Calculation Method and System for the Relationship between the Sun and Planets

Abstract

The present invention relates to the field of astronomical calculation technology, specifically a method and system for calculating the relationship between the sun and planets, which includes the following steps: collecting solar activity observation data, analyzing the time series, intensity, and frequency information of the data, extracting the characteristics of solar activity in different cycles, summarizing the change trends, and calculating the correlation between the cycle and the activity intensity to obtain solar activity correlation data. In the present invention, by extracting the characteristics of solar activity in different cycles and combining with the magnetohydrodynamics method, the velocity and density parameters of electromagnetic radiation and particle flow are deeply analyzed, the perturbation characteristics of the planetary magnetic field are scientifically evaluated, the interference of solar activity on the planetary orbit deviation is clarified by analyzing the gravitational perturbation, the dynamic changes of the orbital velocity and orbital intersection are traced, the correlation between magnetic storms and orbital deviation is incorporated into the prediction analysis, and the analysis of the orbital change trend is further improved by combining the rotation period and gravitational perturbation data, providing a more refined reference basis for predicting planetary motion and astronomical phenomena.

Description

Technical Field

[0001] The present invention relates to the technical field of astronomical calculations, and particularly to a method and system for calculating the relationship between the sun and planets. Background Art

[0002] The field of astronomical calculations studies the precise calculation and modeling of celestial bodies in the universe, including predicting the trajectories of celestial bodies such as stars, planets, and comets, determining the distances and relative positions between celestial bodies, and simulating astronomical phenomena such as solar eclipses, lunar eclipses, and planetary retrograde motion. It is widely applied in astronomy, space science, and astronomical navigation, etc., using high-precision mathematical models and calculation methods to explore the mysteries of the universe. Through astronomical calculations, scientists can accurately predict astronomical phenomena or navigate and position in the universe.

[0003] Among them, the method for calculating the relationship between the sun and planets involves the mutual relationship and dynamics between the sun and planets, mainly used for calculating the orbits, positions, gravitational interactions, etc. between the sun and each planet, and can predict astronomical phenomena such as planetary motion and solar and lunar eclipses based on the calculation results. In addition, it has important applications in aspects such as spacecraft navigation, planetary exploration missions, and stellar research. Through these calculations, scientists can better understand the operating laws of the solar system and the complex connections between celestial bodies.

[0004] In the analysis of the relationship between the sun and planets in the prior art, trajectory prediction is usually based on the time series of a single data source, making it difficult to accurately extract the characteristics of solar activity in different periods. The lack of in-depth analysis of parameters such as electromagnetic radiation and particle flow makes it difficult to comprehensively evaluate the impact of solar activity on the planetary magnetic field, and thus it is impossible to predict the correlation between magnetic storms and planetary orbit offsets. When analyzing the trend of planetary orbit offsets, the existing methods lack a fine evaluation of gravitational perturbations, ignoring the complex correlation between solar activity and orbital parameter changes, resulting in the inability to effectively utilize the rotation period and gravitational perturbation data during orbit deviation adjustment, leading to insufficient accuracy in orbit prediction and adjustment, hindering the comprehensive calculation and modeling of the relationship between the sun and planets, reducing the accuracy of planetary motion prediction, and bringing potential risks to planetary exploration and spacecraft navigation. Summary of the Invention

[0005] The purpose of the present invention is to solve the deficiencies existing in the prior art, and to propose a method and system for calculating the relationship between the sun and planets.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: A method for calculating the relationship between the sun and planets, including the following steps:

[0007] S1: Collect solar activity observation data, analyze the time series, intensity, and frequency information of the data, extract the characteristics of solar activity in different periods, summarize the change trends, and calculate the correlation between the period and the activity intensity to obtain solar activity correlation data;

[0008] S2: Using the solar activity correlation data, combining with the magnetohydrodynamics method, calculate the velocity and density data of electromagnetic radiation and particle flow in solar activity, evaluate the perturbation of the planetary magnetic field, analyze the impact on planetary magnetic storms, and obtain the characteristics of planetary magnetic storm impacts;

[0009] S3: Collect planetary orbit data, combine with the characteristics of planetary magnetic storm impacts, analyze the offset trend of the planetary orbit due to gravitational perturbation in solar activity, compare the dynamic changes of orbital velocity and orbital intersections, track the correlation between magnetic storms and orbital offsets, and obtain the dynamic prediction analysis results of orbital offsets;

[0010] S4: Based on the dynamic prediction analysis results of orbital offsets, combine with the rotation period and gravitational perturbation data, calculate the changes in orbital offset angle and orbital intersections, adjust the planetary orbit parameters, analyze the orbital change trend, calculate the new orbit parameters, and obtain the results of planetary orbit deviation adjustment.

[0011] As a further solution of the present invention, the steps for obtaining the solar activity correlation data are as follows:

[0012] S101: According to the solar activity observation data, obtain the time series, intensity, and frequency information of solar activity, smooth the data, and eliminate short-term fluctuations to retain long-term trend characteristics;

[0013] S102: Extract the period intervals, sort the activity intensities within each period by time, and calculate the correlation degree between the period and the intensity.

[0014]

[0015] where, R i is the correlation degree value of the i-th period interval, γ j is the smoothing coefficient of the activity intensity, x ij is the intensity value of the j-th observation point within the i-th period interval, is the average intensity value of the target period interval, s i is the intensity standard deviation of the target period, and n is the number of observation points within the period interval;

[0016] S103: Make a comprehensive judgment on the correlation degree value R i to screen out the period intervals with significant correlations.

[0017]

[0018] where, S i represents the significance characteristic value of the i-th period interval, x ij is the activity intensity value of the j-th observation point within the i-th period interval is the average intensity value of the target period interval, s i is the standard deviation of the target period interval, and n is the number of observation points in the period interval;

[0019] S104: Set the significance threshold T, and according to the comparison between the significance feature value S i and the threshold, screen the significant period intervals. If |S i |≥T, it means that the activity intensity feature of the i-th period interval is significant and is a significant period;

[0020] S105: Combine the correlation degree value R i and the significance feature value S i , and perform data integration,

[0021]

[0022] where A i represents the comprehensive correlation data value of the i-th period interval, R i is the correlation degree value of the i-th period interval, S i is the significance feature value of the i-th period interval, m is the number of significant period intervals, and the solar activity correlation data is obtained.

[0023] As a further solution of the present invention, the calculation steps of the velocity and density data of the electromagnetic radiation and particle flow are as follows:

[0024] S201: Use the solar activity correlation data to extract the original electromagnetic radiation velocity data v i and the original particle flow density data d i ;

[0025] S202: Perform outlier rejection on the original electromagnetic radiation velocity data v i . Define the outlier as the data point exceeding three times the standard deviation, and calculate the electromagnetic radiation velocity:

[0026]

[0027]

[0028] where V is the electromagnetic radiation velocity value, x i is the timestamp of the data point, μ and σ are the average value and standard deviation of the timestamp respectively, which are used to adjust the weight and highlight the influence of recent data, and n is the total number of data points.

[0029] S203: Perform outlier and data smoothing processing on the original particle flow density data d i . Use the difference method to extract the change trend of the data and calculate the particle flow density:

[0030]

[0031] Among them, D is the particle flow density value, d′ i is the difference value between adjacent data points, t i is the timestamp of the data point, β and τ are adjustment parameters that control the steepness of the weight and the time offset, and n is the total number of data points.

[0032] As a further solution of the present invention, the steps for obtaining the characteristics of the planetary magnetic storm impact are as follows:

[0033] S211: Using the velocity and density data of the electromagnetic radiation and the particle flow, perform data integration using the weighted sum method, and use the integrated data as the input data;

[0034] S212: Calculate the planetary magnetic field perturbation value and analyze the impact of solar activity on the planetary magnetic field:

[0035]

[0036] Among them, ΔB is the planetary magnetic field perturbation value, is the average electromagnetic radiation velocity, is the average particle flow density, α, β, δ, and γ are adjustment parameters used to simulate the nonlinear perturbation of the magnetic field under the condition of high-intensity electromagnetic radiation and particle flow, and θ enhances the influence of nonlinear adjustment and is used to adjust the sensitivity of the logarithmic term;

[0037] S213: Record the time points when the magnetic storm starts and ends, track the change of the magnetic field strength during the magnetic storm, including the maximum magnetic field strength of the magnetic storm, the duration of the magnetic storm, and the magnetic field change rate, and organize to obtain the characteristics M of the planetary magnetic storm impact.

[0038] As a further solution of the present invention, the steps for obtaining the analysis result of the orbital deviation dynamic prediction are as follows:

[0039] S301: Collect the planetary orbit data O through the observatory, integrate it with the characteristics M of the planetary magnetic storm impact, compare the dynamic changes of the orbital velocity and the orbital intersection point, and analyze the direct impact of the gravitational perturbation of solar activity on the planetary orbit deviation;

[0040] S302: Use the numerical simulation method to simulate the planetary orbit deviation during the solar activity cycle and predict the future orbit change trend. The formula is as follows

[0041]

[0042] Among them, ΔR(t) represents the orbital deviation at time t, O(t) is the actual orbital position at time t, P(t) is the expected orbital position at time t, λ is the position deviation adjustment coefficient, ξ is the magnetic storm impact adjustment coefficient, and ζ is the impact accumulation factor;

[0043] S303: Incorporate the deviation between the actual orbit and the expected orbit and the cumulative effect of magnetic storm influence, and output the dynamic prediction analysis result of orbit deviation.

[0044] As a further solution of the present invention, the calculation steps of the change in the orbit deviation angle and the orbit intersection are as follows:

[0045] S401: Collect the rotation period data and gravitational perturbation data, use time series analysis to collect the actual orbit position L(t) at each observation time point, and compare it with the standard orbit position L standard for comparison;

[0046] S402: Calculate the orbit deviation data at each time point,

[0047] P(t) = ω·e θ·t ·(L(t) - L standard ) + λ·cos(φ·t)

[0048] where P(t) is the orbit deviation data, reflecting the deviation from the standard orbit, L(t) is the actual orbit position data at time t, L standard is the standard orbit position, serving as the calculation benchmark, ω is the weight parameter, adjusted based on environmental factors, θ is the time decay coefficient, regulating the intensity of the influence of time on the deviation, and λ, φ are the oscillation adjustment parameters, referring to the influence of periodic changes on the orbit deviation;

[0049] S403: Use the obtained orbit deviation data P(t), and perform operations in combination with the rotation period data α and gravitational perturbation data γ,

[0050]

[0051] where Δ represents the change value of the obtained orbit deviation angle and the orbit intersection, and β and σ are the adjustment parameters for optimizing the prediction.

[0052] As a further solution of the present invention, the steps for obtaining the adjustment result of the planetary orbit deviation are as follows:

[0053] S411: According to the predicted change in the orbit deviation angle and the orbit intersection, analyze the overall change trend Θ of the planetary orbit, and collect the change data at key time points;

[0054] S412: Use the analysis result, in combination with the historical orbit data, to calculate the new orbit parameters of the planet,

[0055] O new = O + δ·(Θ·λ + ∈·ζ)

[0056] where O newDenote the new orbital parameters, Θ is the orbital change trend obtained from the analysis, O is the original orbital parameters, δ and λ are adjustment parameters used to control the adjustment amplitude and speed of the orbital parameters to ensure the accuracy and applicability of the new orbital parameters, ∈ is an adjustment parameter used to adjust the influence of historical trend data, and ζ is an additional trend parameter extracted from historical data analysis to enhance the response sensitivity and stability of the model;

[0057] S413: According to the calculated new orbital parameters, match the new orbital state to obtain the adjustment result of the planetary orbital deviation.

[0058] Solar and planetary relationship calculation system, the system includes:

[0059] The solar activity analysis module is based on the collected solar activity observation data, adopts the time series analysis method, extracts the periodic characteristics of solar activity, determines the correlation between the intensity and period of the activity, and generates solar activity correlation data;

[0060] The magnetic storm perturbation calculation module is based on the solar activity correlation data, combines the magnetohydrodynamics method, calculates the electromagnetic radiation velocity and particle flow density in solar activity, analyzes the influence of the perturbation on the planetary magnetic storm, and generates the planetary magnetic storm influence characteristics;

[0061] The planetary orbit offset analysis module is based on the planetary magnetic storm influence characteristics, collects planetary orbit data, analyzes the offset trend of the planetary orbit caused by the gravitational perturbation of solar activity, compares the dynamic changes of the orbital velocity and orbital intersection point, and generates the orbit offset analysis result;

[0062] The magnetic storm and orbit offset correlation module is based on the orbit offset analysis result, tracks the correlation between the magnetic storm and the orbit offset, evaluates the influence of the magnetic storm on the orbit offset trend, and generates the orbit offset dynamic prediction result;

[0063] The orbital parameter adjustment module is based on the orbit offset dynamic prediction result, combines the rotation period and gravitational perturbation data, calculates the changes of the orbit offset angle and orbital intersection point, adjusts the planetary orbital parameters, conducts trend analysis, and generates the orbit change trend analysis result;

[0064] The new orbit prediction module is based on the orbit change trend analysis result, adopts the orbit dynamics simulation method, calculates and predicts the new planetary orbital parameters, evaluates the future orbit stability and deviation, and generates the planetary orbit deviation adjustment result.

[0065] Compared with the prior art, the advantages and positive effects of the present invention are:

[0066] In the present invention, by using the time series, intensity, and frequency information of solar activity data, the characteristics of solar activity in different cycles are accurately extracted. Combining with magnetohydrodynamics methods, the velocity and density parameters of electromagnetic radiation and particle flows are deeply analyzed, the perturbation characteristics of the planetary magnetic field are scientifically evaluated, the influence of solar activity on planetary magnetic storms is summarized. By analyzing gravitational perturbations, the interference of solar activity on planetary orbit deviation is clarified, the dynamic changes of orbital velocity and orbital intersections are traced, the correlation between magnetic storms and orbital deviation is incorporated into the prediction analysis. Combining the rotation period and gravitational perturbation data, the analysis of the orbital change trend is further improved to ensure the accuracy of the orbital deviation adjustment result. Through multi-level dynamic correlation analysis, the complex relationship between solar activity and planetary orbits is comprehensively integrated, providing a more refined reference basis for predicting planetary motion and astronomical phenomena. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic diagram of the working process of the present invention;

[0068] Figure 2 It is a flowchart for obtaining the associated data of solar activity of the present invention;

[0069] Figure 3 It is a flowchart for calculating the velocity and density data of electromagnetic radiation and particle flows of the present invention;

[0070] Figure 4 It is a flowchart for obtaining the influence characteristics of planetary magnetic storms of the present invention;

[0071] Figure 5 It is a flowchart for obtaining the prediction analysis results of the dynamic orbital deviation of the present invention;

[0072] Figure 6 It is a flowchart for calculating the changes of orbital deviation angle and orbital intersections of the present invention;

[0073] Figure 7 It is a flowchart for obtaining the adjustment results of planetary orbit deviation of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0074] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0075] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0076] Embodiment 1

[0077] Please refer to Figure 1 , the present invention provides a technical solution: a method for calculating the relationship between the sun and planets, including the following steps:

[0078] S1: Collect solar activity observation data, analyze the time series, intensity, and frequency information of the data, extract the characteristics of solar activity in the differential cycle, summarize the change trend, and calculate the correlation between the cycle and the activity intensity to obtain solar activity correlation data;

[0079] S2: Use the solar activity correlation data, combined with the magnetohydrodynamics method, to calculate the velocity and density data of electromagnetic radiation and particle flow in solar activity, evaluate the perturbation of the planetary magnetic field, analyze the impact on planetary magnetic storms, and obtain the characteristics of the impact of planetary magnetic storms;

[0080] S3: Collect planetary orbit data, combined with the characteristics of the impact of planetary magnetic storms, analyze the offset trend of the planetary orbit caused by gravitational perturbation in solar activity, compare the dynamic changes of the orbital velocity and the orbital intersection point, track the correlation between magnetic storms and orbital offset, and obtain the dynamic prediction analysis results of orbital offset;

[0081] S4: Based on the dynamic prediction analysis results of orbital offset, combined with the rotation period and gravitational perturbation data, calculate the changes in the orbital offset angle and the orbital intersection point, adjust the planetary orbit parameters, analyze the orbital change trend, calculate the new orbit parameters, and obtain the results of adjusting the planetary orbit deviation.

[0082] The solar activity correlation data includes the differential cycle of solar activity, the activity intensity, and the analysis results of the change trend. The characteristics of the impact of planetary magnetic storms include the electromagnetic radiation velocity, the particle flow density, and the magnetic field perturbation parameters. The dynamic prediction analysis results of orbital offset include the prediction results of orbital velocity change, the position of the orbital intersection point, and the prediction results of the orbital offset trend. The results of adjusting the planetary orbit deviation are specifically the orbital offset angle, the orbital intersection point, and the new orbit parameters.

[0083] Please refer to Figure 2 , the steps for obtaining the solar activity correlation data are:

[0084] S101: Obtain the time series, intensity, and frequency information of solar activities based on solar activity observation data, smooth the data to eliminate short-term fluctuations and retain long-term trend characteristics;

[0085] S102: Extract the period intervals, sort the activity intensities within each period by time, and calculate the correlation degree between the period and the intensity.

[0086]

[0087] where, R i is the correlation degree value of the i-th period interval, γ j is the smoothing coefficient of the activity intensity, x ij is the intensity value of the j-th observation point within the i-th period interval, is the average intensity value of the target period interval, s i is the intensity standard deviation of the target period, and n is the number of observation points within the period interval;

[0088] S103: Make a comprehensive judgment on the correlation degree value R i to screen out the period intervals with significant correlations.

[0089]

[0090] where, S i represents the significance characteristic value of the i-th period interval, x ij is the activity intensity value of the j-th observation point within the i-th period interval is the average intensity value of the target period interval, s i is the standard deviation of the target period interval, and n is the number of observation points within the period interval;

[0091] S104: Set a significance threshold T, and screen out the significant period intervals based on the comparison between the significance characteristic value S i and the threshold. If |S i |≥T, it means that the activity intensity characteristics of the i-th period interval are significant, and it is a significant period;

[0092] S105: Combine the correlation degree value R i and the significance characteristic value S i to perform data integration.

[0093]

[0094] where, A i represents the comprehensive correlation data value of the i-th period interval, R i is the correlation degree value of the i-th period interval, S iis the significance feature value of the $i$-th cycle interval, $m$ is the number of significant cycle intervals, and solar activity correlation data is obtained.

[0095] Given conditions:

[0096] Number of cycle intervals in the time series data: $m = 3$

[0097] Number of data points in the $i$-th cycle interval: $n$ i $= 5$

[0098] Average intensity value of each cycle:

[0099] Standard deviation of each cycle: $s$ 1 $= 1.0$, $s$ 2 $= 1.2$, $s$ 3 $= 0.9$

[0100] Significance threshold: $T = 0.2$

[0101] Observed data:

[0102] The 1st cycle interval: $x$ 11 $= 3.0$, $x$ 12 $= 4.2$, $x$ 13 $= 5.0$, $x$ 14 $= 3.8$, $x$ 15 $= 3.6$

[0103] The 2nd cycle interval: $x$ 21 $= 4.5$, $x$ 22 $= 5.3$, $x$ 23 $= 6.1$, $x$ 24 $= 4.8$, $x$ 25 $= 4.6$

[0104] The 3rd cycle interval: $x$ 31 $= 3.3$, $x$ 32 $= 3.7$, $x$ 33 $= 4.0$, $x$ 34 $= 2.8$, $x$ 35 $= 3.1$

[0105] Calculate the cycle correlation value:

[0106] Set the smoothing coefficient $\gamma$ j $= 1.0$

[0107] Then for the 1st cycle interval $R$ 1 :

[0108] Calculations for each item:

[0109]

[0110] Sum:

[0111]

[0112] Similarly calculate:

[0113] R 2 = 0.05

[0114] R 3 = -0.132

[0115] Calculate the significant feature value:

[0116] Calculations for each item:

[0117]

[0118] Sum:

[0119]

[0120] Similarly calculate:

[0121] S 2 = 0.25

[0122] S 3 = -0.67

[0123] Here, the absolute values of S 1 , S 2 and S 3 are all greater than T, indicating that all three cycle intervals are significant intervals. Calculate the comprehensive correlation data:

[0124]

[0125] The 1st cycle interval A 1 :

[0126]

[0127] The 2nd cycle interval A 2 :

[0128]

[0129] The 3rd cycle interval A 3 :

[0130]

[0131] Finally, the solar activity correlation data for each cycle interval is obtained.

[0132] Please refer to Figure 3 , the calculation steps for the velocity and density data of electromagnetic radiation and particle fluxes are as follows:

[0133] S201: Extract the original electromagnetic radiation velocity data v using the solar activity correlation data i and the original particle flux density data d i ;

[0134] S202: Remove outliers from the original electromagnetic radiation velocity data v i Define outliers as data points exceeding three standard deviations, and calculate the electromagnetic radiation velocity:

[0135]

[0136] where V is the electromagnetic radiation velocity value, x i is the timestamp of the data point, μ and σ are the mean and standard deviation of the timestamps respectively, used to adjust the weights and highlight the influence of recent data, and n is the total number of data points.

[0137] S203: Perform outlier and data smoothing processing on the original particle flux density data d i Use the difference method to extract the change trend of the data and calculate the particle flux density:

[0138]

[0139] where D is the particle flux density value, d′ i is the difference value between adjacent data points, t i is the timestamp of the data point, β and τ are adjustment parameters to control the steepness of the weight and the time offset, and n is the total number of data points.

[0140] Electromagnetic radiation velocity:

[0141] Suppose there is a set of original data of electromagnetic radiation velocity (unit: km / s):

[0142] v = [120, 130, 125, 135, 110]

[0143] Timestamps of the data points (assumed to be hours counted from the start of observation):

[0144] x = [1, 2, 3, 4, 5]

[0145] Calculate the mean μ and standard deviation σ of the timestamps of the data:

[0146] Mean:

[0147]

[0148] Standard deviation:

[0149]

[0150] Calculate the weight w of each data pointi :

[0151]

[0152] Similarly,

[0153]

[0154] Then,

[0155]

[0156] Particle flux density:

[0157] Suppose there is a set of original data of particle flux density (unit: particles / cm 3 ):

[0158] d = [50, 55, 53, 60, 48]

[0159] Timestamps of data points (assumed to be hours counted from the start of observation):

[0160] x = [1, 2, 3, 4, 5]

[0161] Calculate the difference d′ i = d i - d i-1 :

[0162] d′ = [55 - 50, 53 - 55, 60 - 53, 48 - 60] = [5, -2, 7, -12

[0163] Weight calculation:

[0164] Set β = 0.5 and τ = 3, then,

[0165]

[0166] It is calculated that,

[0167]

[0168] Then the particle flux density,

[0169]

[0170]

[0171] To obtain the velocity and density data of electromagnetic radiation and particle flux in solar activities.

[0172] Please refer to Figure 4 , the steps to obtain the influence characteristics of planetary magnetic storms are:

[0173] S211: Use the velocity and density data of electromagnetic radiation and particle flow, perform data integration using the weighted sum method, and use the integrated data as input data;

[0174] S212: Calculate the planetary magnetic field perturbation value and analyze the influence of solar activity on the planetary magnetic field:

[0175]

[0176] where ΔB is the planetary magnetic field perturbation value, is the average electromagnetic radiation velocity, is the average particle flow density, and α, β, δ, and γ are adjustment parameters used to simulate the nonlinear perturbation of the magnetic field under high-intensity electromagnetic radiation and particle flow, and θ enhances the influence of nonlinear regulation and is used to adjust the sensitivity of the logarithmic term;

[0177] S213: Record the start and end time points of the magnetic storm, track the change in magnetic field strength during the magnetic storm, including the maximum magnetic field strength, magnetic storm duration, and magnetic field change rate of the magnetic storm, and organize to obtain the planetary magnetic storm influence characteristic M.

[0178] Parameter description:

[0179] ΔB represents the perturbation value of the planetary magnetic field and directly reflects the degree of influence of solar activity on the planetary magnetic field;

[0180] V and D start from the perspectives of electromagnetic radiation and particle flow respectively and characterize these two solar activity parameters;

[0181] α, β, δ, and γ are adjustment parameters in the model that help maintain the accuracy and stability of predictions in changing environmental data. Especially the use of the logarithmic term enhances the adaptability and sensitivity of the model under extreme conditions.

[0182] θ provides the ability to enhance the logarithmic response. Especially the use of the logarithmic term enhances the adaptability and sensitivity of the model under extreme conditions.

[0183] Obtain and process the average electromagnetic radiation velocity and the average particle flow density

[0184] Suppose to obtain the sample data of electromagnetic radiation velocity and particle flow density:

[0185] Sample data of electromagnetic radiation velocity V: [120, 130, 110, 100, 105] km / s

[0186] Sample data of particle flow density D: [5, 6, 5.5, 4.5, 5] particles / cm3

[0187] Calculate the average value:

[0188]

[0189] Assume the parameters are set as follows:

[0190] α = 0.8

[0191] β = 1.2

[0192] δ = 0.5

[0193] γ = 2.0

[0194] θ = 0.1

[0195] Calculate and the logarithmic term:

[0196] For and

[0197]

[0198] According to the calculator, is approximately equal to 58.76.

[0199] Then,

[0200] ΔB = 0.8·113 + 1.2·5.2 + 0.5·587.6 - 2.0·58.76

[0201] = 90.4 + 6.24 + 293.8 - 117.52 = 272.92

[0202] The calculated result of the final planetary magnetic field perturbation value is 272.92.

[0203] Please refer to Figure 5 , the steps to obtain the orbital deviation dynamic prediction analysis result are as follows:

[0204] S301: Collect the planetary orbital data O through the observatory, integrate the characteristics of the planetary magnetic storm influence M, compare the dynamic changes of the orbital velocity and the orbital intersection point, and analyze the direct influence of the gravitational perturbation of solar activity on the planetary orbital deviation;

[0205] S302: Use the numerical simulation method to simulate the planetary orbital deviation during the solar activity cycle, predict the future orbital change trend, and the formula is as follows

[0206]

[0207] where, ΔR(t) represents the orbital deviation at time t, O(t) is the actual orbital position at time t, P(t) is the expected orbital position at time t, λ is the position deviation adjustment coefficient, ξ is the magnetic storm influence adjustment coefficient, and ζ is the influence accumulation factor;

[0208] S303: Integrate the deviation between the actual orbit and the expected orbit and the cumulative effect of the magnetic storm impact, and output the dynamic prediction analysis result of the orbit deviation.

[0209] Integrate orbit data and magnetic storm characteristics:

[0210] Collect data: Set O(t) as the actual orbit position at time t, assumed to be (100, 200, 300) units;

[0211] The expected orbit position P(t) is assumed to be (105, 205, 305) units, the position predicted based on historical data and the standard orbit model;

[0212] Set M(t) as the characteristics of the planetary magnetic storm impact, calculated from the magnetic field monitoring data, assumed to be 0.03 units representing the cumulative impact of the magnetic storm.

[0213] Parameter setting and calculation:

[0214] Set λ (position deviation adjustment coefficient) to 0.1, ξ (magnetic storm impact adjustment coefficient) to 0.05, and the parameters are set based on previous empirical data or theoretical models;

[0215] ζ (impact cumulative factor) is assumed to be 1, indicating that the magnetic storm impact is directly added to the calculation.

[0216] Orbit position difference: Calculate O(t) - P(t) to get (-5, -5, -5), and apply the formula to calculate the orbit deviation:

[0217]

[0218] Since M(t) is assumed to be a constant, the integral is simplified to M(t)·t. If t is the observation duration, assumed to be 24 hours (1 day), then:

[0219] ΔR(t) = 0.1·(-5, -5, -5) + 0.05·(0.03·24)

[0220] Calculated as:

[0221] ΔR(t) = (-0.5, -0.5, -0.5) + (0.036, 0.036, 0.036)

[0222] Finally,

[0223] ΔR(t) = (-0.464, -0.464, -0.464)

[0224] Indicates the predicted orbit deviation after considering the magnetic storm and the difference between the actual and expected orbits.

[0225] Please refer toFigure 6 , the calculation steps for the change in the orbital offset angle and the orbital intersection are as follows:

[0226] S401: Collect the rotation period data and gravitational perturbation data, and use time series analysis to collect the actual orbital position L(t) at each observation time point, and compare it with the standard orbital position L standard for comparison;

[0227] S402: Calculate the orbital offset data at each time point,

[0228] P(t) = ω·e θ·t ·(L(t) - L standard ) + λ·cos(φ·t)

[0229] where P(t) is the orbital offset data, reflecting the deviation from the standard orbit, L(t) is the actual orbital position data at time t, L standard is the standard orbital position, serving as the calculation benchmark, ω is the weight parameter, adjusted based on environmental factors, θ is the time decay coefficient, regulating the intensity of the influence of time on the offset, and λ, φ are the oscillation adjustment parameters, referring to the influence of periodic changes on the orbital offset;

[0230] S403: Use the obtained orbital offset data P(t), and perform operations in combination with the rotation period data α and gravitational perturbation data γ,

[0231]

[0232] where Δ represents the change value of the obtained orbital offset angle and the orbital intersection, and β and σ are the adjustment parameters for optimizing the prediction.

[0233] Determine the standard orbital position L standard :

[0234] Assume L standard = 100 units (standard reference value).

[0235] Obtain the actual orbital position L(t):

[0236] Assume that the observation result at time t = 5 hours is L(t) = 105 units.

[0237] Assume that the weight parameter ω = 1.2, the time decay coefficient θ = 0.1, the oscillation adjustment parameter λ = 2, and φ = 0.5.

[0238] Calculate L(t) - L standard = 105 - 100 = 5 units;

[0239] Calculate the decay factor e θ ·t = e 0.1·5 ≈e 0.5≈1.6487;

[0240] Calculate ω·e θ ·t·(L(t) - L standard ) = 1.2·1.6487·5 ≈ 9.8922 units;

[0241] Calculate the oscillating part λ·cos(φ·t) = 2·cos(0.5·5) = 2·cos(2.5) ≈ -1.6022 units;

[0242] Substitute into the formula to get P(t) = 9.8922 - 1.6022 = 8.29 units.

[0243] Set α = 0.03 (rotation period coefficient) and γ = 0.4 (gravitational perturbation coefficient), and at the same time set β = 0.8 and σ = 1.5;

[0244] Calculate α·P(t) = 0.03·8.29 = 0.2487 units,

[0245] Calculate β·γ = 0.8·0.4 = 0.32 units,

[0246] Add the two values: 0.2487 + 0.32 = 0.5687 units;

[0247] Finally, divide the above result by σ to get units.

[0248] It shows that the change in the orbital offset angle and the orbital intersection point at the given time t is 0.3791 units.

[0249] Please refer to Figure 7 , the steps to obtain the adjustment result of the planetary orbit deviation are as follows:

[0250] S411: According to the predicted change in the orbital offset angle and the orbital intersection point, analyze the overall change trend Θ of the planetary orbit, and collect the change data at the key time points;

[0251] S412: Using the analysis result, combined with the historical orbit data, calculate the new orbit parameters of the planet,

[0252] O new = O + δ·(Θ·λ + ∈·ζ)

[0253] where, O newDenote the new orbital parameters, Θ is the orbital change trend obtained from the analysis, O is the original orbital parameters, δ and λ are adjustment parameters used to control the adjustment amplitude and speed of the orbital parameters to ensure the accuracy and applicability of the new orbital parameters, ∈ is an adjustment parameter used to adjust the influence of historical trend data, and ζ is an additional trend parameter extracted from the historical data analysis to enhance the response sensitivity and stability of the model;

[0254] S413: Match the new orbital state according to the calculated new orbital parameters to obtain the adjustment result of the planetary orbital deviation.

[0255] Collect data and calculate the original orbital parameters O:

[0256] Suppose orbital data for the past year has been collected from the orbital monitoring system.

[0257] Suppose the original orbital parameter O is the orbital radius of the Earth around the Sun, which is approximately 1 AU (astronomical unit),

[0258] i.e., O = 1.

[0259] Determine the orbital change trend Θ:

[0260] Calculate the monthly change rate of the orbital radius by performing a linear regression analysis on the data for the past year.

[0261] Suppose Θ, i.e., the annual change rate is 0.0001 AU / year.

[0262] Introduce the additional trend parameter ζ:

[0263] Determine it by analyzing longer-term historical data, for example, based on the data changes in the past decade.

[0264] Suppose ζ represents a small offset of the average orbital radius over a decade, and the calculated value is 0.00005 AU.

[0265] Apply the adjustment parameters δ, λ, and ∈:

[0266] Suppose δ = 0.1, λ = 2, ∈ = 1.

[0267] Then calculate the new orbital parameter O new ,

[0268] O new = O + δ · (Θ · λ + ∈ · ζ) = 1 + 0.1 · (0.0001 · 2 + 1 · 0.00005)

[0269] O new = 1 + 0.1 · (0.0002 + 0.00005) = 1 + 0.1 · 0.00025

[0270] That is, the new orbital parameter Onew is 1.000025 AU.

[0271] A solar - planet relationship calculation system, comprising:

[0272] A solar activity analysis module, based on the collected solar activity observation data, adopts time - series analysis methods, extracts the periodic characteristics of solar activities, determines the correlation between the intensity and the period of the activities, and generates solar activity correlation data;

[0273] A magnetic storm perturbation calculation module, based on the solar activity correlation data, combines magnetohydrodynamics methods, calculates the electromagnetic radiation velocity and the particle - flow density in solar activities, analyzes the influence of the perturbation on planetary magnetic storms, and generates planetary magnetic storm influence characteristics;

[0274] A planetary orbit offset analysis module, based on the planetary magnetic storm influence characteristics, collects planetary orbit data, analyzes the offset trend of the planetary orbit caused by the gravitational perturbation due to solar activities, compares the dynamic changes of the orbital velocity and the orbital intersection, and generates an orbit offset analysis result;

[0275] A magnetic storm - orbit offset correlation module, based on the orbit offset analysis result, tracks the correlation between magnetic storms and orbit offsets, evaluates the influence of magnetic storms on the orbit offset trend, and generates an orbit offset dynamic prediction result;

[0276] An orbit parameter adjustment module, based on the orbit offset dynamic prediction result, combines the rotation period and the gravitational perturbation data, calculates the changes in the orbit offset angle and the orbital intersection, adjusts the planetary orbit parameters, conducts trend analysis, and generates an orbit change trend analysis result;

[0277] A new orbit prediction module, based on the orbit change trend analysis result, adopts orbit dynamics simulation methods, calculates and predicts the new planetary orbit parameters, evaluates the future orbit stability and deviation, and generates a planetary orbit deviation adjustment result.

[0278] The above is only a preferred embodiment of the present invention, and does not impose other forms of limitations on the present invention. Any person skilled in the relevant art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above - mentioned embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for calculating the relationship between the sun and the planets, characterized in that: The following steps are involved: Collect solar activity observation data, analyze the time series, intensity and frequency information of the data, extract the characteristics of solar activity in the difference cycle, summarize the change trend, and calculate the correlation between the cycle and activity intensity to obtain solar activity correlation data; By using the solar activity correlation data and combining it with the magnetohydrodynamic method, the velocity and density data of electromagnetic radiation and particle flow in solar activity are calculated, the disturbance to the planetary magnetic field is evaluated, the impact on the planetary magnetic storm is analyzed, and the impact characteristics of the planetary magnetic storm are obtained; Collect planetary orbit data, analyze the deviation trend of the planetary orbit caused by gravitational perturbations in solar activity in combination with the characteristics of the planetary magnetic storm, compare the dynamic changes of orbital velocity and orbital intersection, track the relationship between magnetic storms and orbital deviation, and obtain dynamic prediction and analysis results of orbital deviation; Based on the dynamic prediction and analysis results of the orbital deviation, combined with the rotation period and gravitational perturbation data, the orbital deviation angle and the orbital intersection changes are calculated, the planetary orbit parameters are adjusted, the orbital change trend is analyzed, the new orbital parameters are calculated, and the planetary orbit deviation adjustment results are obtained; The steps for obtaining the planetary magnetic storm impact characteristics are as follows: Using the velocity and density data of the electromagnetic radiation and particle flow, integrating the data using a weighted sum method, and using the integrated data as input data; Calculate the planetary magnetic field disturbance value and analyze the impact of solar activity on the planetary magnetic field: Where ΔB is the planetary magnetic field disturbance value, is the average electromagnetic radiation speed, is the average particle flow density, α, β, δ and γ are adjustment parameters used to simulate the nonlinear perturbations of the magnetic field in the case of high-intensity electromagnetic radiation and particle flow, θ enhances the effect of nonlinear regulation and is used to adjust the sensitivity of the logarithmic term; The time points when the magnetic storm starts and ends are recorded, and the changes in magnetic field strength during the storm are tracked, including the maximum magnetic field strength, duration and rate of change of the magnetic field, and the impact characteristics M of the planetary magnetic storm are obtained.

2. The method for calculating the relationship between the sun and the planets according to claim 1, characterized in that: The steps for obtaining the solar activity correlation data are as follows: Based on solar activity observation data, the time series, intensity and frequency information of solar activity are obtained, and the data is smoothed to eliminate short-term fluctuations and retain long-term trend characteristics; Extract the cycle intervals, sort the activity intensity within each cycle by time, and calculate the correlation between cycle and intensity. Among them, R i is the correlation value of the i-th period interval, γ j is the smoothing coefficient of activity intensity, x ij is the intensity value of the jth observation point in the i-th period interval, is the average intensity value of the target cycle interval, s i is the intensity standard deviation of the target period, and n is the number of observation points within the period interval; The correlation value R i Make a comprehensive judgment and screen the period intervals with significant correlation. Among them, S i represents the significant eigenvalue of the ith period interval, x ij is the activity intensity value of the jth observation point in the i-th period interval is the average intensity value of the target cycle interval, s i is the standard deviation of the target period interval, and n is the number of observation points in the period interval; Set the significance threshold T, according to the significance feature value S i Compare with the threshold to filter the significant period interval. If |S i |≥T, it means that the activity intensity characteristics of the i-th period interval are significant, and it is a significant period; Combined with the correlation value R i and the significant eigenvalue S i , to integrate data, Among them, A i represents the comprehensive value of the associated data in the i-th period, R i is the correlation value of the ith period interval, S i is the significant characteristic value of the i-th period interval, m is the number of significant period intervals, and the solar activity correlation data are obtained.

3. The method for calculating the relationship between the sun and the planets according to claim 1, characterized in that: The calculation steps of the velocity and density data of the electromagnetic radiation and particle flow are: Utilize the solar activity correlation data to extract the original electromagnetic radiation velocity data v i and the original particle flow density data d i ; For the original electromagnetic radiation velocity data v i Outliers are removed, and outliers are defined as data points that exceed three times the standard deviation, and the electromagnetic radiation speed is calculated: Where V is the electromagnetic radiation velocity value, x i is the timestamp of the data point, μ and σ are the mean and standard deviation of the timestamp, respectively, which are used to adjust the weight to highlight the impact of recent data, and n is the total number of data points; The original particle flow density data d i Perform outlier and data smoothing, use the difference method to extract the data change trend, and calculate the particle flow density: Where D is the particle flow density, d′ i is the difference value of adjacent data points, t i is the timestamp of the data point, β and τ are tuning parameters that control the steepness and time offset of the weights, and n is the total number of data points.

4. The method for calculating the relationship between the sun and the planets according to claim 1, characterized in that: The steps for obtaining the track deviation dynamic prediction analysis results are as follows: Collect planetary orbit data O through the observatory, integrate it with the planetary magnetic storm impact characteristics M, compare the dynamic changes of orbital velocity and orbital intersection, and analyze the direct impact of the gravitational perturbation of solar activity on the planetary orbit deviation; Use numerical simulation methods to simulate the planetary orbital deviation during the solar activity cycle and predict the future orbital change trend. The formula is as follows: Where ΔR(t) represents the orbital offset at time t, O(t) is the actual orbital position at time t, P(t) is the expected orbital position at time t, λ is the position deviation adjustment coefficient, ξ is the magnetic storm impact adjustment coefficient, and ζ is the impact accumulation factor; The deviation between the actual orbit and the expected orbit and the cumulative effect of the magnetic storm are integrated to output the dynamic prediction analysis results of the orbit deviation.

5. The method for calculating the relationship between the sun and the planets according to claim 1, characterized in that: The calculation steps of the track deviation angle and the change of the track intersection point are: The rotation period data and gravitational perturbation data are collected, and the actual orbital position L(t) at each observation time point is collected by time series analysis, and compared with the standard orbital position L standard Make comparisons; Calculate the orbital deviation data at each time point, P(t)=ω·e θ·t ·(L(t)-L standard )+λ·cos(φ·t) Among them, P(t) is the orbital deviation data, reflecting the deviation from the standard orbit, L(t) is the actual orbital position data at time t, and L standard is the standard orbital position, which is used as the calculation basis, ω is the weight parameter, which is adjusted based on environmental factors, θ is the time attenuation coefficient, which adjusts the intensity of the influence of time on the offset, λ and φ are oscillation adjustment parameters, which refer to the influence of periodic changes on the orbital offset; The orbital deviation data P(t) is used to calculate the rotation period data α and the gravitational perturbation data γ. Among them, Δ represents the change value of the orbital deviation angle and the orbital intersection point, and β and σ are adjustment parameters used to optimize the prediction.

6. The method for calculating the relationship between the sun and the planets according to claim 1, characterized in that: The steps for obtaining the planetary orbit deviation adjustment result are: According to the predicted changes in the orbital deviation angle and the orbital intersection point, the overall change trend θ of the planetary orbit is analyzed, and the change data at key time points are collected; Using the analysis results and combining them with historical orbital data, we can calculate the new orbital parameters of the planet. The new =O+δ·(Θ·λ+∈·ζ) Among them, O new represents the new orbital parameters, Θ is the orbital change trend obtained by analysis, O is the original orbital parameters, δ and λ are adjustment parameters used to control the adjustment amplitude and speed of the orbital parameters to ensure the accuracy and applicability of the new orbital parameters, ∈ is an adjustment parameter used to adjust the influence of historical trend data, ζ is an additional trend parameter extracted from historical data analysis, which is used to enhance the response sensitivity and stability of the model; According to the calculated new orbital parameters, the new orbital state is matched to obtain the planetary orbit deviation adjustment result.

7. A system for calculating the relationship between the sun and the planets, characterized in that: According to any one of claims 1 to 6, the method for calculating the relationship between the sun and the planets, the system comprises: The solar activity analysis module uses time series analysis methods based on the collected solar activity observation data to extract the periodic characteristics of solar activity, determine the correlation between the intensity and period of the activity, and generate solar activity correlation data; The magnetic storm disturbance calculation module calculates the electromagnetic radiation speed and particle flow density in solar activities based on the solar activity correlation data and combines the magnetohydrodynamic method, analyzes the impact of disturbances on planetary magnetic storms, and generates the impact characteristics of planetary magnetic storms; The planetary orbit deviation analysis module collects planetary orbit data based on the characteristics of the planetary magnetic storm, analyzes the deviation trend of the planetary orbit caused by the gravitational perturbation caused by solar activity, compares the dynamic changes of the orbital velocity and the orbital intersection, and generates the orbital deviation analysis results; The magnetic storm and orbital deviation association module tracks the association between the magnetic storm and the orbital deviation based on the orbital deviation analysis result, evaluates the impact of the magnetic storm on the orbital deviation trend, and generates a dynamic prediction result of the orbital deviation; The orbit parameter adjustment module calculates the orbital deviation angle and the change of the orbital intersection point based on the orbital deviation dynamic prediction result, combines the rotation period and the gravitational perturbation data, adjusts the planetary orbit parameters, performs trend analysis, and generates orbital change trend analysis results; Based on the orbit change trend analysis results, the new orbit prediction module uses an orbital dynamics simulation method to calculate and predict new planetary orbit parameters, evaluate future orbital stability and deviation, and generate planetary orbit deviation adjustment results.

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