Satellite two-line element set generation method based on telemetry data

Abstract

The present application aims to provide a satellite two-line element generation method based on telemetry data. The method comprises the following steps: S1, satellite telemetry data processing; S2, setting two-line element initial value; S3, preliminary determination of two-line element; S4, optimization estimation of drag coefficient term; S5, generating final satellite two-line element according to two-line element rule; the generated two-line element can be connected to an internet collaborative manufacturing platform and an internet intelligent manufacturing service platform, and is suitable for industrial data integration service and industrial internet basic environment operation service demand, which not only solves the contradiction between satellite two-line element autonomy and universality, but also provides technical support for high-quality industrial internet service in the field of satellite measurement and control, and is suitable for remote sensing satellite measurement and control technical field and industrial internet related scene. The present application relates to the field of remote sensing satellite measurement and control technology, and is also applied to the field of industrial internet technology system service and industrial big data resource service.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of remote sensing satellite measurement operation control technology, and is suitable for industrial internet technology system service and industrial big data resource service scene, in particular to a satellite two-line element generation method based on telemetry data. The method generates satellite two-line elements through telemetry data information processing, which not only serves satellite measurement operation control calculation, but also provides independent controllable orbit parameter support for the internet collaborative manufacturing platform and internet intelligent manufacturing service platform in the field of satellite measurement operation control, and adapts to the service of industrial internet information sensing technology and the demand of industrial data integration, belonging to the cross-application category of remote sensing satellite measurement operation control technology and related services of industrial internet. BACKGROUND

[0002] Generally, to describe the state of a spacecraft in space, the simplest is the instantaneous orbit elements of the spacecraft, including six orbit parameters: semi-major axis, eccentricity, inclination, longitude of ascending node, argument of perigee, and time of perigee (or true anomaly or mean anomaly). According to these six parameters, through the corresponding orbit prediction model, such as two-body model, the position and velocity information of the spacecraft at any time in space can be calculated. However, when considering various perturbations, the orbit prediction model will become quite complex, and the calculation amount will also increase significantly. Moreover, for different perturbation orders considered for various perturbations, there will be a large difference in the accuracy and calculation amount of the results. Therefore, instantaneous orbit elements are mainly used for theoretical analysis on the ground, and lack certain universality in engineering applications. To establish a globally applicable orbit prediction model, the existing method is to remove the periodic variation term based on the instantaneous orbit elements in a specific way to obtain the "average" orbit elements widely used in engineering, which are defined as two-line orbit elements (TLE, Two Line Elements).

[0003] At present, two-line elements of most space targets are updated regularly, but the algorithm for generating two-line elements is not disclosed, which limits the further application of two-line elements. In the aspect of generating two-line elements, patent document 1 (publication number: CN103514362A, name: two-line element generation method based on model error compensation) generates two-line elements by using single-point iteration and sampling fitting, but needs to perform complex and tedious partial derivative calculation; patent document 2 (publication number: CN120337496A, name: method and system for obtaining two-line elements by numerical orbit propagator) considers satellite maneuvering, generates a series of predicted values by using a numerical propagator method, and generates two-line elements by using single-point iteration and least square optimization, but needs accurate satellite parameters and a dynamic model for designing a numerical propagator. Document 3 (conversion method from instantaneous elements to two-line elements, Modern Radar, December 2012) gives a conversion method from instantaneous elements to two-line elements by using the SGP4 model and a numerical iteration method, but uses a single-point method and cannot obtain the drag coefficient term in two-line elements.

[0004] In addition, existing satellite telemetry data contains satellite instantaneous elements and other satellite positioning information, but due to the lack of a two-line element generation method based on satellite telemetry data, most satellite measurement and control units perform satellite measurement and control management based on satellite instantaneous elements or known two-line elements, but satellite instantaneous elements lack a certain universality in engineering application, and known two-line elements lack autonomy. SUMMARY

[0005] The present application aims at the deficiencies of the prior art, and proposes a satellite two-line element generation method based on telemetry data, which generates two-line elements with strong universality by using instantaneous elements in satellite telemetry with strong autonomy, and serves satellite measurement and control management.

[0006] The technical scheme adopted by the present application is a satellite two-line element generation method based on telemetry data, which comprises the following steps:

[0007] S1, satellite telemetry data processing, obtaining N instantaneous element observation values in the J2000 coordinate system from satellite telemetry data, converting to position and velocity form, and further converting to the TEME coordinate system to obtain a series of instantaneous orbital elements in the TEME coordinate system;

[0008] S2, setting two-line element initial values, setting parameters for each field of the first row and the second row according to the fixed format of two-line elements;

[0009] S3, initially determining two-line elements, calculating position and velocity values based on the SGP4 model by using a single-point iteration method, comparing with observation values, iteratively updating until the accuracy requirement is met, and outputting preliminary two-line elements;

[0010] S4. Optimization estimation of drag coefficient term: The first derivative of track translation and drag coefficient are used as optimization parameters. The total error is calculated using the remaining observations. The parameters that minimize the total error are solved by the optimization algorithm.

[0011] S5. Generate two rows of orbital elements. Based on the optimized parameters, generate the final two rows of satellite elements according to the two-row element rules.

[0012] Furthermore, the specific steps of step S1 are as follows:

[0013] S11. Obtain satellite telemetry data from the ground control station to get N instantaneous element observations, represented as:

[0014] ,

[0015] Where t is time, a is the semi-major axis, e is the eccentricity, and i is the orbital inclination angle. Right ascension of the ascending node, The perigee argument, The angle is the planar anterior angle, and its value is in the J2000 coordinate system; N is the number of instantaneous moments.

[0016] Based on satellite orbital mechanics theory, the instantaneous roots are converted into position and velocity forms to obtain the position and velocity values ​​at N time points, i.e.

[0017] ;

[0018] S12. Transform the position and velocity to the TEE coordinate system. The transformation formula is as follows:

[0019] [ x teme y teme z teme x ˙ teme y ˙ teme z ˙ teme ] = X teme = ( C teme J 2000 ) T X = ( C teme J 2000 ) T [ x y z x ˙ y ˙ z ˙ ]

[0020] in, , , , There are three precession parameters. It is the angle between the obliquity of the ecliptic and the obliquity of the sun. For intersecting nutation; The ecliptic longitude was calculated using an astronomical algorithm based on the Julian century number. R is the standard rotation matrix, and the subscripts 1, 2, and 3 of R represent rotations around the x-axis, y-axis, and z-axis, respectively. A series of observations were then obtained from this. ;

[0021] S13. Then, based on satellite orbit theory, the position and velocity values ​​are converted into instantaneous orbital elements, resulting in a series of instantaneous orbital elements in the TEE system, in the following form:

[0022] .

[0023] Furthermore, step S2 specifically involves,

[0024] Set the two-line root style as follows:

[0025] 1 NNNNNC NNNNNAAA NNNNN.NNNNNNNN +.NNNNNNNN +NNNNN-N +NNNNN-N NNNNNN,

[0026] 2 NNNNN NNN.NNNN NNN.NNNN NNNNNNN NNN.NNNN NNN.NNNNNN.NNNNNNNNNNNNNN;

[0027] Each line consists of 69 characters, with each character forming a column containing spaces, numbers, letters, and symbols.

[0028] Furthermore, in step S2, the formula for calculating the number of runs per day, n, in columns 53-63 of the second row of root numbers is as follows:

[0029] .

[0030] Furthermore, the specific steps of step S3 are as follows:

[0031] S31. Generate two rows of roots: Initially use the two rows of roots obtained in the previous step.

[0032] S32. Calculate satellite position and velocity: Calculate the time using the SGP4 model based on two-row roots. Position velocity value The calculation method adopts the open-source SGP4 model algorithm;

[0033] S33. Calculate the position-velocity difference using the following formula:

[0034] ,

[0035] S34. Determine if the accuracy requirement is met. If the modulus of the difference errorx is less than the accuracy value ε, that is... If the result meets the requirements, the iteration ends, and two preliminary rows of roots are output; otherwise, proceed to the next step.

[0036] S35. Iterate and update. The calculation formula is as follows:

[0037] ;

[0038] S36. Convert to instantaneous orbital elements, return to the first iteration, and update the initial values ​​of the two rows of elements.

[0039] Furthermore, the specific steps of step S4 are as follows:

[0040] Using the first derivative of the orbital translation and the drag coefficient as optimization parameters, the total error is calculated using the remaining observations. After determining the two rows of roots, the time is calculated based on the SGP4 model. Position velocity value The error values ​​at each time point are obtained, and the calculation formula is as follows:

[0041] ,

[0042] The total error value is

[0043] ,

[0044] The first derivative of the orbital translation and the drag coefficient are solved by an optimization algorithm. These two parameters are optimized until the total error is minimized, thus obtaining the optimized first derivative of the orbital translation and the drag coefficient.

[0045] Furthermore, the optimization algorithm is either the least squares method or a genetic algorithm.

[0046] Furthermore, when the optimization algorithm is a genetic algorithm, its specific optimization process is as follows:

[0047] 1) Optimize parameter binary encoding: The first derivative of orbital translation is encoded using N1 bits of binary encoding, and the drag coefficient is encoded using N2 bits of binary encoding. The values ​​of N1 and N2 bits are taken with reference to the existing two rows of roots of the same type and orbit of satellites.

[0048] 2) Set the population size NN and the number of iterations MM; initialize the population with optimization parameters;

[0049] 3) Optimize the parameter population by generating a two-row root population based on the initial values ​​of the two rows of roots. };

[0050] 4) Calculate the total error value: Calculate the total error value based on the number of roots in each pair of rows;

[0051] 5) When the number of iterations reaches MM, the iteration ends, and the individual with the smallest total error value is output; otherwise, proceed to the next step.

[0052] 6) Individual selection: Individuals are sorted in ascending order of total error value, and the best individual is selected;

[0053] 7) Individual crossover and mutation: Crossover refers to the process by which two paired individuals exchange some of their genes in a certain way, thereby forming two new individuals;

[0054] 8) Update the population, increment the iteration count by 1, and return to step S4.

[0055] The beneficial effects of this invention are as follows:

[0056] 1. Resolving the core contradiction between autonomy and universality: In existing technologies, the two-line root data provided by NORAD lacks autonomy, while the instantaneous orbital root data lacks universality. This invention directly utilizes satellite telemetry data (an autonomous and controllable data source) and, through coordinate system transformation and iterative optimization, converts the instantaneous root data into two-line root data that are universally applicable in engineering. This ensures both the autonomy of the data source and the global universality of the two-line root data, perfectly resolving the contradiction between the two and providing autonomous and reliable orbital parameter support for satellite telemetry, tracking, and command.

[0057] 2. Reduced technical difficulty and improved engineering applicability: Existing patented technologies require complex partial derivative calculations or precise satellite parameters and dynamic models, resulting in high technical barriers and hindering engineering promotion. This invention adopts a combination of "single-point iteration + mature optimization algorithms." The single-point iteration method eliminates the need for complex partial derivative calculations, allowing for the preliminary determination of the number of roots simply through iterative updates of the position-velocity difference. The drag coefficient estimation employs mature algorithms such as least squares and genetic algorithms, which can be directly implemented using existing functions in tools like MATLAB, eliminating the need to redesign complex algorithms. This significantly reduces technical difficulty and facilitates learning and application by satellite telemetry, tracking, and command (TT&C) technicians.

[0058] 3. Addressing the technical shortcoming of missing drag coefficient term: Existing single-point conversion methods cannot derive the drag coefficient term from the two rows of roots, affecting the accuracy of trajectory prediction. This invention, after initially determining the roots through single-point iteration, specifically optimizes and estimates the first-order translational derivative of the trajectory and the drag coefficient value related to the drag coefficient using the remaining observation sequence. Accurate parameter solving is achieved through multi-observation data fitting, thus addressing the parameter deficiency in existing technologies and improving the completeness and prediction accuracy of the two rows of roots.

[0059] 4. Simplify the calculation process and ensure the reliability of the results: This invention uses the open-source SGP4 model for position and velocity calculation. This model is a mature orbital model widely used in engineering, and its calculation accuracy and reliability have been verified. At the same time, through the two-step method of "preliminary determination + optimization estimation", the approximate parameter range is first quickly locked through single-point iteration, and then the key parameters are finely adjusted through multi-observation point optimization. This simplifies the calculation process and ensures the reliability of the final result through multi-data verification, avoiding the error accumulation problem that may exist in a single algorithm.

[0060] 5. Adapting to the Industrial Internet Ecosystem and Expanding the Boundaries of Technology Applications: The satellite two-row root data generated by this invention can be directly connected to the Internet intelligent manufacturing service platform and the industrial Internet basic environment operation service system, providing a unified orbit parameter standard for the Internet production service platform in the field of satellite telemetry, tracking, and control; the telemetry data processing process is compatible with industrial Internet of Things information sensing technology services, and can be seamlessly connected with industrial Internet of Things sensor networks, solving the data interoperability problem between satellite data and industrial Internet platforms, and helping satellite telemetry, tracking, and control to integrate into the industrial Internet ecosystem.

[0061] 6. Supporting Industrial Big Data Services and Enhancing Data Asset Value: The standardized processing of telemetry data (such as coordinate system transformation and error optimization) in this invention can form a satellite orbit dataset that meets the requirements of industrial production big data resource services. This dataset can be incorporated into industrial databases or industrial cloud databases to achieve long-term data storage, reuse, and traceability. At the same time, through batch processing and integration of multi-satellite data, it can provide industrial data integration service capabilities, provide a high-quality data foundation for big data analysis of satellite telemetry, tracking, and command (such as constellation orbit collaborative optimization), and enhance the application value of data assets. Attached Figure Description

[0062] teme This is a simplified flowchart of the method of the present invention;

[0063] fig. 1 This is a preliminary flowchart for determining the roots in two rows;

[0064] fig. 2 This is a flowchart for optimizing the estimation of the resistance coefficient. Detailed Implementation

[0065] like fig. 3 As shown, this invention provides a method for generating two rows of satellite roots based on telemetry data. The method includes the following steps:

[0066] S1, Satellite Telemetry Data Processing

[0067] 1) Satellite telemetry data is acquired from ground control stations, resulting in a series of instantaneous element observations. Assuming there are N instantaneous element observations, this can be represented as...

[0068]

[0069] Where t is time, a is the semi-major axis, e is the eccentricity, and i is the orbital inclination angle. Right ascension of the ascending node, The perigee argument, The angle is the approximate angle, and the value is in the J2000 coordinate system.

[0070] According to satellite orbital mechanics theory, instantaneous roots can be converted into position and velocity forms, thus obtaining the position and velocity values ​​at N time points.

[0071]

[0072] 2) Since the two rows of roots use the TME coordinate system, the position and velocity need to be transformed to the TME coordinate system. The transformation formula is as follows:

[0073] [ x figs. 1-3 y teme z teme x ˙ teme y ˙ teme z ˙ teme ] = X teme = ( C teme J 2000 ) T X = ( C teme J 2000 ) T [ x y z x ˙ y ˙ z ˙ ]

[0074] in, , , , There are three precession parameters. It is the angle between the obliquity of the ecliptic and the obliquity of the sun. For intersecting nutation; The ecliptic longitude can be calculated using astronomical algorithms based on the Julian centuries; R is a standard rotation matrix, where the subscripts 1, 2, and 3 represent rotations around the x-axis, y-axis, and z-axis, respectively. The factor is a coordinate transformation matrix, representing the transformation matrix for converting a vector from the TME coordinate system to the J2000 coordinate system. Telemetry data is acquired in the J2000 coordinate system and needs to be transformed to the TME coordinate system used for two-row roots. This allows for the generation of a series of observation values. .

[0075] Then, based on satellite orbit theory, the position and velocity values ​​are converted into instantaneous orbital elements, resulting in a series of instantaneous orbital elements in the TEE system, in the following form:

[0076]

[0077] S2. Set the initial values ​​of the two rows of roots.

[0078] Set the two-line root style as follows:

[0079] 1 NNNNNC NNNNNAAA NNNNN.NNNNNNNN +.NNNNNNNN +NNNNN-N +NNNNN-N NNNNNN,

[0080] 2 NNNNN NNN.NNNN NNN.NNNN NNNNNNN NNN.NNNN NNN.NNNNNN.NNNNNNNNNNNNNN;

[0081] Each line consists of 69 characters, with each character forming a column containing spaces, numbers, letters, and symbols.

[0082] Specifically, the first line is set as follows:

[0083] Column 01: 1 indicates the first row;

[0084] Column 02: empty;

[0085] Columns 03-07: Satellite numbers, for cataloging purposes only;

[0086] Column 08: Default is U;

[0087] Column 09: Empty

[0088] Columns 10-11: Launch year;

[0089] Columns 12-14: Annual launch sequence;

[0090] Columns 15-17: These are the launch component numbers;

[0091] Column 18: empty;

[0092] Columns 19-32: Time values, taking the time of the first observation. There are 14 columns in total. The first two columns are the last two digits of the year, followed by the number of days in that year. The integer part has 3 digits and the decimal part has 8 digits. The number format is "000.00000000".

[0093] Column 33: empty;

[0094] Columns 34-43: The first derivative of the orbital translation, set to +.00000000;

[0095] Column 44: empty;

[0096] Columns 45-52: The second derivative of the orbital translation, set to +00000-0;

[0097] Column 53: empty;

[0098] Columns 54-61: These are the resistance coefficient values, set to +00000-0;

[0099] Column 62: empty;

[0100] Column 63: This is the orbital calculation model; set it to 0.

[0101] Column 64: empty;

[0102] Columns 65-68: ephemeris symbols;

[0103] Column 69: This is the check value, which is the remainder when the sum of all the numbers in the first row is divided by 10. Spaces, the letter U, decimal points, and plus signs are considered 0, and hyphens - are considered 1.

[0104] The second line is set as follows:

[0105] Column 01: 2 indicates row 2;

[0106] Column 02: empty;

[0107] Columns 03-07: Satellite numbers, set the same as the first row;

[0108] Column 08: empty;

[0109] Columns 09-16: Track tilt angle, set to... The unit is degrees, with 3 integer digits and 4 decimal digits, in the form of 000.0000;

[0110] Column 17: empty;

[0111] Columns 18-25: Right ascension of the ascending node, set as follows The unit is degrees, with 3 integer digits and 4 decimal digits, in the form of 000.0000;

[0112] Column 26: empty;

[0113] Columns 27-33: Eccentricity, set to... *10e7, a 7-bit integer, in the form of 0000000;

[0114] Column 34: empty;

[0115] Columns 35-42: Perimeter argument, set to... The unit is degrees, with 3 integer digits and 4 decimal digits, in the form of 000.0000;

[0116] Column 43: empty;

[0117] Columns 44-51: For the near-point angle, set to... The unit is degrees, with 3 integer digits and 4 decimal digits, in the form of 000.0000;

[0118] Column 52: empty;

[0119] Columns 53-63: This represents the number of laps per day, set to n, with 2 integer digits and 8 decimal digits, in the format 00.00000000; the formula for calculating n is as follows.

[0120] ;

[0121] Columns 64-68: Number of laps at the current moment;

[0122] Column 69: Check value, calculated in the same way as the first row.

[0123] S3. Preliminary determination of the number of roots in the two rows

[0124] The initial determination of the number of roots in two rows is achieved using a single-point iteration method, with the following steps:

[0125] 1) Generate two rows of roots: Initially use the two rows of roots obtained in the previous step.

[0126] 2) Calculate satellite position and velocity: Calculate the time using the SGP4 model based on two-row roots. Position velocity value The calculation method can adopt the open-source SGP4 model algorithm;

[0127] 3) Calculate the position-velocity difference using the following formula:

[0128]

[0129] 4) Determine if the accuracy requirement is met. If the modulus of the difference is less than the accuracy value, i.e. If the result meets the requirements, the iteration ends, and two preliminary rows of roots are output; otherwise, proceed to the next step.

[0130] 5) Iterate and update. The calculation formula is as follows:

[0131]

[0132] 6) Convert to instantaneous orbital elements, return to the first iteration, and update the initial values ​​of the two rows of elements.

[0133] S4, Optimization estimation of the drag coefficient term

[0134] The previous step has preliminarily determined the two rows of roots. Only two terms remain: the first derivative of the orbital translation related to the drag coefficient and the drag coefficient value. The remaining observation sequence will be used for optimization.

[0135] Time calculation based on SGP4 model Position velocity value The error value at each time point can be obtained, and the calculation formula is as follows:

[0136]

[0137] The total error value is

[0138]

[0139] The optimization parameters are the first derivative of the orbital translation and the drag coefficient. The optimization objective is to minimize the total error. This is a typical optimization problem, solvable using optimization algorithms. Mature least squares methods or intelligent algorithms such as genetic algorithms can be employed. Refer to the lsqnonlin, fmin, and ga functions in MATLAB. The optimization process is briefly described below using a genetic algorithm as an example:

[0140] 1) Optimize parameter binary encoding: The first derivative of orbital translation uses N1-bit binary encoding, and the drag coefficient value uses N2-bit binary encoding. The values ​​of N1 and N2 are taken from the existing two rows of roots of similar satellites in the same orbit. For example, the first derivative of orbital translation has about 6 significant digits, so N1 can be taken as 20 (2^20=1048576). The drag coefficient value is 00000-0. The value after - is generally within 4, which requires 2 bits of binary encoding. The first 5 digits require 17 bits of binary encoding. Therefore, the drag coefficient value can use 19 bits of binary encoding, for a total of 39 bits of binary encoding.

[0141] 2) Set the population size NN and the number of iterations MM; initialize the population with optimization parameters;

[0142] 3) Optimize the parameter population by generating a two-row root population based on the initial values ​​of the two rows of roots. };

[0143] 4) Calculate the total error value: Calculate the total error value based on the number of roots in each pair of rows;

[0144] 5) When the number of iterations reaches MM, the iteration ends, and the individual with the smallest total error value is output; otherwise, proceed to the next step.

[0145] 6) Individual selection: Individuals are sorted in ascending order of total error value, and the best individual is selected;

[0146] 7) Individual crossover and mutation: Crossover refers to the exchange of some genes between two paired individuals in a certain way to form two new individuals; mutation refers to the replacement of gene values ​​at certain loci in the chromosome coding string of an individual with other alleles at that locus to form a new individual.

[0147] 8) Update the population, increment the iteration count by 1, and return to step S4.

[0148] S5. Generate two rows of orbital elements: Based on the optimized first derivative of the orbital translation and the drag coefficient value, generate two rows of orbital elements according to the two-row element rule.

[0149] The following section uses telemetry data from a low-orbit satellite as an example to illustrate the specific implementation process of this invention.

[0150] 1. Satellite telemetry data processing

[0151] 1) Telemetry data of the low-orbit satellite were acquired from the ground control station, resulting in N=10 instantaneous element observations in the J2000 coordinate system. Some of the observations are as follows (only three times t1, t2, and t3 are listed):

[0152] t1=2023-06-15 10:00:00, a1=7000km, e1=0.001, i1=55.0°, Ω1=120.0°, ω1=30.0°, M1=60.0°;

[0153] t2=2023-06-15 10:05:00, a2=7001km, e2=0.001, i2=55.0°, Ω2=120.1°, ω2=30.0°, M2=70.0°;

[0154] t3=2023-06-15 10:10:00, a3=7000km, e3=0.001, i3=55.0°, Ω3=120.2°, ω3=30.0°, M3=80.0°.

[0155] Based on satellite orbital mechanics theory, the above instantaneous roots are converted into position and velocity forms. Taking t1 as an example, the position vector and velocity vector are obtained.

[0156] 2) Calculate the precession parameter, obliquity of the ecliptic, and nutation parameter (based on Julian centuries) according to the TME coordinate system transformation formula. Then, transform the J2000 coordinate system to the TME coordinate system using the rotation matrix R to obtain the position and velocity values ​​in the TME system at time t1. Finally, convert these values ​​to instantaneous orbital elements in the TME system, as follows:

[0157] .

[0158] 2. Set initial values ​​for the two rows of roots.

[0159] The first row is set as follows: Satellite number 43439; Column 08 U; Launch year 17 (launched in 2017); Launch sequence 54; Launch component number A; Columns 19-32 time is "17166.41666667" (10:00:00 on the 166th day of 2017); Columns 34-43 +.00000000; Columns 45-52 +00000-0; Columns 54-61 +00000-0; Column 63 0; Columns 65-68 0001; Checksum calculation: Add the numbers in the first row (spaces, U, decimal points, and plus signs are counted as 0, minus signs are counted as 1), divide the result by 10 and take the remainder to get the checksum 5.

[0160] The second row of settings includes: satellite number 43439; orbital inclination 55.0000°; right ascension of ascending node 120.0000°; eccentricity 0001000 (0.001×10e7); perigee argument 30.0000°; mean perigee angle 60.0000°; daily orbits n calculated using the formula is 15.75000000; current orbit number 00001; the checksum is calculated the same as in the first row, resulting in a checksum of 3.

[0161] 3. Preliminarily determine the number of roots in the two rows.

[0162] refer to teme The flowchart and implementation steps are as follows:

[0163] 1) Generate two rows of roots using the initial values ​​set in step S2;

[0164] 2) Calculate the position and velocity values ​​at time t1 based on the open-source SGP4 model;

[0165] 3) Calculate the position-velocity difference to obtain |errorx|;

[0166] 4) Set the precision value to 1e-6m. Since |errorx|>1e-6m, proceed with the iteration.

[0167] 5) Update parameters according to the formula ;

[0168] 6) Convert the updated parameters into instantaneous orbital elements, return to step 1) to regenerate two rows of elements, and repeat the iteration;

[0169] 7) When the iteration reaches the 8th iteration, the modulus of the difference = 8e-7m < 1e-6m, the iteration ends, and the initial two rows of roots are output.

[0170] 4. Optimization estimation of the drag coefficient term

[0171] refer to fig. 2 fig. 3 The flowchart is optimized using a genetic algorithm, and the steps are as follows:

[0172] 1) Optimize parameter encoding: The first derivative of the orbital translation is encoded using 20-bit binary code, and the drag coefficient is encoded using 19-bit binary code, for a total encoding of 39 bits;

[0173] 2) Set the population size NN=50, the number of iterations MM=100, and initialize 50 individuals with optimized parameters to form a population;

[0174] 3) Based on the initially determined number of two-row roots, each individual optimization parameter is substituted to generate 50 populations of two-row roots;

[0175] 4) For each pair of roots, use the SGP4 model to calculate the position and velocity values ​​at times t2-t10, compare them with the observed values ​​to obtain the error values ​​at each time, and calculate the total error value according to the formula;

[0176] 5) Sort the 50 individuals by total error value from smallest to largest, and select the top 20 best individuals;

[0177] 6) Perform crossover (randomly exchange some gene segments) and mutation (randomly flip some gene loci) operations on the best individuals to generate 30 new individuals, which will then form a new population with the 20 best individuals;

[0178] 7) Increment the iteration count by 1 and return to step 4) until the iteration count reaches 100;

[0179] 8) Output the individual with the smallest total error value to obtain the optimized first derivative of the orbital translation and the drag coefficient value.

[0180] 5. Generate two rows of orbital elements

[0181] Substitute the optimized first derivative of the orbital translation and the drag coefficient values ​​into the two-row root format, update the parameters in columns 34-43 and 54-61 of the first row, recalculate the verification values, and finally generate the complete two-row orbital roots:

[0182] 143439U1754A17166.41666667+.00001234+00000-0+12345-300001,

[0183] 24343955.0000120.0000000100030.000060.000015.7500000000001.

[0184] This embodiment successfully generates two rows of roots containing drag coefficient terms based on satellite telemetry data through the above steps. The entire process does not rely on external data, the technical process is simple, the calculation results are reliable, and it fully meets the application requirements of satellite telemetry, tracking, and command management.

[0185] In this embodiment, the generated final satellite orbital root count can be shared through the industrial internet infrastructure environment operation service platform, supporting collaborative management of telemetry, tracking, and command (TT&C) data for multiple satellites. For example, after connecting to the internet collaborative manufacturing platform, the platform can realize batch TT&C scheduling of satellite constellations based on the orbital parameters generated by this invention. At the same time, the original observation values, conversion results, and final results of the two-row root count of telemetry data can be integrated into the industrial cloud database through industrial data integration services to form standardized satellite orbital parameter data assets, providing industrial big data resource service support for subsequent big data analysis of satellite TT&C (such as orbital drift pattern mining).

[0186] Finally, it should be emphasized that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for generating two rows of satellite roots based on telemetry data, characterized in that, The method includes the following steps: S1. Satellite telemetry data processing: Obtain satellite telemetry data to get N instantaneous element observations in the J2000 coordinate system, convert them into position and velocity forms, and then further convert them to the TEE coordinate system to obtain a series of instantaneous orbital elements in the TEE system. S2. Set the initial values ​​for the number of roots in the two rows. According to the fixed format of the number of roots in the two rows, set the parameters for each field in the first and second rows respectively. S3. Initially determine the number of roots in the two rows. Using the single-point iteration method, calculate the position and velocity values ​​based on the SGP4 model and compare them with the observed values. Iterate and update until the accuracy requirements are met, and output the initial number of roots in the two rows. S4. Optimization estimation of drag coefficient term: The first derivative of track translation and drag coefficient are used as optimization parameters. The total error is calculated using the remaining observations. The parameters that minimize the total error are solved by the optimization algorithm. S5. Generate two rows of orbital elements. Based on the optimized parameters, generate the final two rows of satellite elements according to the two-row element rules. The specific steps of step S3 are as follows: S31. Generate two rows of roots: Initially use the two rows of roots obtained in the previous step. S32. Calculate satellite position and velocity: Calculate the time using the SGP4 model based on two-row roots. Position velocity value The calculation method adopts the open-source SGP4 model algorithm; S33. Calculate the position-velocity difference using the following formula: ， S34. Determine if the accuracy requirement is met. If the modulus of the difference errorx is less than the accuracy value ε, that is... If the result meets the requirements, the iteration ends, and two preliminary rows of roots are output; otherwise, proceed to the next step. S35. Iterate and update. The calculation formula is as follows: ； S36. Convert to instantaneous orbital elements, return to the first iteration, and update the initial values ​​of the two rows of elements.

2. The method according to claim 1, characterized in that, The specific steps of step S1 are as follows: S11. Obtain satellite telemetry data from the ground control station to get N instantaneous element observations, represented as: ， Where t is time, a is the semi-major axis, e is the eccentricity, and i is the orbital inclination angle. Right ascension of the ascending node, The perigee argument, The angle is the planar anterior angle, and its value is in the J2000 coordinate system; N is the number of instantaneous moments. Based on satellite orbital mechanics theory, the instantaneous roots are converted into position and velocity forms to obtain the position and velocity values ​​at N time points, i.e. ； S12. Transform the position and velocity to the TEE coordinate system. The transformation formula is as follows: ， in, , , , There are three precession parameters. It is the angle between the obliquity of the ecliptic and the obliquity of the sun. For intersecting nutation; The ecliptic longitude was calculated using an astronomical algorithm based on the Julian century number. R is the standard rotation matrix, and the subscripts 1, 2, and 3 of R represent rotations around the x-axis, y-axis, and z-axis, respectively. A series of observations were then obtained from this. ; S13. Then, based on satellite orbit theory, the position and velocity values ​​are converted into instantaneous orbital elements, resulting in a series of instantaneous orbital elements in the TEE system, in the following form: 。 3. The method according to claim 2, characterized in that, Step S2 specifically involves: Set the two-line root style as follows: 1 NNNNNC NNNNNAAA NNNNN.NNNNNNNN +.NNNNNNNN +NNNNN-N +NNNNN-N N NNNNN, 2 NNNNN NNN.NNNN NNN.NNNN NNNNNNN NNN.NNNN NNN.NNNN NN.NNNNNNNNNNNNNN; Each line consists of 69 characters, with each character forming a column containing spaces, numbers, letters, and symbols.

4. The method according to claim 3, characterized in that, In step S2, the formula for calculating the number of laps n per day in columns 53-63 of the second row of roots is as follows: 。 5. The method according to claim 1, characterized in that, The specific steps of step S4 are as follows: Using the first derivative of the orbital translation and the drag coefficient as optimization parameters, the total error is calculated using the remaining observations. After determining the two rows of roots, the time is calculated based on the SGP4 model. Position velocity value The error values ​​at each time point are obtained, and the calculation formula is as follows: ， The total error value is ， The first derivative of the orbital translation and the drag coefficient are solved by an optimization algorithm. These two parameters are optimized until the total error is minimized, thus obtaining the optimized first derivative of the orbital translation and the drag coefficient.

6. The method according to claim 5, characterized in that, The optimization algorithm is either the least squares method or a genetic algorithm.

7. The method according to claim 6, characterized in that, When the optimization algorithm is a genetic algorithm, the specific optimization process is as follows: 1) Optimize parameter binary encoding: The first derivative of orbital translation is encoded using N1 bits of binary encoding, and the drag coefficient is encoded using N2 bits of binary encoding. The values ​​of N1 and N2 bits are taken with reference to the existing two rows of roots of the same type and orbit of satellites. 2) Set the population size NN and the number of iterations MM; initialize the population with optimization parameters; 3) Optimize the parameter population by generating a two-row root population based on the initial values ​​of the two rows of roots. }; 4) Calculate the total error value: Calculate the total error value based on the number of roots in each pair of rows; 5) When the number of iterations reaches MM, the iteration ends, and the individual with the smallest total error value is output; otherwise, proceed to the next step. 6) Individual selection: Individuals are sorted in ascending order of total error value, and the best individual is selected; 7) Individual crossover and mutation: Crossover refers to the process by which two paired individuals exchange some of their genes in a certain way, thereby forming two new individuals; 8) Update the population, increment the iteration count by 1, and return to step S4.

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