Geomagnetic daily variation data forecasting method, system, equipment, medium and product

Abstract

The invention discloses a geomagnetic diurnal variation data forecasting method, system, equipment, medium and product, and relates to the technical field of geomagnetic navigation, and the method comprises the steps: collecting and preprocessing global geomagnetic station data, carrying out the fast Fourier transform of geomagnetic field diurnal variation data, solving to obtain a trigonometric function coefficient, and carrying out the calculation of the trigonometric function coefficient; according to the method, geomagnetic daily variation data of any region in the world are obtained through geomagnetic daily variation, then an association Legendre coefficient is obtained through solving, an internal source field Gaussian coefficient and an external source field Gaussian coefficient are obtained through solving according to the Gaussian theory, finally daily variation model forward modeling can be carried out according to the internal source field Gaussian coefficient and the external source field Gaussian coefficient, and geomagnetic daily variation data of any region in the world are output. According to the method, coefficient solution and model forward modeling related to spherical harmonic analysis are utilized, the problems that in the prior art, the control range of a virtual terrestrial magnetism daily variation station is limited, and data are prone to being polluted are solved, and the forecasting precision of terrestrial magnetism daily variation data is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geomagnetic navigation, in particular to a geomagnetic diurnal variation data prediction method, system, device, medium and product. BACKGROUND

[0002] In airborne magnetic survey, shipborne magnetic survey and ground magnetic survey, the geomagnetic field diurnal variation caused by the solar exosphere ionization and the earth rotation needs to be corrected, that is, the geomagnetic diurnal variation correction. At present, the most effective method is to set a geomagnetic diurnal variation station in the center of the test area to measure the magnetic field variation in real time to obtain diurnal variation correction data, and then correct the geomagnetic survey data. This method has high precision and reliable results. However, in actual measurement, for areas such as high mountains, glaciers, virgin forests, deserts, marshes, lakes, land-sea junctions, etc. where it is difficult to set ground diurnal variation stations, or for areas such as ports at sea, deep sea, etc. where it is difficult to set or recover seabed diurnal variation stations, the diurnal variation correction of the measurement data becomes a difficult problem to be solved.

[0003] To solve this problem, some scholars have proposed the concept of "virtual geomagnetic diurnal variation station". However, it should be noted that the correction data obtained by the diurnal variation station is closely related to its environment. If the magnetic field environment around the test area is complex (such as a large number of factories, mines, and residential environment elements), the quality of the diurnal variation data will be significantly reduced, and the correction effect will also be limited. In addition, the effective distance of the geomagnetic diurnal variation station is limited, and covering the far sea area requires a large amount of layout cost.

[0004] Therefore, how to break through the limitation of the effective distance of the virtual geomagnetic diurnal variation station and reduce the sensitivity of the data quality to the observation of the surrounding stations is a technical bottleneck that needs to be broken through in the field. SUMMARY

[0005] The purpose of the present application is to provide a geomagnetic diurnal variation data prediction method, system, device, medium and product, which can solve the problem of area limitation and data quality being easily affected in the prior art, and realize accurate prediction of geomagnetic diurnal variation data in any region of the world.

[0006] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0007] In a first aspect, the present application provides a geomagnetic diurnal variation data prediction method, comprising the following steps:

[0008] Obtaining observation data of global geomagnetic stations and preprocessing the observation data to obtain geomagnetic field diurnal variation data; the observation data includes multiple geomagnetic elements.

[0009] Performing fast Fourier transform on the geomagnetic field diurnal variation data to obtain trigonometric function coefficients.

[0010] According to the relationship between the trigonometric function coefficients and the associated Legendre functions, the associated Legendre coefficients are solved.

[0011] According to the Gauss theory, the endogenous field Gauss coefficients and the exogenous field Gauss coefficients are solved from the associated Legendre coefficients.

[0012] According to the endogenous field Gauss coefficients and the exogenous field Gauss coefficients, the geomagnetic diurnal variation data of any region in the world are outputted.

[0013] Optionally, the observation data of global geomagnetic stations are acquired, and the observation data are preprocessed to obtain the geomagnetic field diurnal variation data, and specifically comprising:

[0014] The observation data of global geomagnetic stations are acquired; the geomagnetic elements of the observation data include the geomagnetic field X component, the geomagnetic field Y component, the geomagnetic field Z component and the geomagnetic field total intensity.

[0015] The format of the observation data is uniformly processed into three-component data under the observation point rectangular coordinate system; the observation data adopt minute value sampling rate.

[0016] The three-component data of the observation data are respectively converted from universal time to local time, and the geomagnetic field variation of each month is averaged by using the time series superposition method to obtain the monthly average of the geomagnetic field.

[0017] For the monthly average of the geomagnetic field of any month, the geomagnetic field diurnal variation data are obtained according to the monthly average of the geomagnetic field and the midnight average of the geomagnetic field of the month.

[0018] Optionally, the trigonometric function form of the geomagnetic field diurnal variation data is as follows:

[0019]

[0020] Wherein, f(l) is the geomagnetic field diurnal variation data of the lth sampling time, T is the total number of sampling times, m is the order of spherical harmonic coefficients, a(m) is the sine function coefficient of the mth harmonic, b(m) is the cosine function coefficient of the mth harmonic, and λ l is the central longitude of the lth sampling time.

[0021] The geomagnetic field diurnal variation data are Fourier transformed by the following formula:

[0022]

[0023] Wherein, F(m) is the mth Fourier transform function of f(l), and i is the imaginary part of the complex number; the real part of the Fourier transform function F(m) divided by T obtains the sine function coefficient a(m), and the negative imaginary part of the Fourier transform function F(m) divided by T obtains the cosine function coefficient b(m).

[0024] Optionally, the Legendre expression of the geomagnetic field diurnal variation three components is:

[0025]

[0026] wherein θ is the geographic co-latitude, λ is the geographic longitude, is an n-order m-degree associated Legendre function, that is, a quasi-sine function of the geographic co-latitude θ, N is the maximum order number of the spherical harmonic coefficients, M is the maximum degree number of the spherical harmonic coefficients, n is the order number of the spherical harmonic coefficients, and m is the degree number of the spherical harmonic coefficients.

[0027] The relationship between the trigonometric function coefficients and the associated Legendre functions is as shown in the following formula:

[0028]

[0029] wherein Xa(m), Xb(m), Ya(m), Yb(m), Za(m), and Zb(m) are the trigonometric function coefficients, is an associated Legendre coefficient, and are the base functions of X, Y, and Z, respectively.

[0030] Optionally, the endogenous field Gauss coefficient and the exogenous field Gauss coefficient are solved according to the following formula:

[0031]

[0032] wherein and are the endogenous field Gauss coefficients, and are the exogenous field Gauss coefficients.

[0033] Optionally, the diurnal variation model forward is performed according to the following formula, and the geomagnetic diurnal variation data of any region in the world is output:

[0034]

[0035] wherein X, Y, and Z represent the geomagnetic field X, Y, and Z components, respectively, n and m represent the order number and the degree number of the spherical harmonic coefficients, N represents the highest order number of the spherical harmonic coefficients, M represents the number of days in a month, a represents the radius of the earth, and r represents the geocentric radius of the calculation point.

[0036] In a second aspect, the present application provides a geomagnetic diurnal variation data prediction system, comprising:

[0037] A geomagnetic observation data acquisition and preprocessing module is configured to acquire observation data of global geomagnetic stations and to preprocess the observation data to obtain geomagnetic field diurnal variation data; the observation data includes multiple geomagnetic elements.

[0038] The trigonometric function coefficient solving module is configured to perform fast Fourier transform on the geomagnetic field diurnal variation data to obtain trigonometric function coefficients.

[0039] The associated Legendre coefficient solving module is configured to obtain associated Legendre coefficients according to a relationship between the trigonometric function coefficients and associated Legendre functions.

[0040] The endogenous and exogenous field Gauss coefficient solving module is configured to obtain endogenous field Gauss coefficients and exogenous field Gauss coefficients from the associated Legendre coefficients according to Gauss theory.

[0041] The geomagnetic diurnal variation data forward solving module is configured to perform diurnal variation model forward according to the endogenous field Gauss coefficients and the exogenous field Gauss coefficients to output geomagnetic diurnal variation data of any region in the world.

[0042] In a third aspect, the present application provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement the steps of the geomagnetic diurnal variation data prediction method described above.

[0043] In a fourth aspect, the present application provides a computer readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement the steps of the geomagnetic diurnal variation data prediction method described above.

[0044] In a fifth aspect, the present application provides a computer program product comprising a computer program, and the computer program is executed by a processor to implement the steps of the geomagnetic diurnal variation data prediction method described above.

[0045] According to the embodiments provided in the present application, the following technical effects are disclosed:

[0046] The present application provides a geomagnetic diurnal variation data prediction method, system, device, medium and product. In the method, global geomagnetic station data is collected and preprocessed, then fast Fourier transform is performed on the geomagnetic field diurnal variation data to obtain trigonometric function coefficients, then associated Legendre coefficients are obtained according to the trigonometric function coefficients, and endogenous field Gauss coefficients and exogenous field Gauss coefficients are obtained according to Gauss theory, and finally diurnal variation model forward is performed according to the endogenous field Gauss coefficients and the exogenous field Gauss coefficients to output geomagnetic diurnal variation data of any region in the world. The present application solves the problems of limited control range of virtual geomagnetic diurnal variation stations and easy pollution of data in the prior art by using spherical harmonic analysis related coefficient solving and model forward, improves the prediction accuracy of geomagnetic diurnal variation data, can predict geomagnetic diurnal variation data of regions difficult for personnel to reach, solves the diurnal variation correction problem of these regions, and avoids the high cost of laying deep-sea anchor diurnal variation stations in the far sea area, and has significant practicality and economy. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings described below only illustrate some of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained from these drawings without creative labor.

[0048] Figure 1 A flow chart of a geomagnetic diurnal data prediction method provided by an embodiment of the present application.

[0049] Figure 2 A flow chart of step S1 of a geomagnetic diurnal data prediction method provided by an embodiment of the present application.

[0050] Figure 3 A curve diagram of a baseline value normalization result of a Beijing 13-lings geomagnetic station in a geomagnetic diurnal data prediction method provided by an embodiment of the present application.

[0051] Figure 4 A comparison diagram of a geomagnetic field diurnal prediction value and an observation value of a Beijing geomagnetic station in a geomagnetic diurnal data prediction method provided by an embodiment of the present application.

[0052] Figure 5 A comparison diagram of a geomagnetic field diurnal prediction value and an observation value of a Quanzhou geomagnetic station in a geomagnetic diurnal data prediction method provided by an embodiment of the present application.

[0053] Figure 6 A comparison diagram of a geomagnetic field diurnal prediction value and an observation value of a Qiongzhong geomagnetic station in a geomagnetic diurnal data prediction method provided by an embodiment of the present application.

[0054] Figure 7 A functional module diagram of a geomagnetic diurnal data prediction system provided by an embodiment of the present application.

[0055] Figure 8 A structural diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0056] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0057] In order to make the above objects, features and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0058] The geomagnetic diurnal variation data prediction method provided by the embodiments of the present application comprises the following steps in an exemplary embodiment, as shown in the figure: Figure 1

[0059] S1, obtaining observation data of global geomagnetic stations and pre-processing the observation data to obtain geomagnetic field diurnal variation data; the observation data comprises various geomagnetic elements. In order to construct a geomagnetic diurnal variation model that can be used in a large range, the observation data of the geomagnetic stations is mainly used in the selection of experimental data. At present, there are more than one thousand global geomagnetic stations, and in this example, the observation data of nearly 150 geomagnetic stations is downloaded, mainly including geomagnetic elements such as geomagnetic field X component Bx, geomagnetic field Y component By, geomagnetic field Z component Bz and geomagnetic field total intensity BF. In this embodiment, as shown in the figure: Figure 2

[0060] S11, obtaining observation data of global geomagnetic stations; the geomagnetic elements of the observation data include geomagnetic field X component, geomagnetic field Y component, geomagnetic field Z component and geomagnetic field total intensity.

[0061] S12, uniformly processing the format of the observation data into three-component data under the observation point rectangular coordinate system; the observation data adopts a minute value sampling rate. In this example, the observation data adopts a minute value sampling rate, so each component data has 1440 data points per day. The data is uniformly formatted to form X, Y and Z three-component data under the observation point rectangular coordinate system.

[0062] S13, converting the three-component data of the observation data from universal time to local time, and applying a time series superposition method to average the geomagnetic field change of each month to obtain a monthly average value of the geomagnetic field.

[0063] S14, for the monthly average value of the geomagnetic field of any month, obtaining the geomagnetic field diurnal variation data according to the monthly average value of the geomagnetic field and the midnight average value of the geomagnetic field of the month. The midnight average value of the geomagnetic field is the observation data of the geomagnetic field from 00:00 to 03:00.

[0064] S2, performing fast Fourier transform on the geomagnetic field diurnal variation data to obtain trigonometric function coefficients. The geomagnetic field change on the earth's surface reflects the current system flowing above it, which is an important prerequisite for establishing a geomagnetic field diurnal variation model by using spherical harmonic analysis method. Fast Fourier transform has the advantages of simplicity and reliability in harmonic analysis, which can express a series of geomagnetic activities changing with time into a series form of sine function and cosine function. Specifically, the trigonometric function form of the geomagnetic field diurnal variation data is as shown in the following formula: ​​

[0065]

[0066] where f(l) is the geomagnetic diurnal variation data at the lth sampling time, T is the total number of sampling times, m is the order of spherical harmonic coefficients, a(m) is the sine function coefficient of the mth harmonic, b(m) is the cosine function coefficient of the mth harmonic, λ l is the geocentric longitude at the lth sampling time.

[0067] The Fourier transform of the geomagnetic diurnal variation data is carried out by the following formula:

[0068]

[0069] where F(m) is the mth Fourier transform function of f(l), i is the imaginary part of the complex exponential term; the real part of the Fourier transform function F(m) divided by T gives the sine function coefficient a(m), and the negative imaginary part of the Fourier transform function F(m) divided by T gives the cosine function coefficient b(m).

[0070] The principle of the above method is that after F(m) is obtained, the inverse Fourier transform is carried out, and the geomagnetic diurnal variation data at the lth sampling time is as follows:

[0071]

[0072] Let Substituting it into the above formula gives:

[0073]

[0074] Substituting C(m) = C r (m) + iC i (m) into the above formula gives:

[0075]

[0076] where C r (m) and C i (m) are the real part and the imaginary part of C(m) respectively, and by comparison, a(m) = C r (m) and b(m) = C i (m). Therefore, a(m) is the real part of the Fourier function divided by T, and b(m) is the imaginary part of the Fourier function divided by T.

[0077] S3, the associated Legendre coefficients are obtained according to the relationship between the trigonometric function coefficients and the associated Legendre functions. In the spherical coordinate system, the scalar potential of the geomagnetic field satisfies the Laplace equation. In fact, the quantity we observe is the magnetic field element, not the magnetic potential. If the geomagnetic element observation values at enough points on the earth's surface or near space are known, the associated Legendre coefficients can be obtained by the least square method.

[0078] Specifically, the Legendre expression of the geomagnetic field diurnal variation three components is as follows:

[0079]

[0080] wherein θ is the geographic co-latitude, λ is the geographic longitude, is an n-order m-degree associated Legendre function, i.e., a quasi-sine function of the geographic co-latitude θ, N is the maximum order number of the spherical harmonic coefficients, M is the maximum degree of the spherical harmonic coefficients, n is the order number of the spherical harmonic coefficients, and m is the degree of the spherical harmonic coefficients.

[0081] By comparison with the trigonometric function coefficients of the FFT, the relationship between the trigonometric function coefficients and the associated Legendre functions is shown in the following formula:

[0082]

[0083] wherein Xa(m), Xb(m), Ya(m), Yb(m), Za(m), and Zb(m) are the trigonometric function coefficients, are the associated Legendre coefficients, and are the base functions of X, Y, and Z, respectively.

[0084] S4, according to the Gauss theory, the internal source field Gauss coefficients and the external source field Gauss coefficients are obtained from the associated Legendre coefficients. According to the Gauss theory, the diurnal variation of the geomagnetic field observed on the ground is derived from the Sq current system of the ionosphere, and the internal source field is derived from the induced magnetic field generated by the Sq current system in the earth interior. In this embodiment, the internal source field Gauss coefficients and the external source field Gauss coefficients are obtained according to the following formula:

[0085]

[0086] wherein and are the internal source field Gauss coefficients, and are the external source field Gauss coefficients.

[0087] S5, the diurnal variation model forward is performed according to the internal source field Gauss coefficients and the external source field Gauss coefficients, and the geomagnetic diurnal variation data of any region in the world is output. According to the obtained internal source field Gauss coefficients and the external source field Gauss coefficients the geomagnetic field diurnal variation value at a certain latitude position at any height from the ground to space with local time in a certain solar cycle activity stage can be calculated. The model takes time, longitude, and latitude as input parameters, and takes each geomagnetic element as output parameter. The diurnal variation model forward is performed according to the following formula, and the geomagnetic diurnal variation data of any region in the world is output:

[0088]

[0089]

[0090] wherein X, Y, Z represent the geomagnetic field X, Y, Z components respectively, n and m represent the order and degree of the spherical harmonic coefficients respectively, N represents the highest order of the spherical harmonic coefficients, M represents the number of days in a month, a represents the radius of the earth, and r represents the geocentric radius of the calculation point. The geomagnetic field total intensity B can be obtained by squaring and square-rooting the sum of the geomagnetic field X, Y, Z components f .

[0091] In order to better illustrate the advantages of the geomagnetic diurnal variation data prediction method provided in the above embodiments of the present application, the accuracy of the geomagnetic diurnal variation data prediction method proposed in the present application is evaluated taking the data of geomagnetic stations in China as an example. The data of 40 reference stations in China range from 2019, and the time resolution is seconds. In the position distribution information of the forty reference stations selected for diurnal correction in this example, except for one station located in Vietnam, the remaining reference stations are located within the territory of China. The base values of the forty reference stations are normalized similar to the base value of the Beijing Shisanling Geomagnetic Station for subsequent model construction.

[0092] Figure 3 The baseline value changes of the three components of the magnetic field of the Beijing Shisanling Geomagnetic Station from January 3, 2019 to December 31, 2019 are shown, and the average value of each day is taken as the baseline value of the station on that day. Based on the data of 40 reference stations in China, the diurnal variation information caused by the external source field of the data of each geomagnetic station is extracted, and the geomagnetic diurnal variation data prediction method provided in the above embodiments of the present application is modeled, and the spherical harmonic coefficients obtained by calculation are used to calculate the external source field at any position.

[0093] Based on the data of 40 geomagnetic reference stations in 2019, the model coefficients of the diurnal variation model are obtained, and the coefficients are one set per month, i.e. 12 diurnal variation model coefficients per year. Similarly, 132 model coefficients can be calculated based on the data of 40 geomagnetic reference stations from 2009 to 2019, corresponding to the 11-year cycle of solar activity. According to the model coefficients of 11 years, the diurnal variation at any time can be calculated. For example, the diurnal variation model of January 2020 is solved using the model of January 2009.

[0094] 1) Internal consistency accuracy evaluation: select the measured data of Beijing, Quanzhou and Qiongzhong geomagnetic stations, compare with the model values, and calculate the root mean square error of the measured data and the model values to evaluate the internal consistency accuracy of the model.

[0095] Figure 4The comparison between the model values and the observed values of Beijing Geomagnetic Observatory in the same time period (local time 0:00-24:00) from 16:00 on September 2, 2014 to 18:00 on September 3, 2014 is shown in FIG. 1, where the solid line is the output value of the daily variation spherical harmonic model, and the dashed line is the observed value of Beijing Geomagnetic Observatory. It can be found from FIG. 1 that the morphological changes of the two are basically consistent. The internal consistency accuracy is better than 3nT. Figure 4

[0096] Figure 5 The comparison between the model values and the observed values of Quanzhou Geomagnetic Observatory in the same time period (local time 0:00-24:00) from 16:00 on August 31, 2014 to 18:00 on September 1, 2014 is shown in FIG. 2, where the solid line is the output value of the daily variation spherical harmonic model, and the dashed line is the observed value of Quanzhou Geomagnetic Observatory. It can be found from FIG. 2 that the morphological changes of the two are basically consistent. The internal consistency accuracy is better than 3nT. Figure 5

[0097] Figure 6 The comparison between the model values and the observed values of Qiongzhong Geomagnetic Observatory in the same time period (local time 0:00-24:00) from 16:00 on December 31, 2018 to 18:00 on January 1, 2019 is shown in FIG. 3, where the solid line is the output value of the daily variation spherical harmonic model, and the dashed line is the observed value of Qiongzhong Geomagnetic Observatory. It can be found from FIG. 3 that the morphological changes of the two are basically consistent. The internal consistency accuracy is better than 5nT. Figure 6

[0098] Table 1 shows the internal consistency accuracy of the daily variation model of the 40 geomagnetic observatories used for modeling, and the statistical results are shown in Table 2. It can be seen that the internal consistency accuracy of the daily variation model is better than 6nT, the average value of three components is better than 3nT, and the average value of the total field is better than 2nT.

[0099] Table 1 Internal consistency accuracy of daily variation model of 40 geomagnetic observatories

[0100]

[0101]

[0102] Table 2 Statistical results of internal consistency accuracy of daily variation model of 40 geomagnetic observatories

[0103]

[0104]

[0105] ​​​2) External coincidence accuracy: Select the ABG, BEL and BDV three foreign geomagnetic station measured data which do not participate in modeling, compare with the model value, and calculate the root mean square error of the measured data and the model value to evaluate the external coincidence accuracy of the model. Table 3 gives the external coincidence accuracy of the daily variation model of the three geomagnetic stations. It can be seen that the external coincidence accuracy of the daily variation model is better than 5nT, the average value of three components is better than 5nT, and the average value of the total field is better than 4nT.

[0106] Table 3 External coincidence accuracy of daily variation model of three geomagnetic stations

[0107] Serial number Station code Bx By Bz Bf 1 ABO 3.29 3.32 4.35 2.74 2 BEL 2.43 4.22 3.64 3.23 3 BDV 3.38 3.24 3.81 3.60

[0108] In order to evaluate the prediction effect of the daily variation model established in the above embodiment, the spherical harmonic coefficients of the daily variation model from 2008 to 2018 are used to predict the daily variation on January 1, 2019 in this example. The model prediction error of 40 geomagnetic stations is shown in Table 4. Table 5 gives the statistical results of the daily variation model prediction error. It can be seen that the average value of the three component precision of the daily variation model prediction is better than 6nT, and the average value of the total field precision is better than 4nT. It also shows the effectiveness of the daily variation prediction model we constructed.

[0109] Table 4 Model prediction error of 40 geomagnetic stations

[0110]

[0111]

[0112] Table 5 Statistical results of daily variation model prediction error

[0113]

[0114] The application uses spherical harmonic analysis related coefficient solving and model forward to solve the problem of limited control range of virtual geomagnetic daily variation station and easy pollution of data in the prior art, and has the following advantages compared with the prior art:

[0115] (1) The spherical harmonic model is fitted by using the observation data of global geomagnetic stations, which can well offset the influence of part of the station data polluted by local environment, and improve the prediction accuracy of geomagnetic daily variation data.

[0116] (2) Based on the observation data of existing stations, the geomagnetic daily variation data of areas difficult to reach by personnel and vehicles (such as virgin forest, marsh, land-sea junction and other areas) can be predicted, which can well solve the daily variation correction problem of aeromagnetic or ship magnetic in the above areas.

[0117] (3) The technology can also correct geomagnetic measurement data in the geomagnetic measurement of the far-sea and far-area ship, avoid the deployment of deep-sea anchor geomagnetic daily variation station with high cost and poor reliability, save the funds of the far-sea and far-area ship magnetic measurement, and reduce the risk.

[0118] Based on the same inventive concept, the embodiments of the present application also provide a system for implementing the geomagnetic daily variation data prediction method described above. The system provides a solution to the problem similar to the implementation scheme described in the above method. In an exemplary embodiment, as shown in Figure 7 , a geomagnetic daily variation data prediction system is provided, including the following functional modules:

[0119] The geomagnetic observation data acquisition and preprocessing module is configured to acquire observation data of global geomagnetic stations and preprocess the observation data to obtain geomagnetic field daily variation data. The observation data includes various geomagnetic elements.

[0120] The trigonometric function coefficient solving module is configured to perform fast Fourier transform on the geomagnetic field daily variation data to obtain trigonometric function coefficients.

[0121] The associated Legendre coefficient solving module is configured to obtain associated Legendre coefficients according to the relationship between the trigonometric function coefficients and the associated Legendre function.

[0122] The endogenous and exogenous field Gauss coefficient solving module is configured to obtain endogenous field Gauss coefficients and exogenous field Gauss coefficients from the associated Legendre coefficients according to the Gauss theory.

[0123] The geomagnetic daily variation data forward solving module is configured to perform daily variation model forward according to the endogenous field Gauss coefficients and the exogenous field Gauss coefficients, and output geomagnetic daily variation data in any region of the world.

[0124] Of course, Figure 7 The architecture shown is only exemplary, and when implementing different functions, one or at least two components in the system shown can be omitted according to actual needs. Figure 7

[0125] In an exemplary embodiment, a computer device is provided, which can be a server or a terminal, and its internal structure diagram can be as shown in Figure 8 ​As shown in the figure. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through the system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capability. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with the terminal outside through the network connection. The computer program is executed by the processor, and the method for predicting geomagnetic diurnal variation data provided by the above-mentioned embodiment can be realized.

[0126] Those skilled in the art can understand that, Figure 8 The skilled in the art can understand that,

[0127] In an exemplary embodiment, a computer device is also provided, including a memory and a processor, the memory stores a computer program, and the processor executes the computer program to realize the steps in the above-mentioned method embodiments.

[0128] In an exemplary embodiment, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by the processor to realize the steps in the above-mentioned method embodiments.

[0129] In an exemplary embodiment, a computer program product is provided, including a computer program, and the computer program is executed by the processor to realize the steps in the above-mentioned method embodiments.

[0130] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant regulations.

[0131] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when the computer program is executed, the processes of the above-mentioned embodiments of the methods can be included. Any reference to memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0132] The database involved in the embodiments provided in the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a blockchain, etc., without being limited thereto. The processor involved in the embodiments provided in the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0133] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0134] The principles and implementation modes of the present application are described by applying specific examples in the present application. The above-mentioned embodiments are only used to help understand the method and its core idea of the present application; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation mode and application range will be changed. In conclusion, the content of the present application should not be understood as a limitation.

Claims

1. A method for forecasting diurnal geomagnetic variation data, characterized in that, include: Observational data from global geomagnetic observatories are acquired and preprocessed to obtain diurnal variation data of the geomagnetic field; the observational data includes various geomagnetic elements. The diurnal variation data of the geomagnetic field were subjected to a fast Fourier transform to obtain the trigonometric function coefficients. Based on the relationship between the trigonometric function coefficients and the associated Legendre function, the associated Legendre coefficients can be obtained. Based on Gaussian theory, the Gaussian coefficients of the internal and external fields are obtained by solving the associated Legendre coefficients. Based on the Gaussian coefficients of the intrinsic field and the extrinsic field, a diurnal variation model is performed to output geomagnetic diurnal variation data for any region globally.

2. The geomagnetic diurnal variation data forecasting method according to claim 1, characterized in that, Acquire observational data from global geomagnetic observatories and preprocess the data to obtain diurnal variation data of the geomagnetic field, specifically including: Acquire observational data from magnetic observatories around the world; the geomagnetic elements of the observational data include the X component, Y component, Z component, and total intensity of the geomagnetic field. The format of the observation data is uniformly processed into three-component data in the Cartesian coordinate system of the observation points; the observation data adopts a minute-value sampling rate. The three components of the observation data were converted from UTC to local time, and the monthly geomagnetic field variation was averaged using the time series superposition method to obtain the monthly average geomagnetic field value. For any given month, the monthly average value of the geomagnetic field is used to obtain the daily variation data of the geomagnetic field based on the monthly average value of the geomagnetic field and the midnight average value of the geomagnetic field in that month.

3. The geomagnetic diurnal variation data forecasting method according to claim 1, characterized in that, The trigonometric function form of the diurnal variation data of the geomagnetic field is shown in the following formula: Where f(l) represents the diurnal variation data of the geomagnetic field at the l-th sampling time, T is the total number of sampling times, m is the order of the spherical harmonic coefficients, a(m) is the sine function coefficient of the m-th harmonic, b(m) is the cosine function coefficient of the m-th harmonic, and λ l The geocentric longitude is the value at the l-th sampling time. The Fourier transform of the diurnal variation data of the geomagnetic field is performed using the following formula: Where F(m) is the m-th Fourier transform function of f(l), and i is the imaginary part of the complex exponential term; the real part of the Fourier transform function F(m) divided by T yields the sine function coefficient a(m), and the negative imaginary part of the Fourier transform function F(m) divided by T yields the cosine function coefficient b(m).

4. The geomagnetic diurnal variation data forecasting method according to claim 3, characterized in that, The Legendre expression for the three parts of the diurnal variation of the geomagnetic field is: Where θ is the geographic co-latitude and λ is the geographic longitude. It is the nth order m degree associated Legendre function, that is, the quasi-sine function of the geographic co-latitude θ, where N is the maximum order of the spherical harmonic coefficients, M is the maximum degree of the spherical harmonic coefficients, n is the order of the spherical harmonic coefficients, and m is the degree of the spherical harmonic coefficients. The relationship between the trigonometric coefficients and the associated Legendre function is shown in the following equation: Where Xa(m), Xb(m), Ya(m), Yb(m), Za(m), and Zb(m) are trigonometric function coefficients. To establish the Legendre coefficient, and These are the basis functions for X, Y, and Z, respectively.

5. The geomagnetic diurnal variation data forecasting method according to claim 4, characterized in that, The Gaussian coefficients of the internal and external fields can be obtained by solving the following formula: in, and The Gaussian coefficients of the intrinsic field are... and is the Gaussian coefficient of the external source field.

6. The geomagnetic diurnal variation data forecasting method according to claim 5, characterized in that, The following formula is used to perform forward modeling of the diurnal variation model, outputting geomagnetic diurnal variation data for any region globally: Where X, Y, and Z represent the X, Y, and Z components of the Earth's magnetic field, respectively; n and m represent the order and degree of the spherical harmonic coefficients, respectively; N represents the highest order of the spherical harmonic coefficients; M represents the number of days in a month; a represents the Earth's radius; and r represents the geocentric radius of the calculation point.

7. A geomagnetic diurnal variation data forecasting system, characterized in that, include: The geomagnetic observation data acquisition and preprocessing module is used to acquire observation data from global geomagnetic stations and preprocess the observation data to obtain daily variation data of the geomagnetic field; the observation data includes various geomagnetic elements. The trigonometric function coefficient solving module is used to perform a fast Fourier transform on the diurnal variation data of the geomagnetic field to obtain the trigonometric function coefficients; The associated Legendre coefficient solving module is used to solve for the associated Legendre coefficients based on the relationship between the trigonometric function coefficients and the associated Legendre function. The module for solving Gaussian coefficients of internal and external fields is used to obtain the Gaussian coefficients of internal and external fields from the associated Legendre coefficients based on Gaussian theory. The geomagnetic diurnal variation data forward modeling module is used to perform diurnal variation model forward modeling based on the Gaussian coefficients of the internal source field and the Gaussian coefficients of the external source field, and output geomagnetic diurnal variation data for any region of the world.

8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the geomagnetic diurnal variation data forecasting method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the geomagnetic diurnal variation data forecasting method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the geomagnetic diurnal variation data forecasting method as described in any one of claims 1-6.

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