Sun difference rotation calculation method and system based on flux modulation method

Abstract

The invention relates to a flux modulation method-based sun worse autorotation calculation method and system, and the method comprises the steps: carrying out the gray conversion of a sun full-solar-surface image obtained every day, and then extracting a sun disc of the sun full-solar-surface image; performing latitude zone division on the extracted solar disc, and calculating an average value of ultraviolet flux of each latitude zone to obtain a flux time sequence of each latitude zone; performing detrending processing on the flux time sequence; fourier transform and band-pass filtering are carried out on the flux time sequence to remove other non-main frequency components; performing autocorrelation function calculation on the flux time sequence after band-pass filtering, and setting the number of lagging days to obtain an autocorrelation image; the number of lagging days corresponding to the maximum autocorrelation coefficient is a main period, and a rotation period is obtained by taking a first peak and adopting Gaussian fitting; calculating a fixed star rotation period and a rotation speed of each latitude zone of the sun; and the sun worse autorotation coefficient is obtained through formula fitting. According to the invention, accurate calculation of the autorotation speed in the solar high-latitude region is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of solar differential rotation calculation, and particularly relates to a calculation method and system for solar differential rotation based on the flux modulation method. Background Art

[0002] The angular velocity of the solar surface at different latitudes is different. This phenomenon that the angular velocity varies with latitude is called differential rotation. The differential rotation of the sun provides an important basis for the solar dynamo theory to understand the origin of solar activity. It plays a key role in the transformation of the poloidal magnetic field to the toroidal magnetic field inside the sun. The solar rotation produces stretching and twisting in the solar magnetic field, generating sunspots and most of the activities on its surface. The phenomena of the earth's atmosphere such as ozone and the revolution of the earth are all closely related to the rotation of the sun.

[0003] For the observation and calculation of solar differential rotation, sunspot groups on the solar surface were first used as tracers. This tracer method mainly measures the rotation speed by determining the position changes of those slowly changing characteristic structures on the solar surface. Different tracers are used for different solar layers. Sunspots and faculas can be used as tracers on the photosphere. Prominences and filaments can be used in the chromosphere, and coronal holes can be used as tracers in the corona. With the development of spectroscopy, later some researchers gradually calculated the rotation speed by measuring the Doppler shift of the photospheric spectral lines detected at different latitudes on the solar limb.

[0004] The accuracy of the tracer method may be affected by various factors, including the type of tracer substance used, the change process of the tracer substance itself, the tracking and recording steps, the observation equipment used, and the observation technology. The tracer method is mainly applicable to the study of the differential rotation of magnetic structures in the low-latitude zone. The spectral method may be interfered by different velocity fields on the solar surface and affected by solar scattered light and spectral line profile changes when applied.

[0005] In addition, tracers such as sunspots usually only appear in the low-latitude zone. Therefore, the traditional tracer method has limitations in calculating the rotation speed in high-latitude regions. The traditional tracer method has the disadvantage that it cannot calculate the rotation speed in high latitudes. Tracers in extreme ultraviolet and ultraviolet solar images such as 304 Å and 1600 Å may not be obvious or difficult to extract. Summary of the Invention

[0006] To overcome the disadvantage that the traditional tracer method cannot calculate the rotation speed in high latitudes, the present invention provides a calculation method and system for solar differential rotation based on the flux modulation method, realizing the accurate calculation of the rotation speed in high-latitude regions of the sun.

[0007] The technical solution adopted by the present invention is as follows:

[0008] Calculation method of solar differential rotation based on the flux modulation method, comprising the following steps:

[0009] Step 1: Image preprocessing: Perform gray-scale transformation on the full-disk solar image obtained daily, and then extract the solar disk of the full-disk solar image;

[0010] Step 2: Flux extraction: Divide the extracted solar disk into latitude zones, calculate the average value of the ultraviolet flux in each latitude zone, and obtain the flux time series of each latitude zone;

[0011] Step 3: Detrending: Since the flux time series will have an obvious overall downward or upward trend, in order to avoid the influence of the trend on the subsequent results, it is necessary to perform detrending on the flux time series;

[0012] Step 4: Band-pass filtering: The extracted flux time series contains various frequency components. Perform Fourier transform on the flux time series, and use band-pass filtering to remove other non-main frequency components and eliminate the influence of clutter;

[0013] Step 5: Autocorrelation calculation: Calculate the autocorrelation function of the flux time series after band-pass filtering, and set the lag days to 0 - 150 days to obtain the autocorrelation image;

[0014] Step 6: Gaussian fitting: The lag days corresponding to the maximum autocorrelation coefficient are the main periods. Take the first peak and use Gaussian fitting to obtain a more accurate rotation period;

[0015] Step 7: Calculate the rotation speed: The solar rotation period obtained by Gaussian fitting is used to calculate the stellar rotation period and rotation speed of each latitude zone of the sun through a formula;

[0016] Step 8: Calculate the differential rotation coefficient: After obtaining the stellar rotation speeds of each latitude zone of the sun, the solar differential rotation coefficient can be obtained by formula fitting.

[0017] In the said Step 1, converting the full-disk solar image into a grayscale image specifically is: converting the RGB value of each pixel point of Figure 2 into a grayscale value to obtain a grayscale image; then perform Hough detection on the grayscale image, specifically: use the Sobel operator to perform edge detection on the grayscale image; determine the center and radius of the solar disk, and then extract the full-disk by using a mask, specifically according to the center and radius obtained by Hough detection, create a mask image with the same size as the original image. In the mask image, only the pixel values of the solar disk area are 1, and the pixel values of the remaining areas are 0. Then multiply the mask image and the original image pixel by pixel to obtain an image containing only the solar disk.

[0018] In step 2, the pre - processed solar disk is divided from the equator to the northern or southern hemisphere. Rectangular bands are divided at intervals of 10° from low latitudes to high latitudes. 16 rectangular strips are divided on the solar disk, covering the range from 80° south latitude to 80° north latitude. The flux is extracted by calculating the average gray value of the pixels in each rectangular strip. Specifically: for Figure 2 each rectangular strip divided in

[0019] its internal pixels are extracted, and the average value of the gray values of all pixels within the rectangular strip is calculated as the flux. Further, the annual - average ultraviolet flux time series at different latitudes is obtained. Specifically: each latitude band corresponds to a flux value every day, thus forming a flux time series. The 16 divided rectangular strips can obtain 16 flux time series at different latitudes. i take the average value of the flux time series D to Then, polynomial fitting is performed on the flux time series. The change trend of the data is approximately described by a polynomial function. The degree of polynomial fitting is 8, and the fitted data f i is obtained. According to formula (1), the detrended flux time series S i is calculated, where i represents the time in days;

[0020]

[0021] In step 4, Fourier transform is performed on the flux time series. Specifically, the flux time series is converted from the time domain to the frequency domain using the discrete Fourier transform formula;

[0022] Observe its frequency distribution in the frequency domain, and find the frequency range of the main components. Specifically, in the frequency - domain graph, the frequency components with high amplitudes correspond to the significant periodicity in the data. By analyzing the frequency - domain graph, the frequency range of the main components can be determined. Assume that the frequency range of the main components is from f1 to f2, then the frequency components within this range can be retained, and other frequency components are regarded as noise;

[0023] Band - pass filtering is used to remove other noise components. Specifically, band - pass filtering is a filtering technique used to retain signals within a specific frequency range from f1 to f2 while removing signals in other frequency ranges;

[0024] Then, inverse Fourier transform is performed, and then the real part is taken. Specifically, the flux time series is converted from the frequency domain to the time domain using the inverse Fourier transform formula. Since the result of the inverse Fourier transform is a complex - valued signal, its real part needs to be taken to obtain the actual time - domain signal; the time - series data after band - pass filtering can be obtained. Specifically, the obtained time - domain signal is the data with noise components removed and the main periodic components retained, which can be used for further analysis and research.

[0025] In step 5, the autocorrelation function of the flux time series after band-pass filtering is calculated as shown in Equation (2):

[0026]

[0027] where ACF(h) represents the autocorrelation coefficient of the time series at time lag h; Cov(y t ,y t-h ) represents the covariance between the time series at times t and t - h, y t represents the flux sequence value at time t, and y t-h represents the sequence value at time t - h; Var(y t ) represents the variance of the time series at time t.

[0028] In step 5, the number of lag days corresponding to the maximum autocorrelation coefficient in the autocorrelation plot is the main period. Specifically, as Figure 5 shown, the maximum autocorrelation coefficient appears at lag h in the autocorrelation plot, and the number of days corresponding to this lag h is the main period of the time series.

[0029] In step 6, the first secondary peak is selected for Gaussian fitting to obtain the accurate solar synodic period T synodic . Specifically Figure 5 as shown, when analyzing the autocorrelation function (ACF) plot of the time series, multiple peaks are usually observed. Among them, the first secondary peak, that is, the first significant non-zero lag peak usually corresponds to the main period of the time series. As Figure 6 shown, selecting this secondary peak for Gaussian fitting can more accurately determine the period.

[0030] In step 7, the solar synodic period T synodic is converted to the stellar rotation period T sidereal , and the conversion formula is as shown in Equation (3):

[0031]

[0032] where T 0 is the orbital period of the satellite.

[0033] The relationship between the rotation speed ω of each solar latitude zone and the stellar rotation period T sidereal of each solar latitude zone is as shown in Equation (4):

[0034]

[0035] In step 8, the function of the solar rotation speed and latitude is fitted using Equation (5) to obtain the values of the differential rotation coefficients A and B;

[0036] ω(φ) = A + Bsin 2 (φ) (5);

[0037] wherein, represents the latitude size, in degrees, represents the angular velocity of rotation at the latitude of ; the coefficient A represents the angular velocity of rotation at the equator of the solar surface, and the coefficient B represents the zonal differential degree.

[0038] A system for solar differential rotation based on the flux modulation method, comprising:

[0039] An image processing device, which is a computer and includes a calculation method for solar differential rotation based on the flux modulation method;

[0040] An image display device, which is a display screen and is used to display the image processed by the calculation method.

[0041] A calculation method and system for solar differential rotation based on the flux modulation method according to the present invention have the following technical effects:

[0042] 1) Step 1 of the present invention efficiently extracts the characteristics of the solar disk: By converting the full-disk solar image into a grayscale image and performing Hough detection, the center and radius of the solar disk can be quickly and accurately determined, and the full-disk is extracted using a mask; preprocessing operations such as grayscale conversion and mask extraction make the characteristics of the solar disk more prominent, facilitating the analysis and processing of subsequent steps. This data preprocessing method has a certain degree of innovation in the field of image processing. This method is applicable to solar images with different resolutions and qualities, and has strong adaptability and versatility;

[0043] 2) Step 2 of the present invention performs refined latitude analysis: The solar disk is divided into rectangular strips at 10° latitude intervals from the equator to the northern and southern hemispheres, covering the range from 80° south latitude to 80° north latitude, realizing refined analysis of different latitude regions of the sun. This division method can more accurately capture the variation law of solar activities at different latitudes, providing more detailed data support for studying the latitude distribution of solar activities. By calculating the average grayscale value of the pixels in each rectangular strip to extract the flux, this method is simple, direct, and effective. Generating the annual average ultraviolet flux time series at different latitudes enriches the research data of solar activities. These data can be used to further analyze the long-term variation trend of solar activities, providing valuable resources for solar physics research;

[0044] 3) Accuracy of the trend analysis in Step 3 of the present invention: By performing polynomial fitting on the flux time series, the noise and random fluctuations in the data can be effectively removed. The fitted data is smoother and can more accurately reflect the long-term change trend of solar activity, providing a more reliable basis for studying the periodicity and regularity of solar activity;

[0045] 4) Remarkable noise removal effect in Step 4 of the present invention: By transforming the flux time series to the frequency domain through Fourier transform, observing its frequency distribution, and using band-pass filtering to remove other noise components, this method can effectively remove high-frequency noise and low-frequency interference in the time series, retain the main periodic components, and improve the quality and usability of the data. The combination of Fourier transform and band-pass filtering can accurately identify the main periodic components in the time series. This efficient processing method can quickly complete the frequency domain analysis and filtering operations of the data, improving the efficiency of the entire analysis process;

[0046] 5) Accuracy of the periodicity identification in Step 5 of the present invention: By calculating the autocorrelation function and analyzing the autocorrelation plot, the main period of the time series can be accurately identified. The lag days corresponding to the maximum autocorrelation coefficient in the autocorrelation plot are the main periods. This method is simple, intuitive, and effective. The autocorrelation function is a commonly used statistical analysis method with high reliability and repeatability. This method can provide reliable statistical analysis results for solar activity research, enhancing the scientificity and credibility of the research;

[0047] 6) Precise determination of the period in Step 6 of the present invention: Selecting the first secondary peak for Gaussian fitting can accurately determine the solar synodic period. Gaussian fitting can effectively reduce the influence of noise and improve the accuracy of period estimation, providing a more accurate time scale for studying the periodicity of solar activity. This helps to more accurately understand the periodic law of solar activity and provides an important reference basis for research in related fields;

[0048] 7) In Step 7 of the present invention, through the conversion formula, the observed synodic period can be accurately converted into the stellar rotation period, thus more accurately reflecting the true rotation situation of the sun. Considering the differential rotation characteristics of the sun, the rotation period can be calculated separately for solar activity regions at different latitudes. This method can provide richer data for studying the latitude distribution of solar activity and helps to deeply understand the internal structure and dynamics of the sun;

[0049] 8) Tracers in extreme ultraviolet and ultraviolet solar images such as 304 Å and 1600 Å may be not obvious or difficult to extract. The flux modulation method of the present invention can provide a new analysis means, enabling effective analysis of the rotation speed in images at these wavelengths, thus overcoming the deficiencies of traditional methods such as tracers. Brief Description of the Drawings

[0050] Figure 1 This is the flowchart of a calculation method and system for solar differential rotation based on the flux modulation method of the present invention.

[0051] Figure 2 This is a schematic diagram of the division of small rectangular strips on the solar disk.

[0052] Figure 3 This is the original flux series and its fitting curve.

[0053] Figure 4 This is the power spectrum diagram after Fourier transform.

[0054] Figure 5 This is the flux autocorrelation diagram at 50° south latitude in 2022.

[0055] Figure 6 This is the Gaussian fitting diagram at 50° south latitude in 2022. Specific implementation manner

[0056] For the calculation method of solar differential rotation based on the flux modulation method, first, preprocess the image by dividing the full-disk solar image into latitude bands. Secondly, use the flux modulation method, that is, obtain the time series data of different latitudes for each year from one picture per day, and process the time series data through techniques such as detrending, band-pass filtering, and Gaussian fitting. Then, perform autocorrelation analysis on it to obtain the rotation period of the sun between 80°S and 80°N. Finally, obtain the function of the solar rotation speed varying with latitude and the differential rotation coefficient.

[0057] As Figure 1 shown, it includes the following steps:

[0058] Step 1, Image preprocessing: Downloaded images with a resolution of 1024×1024 from 2011 to 2023 through the official SDO public website, selected one image at the same time every day with an error of no more than 1 hour, and a total of more than 4000 images were used as experimental data. Perform gray-scale transformation on the full-disk solar image obtained every day, and then extract the solar disk of the full-disk picture. Specifically, convert the full-disk solar image into a gray-scale image, then perform Hough detection on the gray-scale image to determine that the center pixel coordinates of the solar disk are (512, 512) and the radius r = 402 pixels, and then use a mask to extract the full-disk. full-disk solar image into a gray-scale image, then perform Hough detection on the gray-scale image to determine that the center pixel coordinates of the solar disk are (512, 512) and the radius r = 402 pixels, and then use a mask to extract the full-disk.

[0059] Step 2, Flux Extraction: Divide the preprocessed solar disk into latitude zones, calculate the average value of the ultraviolet flux for each latitude zone, and obtain the flux time series for each latitude zone. Specifically, for the preprocessed solar disk, from the equator to the Northern or Southern Hemisphere, rectangular zones are divided at 10° intervals from low to high latitudes. 16 rectangular strips are divided on the solar disk as shown in Figure 2 shown, covering the range from 80°S to 80°N. The flux is extracted by calculating the average gray value of the pixels in each rectangular strip, thereby obtaining the annual average ultraviolet flux time series for different latitudes, such as the flux time series for 50°S in 2022 shown by the blue curve in Figure 3 .

[0060] Step 3, Detrending: Since the flux time series will have an obvious overall downward or upward trend, in order to avoid the influence of the trend on the subsequent results, it is necessary to process the flux time series to eliminate the trend. The detrending process is specifically to process the flux time series to eliminate the trend. First, take the average of the flux time series D i to Then, perform a polynomial fit on the flux time series, with the degree of the fitting polynomial being 8, to obtain the fitting data f i , as shown by the red curve in Figure 3 . Then, according to formula (1) shown, calculate the detrended flux time series S i , where i represents the time in days.

[0061]

[0062] Step 4, Band-Pass Filtering: The extracted flux time series contains various frequency components. We perform a Fourier transform on the flux series and use band-pass filtering to remove other non-dominant frequency components and eliminate the influence of clutter. The band-pass filtering is specifically to perform a Fourier transform on the flux sequence, observe its frequency distribution in the frequency domain, as shown in Figure 4 . Find that the frequency range of the main component is 0.02 - 0.1 / day. Use a band-pass filter in this range to remove other noise components, then perform an inverse Fourier transform, and then take the real part to obtain the time series data after band-pass filtering.

[0063] Step 5, Autocorrelation Calculation: Calculate the autocorrelation function of the filtered flux time series, and set the lag days to 0 - 150 days to obtain the autocorrelation image as shown in Figure 5 . The autocorrelation calculation is specifically: Calculate the autocorrelation function of the filtered flux time series, as shown in formula (2), where ACF(h) represents the autocorrelation coefficient of the time series at time delay h = 150, Cov(y t ,y t-h) represents the covariance of the time series between time t and t - h, and Var(y t ) represents the variance of the time series at time t. The number of lag days corresponding to the maximum autocorrelation coefficient in the autocorrelation plot is the main period.

[0064]

[0065] Step 6, Gaussian fitting: The number of lag days corresponding to the maximum autocorrelation coefficient is the main period. Take the first peak and use Gaussian fitting to obtain a more accurate rotation period, as Figure 6 shown. The specific Gaussian fitting peak is to select the first secondary peak for Gaussian fitting in order to obtain an accurate solar synodic period T synodic = 27.51, and the goodness of fit of Gaussian fitting R-square = 0.9867.

[0066] Step 7, Calculate the rotation speed: The solar rotation period obtained through Gaussian fitting still needs to be calculated through a formula to obtain the stellar rotation periods of each latitude zone of the sun. Specifically, to calculate the rotation speed, the synodic period T synodic needs to be converted to the stellar rotation period T sidereal , and the conversion is as shown in Equation (3). In Equation (3), T 0 = 365.26 days is the orbital period of the satellite. The relationship between the rotation speed ω of each latitude zone of the sun and the stellar rotation period of each latitude zone of the sun is as shown in Equation (3).

[0067]

[0068] Step 8, Calculate the differential rotation coefficient: Generally speaking, there is a close relationship between the solar rotation speed and the latitude. As the latitude increases, the rotation speed decreases, and the rotation speed at the equator is the largest. After obtaining the rotation speed ω of each latitude zone of the sun, the function of the solar rotation speed with respect to the latitude can be fitted by Equation (5) to obtain the average differential rotation coefficient A and B values for each year from 2011 to 2023, as well as the average differential rotation coefficient A and B values for 13 years, as shown in Table 1.

[0069] ω(φ) = A + Bsin 2 (φ) (5);

[0070] Among them, the coefficient A represents the angular velocity of the sun's surface at the equator, and the coefficient B represents the degree of differential rotation in the latitudinal direction.

[0071] Table 1 Fitted A and B values and errors for different years

[0072]

[0073] As can be seen from Table 1, the fitting results of the annual average rotation coefficients A and B and the A value of the average rotation profile coefficient from 2011 to 2023 are roughly between 14.02 and 14.98, and the B value varies between -0.34 and -3.29. The average annual rotation profile coefficient from 2011 to 2023 is A = 14.47 (±0.09), B = -2.24 (±0.18). Coefficient A represents the angular velocity of the solar surface at the equator. B represents the degree of latitudinal differential rotation. A positive B value indicates that the rotation speed at high latitudes is faster than that at the equator, and a negative value is the opposite. Usually, the B value of the sun is negative, indicating that the rotation speed at the equator is the fastest. The results are consistent with the existing research results, indicating that there are certain changes in the solar rotation coefficients A and B in different years, but generally remain within a relatively stable range.

Claims

1. A method for calculating the solar differential rotation based on the flux modulation method, characterized in that The following steps are involved: Step 1: Perform grayscale transformation on the solar full disk image obtained every day, and then extract the solar disk of the solar full disk image; Step 2: Divide the extracted solar disk into latitude bands, calculate the average value of the ultraviolet flux in each latitude band, and obtain the flux time series of each latitude band; Step 3: Detrend the flux time series; Step 4: Perform Fourier transform on the flux time series and use bandpass filtering to remove other non-main frequency components and eliminate the influence of clutter; Step 5: Calculate the autocorrelation function of the flux time series after bandpass filtering, set the lag days, and obtain the autocorrelation image; Step 6: The lag days corresponding to the maximum autocorrelation coefficient are the main period. The first peak is taken and Gaussian fitting is used to obtain the rotation period. Step 7: Based on the solar rotation period obtained by Gaussian fitting, the stellar rotation period and rotation speed of each solar latitude zone are calculated; Step 8: Use the formula to fit and obtain the solar differential rotation coefficient.

2. The method for calculating solar differential rotation based on flux modulation method according to claim 1, characterized in that: In the step 1, the solar full disk image is converted into a grayscale image; then the grayscale image is subjected to Hough detection; the center and radius of the solar disk are determined, and then the full disk is extracted using a mask; According to the center and radius obtained by Hough detection, a mask image with the same size as the original image is created. In the mask image, only the pixel value of the solar disk area is 1, and the pixel values ​​of the rest of the area are 0. Then the mask image is multiplied pixel by pixel by the original image to obtain an image containing only the solar disk.

3. The method for calculating solar differential rotation based on flux modulation method according to claim 1, characterized in that: In the step 2, the solar disk after preprocessing is divided into rectangular bands from low latitudes to high latitudes at intervals of 10° from the equator to the northern hemisphere or the southern hemisphere; 16 rectangular strips are divided on the solar disk, covering the range from 80° south latitude to 80° north latitude; the flux is extracted by calculating the average gray value of the pixels in each rectangular strip; further, the annual average ultraviolet flux time series at different latitudes is obtained; each latitude band corresponds to a flux value every day, thereby constituting a flux time series, and the 16 divided rectangular strips can obtain flux time series at 16 different latitudes.

4. The method for calculating solar differential rotation based on flux modulation method according to claim 1, characterized in that: In step 3, firstly, the flux time series D i Take the average value to Then a polynomial fitting is performed on the flux time series to obtain the fitting data f i According to formula (1), the detrended flux time series S is calculated. i , where i represents the time day; 5. The method for calculating solar differential rotation based on flux modulation method according to claim 1, characterized in that: In the step 4, Fourier transform is performed on the flux time series; Observe its frequency distribution in the frequency domain and find the frequency range of the main component; use bandpass filtering to remove other noise components; then perform inverse Fourier transform and take the real part; you can get the time series data after bandpass filtering.

6. The method for calculating solar differential rotation based on flux modulation method according to claim 1, characterized in that: In step 5, the autocorrelation function of the flux time series after bandpass filtering is calculated, as shown in formula (2): Where ACF(h) represents the autocorrelation coefficient of the time series under time lag h; Cov(y t ,y t-h ) represents the covariance of the time series between time t and th, y t represents the flux sequence value at time t, y t-h Represents the sequence value at time th; Var(y t ) represents the variance of the time series at time t.

7. The method for calculating solar differential rotation based on flux modulation method according to claim 6, characterized in that: The lag days corresponding to the maximum autocorrelation coefficient in the autocorrelation diagram are the main period; the first secondary peak is selected for Gaussian fitting to obtain the accurate solar conjunction period T synodic .

8. The method for calculating solar differential rotation based on flux modulation method according to claim 7, characterized in that: In step 7, the solar conjunction period T synodic Convert to sidereal rotation period T sidereal , the conversion formula is shown in formula (3): Where T0 is the orbital period of the satellite; The rotation speed ω of the sun in each latitude zone and the stellar rotation period T of each latitude zone of the sun sidereal The relationship between is shown in formula (4):

9. The method for calculating solar differential rotation based on flux modulation method according to claim 7, characterized in that: In step 8, equation (5) is used to fit the function between the solar rotation speed and the latitude, thereby obtaining the values ​​of the differential rotation coefficients A and B; ω(φ)=A+Bsin 2 (f) (5); in, Indicates the latitude size, Indicates that at latitude The coefficient A represents the angular velocity of rotation at the equator of the solar surface, and the coefficient B represents the degree of latitudinal difference.

10. A solar differential rotation system based on flux modulation method, characterized in that: include: An image processing device, the image processing device being a computer, comprising a computer for executing the calculation method of solar differential rotation based on the flux modulation method according to any one of claims 1 to 9; An image display device, wherein the image display device is a display screen, used for displaying the image processed by the calculation method.

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