Method and system for quantifying influence of planet orbit change on earth wind field

Abstract

The invention discloses a method and system for quantifying the influence of planet orbit change on an earth wind field, and the method comprises the steps: obtaining global wind speed mean value data, and carrying out the time-frequency analysis and averaging analysis on the global wind speed mean value data based on wavelet transform and Fourier transform; constructing a quantitative model of influence of different planet orbits on the earth wind field under different time scales, and obtaining influence results of different planet orbits on the earth wind field under different time scales within a preset time period; based on the influence ratio of the influence result to the global wind speed mean value in each period, obtaining a quantitative result of the influence of different planet orbits on the earth wind field; constructing wind speed prediction models under different time scales based on the long short-term memory network, and training and evaluating the wind speed prediction models; and based on an evaluation result, screening out an optimal wind speed prediction model to predict a global wind speed change trend. According to the invention, the understanding of the change mechanism of the earth climate system is further deepened, and more references are provided for future climate change research.

Description

Technical Field

[0001] The present invention belongs to the technical field of earth wind field research, and in particular relates to a method and system for quantifying the impact of planetary orbit changes on the earth's wind field. Background Art

[0002] The Earth's climate is a complex and sophisticated system. Among them, wind fields, as an important part of atmospheric circulation, play a vital role in maintaining climate stability and ecological balance. However, as global climate change continues to intensify, people's attention to the climate system has gradually increased, prompting researchers to conduct in-depth research on the mechanism of climate change. In this context, it is particularly important to explore the impact of various factors on the climate system, including external factors such as changes in the orbits of planets in the solar system. Although there have been many studies analyzing the impact of changes in the Earth's internal factors on the wind field, such as Weber, Furger, 2001; Fang, et al. 2006; Lian, et al. 2015; and some studies have focused on the impact of external factors such as solar activity on the Earth's climate, such as Beer, et al. 2000; Rind, 2002; Ding Yihui, 2019; but there are still relatively few studies on how changes in the orbits of different planets affect the Earth's climate.

[0003] The study of wind field changes within the Earth system is relatively mature. Regarding the study of the impact of external factors on the Earth's climate, Liu Dong and Li Jianping (2011) used the wind field standardized seasonal variability index to deeply analyze the horizontal and vertical distribution characteristics of the global monsoon under different Earth rotation speeds or rotation periods, and how these characteristics change regularly with the change of the Earth's rotation. In the study of the impact of the moon's revolution on the Earth's climate, Li (2005) revealed the impact of the moon's revolution on the Earth's atmosphere based on the correlation between the length of the Earth's day (LOD), the Earth's atmospheric potential height field and the moon's revolution period. He found that the Earth's atmosphere exhibits large and small tides based on the moon's revolution period (27.3 days) and the moon's revolution half period (13.6 days). In the study of the Earth's wind field, Iwasaki et al. (2015) observed the relationship between sea surface atmospheric temperature, sea surface temperature and sea surface wind speed, revealing a strong correlation between them. This correlation caused the ocean surface atmosphere to produce atmospheric tides with a period of two weeks; Zeng et al. (2019) studied the changes in global wind speed through ground observation station data. They found that after 1980, the global wind speed showed a continuous downward trend, but after 2010, the global wind speed began to show a clear upward trend, indicating that the changes in the wind field have certain time scale characteristics. In terms of more complex climate system modeling, Xin Guojun and Liang Fuming (1998) abstracted the climate system into a model with highly nonlinear characteristics and cloud radiation dynamic feedback processes. They revealed the equilibrium state and stability of the model through bifurcation theory analysis, which is of great significance for understanding the dynamic processes and interrelationships of the climate system.

[0004] It can be seen that some current studies focus on the impact of external factors of the Earth system on the Earth's climate, but there are very few studies exploring the impact of planetary orbit changes on the Earth's wind field. However, the planets in the solar system interact with each other. Changes in the orbital positions of different planets will cause changes in interactions, which will cause changes in the Earth's wind field. For example, the moon affects the Earth's wind field by affecting the Earth's tidal changes. Therefore, based on the basic fact that each planet in the solar system has an impact on the Earth system, it is urgent to propose a quantitative method and system for the impact of planetary orbit changes on the Earth's wind field. Summary of the invention

[0005] In order to solve the above technical problems, the present invention proposes a method and system for quantifying the impact of planetary orbit changes on the Earth's wind field. By constructing a quantitative analysis model of the impact of different planetary orbits on the Earth's wind field, the degree of impact of different planetary orbits on the Earth's wind field is quantified. In addition, a deep learning method is used to predict the trend of changes in the Earth's wind field in the next 10 years. By analyzing how changes in the orbits of different planets affect the Earth's wind field, the understanding of the change mechanism of the Earth's climate system is further deepened, providing more references for future climate change research, thereby helping to better respond to the challenges brought about by climate change and solve the problems existing in the above-mentioned prior art.

[0006] To achieve the above object, the present invention provides a method for quantifying the impact of planetary orbit changes on the Earth's wind field, comprising the following steps:

[0007] Obtaining global wind speed mean data, and performing time-frequency analysis and averaging analysis on the characteristics of the global wind speed mean data based on wavelet transform and Fourier transform respectively;

[0008] Based on the analyzed global wind speed average data, a quantitative model of the impact of different planetary orbits on the Earth's wind field at different time scales is constructed;

[0009] Based on the quantitative model of the influence of different planetary orbits on the earth's wind field at different time scales, the influence results of different planetary orbits on the earth's wind field at different time scales within a preset time period are obtained;

[0010] Based on the influence ratio of the influence results of the different planetary orbits on the earth's wind field at different time scales on the global wind speed average in each cycle, a quantitative result of the influence of different planetary orbits on the earth's wind field is obtained;

[0011] Building wind speed prediction models at different time scales based on long short-term memory networks, training the wind speed prediction models based on global wind speed mean data at different time scales, and evaluating the wind speed prediction models based on quantitative results of the impact of different planetary orbits on the Earth's wind field;

[0012] Based on the evaluation results, the optimal wind speed prediction model is selected to predict the global wind speed change trend.

[0013] Optionally, the wavelet transform formula is as follows:

[0014]

[0015] Among them, W (a,b) represents the wavelet coefficients, a and b represent the scale and translation parameters respectively, and f ( t ) is the original signal, ψ(t) is the wavelet basis function.

[0016] Optionally, based on the analyzed global mean wind speed data, the process of constructing a quantitative model of the impact of different planetary orbits on the Earth's wind field at different time scales includes:

[0017] The global wind speed mean is obtained by subtracting adjacent hours, and then the absolute average of the difference between the global wind speed means in adjacent hours within the Earth's rotation period is used to obtain the influence of the Earth's rotation on the daily global wind speed; the global wind speed mean is obtained by subtracting adjacent days, and then the absolute average of the difference between the global wind speed means in adjacent days within the lunar revolution period is used to obtain the influence of the lunar revolution on the global wind speed per sidereal month; the global wind speed mean is obtained by subtracting adjacent months, and the absolute average of the difference between the global wind speed means in adjacent months within the Earth's revolution period is used to obtain the influence of the Earth's revolution on the annual global wind speed; the global wind speed mean is obtained by subtracting the interannual scale, and the absolute average of the difference between the global wind speed means on the interannual scale of other planetary orbits is used to obtain the influence of other planetary orbits on the global wind speed.

[0018] Optionally, the specific formula for the quantitative model of the impact of different planetary orbits on the Earth's wind field at different time scales is as follows:

[0019]

[0020] Among them, E d is the influence of the Earth's rotation on the daily global wind speed, E m is the influence of the moon's revolution on the global wind speed per sidereal month, E a is the influence of the Earth's revolution on the annual global wind speed, E i is the influence of other planetary orbits on the global wind speed; d is the d-th day starting from January 1, 1959; m is the m-th sidereal month starting from January 1959; a is the a-th year starting from 1959; i is the 63-year scale starting from 1959; S is the total number of global wind speed means at the corresponding time scale and time granularity; W is the global wind speed mean data; hn, dn, mn, an are the global wind speed means at the n-th hour, day, sidereal month, and year time granularity at the corresponding time scale, respectively.

[0021] Optionally, based on the quantitative model of the impact of different planetary orbits on the earth's wind field at different time scales, a specific formula for obtaining the impact of different planetary orbits on the earth's wind field at different time scales within a preset time period is as follows:

[0022]

[0023]

[0024] Among them, E1 is the effect of the Earth's rotation on the global wind speed, E2 is the effect of the Moon's revolution on the global wind speed, E3 is the effect of the Earth's revolution on the global wind speed, and E4 is the effect of other planetary orbits on the global wind speed; S d , S m , S a , S i They represent the total number of mean wind speeds on the daily, sidereal monthly, annual and interannual scales in the preset year respectively.

[0025] Optionally, the specific formula for obtaining the quantitative results of the impact of different planetary orbits on the Earth's wind field is as follows:

[0026]

[0027] Among them, R is the influence of different planetary orbits on global wind speed in one cycle.

[0028] Optionally, the data processing process of the wind speed prediction model is as follows:

[0029] i t =sigmoid(W i ·[h t-1 ,x t ]+b i )

[0030] f t =sigmoid(W f ·[h t-1 ,x t ]+b f )

[0031] O t =sigmoid(W o ·[h t-1 ,x t ]+b o )

[0032]

[0033] h t =O t ⊙tanh(C t )

[0034] Among them, i is the input gate, f is the forget gate, O is the output gate, C is the internal state, h is the output result, and x is the input data. is the candidate state, t is the current moment, t-1 is the previous moment, ⊙ is the matrix corresponding to the parameter product operation, sigmoid and tanh are activation functions, W is the weight matrix, and b is the bias term.

[0035] The present invention also provides a quantification system for the impact of planetary orbit changes on the earth's wind field, and a quantification method based on the impact of planetary orbit changes on the earth's wind field, comprising: a data acquisition module, a data analysis module, a quantification model construction module, a quantification result generation module, a prediction model construction module, and a prediction result generation module;

[0036] The data acquisition module is used to obtain global wind speed mean data;

[0037] The data analysis module is used to perform time-frequency analysis and averaging analysis on the global wind speed mean data characteristics based on wavelet transform and Fourier transform respectively;

[0038] The quantitative model building module is used to build a quantitative model of the impact of different planetary orbits on the earth's wind field at different time scales based on the analyzed global wind speed mean data;

[0039] The quantitative result generation module is used to obtain the influence results of different planetary orbits on the earth's wind field at different time scales within a preset time period based on the quantitative model of the influence of the different planetary orbits on the earth's wind field at different time scales, and is used to obtain the quantitative results of the influence of different planetary orbits on the earth's wind field based on the influence ratio of the influence results of the different planetary orbits on the earth's wind field at different time scales on the global wind speed mean in each cycle;

[0040] The prediction model building module is used to build wind speed prediction models at different time scales based on the long short-term memory network, train the wind speed prediction models based on the global wind speed mean data at different time scales, and evaluate the wind speed prediction models based on the quantitative results of the impact of different planetary orbits on the earth's wind field;

[0041] The prediction result generation module is used to select the optimal wind speed prediction model based on the evaluation results to predict the global wind speed change trend.

[0042] Optionally, the quantitative model building module includes:

[0043] The first quantitative model building unit is used to obtain the influence of the earth's rotation on the daily global wind speed by subtracting the global wind speed mean values ​​in adjacent hours and then obtaining the influence of the earth's rotation on the daily global wind speed based on the absolute average value of the difference between the global wind speed mean values ​​in adjacent hours within the earth's rotation cycle;

[0044] The second quantitative model building unit is used to obtain the influence of the moon's revolution on the global wind speed per sidereal month by subtracting the global wind speed mean values ​​from those of adjacent days and then obtaining the absolute average value of the difference between the global wind speed mean values ​​of adjacent days within the moon's revolution period;

[0045] The third quantitative model building unit is used to obtain the influence of the earth's revolution on the annual global wind speed by subtracting the global wind speed mean values ​​of adjacent months from each other based on the absolute average value of the difference between the global wind speed mean values ​​of adjacent months within the earth's revolution cycle;

[0046] The fourth quantitative model construction unit is used to obtain the influence of other planetary orbits on the global wind speed by subtracting the global wind speed mean on an interannual scale based on the absolute average value of the difference between the global wind speed mean values ​​on an interannual scale of other planetary orbits.

[0047] Compared with the prior art, the present invention has the following advantages and technical effects:

[0048] The present invention constructs a quantitative analysis model of the impact of different planetary orbits on the Earth's wind field, quantifies the degree of impact of different planetary orbits on the Earth's wind field; in addition, a deep learning method is used to predict the trend of changes in the Earth's wind field. By analyzing how changes in different planetary orbits affect the Earth's wind field, we further deepen our understanding of the mechanism of change in the Earth's climate system, provide more references for future climate change research, and thus help better cope with the challenges brought about by climate change. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0050] Figure 1 A flow chart of a method for quantifying the impact of planetary orbit changes on the Earth's wind field according to an embodiment of the present invention;

[0051] Figure 2 A kernel density map of the influence of different planetary orbits on global wind speed changes according to an embodiment of the present invention;

[0052] Figure 3 A quantitative statistical diagram of the influence of different planetary orbits on global wind speed changes in an embodiment of the present invention;

[0053] Figure 4 A schematic diagram of global wind speed prediction results at different time granularities according to an embodiment of the present invention;

[0054] Figure 5 This is a schematic diagram of the global wind speed mean prediction results for 2022-2031 according to an embodiment of the present invention;

[0055] Figure 6 This is a spatial variation diagram of the mean value of U100 from 8:00 to 9:00 (H9-H8) and from 20:00 to 21:00 (H21-H20) on the global vernal equinox according to an embodiment of the present invention;

[0056] Figure 7The global U100 and V100 hourly mean changes and the solar radiation distribution diagram at 10:00 and 22:00 on the summer solstice in 1959 according to an embodiment of the present invention;

[0057] Figure 8 It is a time-period diagram of the hourly mean global wind speed on an average annual scale according to an embodiment of the present invention;

[0058] Fig. 9 This is a graph showing the monthly and daily average values ​​of 13 stars around the world according to an embodiment of the present invention;

[0059] Fig.10 The graph of the change of the daily average value of global U100 and the solar and lunar eclipses in 1988 according to the embodiment of the present invention is as follows;

[0060] Fig.11 A graph showing the relationship between the moon's revolution speed and the earth's rotation speed according to an embodiment of the present invention;

[0061] Fig.12 It is a 63-year global wind speed daily average time-period diagram of an embodiment of the present invention;

[0062] Fig.13 This is a spatial variation diagram of the mean wind speed from April to July (M7-M4) and from October to January of the following year (M1-M10) in 1988 in the embodiment of the present invention;

[0063] Fig.14 A graph showing the global V100 daily average change and local fluctuations according to an embodiment of the present invention;

[0064] Fig.15 This is a correlation analysis diagram of the 63-year global average V100 daily mean and the earth's revolution parameters according to an embodiment of the present invention;

[0065] Fig.16 This is a correlation analysis diagram of the indirect impact of the distance between the sun and the earth on the 63-year global average U100 daily mean according to an embodiment of the present invention;

[0066] Fig.17 This is a 63-year global wind speed monthly average time-period diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0067] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0068] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0069] This embodiment provides a method for quantifying the impact of planetary orbit changes on the earth's wind field. After analyzing the impact of planetary orbit changes on the earth's wind field at different time scales by using long time series data combined with wavelet transform and Fourier transform, the influence of the earth's internal variability is further eliminated by subtracting adjacent values ​​and constructing a wind field quantification model of global wind speed in the east-west direction (U component) and north-south direction (V component) at different time scales to explore the impact of different planetary orbit changes on the earth's wind field. The results of the model quantitative analysis show that the earth's revolution and the earth's rotation account for approximately 61% and 7% of the impact of different planetary orbit changes on the global wind speed, respectively, and the corresponding wind speed changes are approximately 0.236m / s and 0.025m / s, respectively; while the moon's revolution and other planetary orbits account for approximately 19% and 12% of the impact of different planetary orbit changes on the global wind speed, respectively, and the corresponding wind speed changes are approximately 0.075m / s and 0.047m / s, respectively. In addition, this embodiment also uses a deep learning method to predict the changes in global wind speed in the next ten years. The forecast results show that the global wind speed is generally on a downward trend, with the annual average of the east-west component wind speed (U100) at 100 meters above the ground reaching its lowest point in 2025 and the annual average of the north-south component wind speed (V100) at 100 meters above the ground reaching its lowest point in 2026. These findings provide a new perspective for the present embodiment to better understand the response of the climate system and help the present embodiment to better understand the mechanism of change of the earth's climate.

[0070] As a specific embodiment, the main methods for measuring the earth's wind field include meteorological station observations, balloon observations, aircraft observations, satellite remote sensing, etc. By combining these methods, more comprehensive and accurate earth wind field data can be obtained. The European Centre for Medium-Range Weather Forecasts (ECMWF) Global Climate Fifth Generation (ERA5) atmospheric reanalysis dataset combines model data with observational data from around the world to form a complete and consistent set of high-quality datasets (Graham. 2019). Among them, it covers two types of wind speed data at 100m and 10m above the global surface, and the former has the advantage of being less affected by the surface than the latter. In order to facilitate the study of the impact of planetary orbit changes on the Earth's wind field, this study uses the hourly data of the east-west component wind speed (U100) and the north-south component wind speed (V100) at a height of 100m on the global surface from 1959 to 2021 in the ERA5 data set, and calculates the hourly mean, daily mean, monthly mean, and annual mean of global wind speed as the main research data for exploring the impact mechanism, degree of impact, and prediction of future wind speed trends of different planetary orbit changes on the Earth's wind field. However, the ERA5 wind speed data of 63 years (1959-2021) is difficult to meet the analysis requirements of the interannual variation of global wind speed. In order to expand the time coverage of global wind speed data, this embodiment uses the 1836-2015 (a total of 180 years) U10 and V10 6-hour data from NOAA-CIRES-DOE 20th Century Reanalysis Version 3 (20CR V3) to calculate the global annual mean wind speed as a supplementary data to verify the impact of other planetary orbits on the Earth's wind field. The wind speed data has an interannual periodic variation similar to the ERA5 wind speed data, meeting the requirements of expanding the time range of global wind speed data and conducting global wind speed interannual variation analysis. In addition, this embodiment also uses other auxiliary data including: daily data of the length of the earth's day (LOD) from the Earth's position parameters of the International Earth Rotation (IERS) Speed ​​Service Prediction Center of the U.S. Naval Meteorological and Astronomical Observatory; lunar phase information data from the Swiss Fourmilab website; daily data of the sun-earth distance, solar declination, lunar declination, and lunar right ascension from the NASA Jet Propulsion Laboratory ephemeris. These auxiliary data provide more comprehensive background information for this embodiment, which helps to more accurately analyze the impact of different planetary orbits on the Earth's wind field.

[0071] method:

[0072] The wind field mainly includes two elements: wind speed and wind direction. In the study of the earth's wind field, for the convenience of analysis, the wind direction is usually decomposed into the east-west direction (U component) and the north-south direction (V component). In order to deeply understand the impact of planetary orbit changes on the earth's wind field, this embodiment studies the earth's wind field as a whole, and eliminates the influence of the earth's internal factors as much as possible by calculating the vector mean and adjacent values ​​of the global wind speed in the U component and the V component at different time scales, and analyzes the change law of the global wind speed from a mathematical perspective. Figure 1 The technical flow chart for studying the influence of planetary orbit changes on the earth's wind field includes three parts. In the first part, this embodiment first combines the phenomena of the spatiotemporal changes of the earth's wind field at different time scales with the orbital operation periods of different planets to make a correlation analysis, and then uses the time-frequency information of the global wind speed data obtained by Fourier transform and wavelet transform to verify the correlation analysis results, so as to explore the influence mechanism of different planetary orbit changes on the earth's wind field; in the second part, since the influence of planetary orbit changes on the earth's wind field is a complex process, the earth has been revolving around the sun. In addition to the relatively stable elliptical trajectory with the sun's motion, the relative motion trajectory of other planets and the earth is very complex. In other words, except for the sun, since the wind field of the earth itself is changing, and the influence of other planets is also changing, the influence of other planets on the change of the earth's wind field is very complex, which leads to the use of Fourier transform and wavelet transform. In addition to being able to capture the periodic signal of the earth's revolution around the sun, it is difficult to strip off the orbital signals of other planets for quantitative processing. Therefore, this embodiment establishes a set of quantitative models of the impact of different planetary orbit changes on the earth's wind field by eliminating the influence of other factors through subtracting neighboring values ​​according to the orbital operation periods of different planets in the first part above, which is used to quantify the degree of influence of different planetary orbit changes on global wind speed; in the third part, this embodiment adopts the long short-term memory network (LSTM) with good extraction ability for time series in the deep learning method to predict the trend of global wind speed changes in the next ten years. First, four LSTM time series prediction models are trained respectively using the global wind speed mean data of four different time granularities (hourly, daily, monthly, and annual), and then the performance of these LSTM time series prediction models is evaluated, and then the best prediction model is selected to predict the trend of global wind speed changes in the next ten years.

[0073] Verification of the impact of different planetary orbit changes on global wind speed:

[0074] In studying the impact mechanism of different planetary orbit changes on the earth's wind field, this embodiment deeply analyzes how the planetary orbit affects the earth's wind field. This embodiment also uses the global hourly wind speed data from 1959 to 2021 to calculate the global wind speed hourly mean on a one-year scale, the global wind speed daily mean and the monthly mean on a 63-year scale. By analyzing the time-frequency characteristics of the global wind speed mean at different time scales, the fact of the impact of different planetary orbit changes on the global wind speed is further verified, and at the same time, it also provides a favorable basis for quantifying the time scale division of the impact of different planetary orbit changes on the global wind speed. However, due to the complexity of the process of the planetary orbit affecting the earth's wind field, the superposition of various different regular signals makes it difficult for the overall global wind speed signal to present continuous and stable fluctuations. In view of the characteristics of the global wind speed mean data, this embodiment uses wavelet transform (WT) to perform time-frequency analysis on it. WT can not only analyze the frequency domain characteristics of the signal, but also analyze the time localization characteristics of the signal, which has a good effect on processing non-stationary signals. The formula of wavelet transform is as follows:

[0075]

[0076] Among them, W (a,b) represents the wavelet coefficient, a and b represent the scale parameter and translation parameter respectively. ( t ) is the original signal, ψ(t) is the wavelet basis function.

[0077] It is noteworthy that it is crucial to select suitable wavelet basis function and parameters. First, the present embodiment, after testing wavelet functions such as Morlet, Haar, Meyer, Gaussian, finally selects Gaussian, which has a more significant effect, as the wavelet basis function. Then, the global wind speed hourly mean under the scale of one year, the global wind speed daily mean and the monthly mean under the scale of 63 years are used as input data, wherein the scale parameter a is set to 48 hours, 128 days, and 64 months respectively. Finally, the global wind speed mean time-period image under the corresponding time scale is obtained and used for the time-frequency analysis of the global wind speed mean. It can be seen from the wavelet transform principle that the periodic variation presented by wavelet analysis is no longer limited to sinusoidal variation. The present embodiment can judge the period of wind speed data variation by observing the global wind speed mean time-period image obtained by wavelet analysis, effectively verifying the influence of planetary orbit on the earth's wind field, and fully proving the reliability of the selected time division scale when quantifying the degree of influence of the earth's wind field.

[0078] Quantification of the impact of different planetary orbit changes on global wind speed:

[0079] The wind field is in a dynamic equilibrium state on a global scale. In order to eliminate the randomness and sensitivity of wind speed data as much as possible, the wind speed data is averaged. By applying Fourier transform to conduct in-depth analysis of the global wind speed mean data, the present embodiment can clearly observe cycles such as 24 hours, 365.25 days and their harmonic components, which are consistent with the rotation of the earth and the earth's revolution period. The amplitude of these cycles accounts for more than 65% of the total amplitude. Due to the direct or indirect impact of the earth's climate system and the global wind speed by various channels such as tidal effects, lunar gravity, and lunar precession, other planets, including Mercury, Venus, Mars, Jupiter, Uranus, Neptune and the earth's motion relationship is relatively complex, there is no relatively stable trajectory, and various signals are superimposed together and difficult to peel off. Therefore, there is no obvious lunar revolution period and other planetary orbit period changes in the 63-year (1959-2021) global wind speed mean data.

[0080] In studying the mechanism of the impact of different planetary orbit changes on the Earth's wind field, this embodiment deeply recognizes that the impact of planetary orbits on the Earth's wind field at different time scales is different. The rotation of the Earth mainly affects the change of global wind speed within the day, the revolution of the moon mainly affects the global wind speed within the sidereal month, the revolution of the Earth mainly has a strong impact on the global wind speed within the year, and the orbits of other planets mainly affect the global wind speed from year to year. Therefore, this embodiment constructs 4 analysis models according to different planetary motion cycles: The first is a quantitative model of the impact of the Earth's rotation on the global wind field: For the Earth's rotation, by subtracting the mean global wind speed of adjacent hours, the influence of the Earth's internal variability can be approximately eliminated in theory. At the same time, within an hour, the movement trajectories of the moon's revolution, the Earth's revolution, and other planetary orbits are very small, and their impact on the Earth's wind field can be approximately ignored. Therefore, this embodiment can use the absolute average value of the difference between the mean global wind speeds of adjacent hours in one day, that is, the Earth's rotation period, to express the degree of influence of the Earth's rotation on the global wind speed of that day E d . The second is a quantitative model of the impact of the moon's revolution around the earth on the earth's wind field: for the moon's revolution, by subtracting the global wind speed mean values ​​of adjacent days, the effect of the earth's rotation can be approximately eliminated in theory (the earth rotates once every day), and the impact of the earth's revolution and other planetary orbits can also be approximately eliminated (the earth revolves around the sun 1 / 365 every day, and the movement of other planetary orbits is relatively smaller). Therefore, this embodiment can use the absolute average value of the difference in the global wind speed mean values ​​of adjacent days in a sidereal month, i.e., the moon's revolution period, to approximate the degree of influence of the moon's revolution on the global wind speed in this sidereal month, E m Similarly, in this embodiment, the absolute average value of the difference between the global wind speed averages of adjacent months in a year, i.e., the earth's revolution period (eliminating the effects of the earth's rotation, the moon's revolution around the earth, and other planetary orbits) can be used to represent the degree of influence of the earth's revolution on the global wind speed in this year, E aSimilarly, in this embodiment, the absolute average value of the difference in the global wind speed average values ​​of adjacent years within 63 years (approximately eliminating the effects of the earth's rotation, the moon's revolution around the earth, and the earth's revolution around the sun) can be used to approximately represent the degree of influence of other planetary orbits on the global wind speed E i Thus, this embodiment can obtain the degree of influence of different planetary orbits on the earth's wind field at different time scales. The specific calculation formula is as follows:

[0081]

[0082] Among them, E is the impact degree of global wind speed; d is the d-th day starting from January 1, 1959; m is the m-th sidereal month starting from January 1959; a is the a-th year starting from 1959; i is the 63-year interannual scale starting from 1959; S is the total number of global wind speed means at the corresponding time scale and time granularity; W is the global wind speed mean data; hn, dn, mn, an are the global wind speed n-th hour, day, sidereal month, and year time granularity means at the corresponding time scale, respectively.

[0083] In this embodiment, the influence of the Earth's rotation on the daily scale, the Moon's revolution on the sidereal month, the Earth's revolution on the annual scale, and other planetary orbits on the interannual scale (E d 、E m 、E a 、E i ). Therefore, this embodiment calculates the impact of the earth's rotation on the daily global wind speed E from 1959 to 2021 d The average value of the Earth's rotation is used to obtain the effect of the Earth's rotation on the global wind speed E1. Similarly, the effect of the moon's revolution on the global wind speed E2, the effect of the Earth's revolution on the global wind speed E3, and the effect of other planetary orbits on the global wind speed E4 can be obtained. The specific calculation formula is as follows:

[0084]

[0085] Among them, 1, 2, 3, and 4 represent the Earth's rotation, the Moon's revolution, the Earth's revolution, and the orbits of other planets respectively; S d , S m , S a , S i They represent the total number of mean wind speeds on daily, sidereal monthly, annual and interannual scales from 1959 to 2021 respectively.

[0086] The influence of the Earth's rotation on the day scale, the Moon's revolution on the sidereal month scale, the Earth's revolution on the year scale, and other planetary orbits on the interannual scale on the global wind speed average (E1, E2, E3, E4) are obtained by formulas (6)-(9). This embodiment can quantify the influence of different planetary orbits on the global wind speed by calculating the influence ratio of different planetary orbits on the global wind speed average in each cycle. The formula is as follows:

[0087]

[0088]

[0089] Among them, 1, 2, 3, and 4 represent the Earth's rotation, the Moon's revolution, the Earth's revolution, and other planetary orbits, respectively; R is the degree of influence of different planetary orbits on the global wind speed in one cycle.

[0090] Prediction of future global wind speed change trends:

[0091] The neural network structure of deep learning has natural advantages in processing high-dimensional data and capturing complex patterns. In particular, the LSTM model in the recurrent neural network (Hochreiter and Schmidhuber, 1997) has significant advantages in time series prediction. LSTM is a special recurrent neural network that effectively alleviates the long-term dependency problem by introducing memory units and gating mechanisms. Compared with traditional recurrent neural networks, LSTM can capture long-term dependencies in time series through memory units and gating mechanisms, and use this information for accurate prediction. In addition, LSTM can also model inputs of different time steps by sharing weights, further improving the performance of the model. Therefore, in view of the complex and changeable characteristics of global wind speed, this embodiment selects the LSTM model as a prediction method for the wind speed change trend from 2022 to 2031. The data processing process of the LSTM model can be expressed by the following formula:

[0092] i t =sigmoid(W i ·[h t-1 ,x t ]+b i ) (14)

[0093] f t =sigmoid(W f ·[h t-1 ,x t ]+b f ) (15)

[0094] O t =sigmoid(W o·[h t-1 ,x t ]+b o ) (16)

[0095]

[0096] h t =O t ⊙tanh(C t ) (19)

[0097] Among them, i is the input gate, f is the forget gate, O is the output gate, C is the internal state, h is the output result, and x is the input data. is the candidate state, t is the current moment, t-1 is the previous moment, ⊙ is the matrix corresponding to the parameter product operation, sigmoid and tanh are activation functions, W is the weight matrix, and b is the bias term.

[0098] LSTM modeling generally includes five steps: data preprocessing, data set division, model construction, model training and model evaluation. This embodiment successfully constructs and trains LSTM prediction models at four time granularities: global wind speed hourly mean, daily mean, monthly mean and annual mean, as shown in Table 1. First, this embodiment selects four evaluation indicators, which can intuitively reflect the prediction performance of the model and help this embodiment to more accurately evaluate the pros and cons of the model. Secondly, this embodiment deeply analyzes the possible reasons in combination with the impact mechanism of different planetary orbit changes on the earth's wind field, and further verifies the reliability of the model prediction results. Finally, according to the evaluation results, this embodiment screens out LSTM prediction models with excellent prediction performance, and uses these models to predict the trend of global wind speed changes from 2022 to 2031.

[0099] Table 1

[0100]

[0101]

[0102] result:

[0103] The degree of influence of planetary orbits on the Earth's wind field:

[0104] This embodiment constructs a quantitative model of wind field at different time scales, and explores the degree of influence of the earth's rotation, the revolution of the moon, the revolution of the earth and other planetary orbits on the earth's wind field. First, this embodiment uses global daily-scale wind speed data to analyze the changes in the earth's wind field caused by the difference in the position of solar radiation on land and sea due to the rotation of the earth (see Supplementary Material 2.4.1 for details). Secondly, this embodiment uses global stellar monthly-scale wind speed data to explore the changes in the earth's wind field caused by the earth's tidal effect caused by the revolution of the moon around the earth. Then, this embodiment uses global annual-scale wind speed data to analyze the changes in the earth's wind field caused by the change in solar declination due to the revolution of the earth around the sun. Finally, this embodiment uses global interannual-scale wind speed data to analyze and verify the periodic effects of other planetary orbits on the earth's wind field.

[0105] The two main parameters that describe wind field changes are wind direction and wind speed. The change of wind direction is relatively complex. When studying the earth's wind field as a whole, the wind direction is usually decomposed into north-south direction (V) and east-west direction (U). This also brings convenience to the quantitative analysis of global wind speed changes. In order to avoid the influence of ground human factors (such as buildings, etc.) as much as possible, this embodiment selects the global 100-meter height wind speed dataset of ERA5 for 63 years (1959-2021) as the main research data. Figure 2 The kernel density diagram shows the influence of different planetary orbits on the global wind speed change. From the analysis results, this embodiment can obtain that the influence ranges of the earth's rotation on the daily scale, the moon's revolution on the sidereal month scale, the earth's revolution on the annual scale, and other planetary orbits on the U100 change on the interannual scale are 0.0116-0.0329m / s, 0.0522-0.0674m / s, 0.0717-0.1983m / s, 0.0004-0.1200m / s, respectively, and the influence means are 0.0208m / s, 0. The influence ranges on V100 are 0.0102-0.0194m / s, 0.0437-0.0547m / s, 0.1733-0.2158m / s, 0.0008-0.0888m / s, and the average values ​​are 0.0138m / s, 0.0485m / s, 0.1975m / s, 0.0239m / s. It is worth noting that the influence of the earth's rotation shows a decreasing trend year by year.

[0106] Figure 3The quantitative result diagram of the influence of different planetary orbits on the global wind speed change is shown. Through calculation, this embodiment finds that the influence of the earth's rotation on the daily scale, the moon's revolution on the sidereal month scale, the earth's revolution on the annual scale, and other planetary orbits on the interannual scale on the U100 change account for 8%, 23%, 52%, and 16%, respectively, and the influence on the V100 change account for 5%, 17%, 69%, and 8%, respectively, and the influence on the combined wind speed account for 7%, 19%, 61%, and 12%, respectively. From these data, some interesting phenomena can be observed in this embodiment. The influence of the earth's rotation on the change of U100 is greater than that of V100; the influence of the moon's revolution on the change of U100 is greater than that of V100; the influence of the earth's revolution on the change of V100 is greater than that of U100; the influence of other planetary orbits on the change of U100 is greater than that of V100. The order of the influence of different planetary orbits on the earth's wind field from large to small is the revolution of the earth, the revolution of the moon, the orbits of other planets, and the rotation of the earth.

[0107] Global wind speed forecast based on deep learning:

[0108] First, this embodiment uses the global wind speed mean data at four time granularities (hourly, daily, monthly, and annual) to train the Long Short-Term Memory network (LSTM) time series prediction model in the deep learning method. Figure 4 As shown in Table 2, the LSTM prediction model has a better prediction effect on the global V component mean. By observing the U component wind speed evaluation index, it is found that R2 decreases with the increasing time granularity. The reason may be that the main factors affecting the global U component wind speed are gradually weakened, making the data change law tend to be disordered. Therefore, when predicting global wind speed, it is necessary to fully consider the characteristics and change laws of each component of global wind speed at different time scales.

[0109] Table 2

[0110]

[0111]

[0112] Secondly, under the premise of ensuring prediction accuracy and a longer time scale, this embodiment refers to the evaluation indicators of LSTM prediction models with different time granularities, and selects the global monthly V-component wind speed mean LSTM prediction model and the global daily U-component wind speed mean LSTM prediction model to predict the future global wind speed change trend. Figure 5 The global wind speed average forecast results for 2022-2031 are shown. Through observation, this embodiment finds that the global wind speed has generally shown a downward trend in the past decade. The U100 predicted average oscillation is more violent, while the V100 is relatively stable. This is due to the combined effect of the planetary orbits.

[0113] Through the above analysis, in terms of the influence range of planetary orbits, this embodiment can draw the following conclusions: the influence of the earth's rotation on the change of U100 is greater than that of V100; the influence of the moon's revolution on the change of U100 is greater than that of V100; the influence of the earth's revolution on the change of V100 is greater than that of U100; the influence of other planetary orbits on the change of U100 is greater than that of V100; the order of the influence of planetary orbits on the earth's wind field from large to small is the earth's revolution, the moon's revolution, other planetary orbits and the earth's rotation, which are about 61%, 19%, 12% and 7% respectively. In terms of wind speed prediction, this embodiment finds that the global wind speed generally shows a downward trend from 2022 to 2031, the predicted mean value of U100 oscillates violently, while V100 is relatively stable, especially in 2025 and 2026, U100 and V100 reach the minimum wind speed, respectively, about -0.22m / s and 0.15m / s.

[0114] As a specific example, the impact of planetary orbit changes on the Earth's wind field is analyzed as follows:

[0115] The impact of the Earth's rotation on the Earth's wind field changes:

[0116] The rotation orbit of the earth determines the phenomenon of alternation of day and night. First, this embodiment explores the spatial variation of global wind speed caused by the change of the direct point of the sun caused by the rotation of the earth. In order to eliminate the randomness of the global wind speed change on a single day and reveal its general law, this embodiment takes the spring breeze days in 1959, 1979, 1999 and 2019 as examples, and calculates the hourly average of the average wind speed for five consecutive days adjacent to the vernal equinox, so as to observe the spatial variation of global wind speed during the rotation cycle of the earth. Figure 6 This is the spatial variation diagram of the global U100 mean value from 8:00 to 9:00 and from 20:00 to 21:00 on the vernal equinox. Figure 6 In this embodiment, it can be found that the U100 difference between East 4 District to East 3 District (red rectangular area) and West 8 District to West 9 District (orange rectangular area) of the spatial variation of the U100 mean value from 8 to 9 on the left side of the figure is greater than zero, that is, the U100 acceleration is eastward and the former is greater than the latter. Among them, the red rectangular area is the direct sunlight time zone (the time zone where the direct sunlight point is located), and the orange rectangular area is 12 time zones away from the direct sunlight point. The East 10 District to East 9 District and the West 2 District to West 3 District (blue rectangular area) are the sunset and sunrise areas, respectively, and the U100 difference is less than zero, that is, the U100 acceleration is westward; the U100 mean value from 20 to 21 on the right side of the figure has a similar spatial variation. After statistics, this embodiment found that the above phenomenon was prevalent during the study period (1959-2021). This strongly proves that there is a significant correlation between the change in the direct sunlight point caused by the rotation of the earth and the spatial variation of U100.

[0117] Secondly, this embodiment explores the impact of the change in solar radiation between land and sea caused by the rotation of the earth on the spatiotemporal change of global wind speed. Taking the summer solstice in 1959 as an example, this embodiment calculates the hourly average of the average wind speed for five consecutive days during the summer solstice in order to observe the temporal change of global wind speed during the earth's rotation cycle. Figure 7 It is the global U100 hourly mean change diagram for the summer solstice in 1959. It is observed that the wind speed value is generally close to and less than zero, i.e., mainly easterly wind. This is mainly due to the fact that the earth's wind field is in a dynamic equilibrium state, and the earth rotates from west to east so that the atmosphere around the earth forms a west-to-east motion less than the earth's rotation speed under the long-term action of the earth's gravity, so the overall formation speed is close to zero, relative to the atmospheric motion to the west of the surface (i.e., easterly wind). Meanwhile, the present embodiment can clearly be observed that at 9 to 10 and 21 to 22, the U100 mean has an obvious and consistent mutation phenomenon. Figure 7 This is the global solar radiation distribution map from 9:00 to 10:00 and 21:00 to 22:00 on the summer solstice. Due to the rotation of the earth, the meridian where the sun's direct point is located moves about 15° from east to west every hour. From 9:00 to 10:00, the meridian where the sun's direct point is located passes through the African continent, the European continent and western Asia. At this time, the sun shines on a larger area of ​​the earth's land. However, from 21:00 to 22:00, the meridian where the sun's direct point is located mainly passes through the Pacific Ocean, and the sun shines on a larger area of ​​the earth's ocean at this time. According to statistics, the above phenomenon is prevalent during the study period (1959-2021), which shows that there is a significant correlation between the changes in the position of the earth's land and sea caused by the rotation of the earth and the changes in U100. Figure 7 It is the change diagram of the global V100 hourly mean value on the summer solstice in 1959. It is observed that the wind speed is generally greater than zero, that is, mainly southerly wind. This is mainly due to the fact that the sun is directly shining near the Tropic of Cancer at this time and there are many lands in the northern hemisphere, causing the temperature to rise and the air pressure to decrease, while there is no direct sunlight in the southern hemisphere and there are many oceans, causing the temperature to decrease and the air pressure to rise, forming a pressure gradient force from south to north. At the same time, the present embodiment can also be clearly observed that at 9:00 to 10:00 and 21:00 to 22:00, the V100 mean has an obvious and consistent mutation phenomenon. However, since the corresponding latitudes of the direct sunlight points on the vernal equinox, autumnal equinox, and winter solstice are not in the northern hemisphere, the direct sunlight land area is small, and there is no law similar to the summer solstice. Therefore, it is impossible to observe obvious changes in the V component wind speed within an earth rotation cycle. This also indirectly shows that the change in the position of the land and sea of ​​solar radiation caused by the rotation of the earth has a strong impact on the earth's wind field.

[0118] In the latitude direction of the Earth, the daily rotation of the Earth is about 360°, while the revolution of the Moon is only about 13.17° and the revolution of the Earth is only about 0.98°. In the longitude direction of the Earth, the daily revolution of the Moon is about 4.18° and the revolution of the Earth is about 0.25°. The following can be obtained by wavelet transform: Figure 8The time-period diagram of the hourly mean of global wind speed on a 63-year (1959-2021) annual scale is shown. Within the 48-hour observation range, there is an obvious and continuous 24-hour or 12-hour periodic change, which is completely consistent with the Earth's rotation period. In summary, the Earth's rotation has an impact on the global daily wind speed, and this impact is mainly caused by the change in the position of the Earth's land and sea caused by the Earth's rotation.

[0119] The impact of the moon's revolution on the earth's wind field changes:

[0120] This embodiment draws Fig. 9 The graph of the mean changes of U100 and V100 for 13 sidereal months is shown in the figure, but the changes of wind speed within the sidereal month do not show obvious patterns. This is because the influence of the moon on the earth's wind field is mainly achieved through tidal effects, especially ocean tides (Wood, 1985). First, the gravity of the moon will directly cause atmospheric tides to form on the earth's surface, resulting in the formation of huge and slowly moving wave structures in the earth's atmosphere (Li, 2005; Weber and Thomas, 2017). These wave structures affect the movement of the atmosphere and the diffusion of air, and further act on the global wind speed. More importantly, the moon will also cause tidal phenomena in the ocean, causing periodic changes in ocean currents. These changes are transmitted to the atmospheric system through sea-air interactions (Iwasaki, 2015; Ray, Susanto, 2019), thereby affecting changes in global wind speed. In addition, the moon will also affect the Earth's atmospheric system through geological activities such as earthquakes and volcanic eruptions caused by the Earth's solid tides (Meng, et al. 2019; Stéphanie, et al. 2023), thereby affecting global wind speed changes. Therefore, the impact of the moon on global wind speed is a complex process, involving the direct or indirect effects of multiple factors such as the Earth's atmospheric tides, solid tides, and more intense ocean tides. This complex influence mechanism makes it extremely difficult to find a significant correlation between the moon's revolution and global wind speed. However, under certain circumstances, the phenomenon of the moon's revolution affecting the Earth's wind field can still be observed.

[0121] This embodiment studies the relationship between the relative positions of the sun, the earth and the moon and the changes in the global wind field. Fig.10 Showing the 1988 Global U100 ( Fig.10 ) and the global U100 daily average change diagram for the 30 days adjacent to the solar and lunar eclipses. This embodiment found that the solar and lunar eclipses occurred in March ( Fig.10 ) and September ( Fig.10 ) is near the celestial equator in March and September. When the moon is close to the celestial equator, the sun, earth and moon are near the same straight line. Fig.10In this embodiment, it can be observed that there is a significant change in the daily mean value of U100 between solar and lunar eclipses. In particular, the daily mean value of U100 changes synchronously with the lunar declination. According to astronomical knowledge, during the process of solar and lunar eclipses, the moon passes through the solstice, and the moon's revolution at this time has a significant impact on the change of the earth's wind field. However, due to the complex mechanism of the influence of the moon's revolution on the earth's wind field, the change of U100 does not always follow the same law. After statistical analysis, this embodiment found that a more common phenomenon is as follows Fig.10 The shaded area shows a short-term synchronous variation of 2-3 days between the lunar declination and the U100 daily mean.

[0122] In addition to the direct influence mentioned above, the moon's revolution also affects the Earth's wind field in other indirect ways. The length of day (LOD) is a parameter used to measure the speed of the Earth's rotation. It is calculated by the difference between the actual time required for the Earth to rotate once and 86,400 seconds (or 24 hours). During the study period (1959-2021), the changes in the length of the Earth's day (LOD) consisted of two main oscillations, namely 27.3 days and 13.6 days, which coincided with the lunar revolution period and half-period. Fig.11 The relationship between the moon's revolution and the earth's rotation speed is shown. It is observed that when the moon's declination is zero, the LOD reaches a maximum value (indicated by an orange arrow), and when the moon is at the solstice in the northern or southern hemisphere, the LOD reaches a minimum value (indicated by a green arrow). At the same time, the change in the moon's declination is about 1 day earlier than the change in LOD. In addition, this embodiment finds that the rise and fall times of LOD are different, ranging from 5 to 9 days. In each LOD cycle, the moon's perigee (Perigee) and apogee (Apogee) each appear once, and the peak values ​​of all half-cycles with perigees are higher than those of adjacent half-cycles with apogees. According to the relevant knowledge of kinematics, the moon moves slower near the apogee and the earth rotates faster, while it moves faster near the perigee and the earth rotates slower. Therefore, the LOD of the moon's perigee is slightly larger than the apogee. These findings show that the moon's revolution process affects the earth's rotation speed, which in turn affects the global wind speed.

[0123] In the latitude direction of the Earth, the Earth rotates about 360°×the number of days per month in each sidereal month, while the Moon revolves about 360° and the Earth revolves about 26.93°. In the longitude direction of the Earth, the Moon revolves about 4×23.5° (the average declination range of the Moon) in each sidereal month, while the Earth revolves about 7.049°. Wavelet transform can be used to obtain Fig.12The figure shows the time-period diagram of the daily mean global wind speed for 63 years (1959-2021). There is no obvious periodic change within the 128-day observation range. U100 is more affected by the lunar revolution than V100, which is consistent with the fact that the lunar revolution has a greater impact on the global U component wind speed than the V component. In summary, the lunar orbit mainly affects the global wind speed on the sidereal month scale, and its impact is mainly caused by the tidal effect of the earth caused by the lunar revolution, which in turn affects the earth's climate system.

[0124] The impact of the Earth's revolution on the Earth's wind field changes:

[0125] The Earth's orbit causes the latitude of the direct sunlight to change over time, resulting in the phenomenon of the four seasons. First, this embodiment explores the spatial changes in global wind speed caused by the change in the latitude of direct sunlight caused by the Earth's revolution. In order to eliminate the contingency of global wind speed changes in a single year and reveal universal laws, this embodiment takes 1988 as an example and calculates the average monthly wind speed for five consecutive years to observe the spatial changes in global wind speed during the Earth's revolution cycle. Fig.13 This is a spatial variation diagram of the global mean wind speed from April to July and from October to January of the following year in 1988. Fig.13 In this embodiment, it can be found that the 0° to 20° north latitude (red rectangular area) from April to July on the upper side of the figure is the direct sunlight area, and the differences of U100 and V100 are greater than zero, that is, the acceleration of U100 is eastward and the acceleration of V100 is northward. The differences between the 0° to 20° south latitude area (blue rectangular area) of U100 and other areas of V100 (except the red rectangular area) are less than zero, that is, the acceleration of U100 is westward and the acceleration of V100 is southward; however, the mean wind speed from October to January of the following year on the lower side of the figure has opposite spatial variations. After statistics, this embodiment found that the above phenomenon was prevalent during the study period (1959-2021). This strongly proves that the changes in the direct sunlight point caused by the earth's revolution are significantly correlated with the spatial changes in wind speed.

[0126] Secondly, this embodiment studies the variation pattern of the daily average global wind speed on an annual scale. Fig.14 The overall change of the global V100 daily average and local fluctuations are shown. Fig.14 It was found that within 63 Earth revolution cycles, the global V100 daily average generally showed a quasi-sinusoidal fluctuation trend that first rose and then fell. Specifically, from July to August, when the sun was directly above the Tropic of Cancer, the global V100 daily average was in the high range, which means that the south wind was strong; while from December to January of the following year, when the sun was directly above the Tropic of Capricorn, the global V100 daily average was in the low range, which means that the north wind dominated. In addition, observations Fig.14, this embodiment notes that there are some obvious disturbances in the quasi-sinusoidal trend of the global V100 daily average. These disturbances are mainly due to the impact of the moon's revolution on the earth's wind speed. Therefore, when exploring the impact of the earth's revolution on the global wind speed, this embodiment must fully consider the response of the global V100 daily average to the moon's revolution orbit. For global V100, the solar declination is positive from April to September, that is, the sun shines directly on the northern hemisphere. This causes the northern hemisphere to absorb more solar radiation, which in turn causes changes in atmospheric pressure and affects the global wind speed. When the solar declination reaches the maximum value, the solar radiation absorbed by the northern hemisphere is close to the peak value, and the global south wind also reaches the maximum value. From October to March of the following year, the solar declination is negative, that is, the sun shines directly on the southern hemisphere. This causes the southern hemisphere to absorb more solar radiation, which in turn causes changes in air pressure and affects the global wind speed. When the solar declination reaches the minimum value, the solar radiation absorbed by the southern hemisphere is close to the peak value, and the global north wind also reaches the maximum value. It is worth noting that the change in the global V100 daily average lags behind the change in solar declination by more than one month. In addition, since the Northern Hemisphere is mostly land and the Southern Hemisphere is mostly ocean, the specific heat capacity of land is smaller than that of the ocean. This means that when absorbing the same solar radiation, the temperature in the Northern Hemisphere will be higher than that in the Southern Hemisphere, forming a stronger pressure gradient force. This causes the maximum south wind to be higher than the maximum north wind, at 1.125m / s and -0.732m / s respectively.

[0127] Then, this embodiment analyzes the correlation between the global wind speed average and the earth's revolution parameters. Fig.15 The correlation analysis diagram of the 63-year global average V100 daily mean and the Earth's revolution parameters is shown, and its correlation coefficients R with the solar declination and the distance between the sun and the earth are 0.9218 and 0.8121, respectively. However, due to the low eccentricity of the Earth's orbit, the distance between the sun and the earth at the aphelion is only 1.033 times that at the perihelion, and the solar light power reaching the Earth's surface at the perihelion is only 6.8% higher than that at the aphelion. Therefore, compared with the change in solar declination, the direct effect of the distance between the sun and the earth on the global wind speed can be ignored.

[0128] In addition to the direct influence mentioned above, the Earth's revolution also affects the Earth's wind field indirectly. Fig.16The correlation analysis diagram of the indirect effect of the distance between the sun and the earth on the global average U100 daily average value over 63 years is shown. After careful observation, it is found that the upper side of the figure is the correlation analysis of the change in the distance between the sun and the earth caused by the revolution of the earth and the change in the speed of the earth's rotation. The correlation coefficient between the two is -0.6502, showing a negative correlation. According to the knowledge related to dynamics, this is mainly due to the fact that the earth revolves faster and rotates slower near the perihelion, and the earth revolves slower and rotates faster near the aphelion. In addition, the LOD change has a lag of less than one month relative to the change in the distance between the sun and the earth; the lower side of the figure is the correlation analysis of the earth's rotation speed and the global average U100 daily average value over 63 years. The correlation coefficient between the two is -0.7561, showing a negative correlation. This is mainly caused by the relative motion between U100 and the rotation of the earth. At the same time, the change of U100 has a lag of about one month relative to the change of LOD. Combining the mechanism of the influence of the earth's rotation on the global wind speed, it can be seen that these findings show that the earth's revolution process will affect the earth's rotation speed, and then affect the global wind speed.

[0129] In the latitude direction of the earth, the earth rotates about 360°×days per year, the moon revolves about 360°×sidereal months per year, and the earth revolves about 360°. In the latitude direction of the earth, the moon revolves about 4×23.5°×sidereal months per year, and the earth revolves about 4×23.5° (obliquity of the ecliptic) per year. Wavelet transform can be used to obtain the following Fig.17 The figure shows the time-period diagram of the global monthly mean wind speed for 63 years (1959-2021). Within the 64-month observation range, there is an obvious and continuous 12-month periodic change, which is completely consistent with the Earth's revolution period. In summary, the Earth's revolution mainly affects the global wind speed on an annual scale, and its influence is mainly caused by the change in the latitude of direct sunlight caused by the Earth's revolution.

[0130] The impact of other planets on the changes in the Earth's wind field:

[0131] Existing studies have shown that the orbits of other planets have direct or indirect effects on the Earth's climate change on interannual, centennial and larger time scales (John, 1976; Scafetta, 2010; Westerhold, Marwan, Drury, Liebrand and Agnini, 2020.). Since other planets and the Earth are in relative motion at the same time, the relative trajectory changes are much more complicated than the Earth's rotation and the Earth's revolution around the sun, and the impact period is also very long. Therefore, in order to explore the impact of other planetary orbits on global wind speed, this embodiment performs a fast Fourier transform (FFT) on the annual average of global wind speed for 180 years (1936-2015). In the overall analysis of the impact of other planetary orbits, this embodiment sets the sampling frequency (fs) to 1 year. According to the Nyquist sampling theorem, the minimum change period that can be obtained is 2 years. Since the fluctuation period in the impact of other planetary orbits on global wind speed is greater than 2 years, the FFT method meets the analysis requirements. The analysis results show that the global wind speed fluctuates with periods of 3.75 years, 9.5 years, 10.6 years, 12 years, 18 years, 20 years, 36 years, 60 years and 90 years after 1836. This indicates that other planetary orbits may indirectly affect global wind speed changes by affecting the Earth's orbit. This embodiment compares the obtained wind speed sequence period with the actual planetary orbits, sunspots and other astronomical periods, as shown in Table 3.

[0132] Table 3

[0133]

[0134]

[0135] On the other hand, based on the same inventive concept as the above embodiment, this embodiment also provides a system for quantifying the impact of planetary orbit changes on the earth's wind field. The use method of the system and the quantification method based on the impact of planetary orbit changes on the earth's wind field provided in the above embodiment can be mutually referenced in terms of effect, including: a data acquisition module, a data analysis module, a quantitative model construction module, a quantitative result generation module, a prediction model construction module, and a prediction result generation module;

[0136] The data acquisition module is used to obtain global wind speed mean data;

[0137] The data analysis module is used to perform time-frequency analysis and averaging analysis on the global wind speed mean data characteristics based on wavelet transform and Fourier transform respectively;

[0138] The quantitative model building module is used to build a quantitative model of the impact of different planetary orbits on the earth's wind field at different time scales based on the analyzed global wind speed mean data;

[0139] The quantitative result generation module is used to obtain the influence results of different planetary orbits on the earth's wind field at different time scales within a preset time period based on the quantitative model of the influence of the different planetary orbits on the earth's wind field at different time scales, and is used to obtain the quantitative results of the influence of different planetary orbits on the earth's wind field based on the influence ratio of the influence results of the different planetary orbits on the earth's wind field at different time scales on the global wind speed mean in each cycle;

[0140] The prediction model building module is used to build wind speed prediction models at different time scales based on the long short-term memory network, train the wind speed prediction models based on the global wind speed mean data at different time scales, and evaluate the wind speed prediction models based on the quantitative results of the impact of different planetary orbits on the earth's wind field;

[0141] The prediction result generation module is used to select the optimal wind speed prediction model based on the evaluation results to predict the global wind speed change trend.

[0142] It can be implemented that the quantitative model building module includes:

[0143] The first quantitative model building unit is used to obtain the influence of the earth's rotation on the daily global wind speed by subtracting the global wind speed mean values ​​in adjacent hours and then obtaining the influence of the earth's rotation on the daily global wind speed based on the absolute average value of the difference between the global wind speed mean values ​​in adjacent hours within the earth's rotation cycle;

[0144] The second quantitative model building unit is used to obtain the influence of the moon's revolution on the global wind speed per sidereal month by subtracting the global wind speed mean values ​​from those of adjacent days and then obtaining the absolute average value of the difference between the global wind speed mean values ​​of adjacent days within the moon's revolution period;

[0145] The third quantitative model building unit is used to obtain the influence of the earth's revolution on the annual global wind speed by subtracting the global wind speed mean values ​​of adjacent months from each other based on the absolute average value of the difference between the global wind speed mean values ​​of adjacent months within the earth's revolution cycle;

[0146] The fourth quantitative model construction unit is used to obtain the influence of other planetary orbits on the global wind speed by subtracting the global wind speed mean on an interannual scale based on the absolute average value of the difference between the global wind speed mean values ​​on an interannual scale of other planetary orbits.

[0147] The above are only preferred specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for quantifying the impact of planetary orbit changes on the Earth's wind field, characterized in that: The following steps are involved: Obtaining global wind speed mean data, and performing time-frequency analysis and averaging analysis on the characteristics of the global wind speed mean data based on wavelet transform and Fourier transform respectively; Based on the analyzed global wind speed average data, a quantitative model of the impact of different planetary orbits on the Earth's wind field at different time scales is constructed; Based on the quantitative model of the influence of different planetary orbits on the earth's wind field at different time scales, the influence results of different planetary orbits on the earth's wind field at different time scales within a preset time period are obtained; Based on the influence ratio of the influence results of the different planetary orbits on the earth's wind field at different time scales on the global wind speed average in each cycle, a quantitative result of the influence of different planetary orbits on the earth's wind field is obtained; Building wind speed prediction models at different time scales based on long short-term memory networks, training the wind speed prediction models based on global wind speed mean data at different time scales, and evaluating the wind speed prediction models based on quantitative results of the impact of different planetary orbits on the Earth's wind field; Based on the evaluation results, the optimal wind speed prediction model is selected to predict the global wind speed change trend.

2. The method for quantifying the impact of planetary orbit changes on the earth's wind field according to claim 1, characterized in that: The formula of the wavelet transform is as follows: Among them, W (a,b) represents the wavelet coefficients, a and b represent the scale and translation parameters, respectively, and f ( t ) is the original signal, ψ(t) is the wavelet basis function.

3. The method for quantifying the impact of planetary orbit changes on the earth's wind field according to claim 1, characterized in that: Based on the analyzed global wind speed average data, the process of constructing a quantitative model of the impact of different planetary orbits on the Earth's wind field at different time scales includes: The global wind speed mean is obtained by subtracting adjacent hours, and then the absolute average of the difference between the global wind speed means in adjacent hours within the Earth's rotation period is used to obtain the influence of the Earth's rotation on the daily global wind speed; the global wind speed mean is obtained by subtracting adjacent days, and then the absolute average of the difference between the global wind speed means in adjacent days within the lunar revolution period is used to obtain the influence of the lunar revolution on the global wind speed per sidereal month; the global wind speed mean is obtained by subtracting adjacent months, and the absolute average of the difference between the global wind speed means in adjacent months within the Earth's revolution period is used to obtain the influence of the Earth's revolution on the annual global wind speed; the global wind speed mean is obtained by subtracting the interannual scale, and the absolute average of the difference between the global wind speed means on the interannual scale of other planetary orbits is used to obtain the influence of other planetary orbits on the global wind speed.

4. The method for quantifying the impact of planetary orbit changes on the earth's wind field according to claim 3, characterized in that: The specific formula of the quantitative model of the impact of different planetary orbits on the Earth's wind field at different time scales is as follows: Among them, E d is the influence of the Earth's rotation on the daily global wind speed, E m is the influence of the moon's revolution on the global wind speed per sidereal month, E a is the influence of the Earth's revolution on the annual global wind speed, E i is the influence of other planetary orbits on the global wind speed; d is the d-th day starting from January 1, 1959; m is the m-th sidereal month starting from January 1959; a is the a-th year starting from 1959; i is the 63-year scale starting from 1959; S is the total number of global wind speed means at the corresponding time scale and time granularity; W is the global wind speed mean data; hn, dn, mn, an are the global wind speed means at the n-th hour, day, sidereal month, and year time granularity at the corresponding time scale, respectively.

5. The method for quantifying the impact of planetary orbit changes on the earth's wind field according to claim 4, characterized in that: Based on the quantitative model of the impact of different planetary orbits on the earth's wind field at different time scales, the specific formula for obtaining the impact of different planetary orbits on the earth's wind field at different time scales within a preset time period is as follows: Among them, E1 is the effect of the Earth's rotation on the global wind speed, E2 is the effect of the Moon's revolution on the global wind speed, E3 is the effect of the Earth's revolution on the global wind speed, and E4 is the effect of other planetary orbits on the global wind speed; S d , S m , S a , S i They represent the total number of mean wind speeds on the daily, sidereal monthly, annual and interannual scales in the preset year respectively.

6. The method for quantifying the impact of planetary orbit changes on the earth's wind field according to claim 5, characterized in that: The specific formula for obtaining the quantitative results of the impact of different planetary orbits on the Earth's wind field is as follows: Among them, R is the influence of different planetary orbits on global wind speed in one cycle.

7. The method for quantifying the impact of planetary orbit changes on the earth's wind field according to claim 1, characterized in that: The data processing process of the wind speed prediction model is shown in the following formula: i t =sigmoid(W i ·[h t-1 ,x t ]+b i ) f t =sigmoid(W f ·[h t-1 ,x t ]+b f ) O t =sigmoid(W o ·[h t-1 ,x t ]+b o ) h t =O t ⊙tanh(C t ) Among them, i is the input gate, f is the forget gate, O is the output gate, C is the internal state, h is the output result, and x is the input data. is the candidate state, t is the current moment, t-1 is the previous moment, ⊙ is the matrix corresponding to the parameter product operation, sigmoid and tanh are activation functions, W is the weight matrix, and b is the bias term.

8. A system for quantifying the impact of planetary orbit changes on the Earth's wind field, characterized in that: A method for quantifying the impact of planetary orbit changes on the earth's wind field based on any one of claims 1 to 7, comprising: a data acquisition module, a data analysis module, a quantitative model construction module, a quantitative result generation module, a prediction model construction module, and a prediction result generation module; The data acquisition module is used to obtain global wind speed mean data; The data analysis module is used to perform time-frequency analysis and averaging analysis on the global wind speed mean data characteristics based on wavelet transform and Fourier transform respectively; The quantitative model building module is used to build a quantitative model of the impact of different planetary orbits on the earth's wind field at different time scales based on the analyzed global wind speed mean data; The quantitative result generation module is used to obtain the influence results of different planetary orbits on the earth's wind field at different time scales within a preset time period based on the quantitative model of the influence of the different planetary orbits on the earth's wind field at different time scales, and is used to obtain the quantitative results of the influence of different planetary orbits on the earth's wind field based on the influence ratio of the influence results of the different planetary orbits on the earth's wind field at different time scales on the global wind speed mean in each cycle; The prediction model building module is used to build wind speed prediction models at different time scales based on the long short-term memory network, train the wind speed prediction models based on the global wind speed mean data at different time scales, and evaluate the wind speed prediction models based on the quantitative results of the impact of different planetary orbits on the earth's wind field; The prediction result generation module is used to select the optimal wind speed prediction model based on the evaluation results to predict the global wind speed change trend.

9. The system for quantifying the impact of planetary orbit changes on the earth's wind field according to claim 8, characterized in that: The quantitative model building module includes: The first quantitative model building unit is used to obtain the influence of the earth's rotation on the daily global wind speed by subtracting the global wind speed mean values ​​in adjacent hours and then obtaining the influence of the earth's rotation on the daily global wind speed based on the absolute average value of the difference between the global wind speed mean values ​​in adjacent hours within the earth's rotation cycle; The second quantitative model building unit is used to obtain the influence of the moon's revolution on the global wind speed per sidereal month by subtracting the global wind speed mean values ​​from those of adjacent days and then obtaining the absolute average value of the difference between the global wind speed mean values ​​of adjacent days within the moon's revolution period; The third quantitative model building unit is used to obtain the influence of the earth's revolution on the annual global wind speed by subtracting the global wind speed mean values ​​of adjacent months from each other based on the absolute average value of the difference between the global wind speed mean values ​​of adjacent months within the earth's revolution cycle; The fourth quantitative model construction unit is used to obtain the influence of other planetary orbits on the global wind speed by subtracting the global wind speed mean on an interannual scale based on the absolute average value of the difference between the global wind speed mean values ​​on an interannual scale of other planetary orbits.

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