Geomagnetic diurnal variation prediction method and system based on frequency band kriging interpolation

Abstract

The application provides a geomagnetic diurnal variation prediction method and system based on a frequency band Kriging interpolation, relates to the technical field of geomagnetic diurnal variation data processing, and comprises the following steps: obtaining a measured geomagnetic diurnal variation signal in a target region; based on the measured geomagnetic diurnal variation signal, determining a plurality of frequency data corresponding to the measured geomagnetic diurnal variation signal by adopting wavelet decomposition; based on the plurality of frequency data, predicting interpolation data corresponding to each frequency data by adopting a Kriging interpolation prediction model; and based on the plurality of interpolation data, reconstructing a geomagnetic diurnal variation prediction value of a geomagnetic diurnal variation observation station to be solved in the target region by adopting linear superposition, so that the accuracy and reliability of geomagnetic diurnal variation spatial prediction are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of geomagnetic diurnal variation data processing technology, and more specifically, to a geomagnetic diurnal variation prediction method and system based on frequency band kriging interpolation. Background Technology

[0002] Existing methods for predicting geomagnetic diurnal variations mainly include the weighted distance average method, the latitude difference weighted method, and the two-factor weighted method. The core of these methods lies in calculating fixed weight coefficients based on the spatial relationship between known stations and the points to be predicted (such as distance, latitude difference, etc.) to predict geomagnetic diurnal variations. Due to the limitations of fixed weight coefficients, the prediction accuracy is limited, and they fail to perform adaptive optimization based on the inherent statistical characteristics of geomagnetic field data, thereby reducing the accuracy and reliability when dealing with complex spatial variations. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide a geomagnetic diurnal variation prediction method and system based on frequency band kriging interpolation, so as to improve the accuracy and reliability of geomagnetic diurnal variation spatial prediction.

[0004] Firstly, this application provides a geomagnetic diurnal variation prediction method based on frequency-division kriging interpolation, including:

[0005] Acquire the measured geomagnetic diurnal variation signal within the target area;

[0006] Based on the measured geomagnetic diurnal variation signal, wavelet decomposition is used to determine multiple frequency data corresponding to the measured geomagnetic diurnal variation signal;

[0007] Based on multiple frequency data, a Kriging interpolation prediction model is used to predict the interpolated data corresponding to each frequency data. The Kriging interpolation prediction model is determined based on the mapping relationship between the observation data of each known geomagnetic diurnal variation observation station in the target area, the weight coefficients of each known geomagnetic diurnal variation observation station, and the observation data of the geomagnetic diurnal variation observation station to be determined.

[0008] Based on multiple interpolated data, the predicted values ​​of geomagnetic diurnal variation of the observation stations within the target area are obtained by linear superposition reconstruction.

[0009] Optionally, acquiring the measured geomagnetic diurnal variation signal within the target area includes:

[0010] The geomagnetic diurnal variation signals collected by various geomagnetic diurnal variation observation stations set up in the target area are used as the geomagnetic diurnal variation signals for measurement.

[0011] Optionally, after acquiring the measured geomagnetic diurnal variation signal within the target area, the method further includes:

[0012] Based on the measured geomagnetic diurnal variation signal, the base value corresponding to each day of the measured geomagnetic diurnal variation signal is determined;

[0013] Based on the baseline value and the measured diurnal variation signal of geomagnetism, the relative change of the measured diurnal variation signal of geomagnetism throughout the day is determined as the preprocessed measured diurnal variation signal of geomagnetism.

[0014] Optionally, the Kriging interpolation prediction model includes multiple sub-Kriging interpolation prediction models. One sub-Kriging interpolation prediction model predicts a frequency data to obtain a set of interpolated data corresponding to the frequency data.

[0015] Optionally, the geomagnetic diurnal variation prediction method based on frequency-band kriging interpolation provided in this application further includes:

[0016] Obtain the spatial coordinates of the known geomagnetic diurnal variation observation stations in the target area and the spatial coordinates of the geomagnetic diurnal variation observation stations to be solved;

[0017] Based on the known spatial coordinates of the geomagnetic diurnal variation observation station and the spatial coordinates of the geomagnetic diurnal variation observation station to be solved, the distance between the first station and the distance between the second station are determined; wherein, the distance between the first station is determined based on the spatial coordinates of the known geomagnetic diurnal variation observation station and the geomagnetic diurnal variation observation station to be solved; the distance between the second station is the distance between any two known geomagnetic diurnal variation observation stations.

[0018] Based on the distances between the first and second stations and the frequency data, the semivariance function is obtained;

[0019] The weighting coefficients of the sub-Kriging interpolation prediction model are obtained by solving the Kriging equations based on the semi-variance function.

[0020] Optionally, based on the distances between the first and second stations and the frequency data, the semivariance function is obtained by using the semivariance function, including:

[0021] Based on the distance between the first and second stations and the frequency data, the target empirical semivariance is determined using the empirical semivariance function.

[0022] Based on the target empirical semivariance and the Gaussian semivariance function, the semivariance function is obtained as the parameter of the sub-kriging interpolation prediction model.

[0023] Optionally, the geomagnetic diurnal variation prediction method based on frequency-band kriging interpolation provided in this application further includes:

[0024] In the Kriging interpolation prediction model, the sum of all weight coefficients is 1, and each weight coefficient satisfies the condition of minimizing the estimated variance. The condition of minimizing the estimated variance is determined based on the semivariance between any two known geomagnetic diurnal variation observation stations, the distance between the first stations, and the semivariance between the spatial coordinates of the known geomagnetic diurnal variation observation stations and the geomagnetic diurnal variation observation station to be solved.

[0025] Secondly, this application provides a geomagnetic diurnal variation prediction system based on frequency-division kriging interpolation, comprising:

[0026] The data acquisition module is used to acquire the measured geomagnetic diurnal variation signal within the target area;

[0027] The data processing module is used to determine multiple frequency data corresponding to the measured geomagnetic diurnal variation signal by wavelet decomposition based on the measured geomagnetic diurnal variation signal.

[0028] The data prediction module is used to predict the interpolated data corresponding to each frequency data using a Kriging interpolation prediction model based on multiple frequency data. The Kriging interpolation prediction model is determined based on the mapping relationship between the observation data of each known geomagnetic diurnal variation observation station in the target area, the weight coefficients of each known geomagnetic diurnal variation observation station, and the observation data of the geomagnetic diurnal variation observation station to be determined.

[0029] The frequency band synthesis module is used to obtain the predicted geomagnetic diurnal variation values ​​of the geomagnetic diurnal variation observation stations in the target area by linear superposition reconstruction based on multiple interpolated data.

[0030] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-described geomagnetic diurnal variation prediction method based on frequency band kriging interpolation.

[0031] Fourthly, this application provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the above-described geomagnetic diurnal variation prediction method based on frequency-division kriging interpolation.

[0032] This invention provides a method and system for predicting geomagnetic diurnal variations based on frequency-band Kriging interpolation. The method involves acquiring measured geomagnetic diurnal variation signals within a target area; using wavelet decomposition to determine multiple frequency data corresponding to the measured signals; employing a Kriging interpolation prediction model to predict the interpolated data corresponding to each frequency data point; and using linear superposition and reconstruction based on the multiple interpolated data to obtain the predicted geomagnetic diurnal variation values ​​for the stations within the target area. This effectively improves the accuracy and reliability of spatial prediction of geomagnetic diurnal variations.

[0033] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 A flowchart of a geomagnetic diurnal variation prediction method based on frequency-division kriging interpolation provided by an embodiment of the present invention is shown;

[0036] Figure 2 A schematic diagram of the preprocessed geomagnetic diurnal variation signal provided in an embodiment of the present invention is shown;

[0037] Figure 3 A schematic diagram of the various frequency data provided in the embodiments of the present invention is shown;

[0038] Figure 4 This diagram illustrates the interpolation data corresponding to each frequency data provided in the embodiments of the present invention.

[0039] Figure 5 A schematic diagram showing the comparison of prediction effects provided by embodiments of the present invention is shown;

[0040] Figure 6 A schematic diagram of the reconstructed geomagnetic diurnal variation prediction values ​​provided by an embodiment of the present invention is shown;

[0041] Figure 7 This diagram illustrates the structure of a geomagnetic diurnal variation prediction system based on frequency-division kriging interpolation provided in an embodiment of the present invention.

[0042] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention is shown. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0044] Commonly used geomagnetic diurnal variation prediction methods in existing technologies include the weighted distance averaging method, the latitude difference weighting method, and the two-factor weighting method. The core of these methods lies in calculating fixed weighting coefficients based on the spatial relationship (such as distance and latitude difference) between known stations and the point to be predicted. However, once the fixed weighting coefficients are determined, they remain unchanged throughout the entire time series and are completely independent of the spatial structure characteristics reflected by the geomagnetic diurnal variation observations themselves. The limitations caused by the fixed weighting coefficients restrict their prediction accuracy. Specifically: the weighted distance averaging method fails to fully consider the spatial correlation between known stations; the latitude difference weighting method overemphasizes the influence of latitude while neglecting the contribution of longitude, and its parameter settings rely heavily on experience; while the two-factor weighting method comprehensively considers more factors, its optimal model and parameters also lack theoretical basis and are highly dependent on experience. In summary, existing methods are all empirical deterministic interpolation methods that fail to adaptively optimize based on the inherent statistical characteristics of geomagnetic field data, thus lacking sufficient accuracy and reliability when dealing with complex spatial variations. This application provides a geomagnetic diurnal variation prediction method based on frequency-band Kriging interpolation. The method involves acquiring measured geomagnetic diurnal variation signals within a target area; using wavelet decomposition to determine multiple frequency data corresponding to the measured geomagnetic diurnal variation signals; employing a Kriging interpolation prediction model to predict the interpolated data corresponding to each frequency data based on the multiple frequency data; and using linear superposition reconstruction based on the multiple interpolated data to obtain the predicted geomagnetic diurnal variation values ​​for the geomagnetic diurnal variation observation stations within the target area. This method effectively improves the accuracy and reliability of spatial prediction of geomagnetic diurnal variations.

[0045] This application provides a geomagnetic diurnal variation prediction method based on frequency-band kriging interpolation. (See also...) Figure 1 As shown in the embodiments of this application, the general flow of the geomagnetic diurnal variation prediction method based on frequency band kriging interpolation is as follows:

[0046] Step 110: Obtain the measured geomagnetic diurnal variation signal within the target area.

[0047] In this embodiment of the application, the geomagnetic diurnal variation signal within the target area can be obtained in the following ways:

[0048] The observed geomagnetic diurnal variation signals collected by various geomagnetic diurnal variation observation stations set up in the target area are used as the measured geomagnetic diurnal variation signals.

[0049] Specifically, there are six geomagnetic diurnal variation observation stations in the target area. Among them, the observed geomagnetic diurnal variation signals collected by the five known geomagnetic diurnal variation observation stations are used as the measured geomagnetic diurnal variation signals to calculate the predicted geomagnetic diurnal variation signal of the other geomagnetic diurnal variation observation station.

[0050] Furthermore, after acquiring the measured geomagnetic diurnal variation signal within the target area, the process also includes: preprocessing the measured geomagnetic diurnal variation signal to ensure the consistency and reliability of the measured geomagnetic diurnal variation signal data. In specific implementation, the average value of the measured geomagnetic diurnal variation signal within a preset time period each day (such as 0:00 to 2:00 and 22:00 to 24:00) is first determined as the daily baseline value, and the relative change of the measured geomagnetic diurnal variation signal throughout the day is determined based on the baseline value, thereby eliminating the influence of the long-term stable component of the measured geomagnetic diurnal variation signal.

[0051] Step 120: Based on the measured geomagnetic diurnal variation signal, wavelet decomposition is used to determine multiple frequency data corresponding to the measured geomagnetic diurnal variation signal.

[0052] In this embodiment, the measured geomagnetic diurnal variation signal is decomposed using the db4 wavelet basis function, and the decomposed components at each scale are reconstructed into time-domain signals of the same length as the original signal, thereby obtaining multiple frequency data, each frequency data corresponding to geomagnetic diurnal variation components at different time scales.

[0053] In practice, wavelet decomposition is used to determine multiple frequency data corresponding to the measured geomagnetic diurnal variation signal. Specifically, the db4 wavelet basis function (a fourth-order member of the Daubechies wavelet family (abbreviated as db wavelet)) is selected to perform orthogonal discrete wavelet multiresolution decomposition on the measured geomagnetic diurnal variation signal, and the inverse wavelet reconstruction operator is used to reconstruct the approximate components and detail components at each scale into time-domain sub-signals of the same length as the original signal, thereby obtaining multiple frequency data.

[0054] Specifically, a low-pass filter (LPF) and a high-pass filter (HPF) corresponding to the db4 wavelet basis function are used to decompose the preprocessed measured geomagnetic diurnal variation signal into 10 levels, yielding 11 frequency data points. First, the high-pass filter is used to perform the first level of decomposition, obtaining the first high-frequency detail component, and the low-pass filter is used to perform the first level of decomposition, obtaining the first low-frequency approximation component. Then, starting from the second level, only the low-frequency approximation component obtained from the previous level is repeatedly filtered, progressively decomposing it into lower frequencies. The frequency components are decomposed at each level, generating one high-frequency detail component and one new low-frequency approximation component. After 10 levels of iterative decomposition, the preprocessed measured geomagnetic diurnal variation signal is split into 11 independent frequency data. These 11 independent frequency data include 10 high-frequency detail components and one final low-frequency approximation component. The 10 high-frequency detail components correspond to different frequency band fluctuations from the very short term to the medium and long term, reflecting the dynamic characteristics of geomagnetic diurnal variation. The one final low-frequency approximation component corresponds to the long-term basic trend of geomagnetic diurnal variation, reflecting the overall variation law of the signal.

[0055] In this embodiment of the application, based on, as follows Figure 2 The image shows the preprocessed measured geomagnetic diurnal variation signal. Wavelet decomposition was used to decompose the preprocessed geomagnetic diurnal variation signal to obtain the following result: Figure 3 The multiple frequency data shown demonstrate the frequency band division process.

[0056] Step 130: Based on multiple frequency data and spatial location data, a Kriging interpolation prediction model is used to predict the interpolated data corresponding to each frequency data. The Kriging interpolation prediction model is determined based on the mapping relationship between the observation data of each known geomagnetic diurnal variation observation station in the target area, the weight coefficients of each known geomagnetic diurnal variation observation station, and the observation data of the geomagnetic diurnal variation observation station to be solved. The spatial location data is determined based on the spatial coordinates of the known geomagnetic diurnal variation observation station and the spatial coordinates of the geomagnetic diurnal variation observation station to be solved.

[0057] In this embodiment of the application, the Kriging interpolation prediction model includes multiple sub-Kriging interpolation prediction models. One sub-Kriging interpolation prediction model predicts a frequency data to obtain a set of interpolated data corresponding to the frequency data.

[0058] In practical implementation, the sub-kriging interpolation prediction model can be determined in the following ways:

[0059] Step 1: Obtain the spatial coordinates of the known geomagnetic diurnal variation observation stations in the target area and the spatial coordinates of the geomagnetic diurnal variation observation stations to be solved.

[0060] Step 2: Based on the known spatial coordinates of the geomagnetic diurnal variation observation station and the spatial coordinates of the geomagnetic diurnal variation observation station to be solved, determine the distance between the first station and the distance between the second station; wherein, the distance between the first station is determined according to the spatial coordinates of the known geomagnetic diurnal variation observation station and the geomagnetic diurnal variation observation station to be solved; the distance between the second station is the second distance between any two known geomagnetic diurnal variation observation stations.

[0061] Step 3: Based on the distances between the first and second stations and the frequency data, obtain the semivariogram function. This semivariogram function can be obtained as follows: Based on the distances between the first and second stations and the frequency data, use the empirical semivariogram function to determine the target empirical semivariogram; based on the target empirical semivariogram and the Gaussian semivariogram function, obtain the semivariogram function as the parameter of the sub-Kriging interpolation prediction model. The semivariogram function describes the spatial correlation of geomagnetic diurnal variables; the expression for the target empirical semivariogram is:

[0062]

[0063] In the formula, Let the objective empirical semivariance function be... This refers to the distance between the first and second stations. Let h be the number of pairs of stations in the target area that are separated by a distance h. Known geomagnetic diurnal variation observation stations Frequency data at a given frequency;

[0064] The expression for the Gaussian semivariance function is:

[0065]

[0066] In the formula, It is a Gaussian semivariance function. For sill values, For structural variance, denoted as the distance between the first or second stations, where 'a' is the range parameter.

[0067] Step 4: Solve the Kriging equations based on the semivariance function to obtain the weight coefficients of the sub-Kriging interpolation prediction model. The sum of all weight coefficients in the Kriging interpolation prediction model is 1, and each weight coefficient satisfies the condition for minimizing the estimated variance. The condition for minimizing the estimated variance is determined based on the semivariance between any two known geomagnetic diurnal variation observation stations, the distance between the first stations, and the semivariance between the spatial coordinates of the known geomagnetic diurnal variation observation stations and the geomagnetic diurnal variation observation station to be solved.

[0068] In practical implementation, the Kriging equations are solved based on the target empirical semivariance and the Gaussian semivariance function to obtain the weight coefficients of the sub-Kriging interpolation prediction model. These weight coefficients are obtained by satisfying the unbiasedness and minimization of the estimation variance conditions of the Kriging equations, ensuring the unbiasedness of the interpolated data predicted by the Kriging interpolation prediction model, i.e., the expected value of the estimated value equals the expected value of the true value. The unbiasedness condition is:

[0069]

[0070] The condition for minimizing the estimated variance is:

[0071]

[0072] In the formula, These are the weighting coefficients. The distance between any two known geomagnetic diurnal variation observatories and the second space station. Let be the semivariance between any two known geomagnetic diurnal variation observation stations. For Lagrange multipliers, Let be the spatial coordinates of the known geomagnetic diurnal variation observation station and the distance to the first space station between the geomagnetic diurnal variation observation station to be determined. Let be the semivariance between the known spatial coordinates of the geomagnetic diurnal variation observation station and the geomagnetic diurnal variation observation station to be solved;

[0073] The Kriging interpolation prediction model is obtained based on each sub-Kriging interpolation prediction model. The expression for this Kriging interpolation prediction model is as follows:

[0074]

[0075] In the formula, For the geomagnetic diurnal variation observation station whose spatial location is to be solved The interpolated data at a certain frequency, where m is the number of known geomagnetic diurnal variation observation stations in the target area. Z(x) represents the weighting coefficients of each station, i.e., the weighting coefficients in each sub-kriging interpolation prediction model. i ) is a known geomagnetic diurnal variation observation station (spatial location) xi Frequency data at a specific target frequency.

[0076] In this embodiment of the application, based on multiple frequency data, the Kriging interpolation prediction model constructed in the above manner is used to predict the interpolated data corresponding to each frequency data, such as... Figure 4 As shown, by calculating the spatial correlation structure characteristics between various geomagnetic diurnal variation observation stations, the corresponding semivariance function is obtained, and spatial interpolation is performed on the frequency data of the geomagnetic diurnal variation observation stations to be solved, thereby realizing the spatial estimation of each frequency data.

[0077] To comprehensively evaluate the performance of the Kriging interpolation prediction model in this application embodiment, a comparative experiment was conducted with existing prediction methods. The existing prediction methods include the direct Kriging model and the two-factor DD4 model. The direct Kriging model is a traditional method that directly performs Kriging spatial interpolation on the total geomagnetic diurnal variation signal; the weights of the two-factor DD4 model are determined by the longitude and latitude differences between the known point and the point to be determined. After the comparative experiment, the prediction results and accuracy statistics of each method in the target area are shown in Table 1, and as shown in... Figure 5 The comparison of prediction results shown is given, where accuracy is the root mean square error between the true value and the predicted value, in nT.

[0078] Table 1

[0079]

[0080] Step 140: Based on multiple interpolation data, linear superposition reconstruction is used to obtain the predicted values ​​of geomagnetic diurnal variation of the geomagnetic diurnal variation observation stations in the target area.

[0081] In the embodiments of this application, such as Figure 6As shown, the spatial interpolation results of signals in each frequency band are linearly superimposed to reconstruct the predicted signal.

[0082] In this embodiment, after performing multi-resolution decomposition of the geomagnetic time series using orthogonal discrete wavelet transform, the inverse wavelet reconstruction operator is used to reconstruct the approximate components and detail components at each scale into time-domain sub-signals of the same length as the original signal. Based on multi-resolution analysis theory, the original signal can be represented as a linear superposition of sub-signals at each scale. Therefore, in this embodiment, the spatial interpolation results of each frequency band signal are linearly superimposed to reconstruct the predicted signal. The above process is equivalent to a complete inverse wavelet transform.

[0083] This application provides a geomagnetic diurnal variation prediction system based on frequency-band kriging interpolation. (See also...) Figure 7 As shown, the geomagnetic diurnal variation prediction system based on frequency-band kriging interpolation provided in this application includes:

[0084] The data acquisition module 810 is used to acquire the measured geomagnetic diurnal variation signal within the target area;

[0085] Data processing module 820 is used to determine multiple frequency data corresponding to the measured geomagnetic diurnal variation signal by wavelet decomposition based on the measured geomagnetic diurnal variation signal.

[0086] The data prediction module 830 is used to predict the interpolated data corresponding to each frequency data based on multiple frequency data using a Kriging interpolation prediction model. The Kriging interpolation prediction model is determined based on the mapping relationship between the observation data of each known geomagnetic diurnal variation observation station in the target area, the weight coefficients of each known geomagnetic diurnal variation observation station, and the observation data of the geomagnetic diurnal variation observation station to be determined.

[0087] The frequency band synthesis module 840 is used to obtain the predicted geomagnetic diurnal variation values ​​of the geomagnetic diurnal variation observation stations in the target area by linear superposition reconstruction based on multiple interpolation data.

[0088] It should be noted that the principle of the geomagnetic diurnal variation prediction system based on frequency band kriging interpolation provided in this application embodiment to solve the technical problem is similar to the geomagnetic diurnal variation prediction method based on frequency band kriging interpolation provided in this application embodiment. Therefore, the implementation of the geomagnetic diurnal variation prediction system based on frequency band kriging interpolation provided in this application embodiment can refer to the implementation of the geomagnetic diurnal variation prediction method based on frequency band kriging interpolation provided in this application embodiment, and the repeated parts will not be described again.

[0089] After introducing the geomagnetic diurnal variation prediction method and apparatus based on frequency band kriging interpolation provided in the embodiments of this application, the electronic equipment provided in the embodiments of this application will be briefly introduced next.

[0090] See Figure 8 As shown, the electronic device 500 provided in this application embodiment includes at least a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program, it implements the geomagnetic diurnal variation prediction method based on frequency band kriging interpolation provided in this application embodiment.

[0091] The electronic device 500 provided in this application embodiment may further include a bus 503 connecting different components (including processor 501 and memory 502). The bus 503 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.

[0092] Memory 502 may include a readable storage medium in the form of volatile memory, such as random access memory (RAM) 5021 and / or cache memory 5022, and may further include read-only memory (ROM) 5023. Memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, including but not limited to an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0093] Processor 501 can be a single processing element or a collective term for multiple processing elements. For example, processor 501 can be a central processing unit (CPU), or one or more integrated circuits configured to implement the geomagnetic diurnal variation prediction method based on frequency band kriging interpolation provided in the embodiments of this application. Specifically, processor 501 can be a general-purpose processor, including but not limited to CPUs, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0094] Electronic device 500 can communicate with one or more external devices 504 (e.g., keyboard, remote control, etc.), and also with one or more devices that enable a user to interact with electronic device 500 (e.g., mobile phone, computer, etc.), and / or with devices that enable electronic device 500 to communicate with one or more other electronic devices 500 (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 505. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 506. Figure 8 As shown, network adapter 506 communicates with other modules of electronic device 500 via bus 503. It should be understood that, although... Figure 8 As not shown, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.

[0095] It should be noted that, Figure 8 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0096] The following describes the computer-readable storage medium provided in the embodiments of this application. The computer-readable storage medium provided in the embodiments of this application stores computer instructions, which, when executed by a processor, implement the geomagnetic diurnal variation prediction method based on frequency-band kriging interpolation provided in the embodiments of this application. Specifically, the computer instructions can be built into or installed in the processor, so that the processor can implement the geomagnetic diurnal variation prediction method based on frequency-band kriging interpolation provided in the embodiments of this application by executing the built-in or installed computer instructions.

[0097] In addition, the geomagnetic diurnal variation prediction method based on frequency band kriging interpolation provided in this application embodiment can also be implemented as a computer program product. The computer program product includes program code, which implements the geomagnetic diurnal variation prediction method based on frequency band kriging interpolation provided in this application embodiment when the program code is run on a processor.

[0098] The computer program product provided in this application embodiment may employ one or more computer-readable storage media, which may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. Specifically, more specific examples (a non-exhaustive list) of computer-readable storage media include electrical connections with one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0099] The computer program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on electronic devices such as computers. However, the computer program product provided in this application embodiment is not limited thereto. In this application embodiment, the computer-readable storage medium can be any tangible medium that contains or stores program code, which can be used by or in conjunction with an instruction execution system, device, or apparatus.

[0100] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0101] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0102] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0103] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A geomagnetic diurnal variation prediction method based on sub-band Kriging interpolation, characterized by, The method comprises the following steps: obtaining a measured geomagnetic daily variation signal in a target area; based on the measured geomagnetic daily variation signal, using wavelet decomposition to determine a plurality of frequency data corresponding to the measured geomagnetic daily variation signal; based on a plurality of the frequency data, using a Kriging interpolation prediction model to predict interpolation data corresponding to each of the frequency data; wherein the Kriging interpolation prediction model is determined based on a mapping relationship between observation data of each known geomagnetic daily variation observation station in the target area, a weight coefficient of each of the known geomagnetic daily variation observation stations, and observation data of a to-be-determined geomagnetic daily variation observation station; wherein the Kriging interpolation prediction model comprises a plurality of sub-Kriging interpolation prediction models, one of the sub-Kriging interpolation prediction models predicts one of the frequency data to obtain a set of the interpolation data corresponding to the frequency data; and obtaining spatial coordinates of the known geomagnetic daily variation observation stations in the target area and spatial coordinates of the to-be-solved geomagnetic daily variation observation stations; based on the spatial coordinates of the known geomagnetic daily variation observation stations and the spatial coordinates of the to-be-solved geomagnetic daily variation observation stations, determining a first station-to-station distance and a second station-to-station distance; wherein the first station-to-station distance is determined according to the spatial coordinates of the known geomagnetic daily variation observation stations and the to-be-solved geomagnetic daily variation observation stations; the second station-to-station distance is a second station-to-station distance between any two of the known geomagnetic daily variation observation stations; based on the first station-to-station distance, the second station-to-station distance, and the frequency data, obtaining a semi-variance function; based on the semi-variance function, solving a Kriging equation set to obtain a weight coefficient of the sub-Kriging interpolation prediction model; based on a plurality of the interpolation data, using linear superposition reconstruction to obtain a geomagnetic daily variation prediction value of the to-be-solved geomagnetic daily variation observation station in the target area.

2. The method for geomagnetic diurnal variation prediction based on frequency band- based Kriging interpolation according to claim 1, characterized in that, Obtaining a measured geomagnetic daily variation signal in a target area comprises: obtaining geomagnetic daily variation signals collected by each geomagnetic daily variation observation station arranged in the target area as the measured geomagnetic daily variation signal.

3. The sub-band based Kriging interpolation based geomagnetic diurnal variation prediction method according to claim 1 or 2, characterized in that, After obtaining the measured geomagnetic daily variation signal in the target area, the method further comprises: based on the measured geomagnetic daily variation signal, determining a base value corresponding to each day of the measured geomagnetic daily variation signal; based on the base value and the measured geomagnetic daily variation signal, determining a relative change amount of the full-day measured geomagnetic daily variation signal as the preprocessed measured geomagnetic daily variation signal.

4. The method of claim 1, wherein the method is based on a frequency- band- dependent Kriging interpolation. Based on the first station-to-station distance, the second station-to-station distance, and the frequency data, obtaining a semi-variance function comprises: based on the first station-to-station distance, the second station-to-station distance, and the frequency data, using an empirical semi-variance function to determine a target empirical semi-variance; based on the target empirical semi-variance and a Gaussian-type semi-variance function, obtaining a semi-variance function as a parameter of the sub-Kriging interpolation prediction model.

5. The sub-band based Kriging interpolation based geomagnetic diurnal variation prediction method according to claim 1, characterized in that, The method further comprises: The sum of each weight coefficient in the Kriging interpolation prediction model is 1, and each weight coefficient satisfies an estimated variance minimization condition; wherein the estimated variance minimization condition is determined based on semi-variances between any two known geomagnetic diurnal variation observation stations, the first station distance, and semi-variances between the spatial coordinates of the known geomagnetic diurnal variation observation stations and the to-be-solved geomagnetic diurnal variation observation station.

6. A geomagnetic diurnal variation prediction system based on sub-band Kriging interpolation, characterized by, The method comprises: a data acquisition module configured to acquire a measured geomagnetic diurnal variation signal in a target region; a data processing module configured to determine, based on the measured geomagnetic diurnal variation signal, a plurality of frequency data corresponding to the measured geomagnetic diurnal variation signal by using wavelet decomposition; a data prediction module configured to predict, based on the plurality of frequency data, interpolation data corresponding to each of the frequency data by using a Kriging interpolation prediction model; wherein the Kriging interpolation prediction model is determined based on a mapping relationship between observation data of each known geomagnetic diurnal variation observation station in the target region, weight coefficients of each known geomagnetic diurnal variation observation station, and observation data of a to-be-determined geomagnetic diurnal variation observation station; wherein the Kriging interpolation prediction model comprises a plurality of sub-Kriging interpolation prediction models, one of the sub-Kriging interpolation prediction models is configured to predict one of the frequency data to obtain a set of interpolation data corresponding to the frequency data; and spatial coordinates of the known geomagnetic diurnal variation observation stations in the target region and spatial coordinates of the to-be-solved geomagnetic diurnal variation observation station are acquired; based on the spatial coordinates of the known geomagnetic diurnal variation observation stations and the spatial coordinates of the to-be-solved geomagnetic diurnal variation observation station, a first station distance and a second station distance are determined; wherein the first station distance is determined according to the spatial coordinates of the known geomagnetic diurnal variation observation stations and the to-be-solved geomagnetic diurnal variation observation station; the second station distance is a second station distance between any two of the known geomagnetic diurnal variation observation stations; based on the first station distance, the second station distance, and the frequency data, a semi-variance function is obtained; based on the semi-variance function, a Kriging equation set is solved to obtain weight coefficients of the sub-Kriging interpolation prediction models; a frequency band synthesis module configured to reconstruct, based on the plurality of interpolation data, geomagnetic diurnal variation prediction values of the to-be-solved geomagnetic diurnal variation observation stations in the target region by using linear superposition.

7. An electronic device, comprising: The computer program is stored in the memory and executable on the processor, and the processor executes the computer program to implement the geomagnetic diurnal variation prediction method based on sub-frequency band Kriging interpolation according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are executed by the processor to implement the geomagnetic diurnal variation prediction method based on sub-frequency band Kriging interpolation according to any one of claims 1 to 5.

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