Cross-correlation analysis and time delay estimation method and system for medium and low latitude geomagnetic field SqZ and ionized layer TEC

Abstract

The invention discloses a cross-correlation analysis and time delay estimation method and system for a medium and low latitude geomagnetic field SqZ and an ionosphere TEC, and relates to the technical field of geomagnetism, and the method comprises the following steps: collecting geomagnetic data, ionosphere data, and magnetic quiet day and solar activity parameters; preprocessing the geomagnetic data and the ionosphere data to obtain a geomagnetic field Z component time sequence and a TEC time sequence; and carrying out cross-correlation analysis on the geomagnetic field Z component time sequence and the TEC time sequence during the magnetostatic day, and estimating the correlation time delay of the two. According to the method, the correlation between the geomagnetic field SqZ change and the ionized layer TEC is analyzed, and the method has important theoretical significance and application value for accurately estimating the daily change of the geomagnetic field Z component and the ionized layer TEC, cognizing the change rule of the two and monitoring the irregular change and violent disturbance of the two.

Description

Technical Field

[0001] The present invention relates to the field of geomagnetic technology, and more specifically to a method and system for cross-correlation analysis and time delay estimation of mid- and low-latitude geomagnetic fields SqZ and ionosphere TEC. Background Art

[0002] The geomagnetic field and ionosphere have relatively regular daily changes in calm periods. Severe geomagnetic disturbances are often accompanied by ionospheric disturbances. When the geomagnetic field and ionosphere are seriously disturbed and deviate from the normal state, it has an important impact on communication, navigation and catastrophic weather forecasts. The mechanism of geomagnetic disturbances and ionospheric disturbances is relatively complex. Whether it is morphological analysis of small samples or statistical analysis of large samples, it is inseparable from the judgment of the starting time, amplitude and end mark of the disturbance. Therefore, the study of the relationship between the geomagnetic field and the ionosphere in calm periods is a basic and indispensable work.

[0003] The solar daily variation during the geomagnetic quiet period is called solar quiet daily variation (Sq). This is the most important type of quiet variation in the earth's variable magnetic field. It is mainly caused by the movement of the earth relative to the space current system during its rotation. Ground observations show that it varies according to local time. Previous studies on the variation of the geomagnetic field Sq mainly inverted the Sq equivalent current system based on ground magnetic survey data. For example, Yamazaki et al. used the 10-day international magnetic quiet day data of each month in the 23rd solar week to expand the geomagnetic component into a Fourier series containing interannual variation, annual variation and semi-annual variation, and then inverted the corresponding Sq equivalent current system from the expansion coefficients, and analyzed the similarities and differences of the three components of the Sq equivalent current system, namely, interannual variation, annual variation and semi-annual variation. Stening and Winch used spherical harmonic analysis to study the seasonal effect and longitude effect of the sum of the internal and external source equivalent current systems during the solar low year. Zhao Xudong et al. studied the relationship between the position of the central current vortex and the current intensity of the Sq equivalent current system and solar activity. Vichare et al. adopted a different method from their predecessors, that is, they determined the current magnitude through the H component and the current direction through the D component, thereby finding the approximate position of the Sq equivalent current focus, and analyzed the seasonal variation characteristics of the focus position during the low solar activity year. The observation results obtained by the new method are basically consistent with those of their predecessors. In addition, there are also cases where the Sq changes of the geomagnetic component and the changes of the ionospheric TEC are directly analyzed at the same time and at the same point. For example, Bolaji et al. synchronously analyzed the variation characteristics of the geomagnetic H component and the ionospheric TEC in the equatorial region during the magnetically quiet days of each month, and found that the peak time of the Sq change of the H component is correlated with the pre-noon peak time of the TEC, and demonstrated the mechanism of the equatorial anomaly (Equatorial Ionization Anomal, EIA) and the equatorial counterelectrojet current (CEJ) from the relationship between the changes in the H component and TEC, but such research is currently rare.

[0004] The ionosphere is located in a space affected by the changing magnetic field of the Earth. The ionospheric current is concentrated in the range of 90 to 150 km altitude, which is the current system closest to the ground. During the period of relatively quiet geomagnetic activity, the current flowing in the ionosphere is mainly the Sq current system, and the ionospheric TEC also has typical diurnal changes. Although the equivalent current system theory describes the cause and location of the current that causes the change of geomagnetic Sq well, this type of research only uses geomagnetic data, and does not specifically involve the synchronous change of the ionosphere, and does not directly study the relationship between the ionosphere and the change of geomagnetic field Sq. From the physical cause of Sq change, the change of ionospheric conductivity will directly affect the distribution of current in the ionosphere, thereby affecting the change of geomagnetic field, but there is no way to directly measure the distribution of ionospheric conductivity. The total electric content (TEC) of the ionosphere is one of the important parameters to characterize the change of ionosphere, and it is also a commonly used parameter to study the characteristics of the ionosphere.

[0005] Previous quantitative and statistical analyses of the geomagnetic field and ionospheric TEC were mainly based on the geomagnetic disturbance index and the ionospheric disturbance index, and there has never been a direct correlation analysis using the measured values ​​of the diurnal changes of the geomagnetic field and the ionospheric TEC. The study of the relationship between the change of the geomagnetic field Sq and the diurnal change of the ionospheric TEC is the starting point for exploring the coupling relationship between the geomagnetic field and the ionosphere. When analyzing the quiet changes of the geomagnetic field, the H component of the geomagnetic field is often affected by the ring current and polar current generated by the geomagnetic disturbance. Compared with the H component, the Z component is less affected by the geomagnetic disturbance and solar activity, and its changes are relatively regular. Therefore, the study of the vertical component of the geomagnetic field Sq change, that is, the correlation between SqZ and the ionospheric TEC, has important theoretical significance and application value for accurately estimating the daily changes of the geomagnetic field Z component and the ionospheric TEC, understanding the change laws of the two, and monitoring the irregular changes and violent disturbances of the two. It is a technical problem that technicians in this field urgently need to solve. Summary of the invention

[0006] In view of this, the present invention provides a method and system for cross-correlation analysis and time delay estimation of mid- and low-latitude geomagnetic field SqZ and ionospheric TEC, which solves the problems existing in the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A method for cross-correlation analysis and time delay estimation of mid- and low-latitude geomagnetic field SqZ and ionospheric TEC, comprising the following steps:

[0009] Collect geomagnetic data, ionospheric data, and magnetically quiet days and solar activity parameters;

[0010] Preprocess the geomagnetic data and ionosphere data to obtain the geomagnetic field Z component time series and TEC time series;

[0011] The cross-correlation analysis is performed on the time series of the Z component of the geomagnetic field and the TEC time series during the magnetically quiet days, and the correlation delay between the two is estimated.

[0012] Optionally, the collection of geomagnetic data specifically includes the following steps:

[0013] Based on the geomagnetic station database, the same number of stations are selected in the low-latitude region and the mid-latitude region, and the geomagnetic data of each station with the data type of average absolute data are obtained; wherein the geomagnetic station database includes the name of the geomagnetic station, the longitude and latitude coordinates and the height data of the observation instrument probe from the ground;

[0014] The current magnetic field generated by the unbalanced current of the DC transmission line is projected to the direction of the Z component of the geomagnetic field, and the preliminary collected data is corrected based on the degree of interference of the DC transmission line on the geomagnetic data.

[0015] Optionally, the collection of ionospheric data specifically includes the following steps:

[0016] Based on the data detected by the globally distributed GNSS receivers, plummeting instruments and photometers, the ionospheric total electron content (TEC) data is extracted; the ionospheric total electron content (TEC) data is a time-ordered sequence value;

[0017] For the sequence value of the ionospheric total electron content TEC data at any time, the difference between the sequence value and the original time series mean corresponding to the ionospheric total electron content TEC data is determined, and the standardized ionospheric data is obtained in combination with the sequence standard deviation.

[0018] Optionally, the magnetically quiet days are the 10th International Magnetic Quiet Days of each month, and the solar activity parameters are the solar radio flux F10.7 index and the relative sunspot number.

[0019] Optionally, the geomagnetic data and the ionospheric data are preprocessed, specifically comprising the following steps:

[0020] The cubic spline function interpolation method is used to make the ionospheric data reach the preset spatial resolution, and the grid point closest to the longitude and latitude of the station is determined as the TEC value of the ionosphere directly above the station, forming a TEC time series corresponding to the station position;

[0021] Based on the mean absolute data of the geomagnetic Z component, the mean absolute data with the same time resolution as the ionospheric TEC value is obtained;

[0022] The geomagnetic data and ionospheric data are converted from universal time to local time, and the time series superposition method is used to obtain the average state of Sq changes in magnetically quiet days each month.

[0023] Optionally, obtaining the average state of Sq changes in magnetically quiet days each month specifically includes the following steps:

[0024] Taking the first value of local time every day as the reference value, the diurnal variation series starting from zero is obtained by subtracting the time series of the day from the reference value of the day, which are recorded as dqZ and dqTEC respectively; where d represents diurnal variation and q represents magnetically quiet day;

[0025] Based on all dqZ in each month, the Sq change of the Z component of the geomagnetic field and the average daily variation sequence of ionospheric TEC on the magnetically quiet days of the month are obtained by the time series superposition method, which are denoted as SqZ and SqTEC; among them, SqTEC only represents the TEC time series corresponding to the change of the geomagnetic field Sq.

[0026] Optionally, the cross-correlation analysis specifically includes the following:

[0027] For two time series of geomagnetic field Z components of length N, X = {X1, X2, ..., X N} and TEC time series Y = {Y1,Y2,…,Y N}, their mutual covariance is expressed as:

[0028]

[0029] Where: and is the mean of the series; k represents the time delay relationship between the two time series. When k is positive, it means that the change of X is ahead of Y, and when k is negative, it means that the change of Y is ahead of X.

[0030] The cross-correlation coefficient of two time series is expressed as:

[0031]

[0032] Among them, the size of the mutual correlation coefficient represents the degree of correlation between the two sequences, and the positive or negative value of the time delay represents the phase relationship between the two sequences;

[0033] With time delay as the independent variable and the mutual correlation coefficient as the dependent variable, we get a function of the mutual correlation degree of two random sequences that changes with time delay, namely the mutual correlation function.

[0034] Optionally, estimating the correlation delay between the two specifically includes the following steps:

[0035] The principal component analysis method is used to reconstruct the time series of the Z component of the geomagnetic field and the TEC time series;

[0036] The two reconstructed sequences are subjected to least squares adaptive filtering, and the filtered sequences are subjected to quadratic cross-correlation delay estimation, and the delay value is obtained using the cubic spline interpolation method.

[0037] A system for implementing the cross-correlation analysis and time delay estimation method of the mid- and low-latitude geomagnetic field SqZ and the ionospheric TEC as described in any one of the above items, comprising an acquisition module, a preprocessing module and an analysis module connected in sequence;

[0038] The acquisition module is used to collect geomagnetic data, ionospheric data, and magnetically quiet days and solar activity parameters;

[0039] The preprocessing module is used to preprocess the geomagnetic data and ionosphere data to obtain the geomagnetic field Z component time series and TEC time series;

[0040] The analysis module is used to perform cross-correlation analysis on the Z component time series of the geomagnetic field during the magnetic quiet day and the TEC time series, and estimate the correlation delay between the two.

[0041] Through the above technical solution, it can be known that compared with the prior art, the present invention discloses a method and system for cross-correlation analysis and time delay estimation of geomagnetic field SqZ and ionospheric TEC in mid- and low-latitudes, using geomagnetic data and ionospheric TEC data of multiple stations in mid- and low-latitudes to perform cross-correlation analysis on the vertical component of geomagnetic Sq change during magnetic quiet days and ionospheric TEC, and estimate the correlation time delay between the two. This technical solution has important theoretical significance and application value for accurately estimating the daily changes of geomagnetic field Z component and ionospheric TEC, recognizing the change laws of the two, and monitoring the irregular changes and violent disturbances of the two, and is helpful to explore the interaction laws between geomagnetic field and ionosphere and data assimilation modeling. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0043] Figure 1 A flow chart of the cross-correlation analysis and time delay estimation method of the mid- and low-latitude geomagnetic field SqZ and the ionospheric TEC provided by the present invention;

[0044] Figure 2 This is a structural diagram of the cross-correlation analysis and time delay estimation system of the mid- and low-latitude geomagnetic field SqZ and the ionospheric TEC provided by the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The embodiment of the present invention discloses a method for cross-correlation analysis and time delay estimation of the mid- and low-latitude geomagnetic field SqZ and the ionospheric TEC, such as Figure 1 As shown, the following steps are included:

[0047] Collect geomagnetic data, ionospheric data, and magnetically quiet days and solar activity parameters;

[0048] Preprocess the geomagnetic data and ionosphere data to obtain the geomagnetic field Z component time series and TEC time series;

[0049] The cross-correlation analysis is performed on the time series of the Z component of the geomagnetic field and the TEC time series during the magnetically quiet days, and the correlation delay between the two is estimated.

[0050] Furthermore, the collection of geomagnetic data specifically includes the following steps:

[0051] Based on the geomagnetic station database, the same number of stations were selected in the low-latitude region (N15°~N30°) and the mid-latitude region (N30°~N40°), and the geomagnetic data of each station with the data type of average absolute data were obtained; wherein the latitude of each station increases in sequence, and the geomagnetic station database includes the name of the geomagnetic station, longitude and latitude coordinates and altitude data of the observation instrument probe from the ground;

[0052] The current magnetic field generated by the unbalanced current of the DC transmission line is projected to the direction of the Z component of the geomagnetic field, and the preliminary collected data is corrected based on the degree of interference of the DC transmission line on the geomagnetic data.

[0053] In reality, the DC magnetic field generated by the DC transmission line in normal operation is not easy to distinguish from the geomagnetic field. The increasing number of transmission lines also brings a lot of interference to the geomagnetic station and causes great trouble for subsequent research. To this end, this embodiment proposes a method for automatically correcting the collected interfered data. Specifically, according to the three-dimensional model of the current magnetic field, the current magnetic field generated by the unbalanced current is calculated with the line tower as the node and the line span as the sub-unit. The current magnetic field is projected to the vector direction of the Z component of the geomagnetic field, and the interference value of the transmission line on the geomagnetic field can be obtained. Combined with the measured value of the collected geomagnetic data, the undisturbed geomagnetic data can be obtained, and the correction of the geomagnetic data is realized, avoiding the interference of the transmission line, and providing guarantee for the accuracy of subsequent research.

[0054] Furthermore, the collection of ionospheric data specifically includes the following steps:

[0055] Based on the data detected by the globally distributed GNSS receivers, plummeting instruments, and photometers, the ionospheric total electron content TEC data are extracted; wherein the ionospheric total electron content TEC data are sequence values ​​sorted by time; in this embodiment, the time resolution is 15 minutes, the longitude resolution is 5 degrees, and the latitude resolution is 2.5 degrees;

[0056] For the sequence value of the ionospheric total electron content (TEC) data at any time, the difference between the sequence value and the original time series mean corresponding to the ionospheric total electron content (TEC) data is determined, and the standardized ionospheric data is obtained by combining the sequence standard deviation. This can eliminate specific errors between data and avoid the impact of accidental data on research results.

[0057] In this embodiment, the original time series mean μ and standard deviation σ corresponding to the extracted ionospheric total electron content TEC data are determined by a computer device, and the standardization operation can be expressed as:

[0058]

[0059] Where: x i is the ith value in the TEC data of the total electron content in the ionosphere, μ i is the mean of the time series corresponding to the TEC data of the ionospheric total electron content, σ i is the standard deviation of the time series corresponding to the ionospheric total electron content TEC data, and x' is the standardized ionospheric data.

[0060] Furthermore, the magnetically quiet days are the 10th International Magnetic Quiet Days in each month, and the solar activity parameters are the solar radio flux F10.7 index and the relative sunspot number.

[0061] Further, the geomagnetic data and the ionospheric data are preprocessed, specifically including the following steps:

[0062] The cubic spline function interpolation method is used to make the ionospheric data reach the preset spatial resolution (0.5 degrees), and the grid point closest to the longitude and latitude of the station is determined as the TEC value of the ionosphere directly above the station, forming a TEC time series corresponding to the station position;

[0063] Based on the mean absolute data of the geomagnetic Z component, the mean absolute data with the same time resolution (15 min) as the ionospheric TEC value is obtained;

[0064] The geomagnetic data and ionospheric data are converted from universal time to local time, and the time series superposition method is used to obtain the average state of Sq changes in magnetically quiet days each month.

[0065] Furthermore, the average state of Sq change in each magnetically quiet day of the month is obtained, which specifically includes the following steps:

[0066] In order to more accurately study the daily variations of the geomagnetic field and ionosphere during magnetically quiet days and reduce the impact of long-term changes and daily changes, the first value of local time of each day is taken as the reference value. The daily variation series starting from zero is obtained by subtracting the time series of the day from the reference value of the day, which are recorded as dqZ and dqTEC respectively; where d represents diurnal variation (diurnal) and q represents magnetically quiet day (quiet);

[0067] Based on all dqZ in each month, the Sq change of the Z component of the geomagnetic field and the average daily variation sequence of ionospheric TEC on the magnetically quiet days of the month are obtained by the time series superposition method, which are denoted as SqZ and SqTEC; among them, SqTEC only represents the TEC time series corresponding to the change of the geomagnetic field Sq.

[0068] Furthermore, the cross-correlation analysis specifically includes the following contents:

[0069] For two time series of geomagnetic field Z components of length N, X = {X1, X2, ..., X N} and TEC time series Y = {Y1,Y2,…,Y N}, their mutual covariance is expressed as:

[0070]

[0071] Where: and is the mean of the series; k represents the time delay relationship between the two time series. When k is positive, it means that the change of X is ahead of Y, and when k is negative, it means that the change of Y is ahead of X.

[0072] The cross-correlation coefficient of two time series is expressed as:

[0073]

[0074] Among them, the size of the mutual correlation coefficient represents the degree of correlation between the two sequences, and the positive or negative value of the time delay represents the phase relationship between the two sequences;

[0075] The absolute value of the cross-correlation coefficient can represent the correlation between the two sequences, and the positive or negative value of the time delay can represent the phase relationship between the two sequences. When different time delay values ​​are taken, the cross-correlation coefficient will also change. With the time delay as the independent variable and the cross-correlation coefficient as the dependent variable, we can obtain a function of the cross-correlation between the two random sequences that changes with the time delay, namely the cross-correlation function.

[0076] Furthermore, estimating the relevant delay between the two specifically includes the following steps:

[0077] The principal component analysis method is used to reconstruct the time series of the Z component of the geomagnetic field and the TEC time series;

[0078] The two reconstructed sequences are subjected to least squares adaptive filtering, and the filtered sequences are subjected to quadratic cross-correlation delay estimation, and the delay value is obtained using the cubic spline interpolation method.

[0079] In this embodiment, an observation matrix is ​​constructed according to the geomagnetic field Z component time series and the TEC time series and the mean value is removed, the covariance matrix of the matrix is ​​calculated and the singular value decomposition is performed, the matrix after noise reduction is obtained and the geomagnetic field Z component time series and the TEC time series are reconstructed, and the least squares adaptive filtering output signal is calculated in combination with the filter order, and the error between it and the expected output is determined to confirm the optimal filtered signal. Correlation is performed again according to the mutual correlation results and autocorrelation results of the two, and cubic spline interpolation is performed to obtain the final delay value. This method can improve the anti-noise ability, improve the accuracy of delay estimation, and help explore the interaction law between the geomagnetic field and the ionosphere.

[0080] and Figure 1 Corresponding to the method described above, the embodiment of the present invention also provides a cross-correlation analysis and time delay estimation system of the mid- and low-latitude geomagnetic field SqZ and the ionospheric TEC, for Figure 1 The specific implementation of the method in the embodiment of the present invention provides a cross-correlation analysis and time delay estimation system of the mid- and low-latitude geomagnetic field SqZ and the ionosphere TEC, which can be applied to computer terminals or various mobile devices, such as Figure 2 As shown, it specifically includes a collection module, a preprocessing module and an analysis module connected in sequence;

[0081] The acquisition module is used to collect geomagnetic data, ionospheric data, and magnetically quiet days and solar activity parameters;

[0082] The preprocessing module is used to preprocess the geomagnetic data and ionosphere data to obtain the geomagnetic field Z component time series and TEC time series;

[0083] The analysis module is used to perform cross-correlation analysis on the Z component time series of the geomagnetic field during the magnetic quiet day and the TEC time series, and estimate the correlation delay between the two.

[0084] Through the technical solution provided by this embodiment, the geomagnetic data and ionospheric TEC data of several stations in the middle and low latitudes are used to analyze the relationship between the change of the geomagnetic field SqZ and the ionospheric TEC. It can be found that during the magnetic quiet day, SqZ has a strong negative correlation with the daily variation of the ionospheric TEC, and the change of SqZ is ahead of the change of the ionospheric TEC. Since the negative value of the Z component only represents the vertically upward direction, the smaller the value of the Z component, the larger the absolute value, which indicates that the vertical field strength of the geomagnetic field is greater. Therefore, the negative correlation between SqZ and TEC can be expressed as follows: when the vertical field strength of the change of the geomagnetic field Sq is greater, the value of the ionospheric TEC is also greater. With the increase of latitude, the relevant time delay value will gradually decrease from a positive value to a negative value, that is, SqZ gradually changes from leading the change of TEC to lagging behind the change of TEC. The difference in the spatial distribution of the relevant time delay between SqZ and TEC is mainly related to the height of the latitude, and has little to do with the longitude.

[0085] The cross-correlation relationship and time delay estimation results studied in this embodiment can prove the rationality of using the Z component for modeling in geomagnetic assisted navigation, and provide a theoretical basis for assimilating TEC information into the earth's changing magnetic field model established with the Z component as a parameter; at the same time, it shows that the information on the change of the Z component of the geomagnetic field can also be incorporated into the time series prediction model of TEC.

[0086] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0087] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for cross-correlation analysis and time delay estimation of mid- and low-latitude geomagnetic field SqZ and ionospheric TEC, characterized in that: The following steps are involved: Collect geomagnetic data, ionospheric data, and magnetically quiet days and solar activity parameters; Preprocess the geomagnetic data and ionosphere data to obtain the geomagnetic field Z component time series and TEC time series; The cross-correlation analysis is performed on the time series of the Z component of the geomagnetic field and the TEC time series during the magnetically quiet days, and the correlation delay between the two is estimated.

2. The cross-correlation analysis and time delay estimation method of a mid- and low-latitude geomagnetic field SqZ and ionospheric TEC according to claim 1, characterized in that: The collection of geomagnetic data specifically includes the following steps: Based on the geomagnetic station database, the same number of stations are selected in the low-latitude region and the mid-latitude region, and the geomagnetic data of each station with the data type of average absolute data are obtained; wherein the geomagnetic station database includes the name of the geomagnetic station, the longitude and latitude coordinates and the height data of the observation instrument probe from the ground; The current magnetic field generated by the unbalanced current of the DC transmission line is projected to the direction of the Z component of the geomagnetic field, and the preliminary collected data is corrected based on the degree of interference of the DC transmission line on the geomagnetic data.

3. The cross-correlation analysis and time delay estimation method of a mid- and low-latitude geomagnetic field SqZ and ionospheric TEC according to claim 1, characterized in that: The collection of ionospheric data includes the following steps: Based on the data detected by the globally distributed GNSS receivers, plummeting instruments and photometers, the ionospheric total electron content (TEC) data is extracted; the ionospheric total electron content (TEC) data is a time-ordered sequence value; For the sequence value of the ionospheric total electron content TEC data at any time, the difference between the sequence value and the original time series mean corresponding to the ionospheric total electron content TEC data is determined, and the standardized ionospheric data is obtained in combination with the sequence standard deviation.

4. The cross-correlation analysis and time delay estimation method of a mid- and low-latitude geomagnetic field SqZ and ionospheric TEC according to claim 1, characterized in that: Magnetic quiet days are the 10th International Magnetic Quiet Days in each month, and the solar activity parameters are the solar radio flux F10.7 index and the relative sunspot number.

5. The cross-correlation analysis and time delay estimation method of a mid- and low-latitude geomagnetic field SqZ and ionospheric TEC according to claim 1, characterized in that: Preprocessing geomagnetic data and ionospheric data includes the following steps: The cubic spline function interpolation method is used to make the ionospheric data reach the preset spatial resolution, and the grid point closest to the longitude and latitude of the station is determined as the TEC value of the ionosphere directly above the station, forming a TEC time series corresponding to the station position; Based on the mean absolute data of the geomagnetic Z component, the mean absolute data with the same time resolution as the ionospheric TEC value is obtained; The geomagnetic data and ionospheric data are converted from universal time to local time, and the time series superposition method is used to obtain the average state of Sq changes in magnetically quiet days each month.

6. The method for cross-correlation analysis and time delay estimation of the mid- and low-latitude geomagnetic field SqZ and the ionospheric TEC according to claim 5, characterized in that: The average state of Sq change in magnetically quiet days of each month is obtained, which specifically includes the following steps: Taking the first value of local time every day as the reference value, the diurnal variation series starting from zero is obtained by subtracting the time series of the day from the reference value of the day, which are recorded as dqZ and dqTEC respectively; where d represents diurnal variation and q represents magnetically quiet day; Based on all dqZ in each month, the Sq change of the Z component of the geomagnetic field and the average daily variation sequence of ionospheric TEC on the magnetically quiet days of the month are obtained by the time series superposition method, which are denoted as SqZ and SqTEC; among them, SqTEC only represents the TEC time series corresponding to the change of the geomagnetic field Sq.

7. The cross-correlation analysis and time delay estimation method of the mid- and low-latitude geomagnetic field SqZ and the ionospheric TEC according to claim 1, characterized in that: The cross-correlation analysis specifically includes the following contents: For two time series of geomagnetic field Z components of length N, X = {X1, X2, ..., X N } and TEC time series Y = {Y1,Y2,…,Y N }, their mutual covariance is expressed as: Where: and is the mean of the series; k represents the time delay relationship between the two time series. When k is positive, it means that the change of X is ahead of Y, and when k is negative, it means that the change of Y is ahead of X. The cross-correlation coefficient of two time series is expressed as: Among them, the size of the mutual correlation coefficient represents the degree of correlation between the two sequences, and the positive or negative value of the time delay represents the phase relationship between the two sequences; With time delay as the independent variable and the mutual correlation coefficient as the dependent variable, we get a function of the mutual correlation degree of two random sequences that changes with time delay, namely the mutual correlation function.

8. The method for cross-correlation analysis and time delay estimation of mid- and low-latitude geomagnetic field SqZ and ionospheric TEC according to claim 1, characterized in that: Estimating the relative delay between the two includes the following steps: The principal component analysis method is used to reconstruct the time series of the Z component of the geomagnetic field and the TEC time series; The two reconstructed sequences are subjected to least squares adaptive filtering, and the filtered sequences are subjected to quadratic cross-correlation delay estimation, and the delay value is obtained using the cubic spline interpolation method.

9. A system for implementing the cross-correlation analysis and time delay estimation method of the mid- and low-latitude geomagnetic field SqZ and the ionospheric TEC as claimed in any one of claims 1 to 8, characterized in that: It includes a collection module, a pre-processing module and an analysis module connected in sequence; The acquisition module is used to collect geomagnetic data, ionospheric data, and magnetically quiet days and solar activity parameters; The preprocessing module is used to preprocess the geomagnetic data and ionosphere data to obtain the geomagnetic field Z component time series and TEC time series; The analysis module is used to perform cross-correlation analysis on the Z component time series of the geomagnetic field during the magnetic quiet day and the TEC time series, and estimate the correlation delay between the two.

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