A magnetic field disturbance prediction method, device, equipment and storage medium

Abstract

The application discloses a magnetic field disturbance prediction method and device, equipment and a storage medium, and relates to the field of magnetic field prediction. The method comprises the following steps: acquiring magnetic field observation data, converting the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determining initial magnetic disturbance data from the standard observation data, wherein the magnetic field observation data comprises ground observation data, satellite observation data, observation points corresponding to the ground observation data, and observation points corresponding to the satellite observation data; calculating magnetic field response data generated by each singular point in a current singular point grid at each observation point through the current singular point grid obtained in advance; inversely calculating equivalent current coefficients of each singular point in the current singular point grid according to the initial magnetic disturbance data and the magnetic field response data; and predicting magnetic field disturbance data of any geographical position according to the equivalent current coefficients of each singular point in the current singular point grid. The application solves the problem of low prediction reliability of geomagnetic disturbance.

Description

Technical Field

[0001] This application relates to the field of magnetic field prediction technology, and in particular to a method, apparatus, device and storage medium for predicting magnetic field disturbances. Background Technology

[0002] Geomagnetic disturbances are increasingly impacting space weather forecasting, power grid security, spacecraft navigation, and polar communications. Currently, most methods rely solely on ground-based magnetometer networks for predicting these disturbances. However, the deployment of ground-based magnetometers is limited by geographical conditions and economic costs, resulting in uneven distribution and limited density. In remote areas and ocean regions, the number of magnetometers is sparse, leading to scarce magnetic field observation data and potentially inaccurate or even unpredictable geomagnetic disturbance predictions. Consequently, the current technology suffers from low reliability in geomagnetic disturbance prediction.

[0003] The above content is only used to help understand the technical solutions of the embodiments of this application, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, device, and storage medium for predicting magnetic field disturbances, aiming to solve the technical problem of low reliability in predicting geomagnetic disturbances.

[0005] To achieve the above objectives, embodiments of this application provide a method for predicting magnetic field disturbances, the method comprising:

[0006] Acquire magnetic field observation data, convert the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determine the initial magnetic disturbance data from the standard observation data. The magnetic field observation data includes ground observation data, satellite observation data, observation points corresponding to ground observation data, and observation points corresponding to satellite observation data. Using a pre-constructed current singularity grid, calculate the magnetic field response data generated by each singularity in the current singularity grid at each observation point. Based on the initial magnetic disturbance data and each magnetic field response data, invert the equivalent current coefficient of each singularity in the current singularity grid.

[0007] Based on the equivalent current coefficient of each singular point in the current singularity grid, predict the magnetic field disturbance data for any geographical location.

[0008] In one embodiment, the standard observation data includes standard ground observation data, standard satellite observation data, and standard observation coordinates. The steps of acquiring magnetic field observation data, converting the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determining initial magnetic disturbance data from the standard observation data include: determining the observation points corresponding to the ground observation data and the observation points corresponding to the satellite observation data from the magnetic field observation data, and converting the coordinates of each observation point into transition coordinates in a preset transition coordinate system; converting the ground observation data into standard ground observation data in a preset geomagnetic coordinate system, converting the satellite observation data into standard satellite observation data in a preset geomagnetic coordinate system, and converting each transition coordinate into standard observation coordinates in a preset geomagnetic coordinate system; removing the preset Earth background magnetic field data from the standard ground observation data to obtain initial ground disturbance data, and removing the preset Earth background magnetic field data from the standard satellite observation data to obtain initial satellite disturbance data; and using the initial ground disturbance data, the initial satellite disturbance data, and each standard observation coordinate as initial magnetic disturbance data.

[0009] In one embodiment, the magnetic field response data includes electric field, electric potential, plasma drift velocity, and magnetic disturbance response. The step of calculating the magnetic field response data generated by each singular point in the pre-constructed current singular point grid at each observation point includes: constructing a current singular point grid in a pre-defined observation area based on a preset number of nodes and a preset grid radius, and configuring the initial current coefficient of each singular point in the current singular point grid to a preset unit current coefficient; for each singular point in the current singular point grid, calculating the electric field, electric potential, plasma drift velocity, and magnetic disturbance response generated by the singular point at each observation point based on the preset unit current coefficient of the singular point and the relative distance between the singular point and each observation point.

[0010] In one embodiment, a magnetic field combination matrix is ​​determined based on each magnetic field response data, and the initial magnetic disturbance data is corrected based on the observation error weight of each observation point to obtain weighted magnetic disturbance data.

[0011] Based on the magnetic field combination matrix, weighted magnetic disturbance data, preset regularization matrix, and preset regularization coefficients, a final optimization problem is constructed for the equivalent current coefficient of each singular point in the current singularity grid. The equivalent current coefficient of each singular point is obtained by solving the final optimization problem using a preset matrix decomposition method.

[0012] In one embodiment, the steps of determining a magnetic field combination matrix based on each magnetic field response data and correcting the magnetic field combination matrix according to the observation error weights of each observation point to obtain the magnetic field combination matrix include: constructing an electric field response matrix based on the electric field generated at each observation point by each singularity determined from each magnetic field response data; constructing an electric potential response matrix based on the electric potential generated at each observation point by each singularity determined from each magnetic field response data; constructing a velocity response matrix based on the plasma drift velocity generated at each observation point by each singularity determined from each magnetic field response data; constructing a ground response matrix based on the magnetic disturbance response generated at each ground-based observation point by each singularity determined from each magnetic field response data; and constructing a satellite response matrix based on the magnetic disturbance response generated at each satellite-based observation point by each singularity determined from each magnetic field response data; and combining the electric field response matrix, electric potential response matrix, velocity response matrix, ground response matrix, and satellite response matrix to obtain the magnetic field combination response matrix.

[0013] In one embodiment, the step of calculating the magnetic field disturbance data of any geographical location based on the equivalent current coefficient of each singular point in the current singular point grid includes: for any geographical location, determining the relative distance between the geographical location and each singular point; for each singular point, calculating the single magnetic field disturbance generated by the singular point at the geographical location based on the relative distance between the singular point and the geographical location and the equivalent current coefficient of the singular point; and summing the single magnetic field disturbances generated by each singular point at the geographical location to obtain the magnetic field disturbance data of the geographical location.

[0014] In one embodiment, the method further includes:

[0015] Target magnetic disturbance data for at least one target observation point is determined from the initial magnetic disturbance data. Based on the equivalent current coefficients corresponding to each singular point in the current singularity grid, the magnetic disturbance prediction data for each target observation point is determined. Based on the magnetic disturbance prediction data and target magnetic disturbance data corresponding to each target observation point, the prediction error for each target observation point is calculated. The prediction error is used to determine the reliability of predicting the magnetic field disturbance data of any geographical location through the equivalent current coefficients of each singular point. The prediction error and target magnetic disturbance data of each target observation point are projected onto a preset spatial coordinate system, and the magnetic field disturbance data of at least one geographical location are also projected onto the preset spatial coordinate system to visualize the magnetic field disturbance data of each geographical location, as well as the target magnetic disturbance data and prediction error of each target observation point.

[0016] Furthermore, to achieve the above objectives, embodiments of this application provide a magnetic field disturbance prediction device, the device comprising:

[0017] The acquisition module is used to acquire magnetic field observation data, convert the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determine the initial magnetic disturbance data from the standard observation data. The magnetic field observation data includes ground observation data, satellite observation data, observation points corresponding to ground observation data, and observation points corresponding to satellite observation data.

[0018] The module is used to calculate the magnetic field response data generated by each singularity in the pre-built current singularity grid at each observation point.

[0019] The inversion module is used to invert the equivalent current coefficient of each singular point in the current singularity grid based on the initial magnetic disturbance data and the response data of each magnetic field.

[0020] The prediction module is used to predict magnetic field disturbance data for any geographical location based on the equivalent current coefficient of each singular point in the current singularity grid.

[0021] In addition, to achieve the above objectives, this application also provides a magnetic field disturbance prediction device, which includes a memory, a processor, and a program for a magnetic field disturbance prediction method stored in the memory and executable on the processor. When the program for the magnetic field disturbance prediction method is executed by the processor, it can implement the steps of the magnetic field disturbance prediction method as described above.

[0022] In addition, to achieve the above objectives, embodiments of this application also provide a computer-readable storage medium storing a program for implementing a magnetic field disturbance prediction method. When the program for implementing the magnetic field disturbance prediction method is executed by a processor, it implements the steps of the magnetic field disturbance prediction method as described above.

[0023] In addition, to achieve the above objectives, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the magnetic field disturbance prediction method as described above.

[0024] The one or more technical solutions proposed in this application have at least the following technical effects: This application can acquire magnetic field observation data, which includes ground observation data and its corresponding observation points, satellite observation data and its corresponding observation points. Therefore, this application can acquire multi-source (ground and satellite) magnetic field observation data, thereby improving the richness of the magnetic field observation data and thus enhancing the reliability of subsequent magnetic field disturbance prediction. This application also converts the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, thereby converting both ground and satellite observation data into the same format. This allows for the subsequent joint use of the ground and satellite observation data to invert the equivalent current coefficient, further improving the reliability of subsequent magnetic field disturbance prediction. Since the ground and satellite observation data observed at the observation points include both the Earth's background magnetic field and the magnetic field disturbance component, this application also determines the initial magnetic field disturbance data from the standard observation data to facilitate accurate prediction of magnetic field disturbances at any geographical location.

[0025] Furthermore, this application will use a pre-constructed current singularity grid to calculate the magnetic field response data generated by each singularity in the current singularity grid at each observation point. Then, based on each magnetic field response data and the initial magnetic field disturbance data, the equivalent current coefficient of each singularity in the current singularity grid can be derived, which makes it easier to predict the magnetic field disturbance data of any geographical location through each equivalent current coefficient.

[0026] Each singular point in the pre-constructed current singularity grid is equivalent to a current source, which generates a magnetic field in space. This current source can then be used to simulate the magnetic field disturbance on the ground. However, different singularities generate different magnetic fields in space, and the distance between the observation point and the singularity point varies, resulting in different magnetic field disturbances at the observation point. Therefore, the current magnitude of each singularity point in the pre-constructed current singularity grid is not necessarily accurate. This application first calculates the magnetic field disturbance data of each singularity point at the observation point using the pre-constructed current singularity grid. Then, using the known initial magnetic field disturbance data and the calculated magnetic field disturbance data, the equivalent current coefficient of each singularity point in the current singularity grid is derived. This equivalent current coefficient accurately reflects the current of the singularity point, facilitating the calculation of magnetic field disturbance data at any geographical location using the equivalent current coefficient of each singularity point. This avoids the situation where the prediction of magnetic field disturbance data in sparse areas is inaccurate or impossible due to the limited number of ground magnetometers, thus improving the reliability of magnetic field disturbance prediction. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with those described herein and, together with the specification, serve to explain the principles of those embodiments.

[0028] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating one embodiment of the magnetic field disturbance prediction method of this application;

[0030] Figure 2 This is a schematic diagram of the preset geomagnetic coordinate system in the magnetic field disturbance prediction method of this application embodiment;

[0031] Figure 3 This is a schematic flowchart illustrating the prediction of magnetic field disturbance data in an example of the magnetic field disturbance prediction method according to an embodiment of this application.

[0032] Figure 4 This is a schematic diagram comparing the predicted and observed values ​​of magnetic field disturbance data in an example of the magnetic field disturbance prediction method of this application.

[0033] Figure 5 This is a schematic diagram comparing the predicted and observed values ​​of magnetic field disturbance data in another example of the magnetic field disturbance prediction method of this application.

[0034] Figure 6 This is a schematic diagram of the module structure of the magnetic field disturbance prediction device according to an embodiment of this application;

[0035] Figure 7 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the magnetic field disturbance prediction method in the embodiments of this application.

[0036] The objectives, features, and advantages of the embodiments described in this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0037] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of the embodiments of this application and are not intended to limit the embodiments of this application. To better understand the technical solutions of the embodiments of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0038] With the increasing significance of geomagnetic disturbances in fields such as space weather forecasting, power grid security, spacecraft navigation, and polar communications, accurately retrieving the equivalent current system of the polar ionosphere using limited observational data and predicting magnetic field disturbances at any location on the ground has become an important research direction in geomagnetic modeling and data assimilation. Currently, most geomagnetic disturbance predictions rely solely on ground-based magnetometers. However, the deployment of ground-based magnetometers is limited by geographical conditions and economic costs, resulting in uneven distribution and limited density. In remote areas and ocean regions, the number of magnetometers is sparse, leading to scarce magnetic field observation data and potentially inaccurate or even unpredictable geomagnetic disturbance predictions.

[0039] Furthermore, because different ground magnetometers can be set up at different observation points, and these points have different altitude dimensions, data from different altitude layers and latitudes use inconsistent coordinate systems. This makes it difficult to combine observation data from different points, further increasing the complexity of geomagnetic disturbance prediction. Moreover, it is currently difficult to visually view geomagnetic disturbance data and assess the accuracy of the predicted data. Therefore, current problems include poor data fusion capabilities, limited geomagnetic prediction range, and inconvenient result evaluation and visualization.

[0040] Therefore, this application provides a magnetic field perturbation method, which in this embodiment acquires magnetic field observation data including satellite observation data and ground observation data. This facilitates the combination of vertical magnetic field information provided by polar-orbiting satellites over polar regions, thereby enriching the sources of magnetic field observation data. Furthermore, the ground observation data and satellite observation data are standardized, for example, converted into standard observation data under a preset geomagnetic coordinate system (APEX coordinate system (Adjusted Parabolic Error X-point, magnetosphere-ionosphere coupled coordinate system)). This facilitates the improvement of fitting accuracy in sparse regions and enhances data fusion capabilities.

[0041] Since all magnetic field observation data in this embodiment are converted to data in the APEX coordinate system, the consistency of magnetic field disturbance calculation and equivalent current derivation at different heights and observation platforms can be ensured. This avoids the situation where the data fusion process is complicated and geometric deviations are easily introduced due to the use of different coordinate systems such as geographic and geomagnetic data.

[0042] Furthermore, this embodiment determines the equivalent current coefficient of each singular point in the current singularity grid, thereby facilitating the prediction of magnetic field disturbance data at any geographical location, regardless of the location of the observation point. In other words, magnetic field disturbance prediction can be achieved even at locations other than the observation point. Additionally, this embodiment can calculate the prediction error of geomagnetic disturbances, thus facilitating the evaluation of the reliability of geomagnetic disturbance predictions. Moreover, this embodiment supports the visualization of magnetic field disturbances. For example, the magnetic field disturbance data of at least one geographical location can be projected onto a preset spatial coordinate system to visually display the magnetic field disturbance data of that geographical location. Similarly, the electric field, current, and potential corresponding to a geographical location can be projected onto a preset spatial coordinate system for a holistic view of the data corresponding to that geographical location.

[0043] Based on this, embodiments of this application provide a method for predicting magnetic field disturbances, referring to... Figure 1 , Figure 1 This is a schematic flowchart of the first embodiment of the magnetic field disturbance prediction method according to this application. The magnetic field disturbance prediction method includes steps S10 to S40:

[0044] Step S10: Obtain magnetic field observation data, convert the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determine the initial magnetic disturbance data from the standard observation data. The magnetic field observation data includes ground observation data, satellite observation data, observation points corresponding to ground observation data, and observation points corresponding to satellite observation data.

[0045] It should be noted that the magnetic field observation data may include multiple ground-based observation data or multiple satellite-based observation data. This embodiment does not specifically limit this. Each ground-based observation data has its own corresponding observation point, and each satellite-based observation data also has its own corresponding observation point. The ground-based and satellite-based observation data within the magnetic field observation data can be data observed at the same time or within the same time period; this embodiment does not specifically limit this. Ground-based observation data can be magnetic field data observed by ground stations, and satellite-based observation data can be magnetic field data observed by satellites. The geomagnetic coordinate system is preset to the APEX coordinate system, which unifies the data format of satellite and ground-based observation data, facilitating subsequent fusion of satellite and ground-based observation data, enriching the observation data, and improving the accuracy of subsequent magnetic field disturbances. Different satellite observation data can be observed by the same satellite or by different satellites; this embodiment does not specifically limit this.

[0046] To better understand the APEX coordinate system in this embodiment, please refer to... Figure 2Here is a brief explanation of the APEX coordinate system: The APEX coordinate system can project points on a sphere (such as the position of celestial bodies on the celestial sphere, or the coordinates of data distributed on the sphere) onto the six faces of a cube, transforming them into planar coordinates. Figure 2 Image 'a' shows a schematic diagram of a spherical-cube container model corresponding to the APEX coordinate system. For example, in... Figure 2 In the diagram represented by 'a', there exists a cube, and within that cube is an inscribed sphere, the surface of which exactly covers six faces of the cube. Each face of the cube corresponds to a rectangular region on the sphere. The sphere is divided into different regions using its latitude and longitude coordinates. The cube-spherical projection maps each point on the sphere onto the face of the circumscribed cube by extending a straight line from the Earth's center to the sphere's position. For example, Figure 2 In this context, 'b' represents the projection diagram of the sphere onto the cube face in the APEX coordinate system. Figure 2 In the diagram represented by 'b', there exists a circle within the cube. Lines tangent to this circle form faces of the cube. The circle is... Figure 2 A slice of the inner sphere a. Figure 2 The center of the circle inside b is Figure 2 The center of the ball inside a, in Figure 2 In the diagram represented by 'b', the point where the ray emanating from the center of the sphere intersects the face of the cube is the position where a point on the sphere is projected onto the cube. Angle 'c' is the angle of the ray, used to determine the direction of a point on the sphere. For example, points near the North Pole on the sphere have smaller ray angles, while points near the equator have larger ray angles. Compared to directly using the spherical coordinate system, projecting a cube onto a sphere avoids the singularity problem, which refers to the occurrence of singular values ​​or numerical instability in the coordinate system in polar regions (especially the North and South Poles) when using the spherical coordinate system. Because longitude cannot be defined at poles in spherical coordinates, all longitude values ​​converge to a single point, causing the mesh to become extremely dense near the poles. This can lead to instability or unmanageable singularities in numerical calculations. Using a spherical-to-cube projection avoids these pole singularities: in this projection, poles are no longer singularities in the spherical coordinate system because they are mapped onto the face of a cube, rather than concentrating at a single point. The spherical-to-cube projection ensures a uniform distribution of mesh points across the entire sphere, preventing excessive density of the spherical coordinate system in polar regions and maintaining the stability of numerical calculations. Therefore, using a spherical-to-cube projection solves the pole problem in spherical coordinates, eliminates the numerical difficulties caused by singular values ​​near poles, and thus improves the reliability of subsequent calculations to determine the equivalent current coefficient.

[0047] The standard observation data includes standard data corresponding to each observation point. When the observation point is located on the ground, the standard data corresponding to that observation point is standard ground observation data; when the observation point is located on a satellite, the standard data corresponding to that observation point is standard satellite observation data. The initial magnetic disturbance data includes sub-magnetic disturbance data corresponding to each observation point. When the observation point is located on the ground, the sub-magnetic disturbance data corresponding to that observation point is initial ground disturbance data; when the observation point is located on a satellite, the sub-magnetic disturbance data corresponding to that observation point is initial satellite disturbance data.

[0048] Both standard ground-based observation data and standard satellite-based observation data consist of preset Earth background magnetic field data and disturbance data. Preset Earth background magnetic field data generally accounts for more than 90%, while disturbance data is generated by many factors such as solar activity. Therefore, initial magnetic disturbance data can be determined from standard ground-based observation data and standard satellite-based observation data. For example, initial ground disturbance data can be determined from standard ground-based observation data, and initial satellite disturbance data can be determined from standard satellite-based observation data.

[0049] For example, magnetic field observation data is acquired, including ground observation data from multiple observation points on the ground and satellite observation data from multiple observation points on satellites. The magnetic field observation data is converted into standard observation data in a preset geomagnetic coordinate system, and initial magnetic disturbance data is determined from the standard observation data. For example, ground observation data can be obtained from the SuperMAG (Super Magnetic Observation Network) geomagnetic observation network, and satellite observation data can be obtained from AMPERE (Active Magnetospheric Particle Tracer) and Swarm (Swarm Mission). This embodiment does not specifically limit the specific methods used.

[0050] In a feasible embodiment, step S10 includes steps S11 to S14:

[0051] Step S11: Determine the observation points corresponding to the ground observation data and the observation points corresponding to the satellite observation data from the magnetic field observation data, and convert the coordinates of each observation point into transition coordinates under the preset transition coordinate system;

[0052] It should be noted that because the sources of ground observation data and satellite observation data are different, the coordinate formats of the corresponding observation points may also be different. To avoid confusion during the fusion of ground and satellite observation data, the coordinates of the observation points corresponding to the ground observation data and the satellite observation data can be standardized first. The preset transition coordinate system can be a Cartesian coordinate system or a geomagnetic coordinate system, etc., and this embodiment does not specifically limit it. The preset transition coordinate system is different from the preset geomagnetic coordinate system, and it is not the APEX coordinate system. The transition coordinates are the coordinates of the observation points in the preset transition coordinate system. In this embodiment, the coordinates of the observation points can also carry observation time labels.

[0053] In this embodiment, the ground observation data, satellite observation data, and the transition coordinates corresponding to each observation point in the magnetic field observation data can also be represented using NumPy arrays (numerical arrays). For example, the initial coordinates of an observation point may consist of longitude, latitude, and altitude. Converting the coordinates of the observation point to coordinates in a preset transition coordinate system can transform the coordinates of the observation point into three-dimensional coordinates (xyz) in the Cartesian coordinate system.

[0054] Step S12: Convert the ground observation data into standard ground observation data in a preset geomagnetic coordinate system, convert the satellite observation data into standard satellite observation data in a preset geomagnetic coordinate system, and convert each transition coordinate into standard observation coordinates in a preset geomagnetic coordinate system.

[0055] It should be noted that the standard observation coordinates are transition coordinates in a preset geomagnetic coordinate system. This can be achieved through a preset coordinate transformation relationship between the preset transition coordinate system and the APEX coordinate system. For example, there is a preset coordinate transformation relationship between the Cartesian coordinate system and the APEX coordinate system. The preset coordinate transformation matrix can be obtained directly from this preset transformation relationship. Ground observation data, satellite observation data, and the transition coordinates of each observation point are all transformed to the APEX coordinate system.

[0056] This embodiment first converts the coordinates of all observation points into transition coordinates under a preset transition coordinate system. Since the preset transition coordinate system and the preset geomagnetic coordinate system can be directly converted, after determining the transition coordinates, they can be directly converted into standard observation coordinates under the APEX coordinate system, thereby improving the efficiency of coordinate conversion. Since the direct conversion from geographic coordinates to APEX coordinates requires real-time calculation of magnetic field line tracing, the conversion process is complex. Converting the initial coordinates of the observation points into transition coordinates under the Cartesian coordinate system first improves the efficiency of coordinate conversion. Even if the observation points originate from different ground observation stations or satellites, and the format of the initial coordinates corresponding to the observation points is not uniform, the coordinate conversion method of this embodiment (first converting to transition coordinates under the Cartesian coordinate system, then converting to standard observation coordinates) also improves the efficiency of coordinate conversion.

[0057] Step S13: Remove the preset Earth background magnetic field data from the standard ground observation data to obtain the initial ground disturbance data, and remove the preset Earth background magnetic field data from the standard satellite observation data to obtain the initial satellite disturbance data.

[0058] Step S14: The initial ground disturbance data, initial satellite disturbance data, and standard observation coordinates are used together as the initial magnetic disturbance data.

[0059] It should be noted that the preset Earth background magnetic field data is known. Therefore, by using the preset Earth background magnetic field data, initial ground disturbance data can be obtained from standard ground observation data, and initial satellite disturbance data can be obtained from standard satellite observation data. The initial magnetic disturbance data can be represented as a NumPy array, which facilitates subsequent data analysis.

[0060] For example, the coordinates of each observation point in the magnetic field observation data are converted to transition coordinates in a preset transition coordinate system. Using a preset coordinate transformation relationship between the preset transition coordinate system and the APEX coordinate system, the transition coordinates are converted to standard observation coordinates in the APEX coordinate system. Then, each ground observation data point is converted to standard ground observation data in the APEX coordinate system, and each satellite observation data point is converted to standard satellite observation data in the APEX coordinate system. The preset Earth background magnetic field data in the standard ground observation data is removed to obtain initial ground disturbance data, and the preset Earth background magnetic field data in the standard satellite observation data is removed to obtain initial satellite disturbance data. Each initial ground disturbance data point, each initial satellite disturbance data point, and each standard observation coordinate are combined to form initial magnetic disturbance data, which can be represented as a NumPy array. Each standard observation coordinate is associated with its corresponding magnetic disturbance data. In other embodiments, multiple initial ground disturbance data can be collectively represented as B_ground_obs (3 x N) (ground observation magnetic field vector), where 3 represents the three components of the magnetic field vector (north, east, and vertical directions), and N represents the number of ground observation points. Multiple initial satellite disturbance data can be collectively represented as B_space_obs (3 x M) (satellite observation magnetic field vector), where 3 represents the three components of the magnetic field vector, and M represents the number of observation points in the satellite orbit. Multiple standard observation point coordinates can be collectively represented as: obs_coords, sat_coords, where obs_coords refers to the coordinates of standard observation points belonging to the ground, and sat_coords refers to the coordinates of standard observation points belonging to the data satellite.

[0061] This embodiment can convert both satellite observation data and ground observation data into data in the APEX coordinate system, thereby mapping the satellite observation data and ground observation data to the same reference height surface. This facilitates the fusion of data observed from multiple observation points for the analysis of magnetic field disturbances, thereby improving the accuracy of subsequent geomagnetic disturbance prediction.

[0062] Step S20: Calculate the magnetic field response data generated by each singularity in the pre-constructed current singularity grid at each observation point.

[0063] It should be noted that the current singularity grid is a SECS node grid. The current singularity grid includes multiple singularities, each of which is equivalent to a current source. In the SECS node grid, each grid node is considered a hypothetical current source. These current sources are not actual currents but are artificially defined in the mathematical model; they are cells used to construct the perturbation current field, enabling subsequent prediction of magnetic field perturbations at arbitrary geographical locations using the SECS node grid. Each singularity generates magnetic field response data at each observation point. The magnetic field response data is data related to the magnetic field, such as electric field, electric potential, plasma drift velocity, and magnetic disturbance response. For example, the current singularity grid is constructed, and the magnetic field response data generated by each singularity in the current singularity grid at each observation point is calculated.

[0064] In a feasible embodiment, step S20 further includes steps S21 to S22:

[0065] Step S21: Based on the preset number of nodes and the preset grid radius, construct a current singularity grid in the preset observation area, and configure the initial current coefficient of each singularity in the current singularity grid as a preset unit current coefficient.

[0066] Step S22: For each singular point in the current singular point grid, based on the preset unit current coefficient of the singular point and the relative distance between the singular point and each observation point, calculate the electric field, electric potential, plasma drift velocity and magnetic disturbance response generated by the singular point at each observation point.

[0067] It should be noted that the preset number of nodes represents the number of singularities constructed, and the preset grid radius is the sum of the Earth's radius and the height of the current singularity grid. For example, the current singularity grid can be set at an altitude of Earth's radius + 110 km. Both the preset number of nodes and the preset grid radius can be determined based on actual conditions, and this embodiment does not impose specific limitations on them. The preset observation area generally refers to areas with latitudes above 50 degrees. The preset unit current coefficient can also be determined based on actual conditions, and this embodiment does not impose specific limitations on it. Each grid node in the current singularity grid can be used to place a current source (singularity), and each current source will induce magnetic field disturbances on the Earth's surface or at the satellite's altitude.

[0068] Since magnetic field disturbances are related to activities such as the solar wind, it is assumed that when the solar wind enters the Earth's magnetosphere, it causes changes in the current in the Earth's ionosphere. These changes in the ionospheric current, in turn, lead to part of the magnetic field disturbance. Therefore, magnetic field disturbances can be predicted using a grid of current singularities.

[0069] The relative distance between the singular point and the observation point varies, resulting in different magnetic field response data generated by the singular point at each observation point. Furthermore, the magnitude of the current at the singular point also varies, meaning the magnetic field response data generated at the same observation point may not be identical. Therefore, in this embodiment, the electric field, electric potential, plasma drift velocity, and magnetic disturbance response generated by the singular point at each observation point are calculated based on the preset unit current coefficient of the singular point and the relative distance between the singular point and each observation point.

[0070] Electric field, electric potential, plasma drift velocity, and magnetic disturbance response are all related to the magnetic field. Therefore, this embodiment determines the electric field potential, plasma drift velocity, and magnetic disturbance response of each singularity at each observation point. This facilitates a more accurate derivation of the equivalent current coefficient of each singularity, thereby improving the accuracy of magnetic field disturbance prediction. Plasma drift velocity is the velocity of charged particles at the observation point under the influence of the magnetic field.

[0071] Electric field, electric potential, plasma drift velocity, and magnetic disturbance response can all be calculated based on their respective formulas, and this embodiment does not impose specific limitations on them. For example, the magnetic disturbance response can be calculated using the magnetic dipole field formula, and the plasma drift velocity can be calculated using the E-cross-B Drift Velocity Formula, and this embodiment does not impose specific limitations on them.

[0072] Step S30: Based on the initial magnetic disturbance data and the response data of each magnetic field, the equivalent current coefficient of each singular point in the current singularity grid is obtained by inversion.

[0073] It should be noted that the equivalent current coefficient can characterize the current magnitude at singularities. The initial magnetic disturbance data are the actual observed magnetic field disturbance data, and the magnetic field response data are calculated through the current singularity grid. Furthermore, the equivalent current coefficient of each singularity in the current singularity grid can be obtained by inversion using the initial magnetic disturbance data and the magnetic field response data, which is equivalent to reconstructing the ionospheric current corresponding to the current singularity grid.

[0074] Since the equivalent current coefficient is derived from magnetic field response data and initial magnetic disturbance data (which are actually observed magnetic field disturbances), the derived equivalent current coefficient is accurate. This allows for subsequent prediction of magnetic field disturbance data at any geographical location based on the equivalent current coefficient. Understandably, this embodiment can derive the equivalent current coefficient of the singularity point that best reflects the actual magnetic field disturbance using limited observational data (initial magnetic disturbance data). This improves the accuracy of subsequent predictions of magnetic field disturbance data at any location.

[0075] For example, the initial magnetic disturbance data and the response data of each magnetic field can be used to construct a final optimization problem for the equivalent current coefficient of each singular point in the current singularity grid. The equivalent current coefficient of each singular point in the current singularity grid can be obtained by inversion through the final optimization problem.

[0076] In a feasible embodiment, step S30 further includes steps S31 to S33:

[0077] Step S31: Determine the magnetic field combination matrix based on each magnetic field response data, and correct the initial magnetic disturbance data according to the observation error weight of each observation point to obtain weighted magnetic disturbance data.

[0078] It should be noted that each observation point has its own corresponding magnetic field response data. The magnetic field response data of each observation point includes the electric field, electric potential, plasma drift velocity, and magnetic disturbance response generated by multiple singular points at the observation point. A magnetic field combination matrix can be constructed based on each magnetic field response data.

[0079] The observation error weights can be determined based on the user's trust in the observation points. This embodiment does not impose specific limitations on this. Each observation point has its own corresponding observation error weight. The more trust the user has in the observation point, the smaller the corresponding observation error; the less trust the user has in the observation point, the larger the corresponding observation error. The initial magnetic disturbance data can be corrected based on the observation error weights of each observation point to obtain weighted magnetic disturbance data, thereby improving the accuracy of magnetic field disturbance prediction.

[0080] For example, a magnetic field combination matrix can be constructed based on the magnetic field response data of each observation point to obtain the observation error weight. Each observation error weight is then normalized to obtain a normalized weight. Based on the normalized weight of each observation point, the initial magnetic disturbance data is corrected to obtain weighted magnetic disturbance data. For instance, the largest observation error weight can be determined among the various observation error weights. For each observation error, the ratio of the observation error weight to the largest observation error weight can be used as the normalized weight to normalize the observation error. Other normalization methods are also possible, such as normalization using the standard deviation of the observation error. This embodiment does not specifically limit this method. Furthermore, the initial magnetic disturbance data is corrected using the observation error weight to improve the accuracy of the equivalent current coefficient. In this embodiment, each observation error weight is normalized, thereby eliminating dimensional differences and improving the accuracy of the subsequent calculation of the equivalent current coefficient. The normalized weight is the weight after normalizing the observation error weight.

[0081] For example, initial magnetic disturbance data can also be represented as an observation matrix, where the element Xnm in the observation matrix represents the magnetic field observation data in the nth row and mth column, where n can refer to the longitude coordinates of the magnetic field observation data and m can refer to the latitude coordinates of the magnetic field observation data. When Xnm is data observed by a satellite, the magnetic field observation data of Xnm is satellite observation data; when Xnm is data observed from the ground, the magnetic field observation data of Xnm is ground observation data. For each magnetic field observation data in the initial magnetic disturbance data matrix, it is multiplied by the normalized weight of the observation point where the magnetic field observation data is located to correct the initial magnetic disturbance data.

[0082] In a feasible embodiment, step S31 further includes steps S311 to S313:

[0083] Step S311: Based on the electric field generated by each singularity at each observation point determined from each magnetic field response data, construct an electric field response matrix; based on the electric potential generated by each singularity at each observation point determined from each magnetic field response data, construct an electric potential response matrix; based on the plasma drift velocity generated by each singularity at each observation point determined from each magnetic field response data, construct a velocity response matrix.

[0084] It should be noted that the magnetic field response data includes multiple types of response data, namely electric field, electric potential, plasma drift velocity, and magnetic disturbance response. Each type of magnetic field response data is obtained by calculating the electric field, electric potential, plasma drift velocity, and magnetic disturbance response of each singularity at each observation point. Therefore, a matrix can be constructed using the magnetic field response data corresponding to each observation point.

[0085] For example, response matrices can be constructed for each type of response data. The response matrices for electric field, electric potential, and plasma drift velocity are the electric field response matrix, electric potential response matrix, and velocity response matrix, respectively. The response matrices for magnetic disturbance response include satellite response matrix and ground response matrix.

[0086] Specifically, an electric field response matrix can be constructed based on the electric field generated by each singularity at each observation point, where Cij represents the electric field generated by the i-th singularity at the j-th observation point. Similarly, an electric potential response matrix can be constructed based on the electric potential generated by each singularity at each observation point, where Sij represents the electric potential generated by the i-th singularity at the j-th observation point. Finally, a velocity response matrix can be constructed based on the plasma drift velocity generated by each singularity at each observation point, where Pij represents the plasma drift velocity generated by the i-th singularity at the j-th observation point.

[0087] Step S312: Based on the magnetic disturbance response generated by each singularity determined from each magnetic field response data at each observation point belonging to the ground, construct the ground response matrix; and based on the magnetic disturbance response generated by each singularity determined from each magnetic field response data at each observation point belonging to the satellite, construct the satellite response matrix.

[0088] It should be noted that magnetic disturbance response can include satellite magnetic disturbance response and ground magnetic disturbance response. Satellite magnetic disturbance data can be the magnetic disturbance response generated by singular points at observation points belonging to the satellite, while ground magnetic disturbance response can be the magnetic disturbance response generated by singular points at observation points belonging to the ground. For example, a ground response matrix can be constructed based on the magnetic disturbance response generated by each singular point at each observation point belonging to the ground, or a satellite response matrix can be constructed based on the magnetic disturbance response generated by each singular point at each observation point belonging to the satellite.

[0089] Step S313: Combine the electric field response matrix, electric potential response matrix, velocity response matrix, ground response matrix, and satellite response matrix to obtain the magnetic field combined response matrix;

[0090] It should be noted that the electric field response matrix, electric potential response matrix, velocity response matrix, ground response matrix, and satellite response matrix can all be represented as NumPy arrays. For example, the electric field response matrix, electric potential response matrix, velocity response matrix, ground response matrix, and satellite response matrix can be concatenated to obtain the magnetic field response matrix. This embodiment constructs a combined magnetic field response matrix, thereby improving the efficiency of subsequent calculations of the equivalent current coefficient and simplifying the complexity of calculating the equivalent current coefficient.

[0091] Step S32: Based on the magnetic field combination matrix, weighted magnetic disturbance data, preset regularization matrix, and preset regularization coefficients, construct the final optimization problem corresponding to the equivalent current coefficient of each singular point in the current singularity grid.

[0092] It should be noted that the preset regularization coefficients can be determined in advance based on actual conditions, and this embodiment does not impose specific limitations on this. The preset regularization matrix is ​​used to control the smoothness of the equivalent current coefficients, thereby facilitating the improvement of the accuracy of calculating the equivalent current coefficients. The final optimization problem concerns the equivalent current coefficients corresponding to all singular points in the current singularity grid.

[0093] For example, the final optimization problem can be referred to as Formula 1:

[0094]

[0095] Where x* is the vector composed of all equivalent current coefficients in the current singularity grid, A is the magnetic field combination matrix, b is the weighted magnetic disturbance data, which can also be represented as a matrix; λ is the preset regularization coefficient, x is the equivalent current coefficient, and L is the preset regularization matrix. The preset regularization matrix can be configured based on actual conditions, and this embodiment does not impose specific limitations on it. For example, the preset regularization matrix can be constructed according to the user-input singularity spacing to balance the fitting accuracy of the equivalent current coefficient of each singularity with the physical smoothness of the equivalent current coefficient.

[0096] Step S33: Based on the preset matrix decomposition method, the equivalent current coefficient of each singular point is obtained by solving the final optimization problem.

[0097] It should be noted that the preset matrix decomposition method can be LU (triangular) decomposition, which can be performed on the matrix multiplied by the equivalent current coefficients in the final optimization problem to obtain the equivalent current coefficients at each singular point. In other embodiments, the preset matrix decomposition method can also be SVD (singular value decomposition), and this embodiment does not specifically limit it. In this embodiment, LU triangular decomposition can improve the accuracy of the equivalent current coefficients and avoid redundant calculations, significantly improving the computational efficiency of large-scale time-series magnetic field inversion problems.

[0098] In this embodiment, the final optimization problem is a least squares problem. To better understand the final optimization problem in this embodiment, a brief example is given of the process for determining the final optimization problem: First, a matrix equation is constructed, which is: initial magnetic disturbance data d = magnetic field combination matrix G * equivalent current coefficient x. The problem to be solved in the matrix equation is x. The initial magnetic disturbance data d can be represented as a matrix. Since there are only a few observation points on the ground, this matrix is ​​an ultra-sparse matrix. Assuming that there are observation points at any location on the ground, then d is a completely known matrix. Then, G and d are completely known, and x can be solved by directly x = the inverse of G * d. However, the ground observation equipment is finite, and d must be an ultra-sparse matrix. In this case, x = the inverse of G * d is mathematically invalid, which is an ill-conditioned problem. To solve this ill-conditioned problem, we need to construct a final optimization problem, namely the least squares problem, and solve the least squares problem to obtain the equivalent current coefficient. Solving the least squares problem involves minimizing the sum of squares of the errors to find the best match for the data. Finding the best match for the data means finding the optimal equivalent current coefficient so that the model transition matrix G*model parameters x are closest to the initial magnetic disturbance data d. Minimizing the sum of squares of the errors is to minimize the function of the equivalent current coefficient x. The function of the equivalent current coefficient can be referred to as the matrix equation, which can also be considered as a function of the equivalent current coefficient x. Minimizing the function of the equivalent current coefficient means making the derivative of the equivalent current coefficient zero, which facilitates the least squares problem. Before differentiating the function of the equivalent current coefficient, the initial magnetic disturbance data needs to be corrected to obtain weighted magnetic disturbance data in order to ensure the accuracy of the equivalent coefficient calculation. Furthermore, a preset regularization matrix and preset regularization coefficient need to be added to the matrix equation to improve the smoothness of the equivalent current coefficient and prevent abrupt changes in the equivalent current coefficient at adjacent singular points. The final squared problem can be obtained by referring to Formula 1.

[0099] Step S40: Based on the equivalent current coefficient of each singular point in the current singularity grid, predict the magnetic field disturbance data for any geographical location.

[0100] It should be noted that the equivalent current coefficient can characterize the current magnitude of a singular point. Determining the equivalent current coefficient of each singular point in the current singular point grid is equivalent to reconstructing the grid, making the magnetic field disturbance generated by each singular point at each geographical location more consistent with the actual magnetic field disturbance. This facilitates the prediction of magnetic field disturbance data for any geographical location, improving the comprehensiveness and accuracy of magnetic field disturbance prediction. For example, based on the equivalent current coefficient of each singular point in the current singular point grid, the component magnetic disturbance generated by each singular point at the target geographical location can be determined. The component magnetic disturbance generated by each singular point at the target geographical location can be accumulated to obtain the magnetic field disturbance data for the target geographical location. The target geographical location can be any geographical location where magnetic field disturbance needs to be predicted; the target geographical location can be determined based on actual conditions, and this embodiment does not impose specific limitations on it.

[0101] This application embodiment can acquire magnetic field observation data, including ground observation data and its corresponding observation points, and satellite observation data and its corresponding observation points. Therefore, this application embodiment can acquire multi-source (ground and satellite) magnetic field observation data, thereby increasing the richness of the magnetic field observation data and improving the reliability of subsequent magnetic field disturbance prediction. This application embodiment also converts the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, thus converting both ground and satellite observation data into the same format. This allows for the subsequent use of both ground and satellite observation data to invert equivalent current coefficients, further improving the reliability of subsequent magnetic field disturbance prediction. Since the ground and satellite observation data obtained during observation at the observation points include both the Earth's background magnetic field and magnetic field disturbance components, this application embodiment also determines initial magnetic field disturbance data from the standard observation data to facilitate accurate prediction of magnetic field disturbances at any geographical location.

[0102] Furthermore, in this embodiment, the magnetic field response data generated by each singular point in the pre-constructed current singular point grid at each observation point is calculated. Then, the equivalent current coefficient of each singular point in the current singular point grid is derived from the magnetic field response data and the initial magnetic field disturbance data, which makes it easier to predict the magnetic field disturbance data of any geographical location through the equivalent current coefficient. Each singular point in the pre-constructed current singularity grid is equivalent to a current source. The current source generates a magnetic field in space, which can then be used to simulate the magnetic field disturbance on the ground. However, different singularities generate different magnetic fields in space, and the magnetic field disturbance experienced by the observation point varies depending on the distance between the observation point and the singularity. Therefore, the current magnitude of each singularity in the pre-constructed current singularity grid is not necessarily accurate. Thus, in this embodiment, the magnetic field disturbance data of each singularity in the pre-constructed current singularity grid at the observation point is calculated. Then, using the known initial magnetic field disturbance data and the calculated magnetic field disturbance data, the equivalent current coefficient of each singularity in the current singularity grid is derived. The equivalent current coefficient can accurately reflect the current of the singularity, making it easier to calculate the magnetic field disturbance data of any geographical location using the equivalent current coefficient of each singularity. This avoids the situation where the magnetic field disturbance data prediction is inaccurate or unpredictable in sparse areas due to the limited number of ground magnetometers, thus improving the reliability of magnetic field disturbance prediction.

[0103] In a feasible embodiment, step S40 further includes steps S41 to S43:

[0104] Step S41: For any given geographic location, determine the relative distances between the geographic location and each singular point;

[0105] Step S42: For each singular point, calculate the single magnetic field disturbance generated by the singular point at its geographical location based on the relative distance between the singular point and its geographical location and the equivalent current coefficient of the singular point.

[0106] Step S43: Accumulate the individual magnetic field disturbances generated by each singular point at its geographical location to obtain the magnetic field disturbance data at the geographical location.

[0107] It should be noted that the geographical location can be any location where magnetic field disturbance prediction is required, and the geographical location can be selected based on the actual situation; this embodiment does not impose specific limitations on it. Each singular point can generate magnetic field disturbances at its geographical location. A single magnetic field disturbance is the magnetic field disturbance generated by a singular point at its geographical location. The relative distance between the singular point and the geographical location varies, resulting in different single magnetic field disturbances at the geographical location. Even multiple singular points at the same relative distance with different equivalent current coefficients will generate different single magnetic field disturbances at their respective geographical locations.

[0108] By accumulating the individual magnetic field disturbances generated by each singular point at its geographical location, magnetic field disturbance data for that location can be obtained. In this embodiment, the equivalent current coefficient of each singular point in the inverted singular point grid can be used to simulate the real magnetic field. Therefore, the magnetic field disturbance data for any geographical location can be calculated using the equivalent current coefficient of each singular point in the singular point grid, thus enabling the prediction of magnetic field disturbance data for any point. Furthermore, since the real magnetic field can be simulated using the equivalent current coefficient of each singular point in the singular point grid, and the magnetic field disturbance data calculated by combining the equivalent current coefficients of each singular point reflects the true magnetic field disturbance data at the geographical location, the accuracy of the magnetic field disturbance data prediction is ensured.

[0109] For example, for any given geographic location, the relative distances between the geographic location and each singular point can be determined. For each singular point, the individual magnetic field disturbance generated by the singular point at the geographic location can be calculated based on the relative distance between the singular point and the geographic location, as well as the equivalent current coefficient of the singular point. The individual magnetic field disturbances generated by each singular point at the geographic location are then summed to obtain the magnetic field disturbance data for the geographic location. For instance, a single magnetic field disturbance can be calculated using a magnetic dipole model, which is a formula used to calculate magnetic field disturbances. The magnetic dipole model can be directly obtained, and this embodiment will not elaborate on it. Due to the existence of singular points, the magnetic field strength or magnetic induction intensity of the geographic location changes; this change is the magnetic field disturbance generated by the singular point at the geographic location.

[0110] In a feasible embodiment, the magnetic field disturbance prediction method further includes steps A10 to A30:

[0111] Step A10: Determine the target magnetic disturbance data of at least one target observation point from the initial magnetic disturbance data, and determine the magnetic disturbance prediction data of each target observation point based on the equivalent current coefficients corresponding to each singular point in the current singularity grid.

[0112] It should be noted that the target observation point can be any observation point in the initial magnetic disturbance data, and the magnetic disturbance prediction data is the magnetic field disturbance data of the target observation point predicted by each equivalent current coefficient. In other embodiments, the magnetic disturbance prediction data of each observation point in the initial magnetic disturbance data can also be determined, which facilitates the subsequent determination of the prediction error of each observation point.

[0113] Step A20: Based on the magnetic disturbance prediction data and target magnetic disturbance data corresponding to each target observation point, calculate the prediction error of each target observation point, so as to determine the reliability of predicting the magnetic field disturbance data of any geographical location through the equivalent current coefficient of each singular point by means of the prediction error.

[0114] It should be noted that prediction error can be used to reflect the degree of deviation between the magnetic disturbance prediction data predicted by each equivalent current coefficient and the observed magnetic field disturbance data. The smaller the prediction error for each target observation point, the higher the reliability of the magnetic disturbance prediction data for any geographical location predicted by the equivalent current coefficient of each singular point. The magnetic disturbance prediction data for the target observation point is obtained by predicting the equivalent current coefficient in the current singular point grid. It can be understood that the target observation point corresponds to both observed magnetic field disturbance data and predicted magnetic disturbance prediction data. Since the reliability of the magnetic field disturbance data predicted by the singular point grid can be determined in this embodiment, it is convenient for users to make decision analysis based on the magnetic field disturbance data. For example, it can avoid false alarms (such as unnecessary emergency power grid shutdowns) or missed alarms (such as failure to prevent geomagnetic storms in time) caused by over-reliance on unreliable predictions.

[0115] For example, the prediction error for each target observation point can be obtained, the average value of each prediction error can be calculated to obtain the average error, and the confidence level can be determined based on the average error. The confidence level can be used as the confidence degree. For example, the confidence level corresponding to the average error can be found in the mapping relationship between preset errors and preset levels. In other embodiments, the confidence degree can be determined by region. For example, the magnetic disturbance prediction data and the observed magnetic field disturbance data may be basically the same in some regions, while the difference between the magnetic disturbance prediction data and the observed magnetic field disturbance data may be large in some regions. Therefore, the confidence degree of different regions can also be determined. For example, the prediction error of each target observation point can be determined first, and the regions can be divided according to each prediction error to obtain multiple evaluation regions. For each evaluation region, the confidence level of the evaluation region can be determined based on the average value of each prediction error in the evaluation region to determine the confidence degree of the evaluation region. Among them, target observation points with similar prediction errors can be assigned to the same evaluation region. The evaluation regions do not overlap, and the geographical locations within the same evaluation region are continuous and there are no fragmented geographical locations. Similar prediction errors mean that the difference between two prediction errors is less than a preset error threshold. The preset error threshold can be determined based on the actual situation. Determining credibility by partitioning data makes it easier to provide users with reliable data more effectively, and also makes it easier for users to make decisions based on magnetic disturbance prediction data.

[0116] Step A30: Project the prediction error and target magnetic disturbance data of each target observation point onto a preset spatial coordinate system, and project the magnetic field disturbance data of at least one geographical location onto the preset spatial coordinate system to visualize the magnetic field disturbance data of each geographical location, as well as the target magnetic disturbance data and prediction error of each target observation point.

[0117] It should be noted that the preset spatial coordinate system can be a polar coordinate system or a geographic coordinate system. The geographic coordinate system is a spherical coordinate system that uses latitude and longitude to represent the position on the Earth's surface. Prediction errors can be projected onto the preset spatial coordinate system, as can target magnetic disturbance data and magnetic disturbance prediction data. This enables the visualization of magnetic disturbance prediction data and the display of target magnetic disturbance data and prediction errors at target observation points, thus facilitating users to more intuitively understand the magnetic disturbance prediction data for any geographical location.

[0118] In other embodiments, magnetic field directions, current contour lines, etc., for any geographical location can be drawn in a preset spatial coordinate system. Magnetic disturbance color maps can also be drawn in the preset spatial coordinate system, thereby achieving diverse visualization displays and allowing users to intuitively understand various data within the preset spatial coordinate system. Furthermore, satellite observation data and ground observation data can also be displayed in the preset spatial coordinate system; this embodiment does not specifically limit this. For example, a polar coordinate system or a geographic coordinate system can be obtained, and the prediction error of the target observation point can be directly projected onto the polar coordinate system or the geographic coordinate system. For instance, the prediction error can be marked on the polar coordinate system or the geographic coordinate system, where the geographic coordinates of the target observation point exist. Alternatively, the target magnetic disturbance data can be projected onto the preset spatial coordinate system, and the magnetic field prediction data can be projected onto the preset spatial coordinate system, thereby achieving a visualization display.

[0119] To better understand this embodiment, please refer to Figure 3The following is a brief description of the prediction process (Y10~Y70) for magnetic field disturbance data in this embodiment: Step Y10: Acquire magnetic field observation data; Step Y20: Determine initial magnetic disturbance data based on the magnetic field observation data; Step Y30: Construct a magnetic field combination matrix based on each observation point and the current singularity grid in the magnetic field observation data; alternatively, Step Y30 can be executed first, followed by Step Y20. This embodiment does not specify a particular order for the execution of Steps Y20 and Y30. Step Y40: Correct the initial magnetic disturbance data to obtain weighted magnetic disturbance data based on the observation error weight of each observation point in the magnetic field observation data; Step Y50: Determine a preset regularization matrix based on the distance between adjacent singularities in the current singularity grid; it can be understood that the preset regularization matrix can be determined based on the distance between adjacent singularities in the current singularity grid, thereby improving the smoothness of the equivalent current coefficient of each singularity point, avoiding abnormal data, and improving the accuracy of magnetic field disturbance data prediction. Step Y60: Construct the final optimization problem. The final optimization problem can be constructed based on the initial magnetic disturbance data, the magnetic field combination matrix, and a preset regularization matrix. Step Y70: Solve the final optimization problem to obtain the equivalent current coefficient of each singular point in the current singularity grid. After obtaining the equivalent current coefficient of each singular point, the magnetic field disturbance data at any geographical location can be solved, thereby solving the technical problem of low reliability in geomagnetic disturbance prediction and improving the prediction accuracy of geomagnetic disturbance.

[0120] Furthermore, one can refer to Figure 4 and Figure 5 , Figure 4 and Figure 5 Both provide comparison charts of observed and predicted values. Observed values ​​refer to the observed geomagnetic disturbances, while predicted values ​​refer to the predicted geomagnetic disturbances. Figure 4 The given prediction values ​​are the predicted magnetic field disturbance data when the magnetic field observation data includes ground observation data but excludes satellite observation data. Figure 5 The given prediction values ​​are based on the predicted magnetic field disturbance data obtained when the magnetic field observation data includes both ground-based and satellite-based data. Figure 4 and Figure 5 It can be seen that the predicted values ​​all approach the observed values, and Figure 5 The predicted value is relative to Figure 4 It is getting closer to the observed value. Figure 4 and Figure 5 In Be、 Bn and Bu represents the three components corresponding to the geomagnetic disturbance. Be、 Bn and Bu represents the eastward, northward, and vertical perturbation components, respectively. nT (nanotesla) is the unit of magnetic field strength. Figure 4 and Figure 5 The horizontal axis represents time T.

[0121] Since the embodiments of this application can determine the equivalent current coefficient of each singularity, it can not only determine magnetic field disturbances at different geographical locations, but also be applied to scenarios such as electric fields and plasma velocity fields, thus improving the applicability of the scenarios. Furthermore, in this embodiment, the magnetic field disturbance prediction process can be divided into multiple modules, which facilitates individual maintenance of each module, thereby improving the reliability and efficiency of geomagnetic disturbance prediction. For example, in this embodiment, the magnetic field disturbance prediction process can be divided into an acquisition module, a construction module, an inversion module, a prediction module, and a visualization module. The construction module is used to calculate the magnetic field response data generated by each singularity in the pre-constructed current singularity grid at each observation point. The inversion module is used to invert the equivalent current coefficient of each singularity in the current singularity grid based on the initial magnetic disturbance data and each magnetic field response data. The prediction module is used to predict the magnetic field disturbance data at any geographical location based on the equivalent current coefficient of each singularity in the current singularity grid. The acquisition module can acquire magnetic field observation data, convert it into standard observation data in a preset geomagnetic coordinate system, and determine initial magnetic disturbance data from the standard observation data. The magnetic field observation data includes ground observation data, satellite observation data, observation points corresponding to the ground observation data, and observation points corresponding to the satellite observation data. The visualization module can determine the target magnetic disturbance data for at least one target observation point from the initial magnetic disturbance data. Based on the equivalent current coefficients corresponding to each singular point in the current singularity grid, it determines the predicted magnetic disturbance data for each target observation point. Based on the predicted magnetic disturbance data and the target magnetic disturbance data for each target observation point, it calculates the prediction error for each target observation point. This prediction error is used to determine the reliability of predicting magnetic field disturbance data for any geographical location using the equivalent current coefficients of each singularity point. The prediction error and target magnetic disturbance data for each target observation point are projected onto a preset spatial coordinate system, and the magnetic field disturbance data for at least one geographical location are also projected onto the preset spatial coordinate system. This visualizes the magnetic field disturbance data for each geographical location, as well as the target magnetic disturbance data and prediction error for each target observation point. Therefore, this embodiment achieves a modular design, and the acquisition module can support the input of different observation data, thus expanding the applicable scenarios of this embodiment.

[0122] This application also provides a magnetic field disturbance prediction device. Please refer to... Figure 6 The device includes:

[0123] The acquisition module 10 is used to acquire magnetic field observation data, convert the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determine the initial magnetic disturbance data from the standard observation data. The magnetic field observation data includes ground observation data, satellite observation data, observation points corresponding to ground observation data, and observation points corresponding to satellite observation data. The construction module 20 is used to calculate the magnetic field response data generated by each singular point in the pre-constructed current singular point grid at each observation point. The inversion module 30 is used to invert the equivalent current coefficient of each singular point in the current singular point grid based on the initial magnetic disturbance data and each magnetic field response data. The prediction module 40 is used to predict the magnetic field disturbance data at any geographical location based on the equivalent current coefficient of each singular point in the current singular point grid.

[0124] The magnetic field disturbance prediction device provided in this application adopts the magnetic field disturbance prediction method in the above embodiments, aiming to solve the technical problem of low reliability in geomagnetic disturbance prediction. Compared with the prior art, the beneficial effects of the magnetic field disturbance prediction method provided in this application are the same as those of the magnetic field disturbance prediction method provided in the above embodiments, and other technical features in this magnetic field disturbance prediction device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0125] This application provides a magnetic field disturbance prediction device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the magnetic field disturbance prediction method in Embodiment 1 above.

[0126] The following is for reference. Figure 7 The diagram illustrates a structural schematic of a magnetic field disturbance prediction device suitable for implementing embodiments of this application. The magnetic field disturbance prediction device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The magnetic field disturbance prediction device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0127] like Figure 7As shown, the magnetic field disturbance prediction device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the magnetic field disturbance prediction device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the magnetic field disturbance prediction device to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows magnetic field disturbance prediction devices with various systems, it should be understood that it is not required to implement or possess all of the systems shown. More or fewer systems may be implemented alternatively.

[0128] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0129] The magnetic field disturbance prediction device provided in this application, employing the magnetic field disturbance prediction method in the above embodiments, can solve the technical problem of low reliability in geomagnetic disturbance prediction. Compared with the prior art, the beneficial effects of the magnetic field disturbance prediction device provided in this application are the same as those of the magnetic field disturbance prediction method provided in the above embodiments, and other technical features in this magnetic field disturbance prediction device are the same as those disclosed in the method of the previous embodiment, and will not be repeated here. It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or combinations thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples. The above descriptions are merely specific embodiments of this application, but the protection scope of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.

[0130] This embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon, which are used to execute the magnetic field disturbance prediction method in Embodiment 1 above. The computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor devices, apparatuses, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable EPROM (Electrical Programmable Read Only Memory) or flash memory, optical fiber, portable compact disk CD-ROM (compact disc read-only memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution device, apparatus, or apparatus. The program code contained on the computer-readable storage medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (Radio Frequency), etc., or any suitable combination thereof. The aforementioned computer-readable storage medium may be included in the magnetic field disturbance prediction device; or it may exist independently, not assembled into the magnetic field disturbance prediction device. The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the magnetic field disturbance prediction device, the magnetic field disturbance prediction device: acquires magnetic field observation data, converts the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determines initial magnetic disturbance data from the standard observation data. The magnetic field observation data includes ground observation data, satellite observation data, observation points corresponding to the ground observation data, and observation points corresponding to the satellite observation data. It also calculates the magnetic field response data generated by each singular point in the pre-constructed current singular point grid at each observation point; based on the initial magnetic disturbance data and each magnetic field response data, it inverts to obtain the equivalent current coefficient of each singular point in the current singular point grid; and based on the equivalent current coefficient of each singular point in the current singular point grid, it predicts the magnetic field disturbance data for any geographical location.

[0131] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a LAN (local area network) or WAN (wide area network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0132] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of the present application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based device that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Modules described in the embodiments of this disclosure may be implemented in software or hardware. The names of modules do not, in some cases, constitute a limitation on the unit itself. The computer-readable storage medium provided in this application embodiment stores computer-readable program instructions for executing the above-described magnetic field disturbance prediction method, aiming to solve the technical problem of low reliability in geomagnetic disturbance prediction. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application embodiment are the same as the beneficial effects of the magnetic field disturbance prediction method provided in the above embodiments, and will not be repeated here.

[0133] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the magnetic field disturbance prediction method described above. The computer program product provided in this application aims to solve the technical problem of low reliability in geomagnetic disturbance prediction. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the magnetic field disturbance prediction method provided in the above embodiments, and will not be repeated here.

[0134] The above are merely preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structural or procedural transformations made using the description and drawings of the present application, or direct or indirect applications in other related technical fields, are similarly included within the patent processing scope of the present application.

Claims

1. A method for predicting magnetic field disturbances, characterized in that, The method includes: Acquire magnetic field observation data, convert the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determine initial magnetic disturbance data from the standard observation data. The magnetic field observation data includes ground observation data, satellite observation data, observation points corresponding to the ground observation data, and observation points corresponding to the satellite observation data. By using a pre-constructed current singularity grid, the magnetic field response data generated by each singularity in the current singularity grid at each observation point is calculated; Based on the initial magnetic disturbance data and the magnetic field response data, the equivalent current coefficient of each singular point in the current singularity grid is obtained by inversion. Based on the equivalent current coefficient of each singular point in the current singularity grid, predict the magnetic field disturbance data at any geographical location; The standard observation data includes standard ground observation data, standard satellite observation data, and standard observation coordinates; the steps of acquiring magnetic field observation data, converting the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determining initial magnetic disturbance data from the standard observation data include: The observation points corresponding to the ground observation data and the observation points corresponding to the satellite observation data are determined from the magnetic field observation data, and the coordinates of each observation point are converted into transition coordinates under a preset transition coordinate system. The ground observation data is converted into standard ground observation data in a preset geomagnetic coordinate system, the satellite observation data is converted into standard satellite observation data in a preset geomagnetic coordinate system, and each transition coordinate is converted into standard observation coordinates in a preset geomagnetic coordinate system. The preset Earth background magnetic field data is removed from the standard ground observation data to obtain the initial ground disturbance data, and the preset Earth background magnetic field data is removed from the standard satellite observation data to obtain the initial satellite disturbance data; The initial ground disturbance data, the initial satellite disturbance data, and each of the standard observation coordinates are used together as the initial magnetic disturbance data; The step of inverting the equivalent current coefficient of each singular point in the current singularity grid based on the initial magnetic disturbance data and each of the magnetic field response data includes: The magnetic field combination matrix is ​​determined based on the magnetic field response data, and the initial magnetic disturbance data is corrected according to the observation error weight of each observation point to obtain weighted magnetic disturbance data. Based on the magnetic field combination matrix, weighted magnetic disturbance data, preset regularization matrix, and preset regularization coefficient, a final optimization problem is constructed that corresponds to the equivalent current coefficient of each singular point in the current singularity grid. The equivalent current coefficient of each singular point is obtained by solving the final optimization problem according to the preset matrix decomposition method.

2. The magnetic field disturbance prediction method as described in claim 1, characterized in that, The magnetic field response data includes electric field, electric potential, plasma drift velocity, and magnetic disturbance response; the step of calculating the magnetic field response data generated by each singularity in the pre-constructed current singularity grid at each observation point includes: Based on the preset number of nodes and the preset grid radius, a current singularity grid is constructed in the preset observation area, and the initial current coefficient of each singularity in the current singularity grid is configured as a preset unit current coefficient. For each singular point in the current singularity grid, based on the preset unit current coefficient of the singular point and the relative distance between the singular point and each observation point, the electric field, electric potential, plasma drift velocity and magnetic disturbance response generated by the singular point at each observation point are calculated.

3. The magnetic field disturbance prediction method as described in claim 1, characterized in that, The steps of determining the magnetic field combination matrix based on the magnetic field response data and correcting the magnetic field combination matrix according to the observation error weight of each observation point to obtain the magnetic field combination matrix include: An electric field response matrix is ​​constructed based on the electric field generated at each observation point by each singularity determined from the magnetic field response data; an electric potential response matrix is ​​constructed based on the electric potential generated at each observation point by each singularity determined from the magnetic field response data; and a velocity response matrix is ​​constructed based on the plasma drift velocity generated at each observation point by each singularity determined from the magnetic field response data. Based on the magnetic disturbance response generated by each singularity determined from the magnetic field response data at each observation point belonging to the ground, a ground response matrix is ​​constructed, and based on the magnetic disturbance response generated by each singularity determined from the magnetic field response data at each observation point belonging to the satellite, a satellite response matrix is ​​constructed. The combined electric field response matrix, electric potential response matrix, velocity response matrix, ground response matrix, and satellite response matrix are combined to obtain the magnetic field combined response matrix.

4. The magnetic field disturbance prediction method as described in claim 1, characterized in that, The step of calculating the magnetic field disturbance data at any geographical location based on the equivalent current coefficient of each singular point in the current singularity grid includes: For any given geographic location, determine the relative distance between that geographic location and each of the singular points; For each singularity, based on the relative distance between the singularity and the geographical location, and the equivalent current coefficient of the singularity, calculate the single magnetic field disturbance generated by the singularity at the geographical location; The magnetic field disturbance data of the geographical location is obtained by summing up the individual magnetic field disturbances generated by each singular point at the geographical location.

5. The magnetic field disturbance prediction method as described in claim 1, characterized in that, The method further includes: The target magnetic disturbance data of at least one target observation point is determined from the initial magnetic disturbance data, and the magnetic disturbance prediction data of each target observation point is determined based on the equivalent current coefficients corresponding to each singular point in the current singular point grid. Based on the magnetic disturbance prediction data and target magnetic disturbance data corresponding to each target observation point, the prediction error of each target observation point is calculated, so as to determine the credibility of predicting the magnetic field disturbance data of any geographical location through the equivalent current coefficient of each singular point by means of the prediction error. The prediction error and target magnetic disturbance data of each target observation point are projected onto a preset spatial coordinate system, and the magnetic field disturbance data of at least one geographical location are projected onto the preset spatial coordinate system to visualize the magnetic field disturbance data of each geographical location, as well as the target magnetic disturbance data and prediction error of each target observation point.

6. A magnetic field disturbance prediction device, characterized in that, The device includes: The acquisition module is used to acquire magnetic field observation data, convert the magnetic field observation data into standard observation data in a preset geomagnetic coordinate system, and determine initial magnetic disturbance data from the standard observation data. The magnetic field observation data includes ground observation data, satellite observation data, observation points corresponding to the ground observation data, and observation points corresponding to the satellite observation data. The standard observation data includes standard ground observation data, standard satellite observation data, and standard observation coordinates. A construction module is used to calculate the magnetic field response data generated by each singularity in the current singularity grid at each observation point using a pre-constructed current singularity grid. The inversion module is used to invert the equivalent current coefficient of each singular point in the current singularity grid based on the initial magnetic disturbance data and the magnetic field response data. The prediction module is used to predict magnetic field disturbance data at any geographical location based on the equivalent current coefficient of each singular point in the current singularity grid. The acquisition module is further configured to determine the observation points corresponding to the ground observation data and the observation points corresponding to the satellite observation data from the magnetic field observation data, and convert the coordinates of each observation point into transition coordinates under a preset transition coordinate system; convert the ground observation data into standard ground observation data under a preset geomagnetic coordinate system, convert the satellite observation data into standard satellite observation data under a preset geomagnetic coordinate system, and convert each transition coordinate into standard observation coordinates under a preset geomagnetic coordinate system; remove the preset Earth background magnetic field data from the standard ground observation data to obtain initial ground disturbance data, and remove the preset Earth background magnetic field data from the standard satellite observation data to obtain initial satellite disturbance data; and use the initial ground disturbance data, the initial satellite disturbance data, and each of the standard observation coordinates together as the initial magnetic disturbance data; The inversion module is further configured to determine the magnetic field combination matrix based on the magnetic field response data, and to correct the initial magnetic disturbance data to obtain weighted magnetic disturbance data based on the observation error weight of each observation point; based on the magnetic field combination matrix, the weighted magnetic disturbance data, the preset regularization matrix, and the preset regularization coefficient, to construct a final optimization problem corresponding to the equivalent current coefficient of each singular point in the current singular point grid; and to solve the final optimization problem using a preset matrix decomposition method to obtain the equivalent current coefficient of each singular point.

7. A magnetic field disturbance prediction device, characterized in that, The magnetic field disturbance prediction device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the steps of the magnetic field disturbance prediction method according to any one of claims 1 to 5.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and the computer-readable storage medium stores a program for implementing the magnetic field disturbance prediction method. The program for implementing the magnetic field disturbance prediction method is executed by a processor to implement the steps of the magnetic field disturbance prediction method as described in any one of claims 1 to 5.

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