Space environment element customized profile evaluation method for spacecraft orbit

Abstract

This invention specifically relates to a method for customized profile assessment of space environment elements for spacecraft orbits. The method includes: acquiring a user's orbital requirement parameter set; acquiring a current global space environment forecast / reanalysis data cube in response to the user's orbital requirement parameter set; performing four-dimensional spatiotemporal interpolation on the current global space environment forecast / reanalysis data cube based on the user's orbital requirement parameter set to obtain time-varying orbital environment element profile data, which includes timestamps, latitude and longitude, altitude, and element field values ​​for each space environment element; and substituting the time-varying orbital environment element profile data into a comparison calculation module to obtain forecast accuracy assessment indicators, potential risk level assessment results, and impact index calculation results. This transforms macroscopic space weather numerical forecast products into customized and quantitative decision support information directly serving spacecraft design, enhancing the application value of forecast products.

Description

Technical Field

[0001] This invention relates to the field of spacecraft space environment forecasting technology, specifically to a customized profile assessment method for space environment elements in spacecraft orbits. Background Technology

[0002] With the continuous expansion and deepening of human space activities, including manned space missions, large-scale satellite constellation deployments, and frequent on-orbit services and rendezvous and docking, space missions are increasingly reliant on space environment information. High-timeliness and high-precision space environment situational awareness and forecasting have become key technological supports for ensuring spacecraft on-orbit safety, optimizing mission planning, and extending satellite lifespan.

[0003] Spacecraft designers, orbit engineers, and mission planners not only need to acquire global-scale space environment status data, but also urgently need to obtain refined forecasts of space environment elements (such as radiation dose rate, plasma density, surface charging potential, atomic oxygen flux, etc.) along the orbital path for the specific orbits involved in their missions (such as low Earth orbit, medium Earth orbit, geostationary orbit, deep space transfer orbits such as lunar transfer orbits, etc.). They also need to be able to compare and analyze environmental data with historical data of similar orbits or similar space weather events, so as to provide quantitative basis for spacecraft protection design, on-orbit operation avoidance, and life assessment.

[0004] However, existing space environment numerical prediction systems cannot meet the above requirements.

[0005] Accordingly, there is a need in the field for a new customized profile assessment scheme for space environment elements in spacecraft orbits to address the above problems. Summary of the Invention

[0006] Based on this, the present invention proposes a customized profile assessment method for space environment elements in spacecraft orbits. This method solves the technical problem that existing space environment numerical prediction systems can only provide current and short-term forecasts of global or multi-regional space environment parameters based on physical models or data assimilation methods, and cannot provide customized requirements. The method extracts environmental element profiles along the orbital path within global forecast / reanalysis data, and evaluates forecast accuracy, assesses potential risk levels, and calculates impact indices based on profile analysis.

[0007] According to a first aspect of the present invention, a method for customized profile assessment of space environment elements for spacecraft orbits is provided, comprising:

[0008] Obtain the user's orbital requirement parameter set, wherein the orbital requirement parameter set includes at least spacecraft orbital ephemeris data, and the data format is a standardized time-position sequence;

[0009] In response to the user's orbital requirement parameter set, acquire the current global space environment forecast / reanalysis data cube;

[0010] Based on the user's orbital requirement parameter set, four-dimensional spatiotemporal interpolation is performed on the current global space environment forecast / reanalysis data cube to obtain orbital environment element profile data that changes over time. The profile data includes timestamps, latitude and longitude, altitude, and element field values ​​of each space environment element.

[0011] By substituting the time-varying orbital environment profile data into the comparison and calculation module, the forecast accuracy assessment index, potential risk level assessment results, and impact index calculation results are obtained.

[0012] According to an embodiment of the present invention, the step of performing four-dimensional spatiotemporal interpolation on the current global space environment forecast / reanalysis data cube based on the user's orbital demand parameter set to obtain time-varying orbital environment element profile data includes:

[0013] Based on the user's orbital requirement parameter set, determine the orbital type, which includes at least one of the following: low Earth orbit, medium Earth orbit, geostationary orbit, and lunar orbit.

[0014] Adaptively adjust the spatiotemporal interpolation resolution based on the orbit type;

[0015] By combining Kriging interpolation with neural network fitting, four-dimensional spatiotemporal interpolation is performed along the orbit ephemeris path in the user's orbital requirement parameter set to obtain the interpolation result.

[0016] The interpolation accuracy is verified, and based on the verification results, the following can be selectively re-executed: "Using Kriging interpolation combined with neural network fitting, four-dimensional spatiotemporal interpolation is performed along the orbital ephemeris path in the user's orbital requirement parameter set to obtain the interpolation result", or, orbital environment element profile data that changes over time can be generated.

[0017] According to an embodiment of the present invention, the adaptive adjustment of spatiotemporal interpolation resolution based on orbit type includes:

[0018] Based on the orbit type, determine the spatiotemporal parameter characteristics corresponding to the orbit type;

[0019] Based on the spatiotemporal parameter characteristics corresponding to the orbit type, determine the trade-off index between the accuracy and efficiency of temporal resolution and spatial resolution;

[0020] Furthermore, by monitoring orbital parameters in real time, the resolution can be selectively adjusted. The real-time monitoring of orbital parameters includes at least speed, altitude, and maneuver status.

[0021] To achieve adaptive adjustment of spatiotemporal interpolation resolution.

[0022] According to an embodiment of the present invention, the step of performing four-dimensional spatiotemporal interpolation along the orbital ephemeris path in the user's orbital requirement parameter set using Kriging interpolation combined with neural network fitting to obtain the interpolation result includes:

[0023] Four-dimensional spatiotemporal interpolation includes time-dimension interpolation and spatial-dimension interpolation;

[0024] Time-dimension interpolation includes:

[0025] The time series is nonlinearly fitted using an LSTM neural network to process data on abrupt changes caused by orbital maneuvers.

[0026] Spatial dimension interpolation includes:

[0027] Spatial interpolation grids are generated based on Delaunay triangulation, and then spatial distribution anomalies are corrected by combining them with geomagnetic field models.

[0028] The interpolation results of the time dimension and the interpolation results of the spatial dimension are linked through the star ephemeris path to form a four-dimensional dataset containing timestamps, latitude and longitude, altitude and field values ​​of various elements, so as to obtain the interpolation results.

[0029] According to an embodiment of the present invention, the step of substituting time-varying orbital environmental element profile data into the comparison calculation module to obtain forecast accuracy assessment indicators, potential risk level assessment results, and impact index calculation results includes:

[0030] Based on the time-varying orbital environment profile data, obtain in-situ satellite observation data that matches the user's orbital requirement parameter set;

[0031] Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set, a forecast accuracy evaluation index is obtained.

[0032] Based on the time-varying orbital environmental element profile data, historical event slices that match the profile data are selected from the preset historical space weather event database;

[0033] Based on the time-varying profile data of orbital environmental elements and the selected historical event slices, the potential risk level assessment results are obtained.

[0034] The impact index is calculated based on the time-varying profile data of orbital environmental elements, the in-situ satellite observation data that matches the user's orbital requirement parameter set, and the selected historical event slices.

[0035] According to an embodiment of the present invention, obtaining in-situ satellite observation data that matches the user's orbital requirement parameter set based on time-varying orbital environment feature profile data includes:

[0036] Obtain actual observation data;

[0037] The dynamic time warping algorithm is used to align the time-varying orbital environment profile data with the time series of actual observation data, and then filter out the time-aligned satellite in-situ observation data.

[0038] By performing spatial matching on time-aligned satellite in-situ observation data through affine transformation, satellite in-situ observation data that is spatiotemporally aligned with the orbital environment element profile data that changes over time is obtained, which is satellite in-situ observation data that matches the user's orbital requirement parameter set.

[0039] According to an embodiment of the present invention, the process of obtaining forecast accuracy evaluation indicators based on time-varying orbital environment feature profile data and in-situ satellite observation data matched with the user's orbital requirement parameter set includes:

[0040] Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set, a multi-dimensional accuracy index set is obtained. The multi-dimensional types include at least the time dimension, spatial dimension, position dimension, radiation dose dimension, and plasma density dimension.

[0041] Based on the multi-dimensional accuracy index set, forecast accuracy evaluation indicators are obtained.

[0042] According to an embodiment of the present invention, selecting historical event slices that match the profile data from a preset historical space weather event database based on time-varying orbital environmental element profile data includes:

[0043] Based on the time-varying profile data of orbital environmental elements, multi-dimensional screening conditions are determined, which include at least time range, event type, and impact level.

[0044] Based on multi-dimensional filtering criteria, historical event slices that match the multi-dimensional filtering criteria are selected from the preset historical spatial weather event database.

[0045] According to an embodiment of the present invention, obtaining the potential risk level assessment result based on the time-varying orbital environmental element profile data and selected historical event slices includes:

[0046] Based on the time-varying orbital environment profile data and selected historical event slices, multi-dimensional difference indicators are obtained through pixel-level comparison. Among them, the types of multi-dimensional indicators include at least flare intensity, geomagnetic storm index, and duration.

[0047] By inputting multi-dimensional difference indicators into the Mamdani fuzzy inference system, the multi-dimensional difference indicators are mapped to risk levels through triangular membership functions, thereby obtaining the potential risk level, i.e., the potential risk level assessment result.

[0048] According to an embodiment of the present invention, the step of obtaining the impact index calculation result based on time-varying orbital environment element profile data, satellite in-situ observation data matching the user's orbital demand parameter set, and selected historical event slices includes:

[0049] The cumulative radiation dose is obtained based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set.

[0050] The single-event flip rate is obtained by using satellite in-situ observation data that matches the user's orbital requirement parameter set and selecting historical event slices.

[0051] The surface charging potential distribution is obtained based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set.

[0052] To obtain the engineering impact index.

[0053] As can be seen from the above technical solution, the customized profile evaluation method for space environment elements in spacecraft orbits provided by the present invention has the following beneficial effects:

[0054] This invention uses the acquired user orbital requirement parameter set to perform four-dimensional spatiotemporal interpolation on the current global space environment forecast / reanalysis data cube, obtaining time-varying orbital environment element profile data. This achieves adaptive spatiotemporal resolution adjustment for the differentiated requirements of low Earth orbit / lunar orbits, improves the nonlinear fitting ability of the interpolation algorithm for orbital maneuvering abrupt changes, and ensures high-precision generation of orbital slices. Furthermore, by substituting the time-varying orbital environment element profile data into the comparison calculation module, forecast accuracy evaluation indicators, potential risk level assessment results, and impact index calculation results are obtained. This allows for the evaluation of forecast accuracy by overlaying and comparing forecast slices with actual observations along the orbit. This technology improves the quantitative reliability of multi-dimensional accuracy assessment indicators, enables comparison of different event scenarios (i.e., comparing the current forecast orbit slice with orbit slices during typical historical space weather events to assess potential risk levels), ensures the accurate output of potential risk levels in the fuzzy logic risk assessment model, and allows the calculation of engineering impact indices such as cumulative radiation dose and single-event rollover rate through orbit slices. This meets the quantitative needs of impact assessment throughout the entire aerospace engineering process and transforms macroscopic space weather numerical forecast products into customized and quantitative decision support information that directly serves spacecraft design, on-orbit management, and mission planning, greatly enhancing the application value of forecast products. Attached Figure Description

[0055] The disclosure of this invention will become more readily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of this invention. Furthermore, similar numbers in the drawings are used to denote similar components, wherein:

[0056] Figure 1 This is a schematic diagram of the main process of a customized profile assessment method for space environment elements in spacecraft orbits according to an embodiment of the present invention.

[0057] Figure 2 This is a schematic diagram of the four-dimensional spatiotemporal interpolation process of a customized profile evaluation method for space environment elements of spacecraft orbits according to an embodiment of the present invention.

[0058] Figure 3 This is a flowchart illustrating the comparison calculation module of a customized profile evaluation method for space environment elements in spacecraft orbits according to an embodiment of the present invention. Detailed Implementation

[0059] Some embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0060] In the description of this invention, "module" and "processor" can include hardware, software, or a combination of both. A module can include hardware circuitry, various suitable sensors, communication ports, memory, and may also include software components, such as program code, or a combination of software and hardware. A processor can be a central processing unit, microprocessor, image processor, digital signal processor, or any other suitable processor. The processor has data and / or signal processing capabilities. The processor can be implemented in software, in hardware, or a combination of both. Non-transitory computer-readable storage media includes any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, etc. The term "A and / or B" means all possible combinations of A and B, such as only A, only B, or A and B. The terms "at least one A or B" or "at least one of A and B" have a similar meaning to "A and / or B" and can include only A, only B, or A and B. The singular terms "a" or "this" can also include plural forms.

[0061] like Figures 1 to 3 As shown in this embodiment, a customized profile assessment method for space environment elements in spacecraft orbits is disclosed. The method includes the following steps:

[0062] Step S1: Obtain the user's orbital requirement parameter set, wherein the orbital requirement parameter set includes at least spacecraft orbital ephemeris data, and the data format is a standardized time-position sequence;

[0063] Specifically, obtaining the user's track requirement parameter set includes:

[0064] Acquire spacecraft orbital ephemeris data input by the user or uploaded by the user, wherein the data format is a standardized time-position sequence.

[0065] Step S2: In response to the user's orbital requirement parameter set, obtain the current global space environment forecast / reanalysis data cube;

[0066] Specifically, in response to the user's orbital requirement parameter set, the current global space environment forecast / reanalysis data cube includes:

[0067] In response to the user's orbital requirement parameter set, the system automatically retrieves the global space environment forecast / reanalysis data cube from the distributed storage cluster. The data covers the entire link from the Sun to interplanetary space, magnetosphere, ionosphere, and near-Earth space, with a time resolution of ≤1 minute and a spatial resolution of ≤1 km. The data types include at least elements such as radiation dose, plasma density, and surface charging potential.

[0068] Step S3: Based on the user's orbital requirement parameter set, perform four-dimensional spatiotemporal interpolation on the current global space environment forecast / reanalysis data cube to obtain orbital environment element profile data that changes over time. The profile data includes timestamps, latitude and longitude, altitude, and element field values ​​of each space environment element.

[0069] Specifically, the step of performing four-dimensional spatiotemporal interpolation on the current global space environment forecast / reanalysis data cube based on the user's orbital requirement parameter set to obtain time-varying orbital environment element profile data includes:

[0070] Based on the user's orbital requirement parameter set, determine the orbital type, which includes at least one of the following: low Earth orbit, medium Earth orbit, geostationary orbit, and lunar orbit.

[0071] Depending on the orbit type, the spatiotemporal interpolation resolution is adaptively adjusted. Specifically, the time resolution is adjustable from 1 to 60 seconds, and the spatial resolution is adjustable from 100m to 10km.

[0072] By combining Kriging interpolation with neural network fitting, four-dimensional spatiotemporal interpolation is performed along the orbit ephemeris path in the user's orbital requirement parameter set to obtain the interpolation result.

[0073] The interpolation accuracy is verified, and based on the verification results, the following can be selectively re-executed: "Using Kriging interpolation combined with neural network fitting, four-dimensional spatiotemporal interpolation is performed along the orbital ephemeris path in the user's orbital requirement parameter set to obtain the interpolation result", or, orbital environment element profile data that changes over time can be generated.

[0074] Specifically, before determining the track type based on the user's track requirement parameter set, the method further includes:

[0075] By using the onboard attitude determination system, the attitude determination accuracy is determined to meet the preset requirements, which can be less than 0.1m. This achieves physical integration of the star sensor and the payload, reduces the transmission of attitude measurement errors, and achieves post-orbit determination accuracy of <0.1m, ensuring the accuracy of the spatiotemporal coordinate system.

[0076] Among them, the post-orbit determination accuracy of the on-board attitude determination system It can be obtained through the following formula:

[0077] ;

[0078] In the formula, All of these are spatial dimension parameters in the geocentric inertial coordinate system.

[0079] Specifically, the adaptive adjustment of spatiotemporal interpolation resolution based on orbit type includes:

[0080] Based on the orbit type, determine the spatiotemporal parameter characteristics corresponding to the orbit type;

[0081] Based on the spatiotemporal parameter characteristics corresponding to the orbit type, determine the trade-off index between the accuracy and efficiency of temporal resolution and spatial resolution;

[0082] Furthermore, by monitoring orbital parameters in real time, the resolution can be selectively adjusted. The real-time monitoring of orbital parameters includes at least speed, altitude, and maneuver status.

[0083] To achieve adaptive adjustment of spatiotemporal interpolation resolution.

[0084] Specifically, based on the spatiotemporal parameter characteristics corresponding to the orbit type, the trade-off between the accuracy and efficiency of temporal and spatial resolution is determined through the following formula:

[0085] When the orbit type is low orbit:

[0086] ;

[0087] ;

[0088] When the track type is medium track:

[0089] ;

[0090] ;

[0091] When the orbit type is geosynchronous orbit:

[0092] ;

[0093] ;

[0094] When the orbit type is a lunar orbit:

[0095] ;

[0096] ;

[0097] In the formula, , , , , , , , All represent preset weighting coefficients. Represents time resolution. Represents spatial resolution.

[0098] Specifically, the method of using Kriging interpolation combined with neural network fitting to perform four-dimensional spatiotemporal interpolation along the orbital ephemeris path in the user's orbital requirement parameter set yields the following interpolation results:

[0099] Four-dimensional spatiotemporal interpolation includes time-dimension interpolation and spatial-dimension interpolation;

[0100] Time-dimension interpolation includes:

[0101] The LSTM neural network is used to perform nonlinear fitting on time series to process abrupt change data caused by orbital maneuvers (such as orbit change or attitude adjustment), in order to overcome the limitations of traditional linear interpolation in non-stationary time series.

[0102] Spatial dimension interpolation includes:

[0103] A spatial interpolation grid is generated based on the Delaunay triangulation, and then combined with the geomagnetic field model to correct spatial distribution anomalies (which may be data distortion in geomagnetic interference areas) to ensure the continuity and physical rationality of the spatial interpolation.

[0104] The interpolation results of the time dimension and the interpolation results of the spatial dimension are linked through the star ephemeris path to form a four-dimensional dataset containing timestamps, latitude and longitude, altitude and field values ​​of various elements, so as to obtain the interpolation results.

[0105] Specifically, Kriging interpolation is achieved through the following formula:

[0106] ;

[0107] In the formula, Representative point to be estimated The interpolation result at that point, Represents the weighting coefficient, used to describe the first... Known observation points For the estimated point Contribution Representing the Known observation points The measured value at that location Representing the Lagrange multipliers, This represents the number of known observation points.

[0108] The nonlinear fitting of a time series by an LSTM neural network is obtained using the following formula:

[0109] ;

[0110] In the formula, The output of the forget gate controls the hidden state at the previous moment. The information retention ratio (0~1) is used to handle abrupt changes caused by orbital maneuvers. This represents the activation function, used to implement non-linear mapping. The weight matrix representing the forget gate, The input vector represents the hidden state from the previous time step. Input at the current time It is pieced together. The bias term representing the forget gate is used to adjust the threshold of the activation function in order to optimize the convergence speed and accuracy of the network.

[0111] Specifically, the steps for generating a spatial interpolation mesh based on a Delaunay triangulation include:

[0112] Obtain the set of known observation points, point set P = {p1, p2, ..., p...} n} is used to construct spatial interpolation grids;

[0113] Performing a Delaunay triangulation on the point set yields a set of triangles T = {t1, t2, ..., t}. m}, satisfying the "empty circle property", that is, the circumcircle of any triangle contains no other points, and the "maximum and minimum angle property", to avoid the occurrence of narrow and elongated triangles;

[0114] Determine the sides of the triangle, with the side set E = {e1, e2, ..., e}. k} is used to define the topology of the spatial mesh to ensure the continuity and smoothness of the mesh;

[0115] The spatial interpolation grid is then subjected to grid quality inspection to obtain grid quality indicators;

[0116] Determine whether the mesh quality index meets the preset threshold, i.e., the minimum angle θ. min ≥30∘, to ensure the numerical stability of the grid and the accuracy of interpolation.

[0117] In the above embodiments, spatial autocorrelation modeling is used to achieve nonlinear interpolation in the spatial dimension, correcting outliers such as geomagnetic interference zones to ensure the physical rationality of spatial distribution. By capturing the nonlinear characteristics of time series through long short-term memory units, the limitations of traditional linear interpolation in non-stationary time series are overcome, achieving high-precision fitting in the time dimension. Spatial interpolation is achieved through high-quality spatial grids, and spatial distribution outliers are corrected by combining geomagnetic field models, ensuring the continuity and physical rationality of spatial interpolation.

[0118] Specifically, verifying the interpolation accuracy of the interpolation results includes: using cross-validation to ensure that the relative error between the interpolation results and the satellite in-situ observation data is ≤5%, which is used to verify the reliability and generalization ability of the algorithm.

[0119] Specifically, based on the verification results, the process of "performing four-dimensional spatiotemporal interpolation along the orbital ephemeris path in the user's orbital requirement parameter set using Kriging interpolation combined with neural network fitting to obtain the interpolation result" is selectively re-executed, or, the process of generating time-varying orbital environment feature profile data includes:

[0120] If the relative error between the interpolation result and the satellite in-situ observation data is ≤5%, then time-varying orbital environment element profile data is generated, i.e., the data format is NetCDF-4 standard. The profile data includes timestamps, latitude and longitude, altitude and element field values ​​of various space environment elements, so that the profile data can support cross-platform data sharing and integrated analysis.

[0121] Otherwise, the process of “using Kriging interpolation combined with neural network fitting to perform four-dimensional spatiotemporal interpolation along the orbital ephemeris path in the user’s orbital requirement parameter set to obtain the interpolation result” and subsequent steps will be repeated.

[0122] In the above embodiments, by performing four-dimensional spatiotemporal interpolation on the current global space environment forecast / reanalysis data cube, the orbital environment element profile data that changes over time is obtained, realizing the whole process optimization from on-board raw data acquisition to high-precision spatiotemporal reconstruction, which facilitates subsequent comparison and calculation of the profile data.

[0123] Step S4: Substitute the time-varying orbital environment profile data into the comparison calculation module to obtain the forecast accuracy assessment index, potential risk level assessment results, and impact index calculation results.

[0124] Specifically, the step of substituting time-varying orbital environmental element profile data into the comparison and calculation module to obtain forecast accuracy assessment indicators, potential risk level assessment results, and impact index calculation results includes:

[0125] Based on the time-varying orbital environment profile data, obtain in-situ satellite observation data that matches the user's orbital requirement parameter set;

[0126] Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set, a forecast accuracy evaluation index is obtained.

[0127] Based on the time-varying orbital environmental element profile data, historical event slices that match the profile data are selected from the preset historical space weather event database;

[0128] Based on the time-varying profile data of orbital environmental elements and the selected historical event slices, the potential risk level assessment results are obtained.

[0129] The impact index is calculated based on the time-varying profile data of orbital environmental elements, the in-situ satellite observation data that matches the user's orbital requirement parameter set, and the selected historical event slices.

[0130] Specifically, obtaining in-situ satellite observation data that matches the user's orbital requirement parameter set based on time-varying orbital environment element profile data includes:

[0131] Obtain actual observation data;

[0132] The dynamic time warping algorithm is used to align the time-varying orbital environment profile data with the time series of actual observation data, and then filter out the time-aligned satellite in-situ observation data.

[0133] By performing spatial matching on time-aligned satellite in-situ observation data through affine transformation, satellite in-situ observation data that is spatiotemporally aligned with the orbital environment element profile data that changes over time is obtained, which is satellite in-situ observation data that matches the user's orbital requirement parameter set.

[0134] Specifically, the process of aligning the time-varying orbital environment feature profile data with the time series of actual observation data using a dynamic time warping algorithm, and then filtering out the time-aligned in-situ satellite observation data includes:

[0135] The optimal matching path between the time series of orbital environment feature profile data and actual observation data that change over time is calculated by dynamic programming. Step size constraints of ±1 second accuracy and slope limits are set to avoid overstretching, while nonlinear time stretching is allowed to solve the time series offset problem caused by orbital maneuvers.

[0136] And computational efficiency is optimized using the Sakoe-Chiba band;

[0137] Furthermore, it also includes: removing impulse noise by combining median filtering and unifying the units through Z-score standardization to achieve outlier handling.

[0138] Specifically, by performing spatial matching on time-aligned satellite in-situ observation data through affine transformation, the resulting satellite in-situ observation data, spatiotemporally aligned with the time-varying orbital environment feature profile data, includes:

[0139] By fitting the transformation matrix using the least squares method, the rotation, translation, and scaling of latitude and longitude coordinates are achieved to ensure a spatial accuracy of ±100m.

[0140] Key points of the orbit can be selected, such as nadir points or the center of geomagnetic anomalies, as landmark control points. Mismatched points are eliminated using the RANSAC algorithm to improve matching robustness.

[0141] Specifically, the forecast accuracy evaluation indicators obtained based on time-varying orbital environment element profile data and in-situ satellite observation data matched with the user's orbital requirement parameter set include:

[0142] Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set, a multi-dimensional accuracy index set is obtained. The multi-dimensional types include at least the time dimension, spatial dimension, position dimension, radiation dose dimension, and plasma density dimension.

[0143] Based on the multi-dimensional accuracy index set, forecast accuracy evaluation indicators are obtained.

[0144] Specifically, the precision indices for the time dimension include:

[0145] Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital demand parameter set, the absolute difference between the predicted event occurrence time of the time-varying orbital environment profile data and the measured time of the satellite in-situ observation data that matches the user's orbital demand parameter set is calculated to obtain the lead deviation, which is required to be ≤5 minutes.

[0146] The duration error is assessed using a relative error formula based on time-varying orbital environment profile data and in-situ satellite observation data matched with the user's orbital requirement parameter set. The duration error is required to be ≤10%.

[0147] The precision indices for spatial dimensions include:

[0148] The similarity of element distribution is comprehensively evaluated by considering three factors: brightness, contrast, and structure. The SSIM structural similarity index is obtained using the following formula, requiring a similarity of ≥85%:

[0149] ;

[0150] In the formula, Representative dataset and The SSIM structural similarity index, , Representative dataset and The average value is used to reflect brightness information. , Representative dataset and The standard deviation is used to reflect the contrast / dispersion. Representative dataset and The covariance is used to reflect structural correlation. , These all represent stability constants, used to avoid numerical instability caused by a denominator of zero.

[0151] The precision index for the location dimension includes:

[0152] Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set, calculate the Euclidean distance between the predicted center of the time-varying orbital environment profile data and the measured event center of the satellite in-situ observation data that matches the user's orbital requirement parameter set, and obtain the position deviation, which is required to be ≤1km.

[0153] The accuracy index of radiation dose includes:

[0154] Based on time-varying orbital environment profile data and in-situ satellite observation data matching the user's orbital requirement parameter set, absolute error control is used to obtain the radiation dose error, which is required to be ≤8%.

[0155] Among them, the accuracy of radiation dose The index is obtained using the following formula:

[0156] ;

[0157] In the formula, Radiation dose representing the time-varying profile data of orbital environmental elements. The radiation dose represented by satellite in-situ observation data that matches the user's orbital requirement parameter set;

[0158] Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set, absolute error control is used to obtain the plasma density error, which is required to be ≤10%.

[0159] Specifically, based on the multi-dimensional accuracy index set, the forecast accuracy evaluation indicators include:

[0160] The forecast accuracy evaluation index is obtained by determining the proportion of each accuracy index in the multi-dimensional accuracy index set that meets the preset threshold requirements.

[0161] Specifically, the method further includes: feeding back the forecast accuracy assessment index to the forecast center.

[0162] Specifically, before selecting historical event slices matching the profile data from a preset historical space weather event database based on time-varying orbital environmental element profile data, the method further includes:

[0163] Set the range of historical time values;

[0164] Based on the historical time range, obtain the event data corresponding to all historical spatial weather events within the historical time range;

[0165] The event data includes at least metadata, namely event ID, time range, spatial range, and event type, specifically including flare / geostorm and impact level;

[0166] Spatiotemporal data, namely orbital slice data stored at a resolution of 1 minute / 1km, is distributed and stored via IPFS to ensure that the data is immutable;

[0167] Feature labels are event features that are automatically extracted through natural language processing.

[0168] Specifically, the step of selecting historical event slices that match the profile data from a pre-defined historical space weather event database based on time-varying orbital environmental element profile data includes:

[0169] Based on the time-varying profile data of orbital environmental elements, multi-dimensional screening conditions are determined, which include at least time range, event type, and impact level.

[0170] Based on multi-dimensional filtering criteria, historical event slices that match the multi-dimensional filtering criteria are selected from the preset historical spatial weather event database.

[0171] Specifically, the potential risk level assessment results obtained based on the time-varying orbital environmental element profile data and selected historical event slices include:

[0172] Based on the time-varying orbital environment profile data and selected historical event slices, multi-dimensional difference indicators are obtained through pixel-level comparison. Among them, the types of multi-dimensional indicators include at least flare intensity, geomagnetic storm index, and duration.

[0173] By inputting multi-dimensional difference indicators into the Mamdani fuzzy inference system, the multi-dimensional difference indicators are mapped to risk levels through triangular membership functions, thereby obtaining the potential risk level, i.e., the potential risk level assessment result.

[0174] Specifically, by inputting multi-dimensional difference indicators into the Mamdani fuzzy inference system, the multi-dimensional difference indicators are mapped to risk levels through a triangular membership function, resulting in potential risk levels including:

[0175] Input multi-dimensional difference indicators into the Mamdani fuzzy inference system, namely, difference in flare intensity, difference in duration, and difference in geomagnetic storm index;

[0176] The membership degree levels of the difference indicators in each dimension are obtained by using the triangular membership function.

[0177] The potential risk level is obtained based on the degree of membership of the differences in each dimension.

[0178] Specifically, the intensity of the flares is different: ;

[0179] Duration difference: ;

[0180] Geomagnetic storm index difference: .

[0181] Specifically, the calculation results of the impact index, obtained based on the time-varying orbital environment element profile data, satellite in-situ observation data matching the user's orbital requirement parameter set, and selected historical event slices, include:

[0182] The cumulative radiation dose is obtained based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set.

[0183] The single-event flip rate is obtained by using satellite in-situ observation data that matches the user's orbital requirement parameter set and selecting historical event slices.

[0184] The surface charging potential distribution is obtained based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set.

[0185] To obtain the engineering impact index.

[0186] Specifically, the cumulative radiation dose is obtained using the following formula:

[0187] ;

[0188] In the formula, This represents the orbital period calculated from ephemeris data. This represents the shielding layer thickness coefficient, which is calibrated using a material attenuation curve. Represents cumulative radiation dose;

[0189] The single-event flip rate is obtained using the following formula:

[0190] ;

[0191] In the formula, Represents the single-event flip rate. Representing the The flip event of the observed event, where 1 indicates that it occurred and 0 indicates that it did not occur. Represents the total number of observations;

[0192] The surface charging potential distribution is obtained by the following formula:

[0193] ;

[0194] In the formula, Represents the surface charging potential distribution. Represents surface potential. Represents space charge density, This represents the vacuum permittivity, which is 8.85 × 10⁻⁶ in this case. −12 F / m.

[0195] In summary, the customized profile assessment method for space environment elements in spacecraft orbits of the present invention transforms macroscopic space weather numerical forecast products into customized and quantitative decision support information that directly serves spacecraft design, on-orbit management, and mission planning, greatly enhancing the application value of forecast products.

[0196] For execution Figures 1-3 The embodiments of the customized profile assessment method for space environment elements in spacecraft orbits shown are similar in technical principles, the technical problems they solve, and the technical effects they produce. Those skilled in the art can clearly understand this. For the sake of convenience and brevity, the specific working process of the system and related explanations can be found in the embodiments of the customized profile assessment method for space environment elements in spacecraft orbits, and will not be repeated here.

[0197] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0198] Furthermore, the present invention also provides a system that further includes a computer-readable storage medium. In one embodiment of the present invention, the computer-readable storage medium can be configured to store program code for performing the customized profile assessment method for space environment elements oriented towards spacecraft orbits according to the above-described method embodiments. This program code can be loaded and run by a processor to implement the above-described customized profile assessment method for space environment elements oriented towards spacecraft orbits. For ease of explanation, only the parts related to the embodiments of the present invention are shown; for specific technical details not disclosed, please refer to the method section of the embodiments of the present invention. The computer-readable storage medium can be a memory device comprising various electronic devices. Optionally, in the embodiments of the present invention, the computer-readable storage medium is a non-transitory computer-readable storage medium.

[0199] Those skilled in the art will understand that all or part of the processes in the method of the above embodiment of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable storage medium can include any entity or device capable of carrying the computer program code, a medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory, a random access memory, an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the computer-readable storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable storage medium does not include electrical carrier signals and telecommunication signals.

[0200] Furthermore, it should be understood that since the various modules are only provided to illustrate the functional units of the device of the present invention, the physical devices corresponding to these modules may be the processor itself, or a part of the processor's software, hardware, or a combination of software and hardware. Therefore, the number of modules shown in the figures is merely illustrative.

[0201] Those skilled in the art will understand that the various modules in the device can be adaptively split or combined. Such splitting or combining of specific modules will not cause the technical solution to deviate from the principles of the present invention; therefore, the technical solutions after splitting or combining will fall within the protection scope of the present invention.

[0202] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for customized profile assessment of space environment elements for spacecraft orbits, characterized in that, include: Obtain the user's orbital requirement parameter set, wherein the orbital requirement parameter set includes at least spacecraft orbital ephemeris data, and the data format is a standardized time-position sequence; In response to the user's orbital requirement parameter set, acquire the current global space environment forecast / reanalysis data cube; Based on the user's orbital requirement parameter set, four-dimensional spatiotemporal interpolation is performed on the current global space environment forecast / reanalysis data cube to obtain orbital environment element profile data that changes over time. The profile data includes timestamps, latitude and longitude, altitude, and element field values ​​of each space environment element. By substituting the time-varying orbital environment profile data into the comparison and calculation module, the forecast accuracy assessment index, potential risk level assessment results, and impact index calculation results are obtained.

2. The customized profile assessment method for space environment elements in spacecraft orbits according to claim 1, characterized in that, The process of performing four-dimensional spatiotemporal interpolation on the current global space environment forecast / reanalysis data cube based on the user's orbital requirement parameter set to obtain time-varying orbital environment element profile data includes: Based on the user's orbital requirement parameter set, determine the orbital type, which includes at least one of the following: low Earth orbit, medium Earth orbit, geostationary orbit, and lunar orbit. Adaptively adjust the spatiotemporal interpolation resolution based on the orbit type; By combining Kriging interpolation with neural network fitting, four-dimensional spatiotemporal interpolation is performed along the orbit ephemeris path in the user's orbital requirement parameter set to obtain the interpolation result. The interpolation accuracy is verified, and based on the verification results, the following can be selectively re-executed: "Using Kriging interpolation combined with neural network fitting, four-dimensional spatiotemporal interpolation is performed along the orbital ephemeris path in the user's orbital requirement parameter set to obtain the interpolation result", or, orbital environment element profile data that changes over time can be generated.

3. The customized profile assessment method for space environment elements in spacecraft orbits according to claim 2, characterized in that, The adaptive adjustment of spatiotemporal interpolation resolution based on orbit type includes: Based on the orbit type, determine the spatiotemporal parameter characteristics corresponding to the orbit type; Based on the spatiotemporal parameter characteristics corresponding to the orbit type, determine the trade-off index between the accuracy and efficiency of temporal resolution and spatial resolution; Furthermore, by monitoring orbital parameters in real time, the resolution can be selectively adjusted. The real-time monitoring of orbital parameters includes at least speed, altitude, and maneuver status. To achieve adaptive adjustment of spatiotemporal interpolation resolution.

4. The customized profile assessment method for space environment elements in spacecraft orbits according to claim 3, characterized in that, The method of using Kriging interpolation combined with neural network fitting to perform four-dimensional spatiotemporal interpolation along the orbital ephemeris path in the user's orbital requirement parameter set yields the following interpolation results: Four-dimensional spatiotemporal interpolation includes time-dimension interpolation and spatial-dimension interpolation; Time-dimension interpolation includes: The time series is nonlinearly fitted using an LSTM neural network to process data on abrupt changes caused by orbital maneuvers. Spatial dimension interpolation includes: Spatial interpolation grids are generated based on Delaunay triangulation, and then spatial distribution anomalies are corrected by combining them with geomagnetic field models. The interpolation results of the time dimension and the interpolation results of the spatial dimension are linked through the star ephemeris path to form a four-dimensional dataset containing timestamps, latitude and longitude, altitude and field values ​​of various elements, so as to obtain the interpolation results.

5. The customized profile assessment method for space environment elements in spacecraft orbits according to claim 4, characterized in that, The process of substituting time-varying orbital environmental element profile data into the comparison and calculation module to obtain forecast accuracy assessment indicators, potential risk level assessment results, and impact index calculation results includes: Based on the time-varying orbital environment profile data, obtain in-situ satellite observation data that matches the user's orbital requirement parameter set; Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set, a forecast accuracy evaluation index is obtained. Based on the time-varying orbital environmental element profile data, historical event slices that match the profile data are selected from the preset historical space weather event database; Based on the time-varying profile data of orbital environmental elements and the selected historical event slices, the potential risk level assessment results are obtained. The impact index is calculated based on the time-varying profile data of orbital environmental elements, the in-situ satellite observation data that matches the user's orbital requirement parameter set, and the selected historical event slices.

6. The customized profile assessment method for space environment elements in spacecraft orbits according to claim 5, characterized in that, The acquisition of in-situ satellite observation data matching the user's orbital requirement parameter set based on time-varying orbital environment feature profile data includes: Obtain actual observation data; The dynamic time warping algorithm is used to align the time-varying orbital environment profile data with the time series of actual observation data, and then filter out the time-aligned satellite in-situ observation data. By performing spatial matching on time-aligned satellite in-situ observation data through affine transformation, satellite in-situ observation data that is spatiotemporally aligned with the orbital environment element profile data that changes over time is obtained, which is satellite in-situ observation data that matches the user's orbital requirement parameter set.

7. The method for customized profile assessment of space environment elements for spacecraft orbits according to claim 6, characterized in that, The forecast accuracy evaluation indicators obtained based on time-varying orbital environment profile data and in-situ satellite observation data matched with the user's orbital requirement parameter set include: Based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set, a multi-dimensional accuracy index set is obtained. The multi-dimensional types include at least the time dimension, spatial dimension, position dimension, radiation dose dimension, and plasma density dimension. Based on the multi-dimensional accuracy index set, forecast accuracy evaluation indicators are obtained.

8. The method for customized profile assessment of space environment elements for spacecraft orbits according to claim 7, characterized in that, The step of selecting historical event slices that match the profile data from a pre-set historical space weather event database based on time-varying orbital environmental element profile data includes: Based on the time-varying profile data of orbital environmental elements, multi-dimensional screening conditions are determined, which include at least time range, event type, and impact level. Based on multi-dimensional filtering criteria, historical event slices that match the multi-dimensional filtering criteria are selected from the preset historical spatial weather event database.

9. The method for customized profile assessment of space environment elements for spacecraft orbits according to claim 8, characterized in that, The potential risk level assessment results obtained based on the time-varying orbital environmental element profile data and selected historical event slices include: Based on the time-varying orbital environment profile data and selected historical event slices, multi-dimensional difference indicators are obtained through pixel-level comparison. Among them, the types of multi-dimensional indicators include at least flare intensity, geomagnetic storm index, and duration. By inputting multi-dimensional difference indicators into the Mamdani fuzzy inference system, the multi-dimensional difference indicators are mapped to risk levels through triangular membership functions, thereby obtaining the potential risk level, i.e., the potential risk level assessment result.

10. The method for customized profile assessment of space environment elements for spacecraft orbits according to claim 9, characterized in that, The impact index calculation results, obtained based on time-varying orbital environment profile data, satellite in-situ observation data matching the user's orbital requirement parameter set, and selected historical event slices, include: The cumulative radiation dose is obtained based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set. The single-event flip rate is obtained by using satellite in-situ observation data that matches the user's orbital requirement parameter set and selecting historical event slices. The surface charging potential distribution is obtained based on the time-varying orbital environment profile data and the satellite in-situ observation data that matches the user's orbital requirement parameter set. To obtain the engineering impact index.

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