetic field data for manual judgment. When two or more geomagnetic storm events overlap, the end time of the main phase or the start time of the recovery phase also depends entirely on manual judgment. Moreover, for new observation data, the workload of screening new geomagnetic storms is large, which is inconvenient for maintaining the geomagnetic storm event list. Summary of the Invention

[0004] The present application provides a method, apparatus, device and storage medium for extracting geomagnetic storm events. Through wavelet scale spectrum and dynamic threshold method, the time node information of the initial phase (acute onset), main phase and recovery phase of geomagnetic storm events is extracted more accurately and automatically, and a complete list of geomagnetic storm events in the past four solar activity cycles is constructed.

[0005] According to a first aspect of the present application, a method for extracting geomagnetic storm events is provided, the method comprising:

[0006] Acquire Dst index data and SYM-H index data, and preprocess the Dst index data and SYM-H index data to obtain a Dst index time series and a SYM-H index time series;

[0007] Extracting geomagnetic storm event characteristics defined by the Dst index according to the Dst index time series;

[0008] According to the geomagnetic storm event characteristics defined by the Dst index, extracting the geomagnetic storm event characteristics defined by the SYM-H index from the SYM-H index time series based on the wavelet scale spectrum method;

[0009] According to the characteristics of geomagnetic storm events defined by the Dst index and the SYM-H index, a geomagnetic storm event list is constructed, wherein the geomagnetic storm event list includes the time information and intensity level information of the initial phase, main phase and recovery phase of the geomagnetic storm event.

[0010] According to the above aspects and any possible implementation, an implementation is further provided, wherein extracting the geomagnetic storm event characteristics defined by the Dst index according to the Dst index time series includes:

[0011] According to the Dst index time series, the intensity levels of geomagnetic storm events in various places are determined, and the characteristics of geomagnetic storm events in various places defined by the Dst index are extracted based on dynamic thresholds, wherein the geomagnetic storm event characteristics include the time information of the initial phase, main phase and recovery phase of the geomagnetic storm.

[0012] According to the above aspects and any possible implementation, an implementation is further provided, wherein determining the intensity level of magnetic storm events in various locations based on the Dst index time series includes:

[0013] Traversing the Dst index time series, determining all time periods where the Dst index data is less than a preset threshold and the minimum Dst value within each time period;

[0014] The intensity level of the geomagnetic storm event corresponding to each time period is determined by mapping the minimum Dst value in each time period with the geomagnetic storm level.

[0015] According to the above aspects and any possible implementation, a further implementation is provided for extracting the characteristics of magnetic storm events in various regions defined by the Dst index based on a dynamic threshold, including:

[0016] Construct a minimum Dst value time series based on the minimum Dst value and its corresponding time in each time period;

[0017] Determine the end time sequence of the main phase and the start time sequence of the recovery phase of the magnetic storm events at various locations based on the minimum Dst value time sequence;

[0018] According to the end time series of the main phase of the magnetic storm events in various places, the start time series of the main phase corresponding to the magnetic storm events in various places are determined;

[0019] According to the main phase start time sequence of magnetic storm events in various places, determine the initial phase start time sequence corresponding to magnetic storm events in various places;

[0020] According to the initial phase start time series of magnetic storm events in various places, the recovery phase end time series corresponding to the magnetic storm events in various places is determined.

[0021] According to the above aspects and any possible implementation, an implementation is further provided, wherein determining the initial phase start time sequence corresponding to the magnetic storm events in each region based on the main phase start time sequence of the magnetic storm events in each region comprises:

[0022] Based on the main phase start time series of magnetic storm events in various places, query the Dst index value within the preset time range corresponding to the start time of each main phase;

[0023] Based on the Dst index value within the preset time range corresponding to the start time of each main phase, the initial phase start time corresponding to the magnetic storm event in each place is determined, and the corresponding initial phase start time sequence is determined according to the initial phase start time corresponding to the magnetic storm event in each place.

[0024] According to the above aspects and any possible implementation, a further implementation is provided, which determines the initial phase start time corresponding to the magnetic storm event in each location based on the Dst index value within a preset time range corresponding to the start time of each main phase, including:

[0025] If there is an i-th moment within the preset time range, Dst i+1 -Dst i ≥n and Dst i ≥0, then the moment i is regarded as the starting time of the initial phase of the geomagnetic storm with a sudden onset;

[0026] If there is an i-th moment within the preset time range, Dst i ≥0 but does not meet Dst i+1 -Dst i ≥n, then Dst i =0 is taken as the starting time of the initial phase of the geomagnetic storm without an acute onset;

[0027] If at any time i within the preset time range, Dst i+1 -Dst i ≥n and Dst i ≥0, the moment in the middle of a time window forward of the main phase start time is selected as the initial phase start time of the geomagnetic storm without an abrupt onset, and the time window extends from the main phase start time forward to the initial phase end time.

[0028] According to the above aspects and any possible implementation, a further implementation is provided, which determines the recovery phase end time sequence corresponding to the magnetic storm events in each region based on the initial phase start time sequence of the magnetic storm events in each region, including:

[0029] According to the initial phase start time of magnetic storm events in various places, find the Dst index data within the preset time range corresponding to each initial phase start time;

[0030] Determine the Dst background value of magnetic storm events in various locations based on the Dst index data within the preset time ranges;

[0031] According to the Dst background values of the magnetic storm events in each place, the recovery phase end time corresponding to the magnetic storm events in each place is determined, and a recovery phase end time series is constructed.

[0032] According to the above aspects and any possible implementation, an implementation is further provided, wherein, according to the geomagnetic storm event characteristics defined by the Dst index, the geomagnetic storm event characteristics defined by the SYM-H index are extracted from the SYM-H index time series based on a wavelet scale spectrum method, including:

[0033] According to the characteristics of the geomagnetic storm events defined by the Dst index, a corresponding time period is selected from the SYM-H index time series to construct the SYM-H index time series of geomagnetic storm events in various regions;

[0034] According to the SYM-H index time series of geomagnetic storm events in various places, the intensity levels of geomagnetic storm events defined by the SYM-H index are determined, and the characteristics of geomagnetic storm events defined by the SYM-H index are extracted based on the wavelet scale spectrum method. The characteristics of geomagnetic storm events defined by the SYM-H index include the time information of the initial phase, main phase and recovery phase of the geomagnetic storm events in various places.

[0035] According to the above aspects and any possible implementation, an implementation is further provided, wherein determining the intensity level of the magnetic storm events in each region defined by the SYM-H index based on the SYM-H index time series of the magnetic storm events in each region comprises:

[0036] Traversing the SYM-H index time series of magnetic storm events in the various locations, and determining the minimum SYM-H index in each SYM-H index time series;

[0037] The intensity levels of the geomagnetic storm events in each region defined by the SYM-H index are determined by mapping the minimum values of the SYM-H index in each SYM-H index time series with the geomagnetic storm levels.

[0038] According to the above aspects and any possible implementation, an implementation is further provided for extracting the characteristics of geomagnetic storm events defined by the SYM-H index based on the wavelet scale spectrum method, wherein the characteristics of the geomagnetic storm events defined by the SYM-H index include the time information of the initial phase, main phase and recovery phase of the geomagnetic storm events in each region, including:

[0039] According to the Morlet wavelet basis function, continuous wavelet transform is performed on the SYM-H index time series of magnetic storm events in various places, and the wavelet coefficients at each scale are calculated respectively.

[0040] Based on the wavelet coefficients at different scales, determining the initial phase start time series and the main phase start time series of magnetic storm events in various locations;

[0041] Based on the initial phase start time series of the magnetic storm events in each location, the recovery phase end time series of the magnetic storm events in each location defined by the SYM-H index is determined.

[0042] According to a second aspect of the present application, a device for extracting geomagnetic storm events is provided. The device comprises:

[0043] a data acquisition unit, configured to acquire Dst index data and SYM-H index data, and preprocess the Dst index data and SYM-H index data to obtain a Dst index time series and a SYM-H index time series;

[0044] a geomagnetic storm event feature extraction unit, configured to extract geomagnetic storm event features defined by the Dst index according to the Dst index time series;

[0045] The geomagnetic storm event feature extraction unit is further configured to extract the geomagnetic storm event features defined by the SYM-H index from the SYM-H index time series based on the wavelet scale spectrum method according to the geomagnetic storm event features defined by the Dst index;

[0046] The geomagnetic storm event list construction unit is used to construct a geomagnetic storm event list based on the geomagnetic storm event characteristics defined by the Dst index and the geomagnetic storm event characteristics defined by the SYM-H index, wherein the geomagnetic storm event list includes the time information and intensity level information of the initial phase, main phase and recovery phase of the geomagnetic storm event.

[0047] According to a third aspect of the present application, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the program.

[0048] According to a fourth aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method according to the first aspect of the present application is implemented.

[0049] Compared with the prior art, the present disclosure achieves the following beneficial effects:

[0050] This paper uses the wavelet scaling spectrum and dynamic threshold method, and utilizes the Dst index and SYM-H index for relevant calculations and judgments, to more accurately realize the automatic extraction of information at each stage of geomagnetic storm events, and constructs a complete list of geomagnetic storm events for nearly four solar activity cycles, reducing manual intervention, lowering the workload of screening new geomagnetic storms, and facilitating the maintenance of the geomagnetic storm event list.

[0051] It should be understood that the contents described in the Summary of the Invention are not intended to limit the key or important features of the embodiments of the present application, nor are they intended to limit the scope of the present application. Other features of the present application will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are provided for a better understanding of the present invention and do not constitute a limitation of the present application. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0053] Figure 1 A flow chart of a method for extracting geomagnetic storm events according to an embodiment of the present application is shown;

[0054] Figure 2 A typical process diagram of a geomagnetic storm event defined according to the SYM-H index according to an embodiment of the present application is shown;

[0055] Figure 3 A block diagram of a device for extracting geomagnetic storm events according to an embodiment of the present disclosure;

[0056] Figure 4 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0057] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0058] In this document, the term "and / or" simply describes a relationship between related objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the related objects are in an "or" relationship.

[0059] Example 1

[0060] Figure 1 A flow chart of a method 100 for processing geomagnetic storm event extraction is shown. Figure 1 As shown, the method includes the following steps:

[0061] S110 , acquiring Dst index data and SYM-H index data, and preprocessing the Dst index data and SYM-H index data to obtain a Dst index time series and a SYM-H index time series.

[0062] In some embodiments, the Dst index data and SYM-H index data obtained come from the final values of the Dst index from 1986 to 2020, the final values of the Dst index from 2021 to June 2024, and the final values of the SYM-H index from 1986 to June 2024 released by the world data center Kyoto.

[0063] In order to improve data accuracy, improve the accuracy of geomagnetic storm event feature extraction, and reduce errors, it is necessary to preprocess the Dst index data and SYM-H index data, that is, perform integrity checks on the Dst index data and SYM-H index data, perform linear interpolation on the default positions, and generate preprocessed Dst index time series and SYM-H index time series to avoid misjudgment or missed judgment caused by data interruptions in rare cases.

[0064] S120: Extracting geomagnetic storm event characteristics defined by the Dst index according to the Dst index time series.

[0065] In some embodiments, to accurately extract geomagnetic storm events, it is necessary to clearly define the intensity level of geomagnetic storm events. The geomagnetic storm intensity classification standard adopted in this embodiment is derived from the national standard GB / T31160-2014. The specific geomagnetic storm intensity classification standard is shown in Table 1. This application currently only extracts events of moderate geomagnetic storm level and above. At the same time, the geomagnetic storm level defined by the SYM-H index is completely consistent with the classification standard based on the Dst index.

[0066] Table 1 Geomagnetic storm event intensity classification standards

[0067] level Event classification threshold range Minor geomagnetic storm -50nT<Dst≤-30nT moderate geomagnetic storm -100nT<Dst≤-50nT Large geomagnetic storm -200nT<Dst≤-100nT Extreme geomagnetic storm -300nT<Dst≤-200nT Super geomagnetic storm Dst≤-300nT

[0068] In some embodiments, extracting geomagnetic storm event characteristics based on the Dst index includes: determining the intensity level of geomagnetic storm events in various places based on the Dst index time series preprocessed in the above step S110, and extracting the characteristics of geomagnetic storm events in various places defined by the Dst index based on a dynamic threshold. The geomagnetic storm event characteristics include the time information of the initial phase, main phase and recovery phase of the geomagnetic storm, completing the entire process of identifying geomagnetic storm events using the Dst index.

[0069] Specifically, according to the Dst index time series pre-processed in step S110 above, determining the intensity level of magnetic storm events in various places includes the following steps:

[0070] Traverse the Dst index time series {Dst i}, where i represents a time point, the sequence interval period is 1 hour, and all time periods in which the Dst index data is less than a preset threshold are queried. Since this embodiment only extracts events of moderate geomagnetic storms and above, the preset threshold is selected as -50nT.

[0071] Determine the minimum Dst value in each time period and record its corresponding time t (if there are two or more minimum Dst values, select the relatively later time t), and construct the minimum Dst value time series {minDst i}, the corresponding time in the sequence {T_MainPhase_End i} is the end time of the main phase of the geomagnetic storm event, and also the start time of the recovery phase {T_RecoveryPhase_Start i}.

[0072] According to the mapping between the minimum value of Dst in each time period and the geomagnetic storm level, the intensity level of the geomagnetic storm event corresponding to each time period is determined. For example, if the minimum value of Dst in a certain time period is -151nT, according to the intensity grading standard shown in Table 1, the intensity level of the geomagnetic storm event corresponding to the time period is a large geomagnetic storm.

[0073] Furthermore, the characteristics of geomagnetic storm events in various regions defined by the Dst index are extracted based on a dynamic threshold, wherein the characteristics of the geomagnetic storm event include the time information of the initial phase, main phase, and recovery phase of the geomagnetic storm, including the following steps:

[0074] According to the minimum Dst value time series {minDst i}, determine the end time series of the main phase and the start time series of the recovery phase of magnetic storm events in various places;

[0075] According to the end time series of the main phase of the magnetic storm events in various places, the start time series of the main phase corresponding to the magnetic storm events in various places are determined;

[0076] According to the main phase start time sequence of magnetic storm events in various places, determine the initial phase start time sequence corresponding to magnetic storm events in various places;

[0077] According to the initial phase start time series of magnetic storm events in various places, the recovery phase end time series corresponding to the magnetic storm events in various places is determined.

[0078] In some embodiments, determining the main phase start time sequence corresponding to the magnetic storm events in each region based on the main phase end time sequence of the magnetic storm events in each region includes:

[0079] Main phase end time {T_MainPhase_End i} is the starting point, and the query is within 24 hours forward |Dst i+1 -Dst i | the maximum moment, the corresponding moment i is taken as the main phase start time of the geomagnetic storm event {T_MainPhase_Start i}, or the initial phase end time {T_InitialPhase_End i}.

[0080] In some embodiments, determining the initial phase start time sequence corresponding to the magnetic storm events in each region based on the main phase start time sequence of the magnetic storm events in each region includes:

[0081] Geomagnetic storms can be divided into geomagnetic storms with sudden onset and geomagnetic storms without sudden onset according to whether they have a sudden onset or not. Correspondingly, there are also differences in the determination of the starting time of the initial phase of the two types of geomagnetic storms.

[0082] First, according to the main phase start time {T_MainPhase_Start i} as the starting point, query the Dst index value within 24 hours;

[0083] Based on the found Dst index values, determine the initial phase start time corresponding to the magnetic storm events in various places;

[0084] If there exists at time i that satisfies Dst i+1 -Dst i ≥n and Dst i ≥0, then the i-th moment is defined as the initial phase start time of the geomagnetic storm with an abrupt onset {T_InitialPhase_Start i};

[0085] If only Dst i ≥0, then select Dst i = 0, as the starting time of the initial phase of the geomagnetic storm without an acute onset;

[0086] If neither condition is met, select {T_MainPhase_Start i The time corresponding to 12 hours before is the beginning time of the initial phase of the geomagnetic storm.

[0087] In this embodiment, the value of n can be selected to be a value that can better identify the characteristics of the Dst index change at the beginning of the initial phase of a sudden geomagnetic storm. For example, based on a lot of experience and summary, n can be 8.

[0088] In some embodiments, determining the recovery phase end time sequence corresponding to the magnetic storm events in each region based on the initial phase start time sequence of the magnetic storm events in each region includes:

[0089] After determining the initial phase start time of the magnetic storm event in each place, the initial phase start time {T_InitialPhase_Start i} is the starting point, and the previous 24 hours {Dst i} Take the average value as the Dst background value of the corresponding time period {backgroundDst i};

[0090] The recovery phase start time {T_RecoveryPhase_Start i} as the starting point, search backward for {Dst i} first reaches the Dst background value {backgroundDst i}, and record this moment as the end time of the corresponding geomagnetic storm event recovery phase {T_RecoveryPhase_End i}.

[0091] S130 , extracting the geomagnetic storm event characteristics defined by the SYM-H index from the SYM-H index time series based on a wavelet scalogram method according to the geomagnetic storm event characteristics defined by the Dst index.

[0092] In some embodiments, according to the geomagnetic storm event characteristics defined by the Dst index, extracting the geomagnetic storm event characteristics defined by the SYM-H index from the SYM-H index time series based on the wavelet scale spectrum method includes the following steps:

[0093] First, the Dst index is used to extract the initial phase start time series and the recovery phase end time series of the geomagnetic storm event. Based on the initial phase start time, it is extended forward 2 hours, and based on the recovery phase end time, it is extended backward 2 hours to construct the geomagnetic storm event time series defined by the SYM-H index {SYMH k};

[0094] Secondly, according to the SYM-H index time series of magnetic storm events in various places, the minimum value of the SYM-H index in each SYM-H index time series is determined, and the intensity level of the magnetic storm events in various places defined by the SYM-H index is determined according to the mapping between the minimum value of the SYM-H index in each SYM-H index time series and the geomagnetic storm level. For example, if the minimum value of Dst in a certain time period is -252nT, according to the intensity classification standard shown in Table 1, the intensity level of the geomagnetic storm event corresponding to the time period is an extremely large geomagnetic storm.

[0095] Finally, based on the SYM-H index time series of geomagnetic storm events in various places, the characteristics of geomagnetic storm events defined by the SYM-H index are extracted based on the wavelet scaling spectrum method. The characteristics of geomagnetic storm events defined by the SYM-H index include the time information of the initial phase, main phase and recovery phase of the geomagnetic storm events in various places.

[0096] Specifically, the characteristics of magnetic storm events in various regions defined by the SYM-H index are extracted based on the wavelet scale spectrum method, including:

[0097] First, the Morlet wavelet basis function is selected to calculate the {SYMH k}Continuous wavelet transform is performed on the time series. According to the characteristics of the geomagnetic storm and the expected mutation scale, the appropriate wavelet transform scale range is selected, the wavelet coefficients at each scale are calculated, and the wavelet coefficients at different scales are combined to determine a reasonable threshold based on the noise level and signal characteristics to distinguish normal geomagnetic fluctuations from abnormal signals related to geomagnetic storms.

[0098] Secondly, find the moment when the absolute value of the wavelet coefficient exceeds the threshold, and determine the initial phase start time {T_InitialPhase_Start k} and the main phase start time {T_MainPhase_Start k For geomagnetic storm events without sudden onset, the same method as the Dst index introduced in step S120 is used to determine the starting time of the initial phase of the geomagnetic storm event without sudden onset.

[0099] Finally, according to the initial phase start time of the magnetic storm events in various places defined by the SYM-H index, the end time of the recovery phase corresponding to the magnetic storm events in various places defined by the SYM-H index is determined, including:

[0100] The initial phase start time of magnetic storm events in various places defined by the SYM-H index {T_InitialPhase_Start k} as the starting point, the first 24 hours {SYMH k} Take the average value as the background value of SYM-H index in this period {backgroudSYMH k},

[0101] Recovery phase start time {T_RecoveryPhase_Start k} as the starting point, search backward for {SYMH k} first reached the SYM-H index background value {backgroudSYMH k}, recorded as the end time of the recovery phase of the geomagnetic storm event {T_RecoveryPhase_End k}.

[0102] S140, constructing a geomagnetic storm event list based on the geomagnetic storm event characteristics defined by the Dst index and the geomagnetic storm event characteristics defined by the SYM-H index, wherein the geomagnetic storm event list includes time information and intensity level information of the initial phase, main phase and recovery phase of the geomagnetic storm event.

[0103] In some embodiments, the initial phase start time, initial phase end time (i.e., main phase start time), main phase end time (i.e., recovery phase start time), recovery phase end time and determined intensity level information of each geomagnetic storm event are recorded in a list in a certain format.

[0104] According to the embodiments of the present disclosure, the following beneficial effects are achieved:

[0105] This paper uses the wavelet scaling spectrum and dynamic threshold method, and utilizes the Dst index and SYM-H index for relevant calculations and judgments, to more accurately realize the automatic extraction of information at each stage of geomagnetic storm events, and constructs a complete list of geomagnetic storm events for nearly four solar activity cycles, reducing manual intervention, lowering the workload of screening new geomagnetic storms, and facilitating the maintenance of the geomagnetic storm event list.

[0106] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all optional embodiments, and the actions and modules involved are not necessarily required by this application.

[0107] The above is an introduction to the method embodiment. The following is a device embodiment to further illustrate the solution described in this application.

[0108] Figure 3 FIG. 3 shows a block diagram of a geomagnetic storm event extraction device 300 according to an embodiment of the present application, as shown in FIG. Figure 3 As shown, the apparatus 300 includes:

[0109] A data acquisition unit 310 is configured to acquire Dst index data and SYM-H index data, and preprocess the Dst index data and SYM-H index data to obtain a Dst index time series and a SYM-H index time series;

[0110] a geomagnetic storm event feature extraction unit 320, configured to extract geomagnetic storm event features defined by the Dst index according to the Dst index time series;

[0111] The geomagnetic storm event feature extraction unit 320 is further configured to extract the geomagnetic storm event features defined by the SYM-H index from the SYM-H index time series based on the wavelet scale spectrum method according to the geomagnetic storm event features defined by the Dst index;

[0112] The geomagnetic storm event list construction unit 330 is used to construct a geomagnetic storm event list based on the geomagnetic storm event characteristics defined by the Dst index and the geomagnetic storm event characteristics defined by the SYM-H index, wherein the geomagnetic storm event list includes the time information and intensity level information of the initial phase, main phase and recovery phase of the geomagnetic storm event.

[0113] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the described module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0114] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0115] Figure 4 A schematic block diagram of an electronic device 800 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0116] The electronic device 400 includes a computing unit 401, which can perform various appropriate actions and processes according to a computer program stored in a ROM 402 or a computer program loaded from a storage unit 408 into a RAM 403. The RAM 403 may also store various programs and data required for the operation of the electronic device 400. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An I / O interface 405 is also connected to the bus 404.

[0117] Multiple components in the electronic device 400 are connected to the I / O interface 405, including an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0118] The computing unit 401 may be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as method 100. For example, in some embodiments, the method 100 may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method 100 described above may be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the method 100 in any other appropriate manner (e.g., by means of firmware).

[0119] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system comprising at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0120] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0121] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0122] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0123] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0124] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0125] In the technical solution of this application, the acquisition, storage and application of user personal information involved are in compliance with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0126] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this application can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this application can be achieved. This is not a limitation herein.

[0127] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. A method for extracting geomagnetic storm events based on wavelet scale spectrum, characterized in that: include: Acquire Dst index data and SYM-H index data, and preprocess the Dst index data and SYM-H index data to obtain a Dst index time series and a SYM-H index time series; Extracting geomagnetic storm event characteristics defined by the Dst index according to the Dst index time series; According to the geomagnetic storm event characteristics defined by the Dst index, extracting the geomagnetic storm event characteristics defined by the SYM-H index from the SYM-H index time series based on the wavelet scale spectrum method; According to the characteristics of geomagnetic storm events defined by the Dst index and the SYM-H index, a geomagnetic storm event list is constructed, wherein the geomagnetic storm event list includes the time information and intensity level information of the initial phase, main phase and recovery phase of the geomagnetic storm event.

2. The method according to claim 1, characterized in that Extracting the geomagnetic storm event characteristics defined by the Dst index according to the Dst index time series includes: According to the Dst index time series, the intensity levels of geomagnetic storm events in various places are determined, and the characteristics of geomagnetic storm events in various places defined by the Dst index are extracted based on dynamic thresholds, wherein the geomagnetic storm event characteristics include the time information of the initial phase, main phase and recovery phase of the geomagnetic storm.

3. The method according to claim 2, characterized in that Determining the intensity level of magnetic storm events in various locations based on the Dst index time series includes: Traversing the Dst index time series, determining all time periods where the Dst index data is less than a preset threshold and the minimum Dst value within each time period; The intensity level of the geomagnetic storm event corresponding to each time period is determined by mapping the minimum Dst value in each time period with the geomagnetic storm level.

4. The method according to claim 3, characterized in that The characteristics of magnetic storm events in various regions defined by the Dst index are extracted based on dynamic thresholds, including: Construct a minimum Dst value time series based on the minimum Dst value and its corresponding time in each time period; Determine the end time sequence of the main phase and the start time sequence of the recovery phase of the magnetic storm events at various locations based on the minimum Dst value time sequence; According to the end time series of the main phase of the magnetic storm events in various places, the start time series of the main phase corresponding to the magnetic storm events in various places are determined; According to the main phase start time sequence of magnetic storm events in various places, determine the initial phase start time sequence corresponding to magnetic storm events in various places; According to the initial phase start time series of magnetic storm events in various places, the recovery phase end time series corresponding to the magnetic storm events in various places is determined.

5. The method according to claim 4, characterized in that Determining the initial phase start time sequence corresponding to the magnetic storm events in each region based on the main phase start time sequence of the magnetic storm events in each region includes: Based on the main phase start time series of magnetic storm events in various places, query the Dst index value within the preset time range corresponding to the start time of each main phase; Based on the Dst index value within the preset time range corresponding to the start time of each main phase, the initial phase start time corresponding to the magnetic storm event in each place is determined, and the corresponding initial phase start time sequence is determined according to the initial phase start time corresponding to the magnetic storm event in each place.

6. The method according to claim 5, characterized in that Based on the Dst index values within the preset time range corresponding to the start time of each main phase, the initial phase start time corresponding to the magnetic storm event in each location is determined, including: If there is an i-th moment within the preset time range, Dst i+1 -Dst i ≥n and Dst i ≥0, then the moment i is regarded as the starting time of the initial phase of the geomagnetic storm with a sudden onset; If there is an i-th moment within the preset time range, Dst i ≥0 but does not meet Dst i+1 -Dst i ≥n, then Dst i =0 is taken as the starting time of the initial phase of the geomagnetic storm without an acute onset; If at any time i within the preset time range, Dst i+1 -Dst i ≥n and Dst i ≥0, the moment in the middle of a time window forward of the main phase start time is selected as the initial phase start time of the geomagnetic storm without an abrupt onset, and the time window extends from the main phase start time forward to the initial phase end time.

7. The method according to claim 4, characterized in that According to the initial phase start time series of magnetic storm events in various places, the corresponding recovery phase end time series of magnetic storm events in various places are determined, including: According to the initial phase start time of magnetic storm events in various places, find the Dst index data within the preset time range corresponding to each initial phase start time; Determine the Dst background value of magnetic storm events in various locations based on the Dst index data within the preset time ranges; According to the Dst background values of the magnetic storm events in each place, the recovery phase end time corresponding to the magnetic storm events in each place is determined, and a recovery phase end time series is constructed.

8. The method according to claim 2, characterized in that According to the geomagnetic storm event characteristics defined by the Dst index, the geomagnetic storm event characteristics defined by the SYM-H index are extracted from the SYM-H index time series based on the wavelet scale spectrum method, including: According to the characteristics of the geomagnetic storm events defined by the Dst index, a corresponding time period is selected from the SYM-H index time series to construct the SYM-H index time series of geomagnetic storm events in various regions; According to the SYM-H index time series of geomagnetic storm events in various places, the intensity levels of geomagnetic storm events defined by the SYM-H index are determined, and the characteristics of geomagnetic storm events defined by the SYM-H index are extracted based on the wavelet scale spectrum method. The characteristics of geomagnetic storm events defined by the SYM-H index include the time information of the initial phase, main phase and recovery phase of the geomagnetic storm events in various places.

9. The method according to claim 8, characterized in that Determining the intensity level of the magnetic storm events in each region as defined by the SYM-H index based on the SYM-H index time series of the magnetic storm events in each region includes: Traversing the SYM-H index time series of magnetic storm events in the various locations, and determining the minimum SYM-H index in each SYM-H index time series; The intensity levels of the geomagnetic storm events in each region defined by the SYM-H index are determined by mapping the minimum values of the SYM-H index in each SYM-H index time series with the geomagnetic storm levels.

10. The method according to claim 9, characterized in that The characteristics of geomagnetic storm events defined by the SYM-H index are extracted based on the wavelet scalogram method. The characteristics of geomagnetic storm events defined by the SYM-H index include the time information of the initial phase, main phase and recovery phase of the geomagnetic storm events in various places, including: According to the Morlet wavelet basis function, continuous wavelet transform is performed on the SYM-H index time series of magnetic storm events in various places, and the wavelet coefficients at each scale are calculated respectively. Based on the wavelet coefficients at each scale, determining the initial phase start time series and the main phase start time series of magnetic storm events in various locations; Based on the initial phase start time series of the magnetic storm events in each location, the recovery phase end time series of the magnetic storm events in each location defined by the SYM-H index is determined.

Images