Satellite multi-tense visualization method based on SGP4 orbit model and database

Abstract

The invention relates to the technical field of spacecraft orbit and attitude visualization, in particular to a satellite multi-tense visualization method based on an SGP4 orbit model and a database, and the method comprises the steps: receiving target time inputted by a user, judging whether telemetering data in a table form corresponding to the target time is stored in a preset database or not to obtain a judgment result; if the judgment result is that the telemetry data in the table form corresponding to the target time is not stored in the preset database, obtaining the telemetry data in the table form in a specified time period in the preset database, and displaying the attitude and orbit of the satellite at the target time according to the table-form telemetry data in a preset database in a specified time period, the pre-acquired TLE data and the specified pose of the satellite at the target time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of spacecraft orbit and attitude visualization, and particularly relates to a satellite multi-temporal visualization method based on an SGP4 orbit model and a database, a computer device and a computer readable storage medium. BACKGROUND

[0002] In many space missions such as spacecraft orbit monitoring, mission planning and on-orbit control, accurate visualization of satellite orbit and attitude is an important basis for task execution and evaluation. At present, orbit visualization is mostly based on dynamic modeling and three-dimensional graphic rendering of telemetry data (such as orbit position and velocity output by GNSS receivers) and attitude sensor data (such as attitude quaternion output by star sensors). With the rapid development of commercial space and on-orbit service scenarios, there is a higher demand for visualization of satellite orbit and attitude states at any time, especially under the conditions of non-continuous data, missing or insufficient sampling of orbit / attitude data, which still requires high-precision and multi-temporal visualization capabilities.

[0003] However, in the current technology, the visualization method based on telemetry data driving generally relies on the integrity and continuity of telemetry data, that is, it is assumed that the database can provide direct measurement values or equally spaced sampling values at the queried time point. In actual applications, the acquisition of telemetry data is intermittent and uncertain, for example, due to factors such as limited satellite orbit communication window, telemetry data backhaul bandwidth or ground receiving frequency, the target time point cannot be directly matched to the existing sampling data in the telemetry database, resulting in the inability of the visualization system to effectively reconstruct the attitude and orbit state at that time point. This dependence on the integrity of the data time sequence seriously limits the universality and timeliness of the orbit visualization system.

[0004] To solve this problem, some solutions attempt to compensate for the orbit state at the target time based on an orbit prediction model (such as SGP4), but this method usually uses fixed parameters or a single TLE data for extrapolation, lacking dynamic fusion of historical telemetry data, resulting in large prediction trajectory deviation. At the same time, since TLE data is regularly published in intervals, the current related methods have not effectively solved the orbit update selection strategy when the target time is in the middle of the TLE data publishing interval, causing the timeliness of orbit prediction to decline or the error to accumulate.

[0005] In addition, the existing system is based on a structured telemetry database to query and display the orbit / attitude state, and when the data corresponding to the target time point is missing, interpolation or estimation is often used to recover the trajectory. For example, linear interpolation, polynomial fitting or spline curve is used to smooth the position / velocity data to generate a trajectory for visualization, but such "virtual data" deviates significantly from the actual dynamic characteristics, especially when the attitude changes rapidly or the orbit perturbation is significant, which can easily introduce error propagation and even lead to inaccurate orbit prediction. In addition, such methods have strong dependence on telemetry data structure, poor universality, and cannot adapt to the processing requirements of multi-source data fusion and non-uniform sampling. SUMMARY

[0006] (1) Technical problems to be solved

[0007] In view of the above-mentioned defects and deficiencies of the prior art, the present application provides a satellite multi-temporal visualization method, device and medium based on SGP4 orbit model and database, which solves the technical problem that the system cannot accurately restore the orbit and attitude state of the satellite at the target time point when the target time point is outside the telemetry data sampling interval or the data is missing, resulting in distorted visualization results or rendering failure.

[0008] (2) Technical solutions

[0009] In order to achieve the above-mentioned purpose, the main technical scheme adopted by the present application includes:

[0010] In a first aspect, the present application provides a satellite multi-temporal visualization method based on SGP4 orbit model and database, comprising:

[0011] S1, receiving a target time input by a user, and determining whether the pre-set database stores telemetry data in table form corresponding to the target time, to obtain a determination result;

[0012] S2, if the determination result is that the pre-set database does not store telemetry data in table form corresponding to the target time, obtaining telemetry data in table form of a specified time period in the pre-set database, and displaying the attitude and orbit of the satellite at the target time according to the telemetry data in table form of the specified time period in the pre-set database, the pre-obtained TLE data and the specified attitude of the satellite at the target time.

[0013] Optionally, in some embodiments of the present application, the telemetry data includes:

[0014] the sampling time of the star sensor;

[0015] the attitude quaternion of the satellite corresponding to the sampling time of the star sensor;

[0016] a sampling time of the GNSS receiver;

[0017] an orbital position and a velocity of a satellite corresponding to the sampling time of the GNSS receiver.

[0018] Optionally, in some embodiments of the present application, the S2 specifically comprises:

[0019] S21, if the judgment result is that the pre-set database does not store the telemetry data in the form of a table corresponding to the target time, acquiring the telemetry data in the form of a table in a specified time period in the pre-set database, and converting the telemetry data in the form of a table in the specified time period in the pre-set database into corresponding orbital elements;

[0020] S22, based on the orbital elements and aerodynamic parameters in the pre-acquired TLE data, performing orbital recursion by using an SGP4 orbit model to acquire the position and velocity of the satellite corresponding to the target time;

[0021] S23, based on the position and velocity of the satellite corresponding to the target time and the specified pose of the target time, acquiring an attitude quaternion corresponding to the target time;

[0022] S24, inputting the position and velocity of the satellite corresponding to the target time, the attitude quaternion corresponding to the target time, and the target time into a Cesium open source library to display the attitude and orbit of the satellite at the target time.

[0023] Optionally, in some embodiments of the present application, the specified time period is [t-300 seconds, t];

[0024] wherein t is a time corresponding to the last recorded telemetry data in the form of a table stored in the pre-set database before the target time;

[0025] The pre-acquired TLE data is the latest published TLE data collected according to a pre-set time interval.

[0026] Optionally, in some embodiments of the present application, the S21 specifically comprises:

[0027] If the judgment result is that the pre-set database does not store the telemetry data corresponding to the target time, acquiring the telemetry data in the form of a table in a specified time period in the pre-set database, and performing 7-order Lagrange interpolation on the orbital position and velocity of the satellite corresponding to the GNSS receiver in the specified time period to generate an initial value of the orbital position and an initial value of the velocity, and converting the initial value of the orbital position and the initial value of the velocity into orbital elements based on the initial value of the orbital position and the initial value of the velocity.

[0028] Optionally, in some embodiments of the present application, the specified pose of the target time is a preset standby pose or a preset design pose of the satellite corresponding to the target time.

[0029] Optionally, in some embodiments of the present application, the method further comprises:

[0030] S3, if the judgment result is that the preset database stores the telemetry data corresponding to the target time, input the telemetry data corresponding to the target time and the TLE data corresponding to the target time which is acquired in advance into the Cesium open source library, and display the attitude and orbit of the satellite at the target time.

[0031] Optionally, in some embodiments of the present application, the preset database is in communication connection with a ground station.

[0032] The ground station parses the telemetry data in file form into telemetry data in table form and stores it into the preset database.

[0033] The telemetry data in file form is acquired by the satellite after entering the orbit and transmitted to the ground station through a data transmission station in communication connection with the satellite and the ground station respectively.

[0034] In a second aspect, the present embodiment also provides a computer device, which comprises a processor and a memory, and the processor is configured to execute instructions stored in the memory, so that the computer device executes the satellite multi-time state visualization method based on the SGP4 orbit model and the database according to the first aspect.

[0035] In a third aspect, the present embodiment also provides a computer readable storage medium comprising computer program instructions, which, when executed by a processor, implement the satellite multi-time state visualization method based on the SGP4 orbit model and the database according to the first aspect.

[0036] (III) Beneficial Effects

[0037] The satellite multi-time state visualization method based on the SGP4 orbit model and the database provided by the present embodiment can realize dynamic identification and processing of telemetry data missing scenarios through the introduction of a target time telemetry data integrity judgment mechanism in the data judgment stage, avoid the problem of orbit and attitude calculation failure caused by the lack of key data, and reconstruct the orbit and attitude at the target time point by fusing historical telemetry data in a specified time period, pre-acquired TLE orbit parameters and satellite specified poses, constructing orbit elements and driving the SGP4 model for orbit recursion.

[0038] Meanwhile, the application introduces standby posture or design posture as reference posture information of target time in the posture calculation process, ensuring that the compensation of the posture quaternion can be generated when the posture sensor data is missing. In addition, by establishing a dynamic selection mechanism of TLE data, the latest TLE data closest to the release time is automatically used preferentially, ensuring the accuracy and timeliness of the orbit calculation.

[0039] In summary, the method can independently complete the completion and visual rendering of the orbit and posture at each target time point even if there is an interval, discontinuity or sampling interruption in the telemetry data, ensuring the stability, accuracy and wide applicability of the entire satellite visualization process, and having good robustness and engineering practical value. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 FIG. 1 is a flowchart of a satellite multi-temporal visualization method based on SGP4 orbit model and database according to an embodiment of the application;

[0041] Figure 2 FIG. 2 is a flowchart of displaying the posture and orbit of a satellite at a target time according to pre-set telemetry data in a table form of a specified time period in a database, pre-acquired TLE data and a specified posture of the satellite at the target time according to an embodiment of the application. DETAILED DESCRIPTION

[0042] In order to better explain the application and facilitate understanding, the application is described in detail below in conjunction with the drawings through specific embodiments.

[0043] In related art, the visualization processing of satellite orbit and posture state can be mainly summarized into three types:

[0044] The first type is a visualization method directly driven by complete telemetry data. This type of method relies on satellite on-orbit posture and orbit parameter data collected by star sensors, GNSS receivers and other devices, reconstructs the satellite state through accurate sampling time points, and inputs the results into a three-dimensional visualization platform to realize rendering. However, this type of method has high dependence on the time alignment and completeness of telemetry data. If the target time point requested by the user is not within the coverage of the telemetry data, i.e., the corresponding orbit or posture information cannot be obtained, the visualization fails or is misdisplayed, especially in the initial stage of satellite operation, data back transmission delay or large sampling interval.

[0045] The second type is a method of fitting prediction based on an orbit model and historical telemetry data. This type of method attempts to utilize historical GNSS orbit data, fit the orbit change trend, and use Kalman filtering, Bayesian inference, and other algorithms for orbit prediction to estimate the state at the target time. However, this type of method usually only deals with the orbit position and does not involve attitude compensation, and when the data is sparse or the orbit changes dramatically, the prediction error is large and the stability is poor, which is difficult to meet the accuracy requirements of engineering visualization. In addition, in the case of missing attitude information, the system usually uses the default static attitude to fill in, which lacks flexibility and authenticity.

[0046] The third type is a method of orbit deduction based only on TLE data and SGP4 model. The SGP4 model is a widely used satellite orbit rapid calculation model, which can realize short-term orbit prediction and display in combination with TLE data. However, existing methods often use a fixed set of TLE data, lacking a strategy for dynamically selecting the optimal TLE data according to the target time; at the same time, the SGP4 model itself does not provide attitude information, resulting in only orbit visualization and separate attitude completion. In addition, in the case of lack of historical telemetry data fusion for orbit initial value, there is a cumulative error in orbit prediction, which affects the visualization accuracy in critical periods.

[0047] Therefore, an embodiment of the present application provides a satellite multi-temporal visualization method based on SGP4 orbit model and database. By introducing a target time telemetry data existence judgment mechanism in the visualization preparation stage, the orbit model prediction path can be automatically switched when telemetry data is missing. By extracting GNSS orbit data in the near period, the orbit initial value and velocity initial value are generated using 7-order Lagrange interpolation, which is then converted into orbit elements. Combined with the aerodynamic parameters in TLE and the SGP4 orbit model, the orbit position and velocity at the target time are recursively calculated.

[0048] At the same time, the present application introduces a standby attitude or a designed attitude as the specified pose of the target time attitude, and calculates the attitude quaternion in combination with the orbit state in the visualization stage, thereby completing the attitude information. More importantly, the present application constructs a dynamic selection mechanism for TLE data, which preferentially uses TLE data closest to the release time, thereby improving the timeliness and accuracy of orbit deduction.

[0049] In summary, even in complex scenarios such as telemetry data breakpoints, sampling delays, or misalignment, the present application can independently complete the orbit and attitude reconstruction and visualization at any target time, ensuring the flexibility of system response, the accuracy of orbit and attitude calculation, and the real-time of overall display. Not only is the method stable and reliable, but it also has high calculation accuracy, good universality and scalability, and is suitable for various on-orbit satellite visualization systems.

[0050] For better understanding of the above technical solutions, the exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present application can be more clearly, thoroughly understood and the scope of the present application can be completely conveyed to those skilled in the art.

[0051] Figure 1 The flowchart of the satellite multi-temporal visualization method based on SGP4 orbit model and database according to an embodiment of the present application is shown in FIG. 1. As shown in FIG. 1, the satellite multi-temporal visualization method based on SGP4 orbit model and database includes: Figure 1

[0052] S1, receiving a target time input by a user, and determining whether the pre-set database stores the telemetry data in table form corresponding to the target time, to obtain a determination result;

[0053] The telemetry data includes: sampling time of a star sensor; attitude quaternion of the satellite corresponding to the sampling time of the star sensor; sampling time of a GNSS receiver; orbit position and velocity of the satellite corresponding to the sampling time of the GNSS receiver.

[0054] Optionally, in some embodiments of the present application, the pre-set database is in communication connection with a ground station;

[0055] The ground station parses the telemetry data in file form into the telemetry data in table form, and stores the telemetry data in table form into the pre-set database;

[0056] The telemetry data in file form is acquired by the satellite after the satellite enters an orbit, and is transmitted to the ground station by a data transmission station in communication connection with the satellite and the ground station, respectively.

[0057] S2, if the determination result is that the pre-set database does not store the telemetry data in table form corresponding to the target time, acquiring the telemetry data in table form of a specified time period in the pre-set database, and displaying the attitude and orbit of the satellite at the target time according to the telemetry data in table form of the specified time period in the pre-set database, pre-acquired TLE data, and a specified attitude of the satellite at the target time.

[0058] Specifically, in the present embodiment, first, a target time input by a user is received, and it is determined whether the pre-set database stores the telemetry data in table form corresponding to the target time. The telemetry data includes sampling time of a star sensor and corresponding attitude quaternion, and sampling time of a GNSS receiver and corresponding orbit position and velocity information. ​

[0059] If the telemetry data corresponding to the target time is not stored in the database, the telemetry data of a specified time period (e.g., from 300 seconds before the target time to the last valid data before the target time) in the database is further acquired, and the GNSS data in the time period is calculated by a seven-order Lagrange interpolation to obtain the initial value of the orbital position and the initial value of the velocity of the target time, which are then converted into the orbital element parameters.

[0060] Subsequently, based on the orbital element and the aerodynamic parameters in the latest TLE (two-line element) data published at fixed time intervals, the SGP4 orbit model is used for orbit recursion to calculate the position and velocity of the satellite at the target time point.

[0061] For the attitude part, a preset design attitude corresponding to the target time or a standby attitude of the satellite can be selected according to business needs, and the attitude quaternion in the three-dimensional coordinate system is determined in combination with the orbit information. Finally, the satellite orbit position, velocity and attitude information at the target time are input into the Cesium open source three-dimensional geographic visualization platform for three-dimensional dynamic rendering, so as to realize the attitude and orbit visualization of the satellite at the target time point.

[0062] Specifically, referring to Figure 2 , the S2 specifically includes:

[0063] S21, if the judgment result is that the pre-set database does not store the table form telemetry data corresponding to the target time, the table form telemetry data of a specified time period in the pre-set database is acquired, and the table form telemetry data of the specified time period in the pre-set database is converted into corresponding orbital elements;

[0064] The specified time period is [t-300 seconds, t];

[0065] Wherein, t is the time corresponding to the last recorded table form telemetry data before the target time stored in the pre-set database;

[0066] The S21 specifically includes: if the judgment result is that the pre-set database does not store the telemetry data corresponding to the target time, the telemetry data of a specified time period in the pre-set database is acquired, and the seven-order Lagrange interpolation is performed on the orbital position and velocity of the satellite corresponding to the GNSS receiver in the specified time period to generate the initial value of the orbital position and the initial value of the velocity, and the orbital element is converted based on the initial value of the orbital position and the initial value of the velocity.

[0067] In the prior art, the visualization of satellite orbit and attitude is highly dependent on telemetry data (such as GNSS orbital position and velocity) at the target time point. Once there is no data at the target time point, the system will not be able to render the satellite state, resulting in interruption or error in visualization. In the present application, in the case where the database does not store telemetry data corresponding to the target time, the initial values of the orbit and velocity at the target time point are calculated by backtracking the historical telemetry data within a certain time window (for example, 300 seconds before the target time) and using a 7th order Lagrange interpolation method. This method not only enables reconstruction of missing data, but also improves fault tolerance and data utilization efficiency to some extent.

[0068] In addition, compared with low-order interpolation methods (such as linear or cubic spline), the 7th order Lagrange interpolation can utilize more data at time nodes for interpolation fitting, and has higher fitting accuracy and time consistency when dealing with high dynamic and high nonlinear orbit changes. In particular, in LEO (low earth orbit) satellites or satellites with rapid attitude and orbit changes, the orbit parameters change dramatically over time, and low-order interpolation can easily cause large initial value deviation. The 7th order interpolation can make full use of historical GNSS trajectory points to construct a smoother and more accurate estimation curve, providing reliable initial values for subsequent orbit parameter conversion and further improving the recursion accuracy of the SGP4 orbit model.

[0069] The SGP4 orbit model does not directly use position-velocity as input, but uses orbital elements (such as semi-major axis, eccentricity, inclination, etc.) as input parameters. By converting the interpolated initial orbit values into orbital elements, a data structure that matches the input format of the SGP4 orbit model can be constructed, enabling seamless connection for subsequent orbit recursion. Satellite telemetry data is limited by communication bandwidth, transmission window, etc., and cannot cover every moment. After using this scheme, only data within 300 seconds before the target time point is needed to reconstruct the orbit information through interpolation and modeling, significantly reducing the dependence on high-frequency and full-time telemetry data.

[0070] S22, based on the orbital elements and the aerodynamic parameters in the pre-acquired TLE data, using the SGP4 orbit model to perform orbit recursion to obtain the position and velocity of the satellite corresponding to the target time;

[0071] The pre-acquired TLE data is the latest published TLE data collected at a pre-set time interval.

[0072] SGP4 (Simplified General Perturbations model 4) is a high-precision orbit prediction model for near-earth orbit satellites. It can recursively calculate the orbit state at the target time based on the orbital elements and perturbation parameters (such as atmospheric drag coefficient, perigee rate, etc.) in the TLE data. By using the SGP4 orbit model to recursively calculate, it is no longer dependent on whether the real-time telemetry data at the target time point is available, but can be based on historical data and TLE aerodynamic parameters to extrapolate and predict the orbit at any time point, thereby ensuring the continuity and integrity of the orbit visualization in the time domain.

[0073] S23, based on the position and velocity of the satellite corresponding to the target time and the specified pose of the target time, obtaining the attitude quaternion corresponding to the target time;

[0074] The specified pose of the target time is a pre-set standby attitude or a pre-set design attitude of the satellite at the target time.

[0075] In actual satellite operation, satellite attitude data (such as quaternion) may be temporarily missing due to communication delay, abnormal attitude sensor, or data not yet in the database, etc. This step proposes:

[0076] When the telemetry attitude data at the target time point is missing, it can be supplemented by the "position + velocity + preset attitude strategy"; a "computable alternative" mechanism for attitude is realized, thereby ensuring the continuity and integrity of the visualization; avoiding the interruption of the attitude trajectory due to data loss, improving the user interaction experience and the professionalism of the orbit visualization. The attitude of the satellite is not isolated, but usually coupled with the orbit position and velocity in terms of function or rule (such as "flight direction stabilization" mode, earth pointing mode, etc.). Through this step, based on the orbit state of "where the satellite is located at this moment and at what speed it is flying", the pre-set attitude scheme (such as standby state, camera observation attitude, etc.) matching the task can be selected; then it is converted into the corresponding attitude quaternion, ensuring that the physical meaning of the attitude is coordinated with the orbit, avoiding the display of "suspended" or "jumping" state that violates the flight logic.

[0077] Through the "orbit information + preset attitude" attitude calculation method, complete three-dimensional modeling and display can still be performed when real-time attitude telemetry data is lacking, thereby having the following advantages: supporting data simulation and task deduction in offline environment, such as achieving simulation visualization in the task preparation stage and orbit pre-play stage; improving fault tolerance, still having "optimal guessing" display capability under the condition of partial telemetry data loss or delay, and not completely failing.

[0078] S24, inputting the satellite position and speed corresponding to the target time, the attitude quaternion corresponding to the target time and the target time into the Cesium open source library to display the attitude and orbit of the satellite at the target time.

[0079] In this embodiment, by inputting the calculated physical parameters such as "position, speed, attitude quaternion" into the Cesium three-dimensional open source visualization engine, the satellite's trajectory line and motion direction in three-dimensional space can be rendered in real time; the attitude orientation of the satellite (such as the antenna pointing direction and the load direction) is synchronously displayed, which facilitates understanding of the working state; the operator can intuitively master the spatial attitude and motion trajectory of the satellite at the target time point, and the interactive experience is improved.

[0080] The method further comprises:

[0081] S3, if the judgment result is that the telemetry data corresponding to the target time is stored in the pre-set database, inputting the telemetry data corresponding to the target time and the pre-acquired TLE data corresponding to the target time into the Cesium open source library to display the attitude and orbit of the satellite at the target time.

[0082] Compared with the existing satellite visualization method relying on complete telemetry data, the embodiments of the present application have the following significant advantages: when the target time point lacks telemetry data, the present method automatically completes the orbit and attitude state by interpolation and orbit modeling, avoids interruption of rendering, and guarantees temporal continuity. The seventh-order Lagrange interpolation is used to generate the orbit initial value, and the mature SGP4 orbit recursive model is combined for accurate calculation, and the result is more real and reliable. The attitude information supports flexible configuration of "standby attitude" or "design attitude", and does not rely on actual data of the attitude sensor, which is suitable for multiple application scenarios. Through automatic docking of the database and the ground station, telemetry data analysis is automated, which is suitable for orbit and attitude management requirements of most small satellites and constellation systems. Combined with the Cesium three-dimensional map rendering engine, the user can interactively view the satellite state at any time point, improving the efficiency and intuitiveness of operation control and task simulation.

[0083] In summary, the embodiments of the present application not only break through the problem of unavailable orbit and attitude information caused by interruption of satellite telemetry data, but also construct a highly adaptable multi-temporal orbit and attitude prediction and reconstruction mechanism for future space information visualization tasks, which has strong engineering practical value and technical advancement.

[0084] It should be noted that in some embodiments of the present application, the step S23 of acquiring the attitude quaternion corresponding to the target time based on the specified attitude of the satellite corresponding to the target time and the target time can be realized by the following way.

[0085] According to the satellite position and velocity information obtained from the orbit calculation result, and in combination with a preset specified pose strategy, an attitude quaternion corresponding to a target time is calculated. The specified pose can be a standard control attitude mode of the satellite, such as an earth-pointing attitude mode or a sun-pointing attitude mode.

[0086] The following takes a three-axis stabilized earth-pointing attitude as an example for illustration:

[0087] The three-axis stabilized earth-pointing attitude refers to that the +Z axis in the satellite body coordinate system points to the direction of the Earth's center of mass, i.e., the direction opposite to the position vector of the satellite; the +X axis points to the direction of the orbital velocity, or is constrained in the direction of the velocity vector; and the +Y axis is obtained by the cross product of the +Z and +X axes to meet the requirement of the right-hand coordinate system.

[0088] According to the orbit state information of the satellite at the target time obtained in step S22, let the position vector of the satellite in the Earth-centered inertial coordinate system (such as the J2000 system) be

[0089]

[0090] where px is the component of the satellite position in the x direction (unit: km); py is the component of the satellite position in the y direction (unit: km); and pz is the component of the satellite position in the z direction (unit: km).

[0091] The velocity vector of the satellite in the Earth-centered inertial coordinate system is

[0092]

[0093] where vx is the component of the satellite velocity in the X direction (unit: km / s); vy is the component of the satellite velocity in the Y direction (unit: km / s); and vz is the component of the satellite velocity in the Z direction (unit: km / s).

[0094] According to the above orbit information, the attitude direction cosine matrix (DCM) of the satellite is calculated, and the steps are as follows:

[0095] The +Z axis direction of the satellite body coordinate system is calculated as which indicates that the +Z axis of the satellite is aligned in the opposite direction of the Earth-centered direction, and the unit vector satisfies the definition of the attitude mode.

[0096] The orbital velocity direction unit vector is calculated as This vector is used to define the +X direction in the satellite body coordinate system.

[0097] The +Y axis direction of the satellite body coordinate system is calculated as where "×" represents the cross product of vectors, and a transverse direction perpendicular to the Earth-centered direction and the velocity direction is obtained.​

[0098] Normalized +Y axis direction:

[0099] Calculate +X axis direction (ensure orthogonal to +Z and +Y):

[0100] Construct the direction cosine matrix DCM: The column vectors of this matrix are respectively the projections of the +X, +Y, +Z axes in the satellite body coordinate system in the inertial system.

[0101] Convert the DCM matrix to the attitude quaternion, and specifically, a standard DCM-to-quaternion conversion formula can be used to obtain: q = [q0, q1, q2, q3];

[0102] Wherein: q0 is the scalar part; q1, q2, q3 are the vector parts, corresponding to the rotation axis direction.

[0103] The final calculated quaternion q is the attitude quaternion corresponding to the target time, which can be used to drive the visualization engine to display the attitude state of the satellite at the time point.

[0104] If the sun-pointing attitude mode is adopted, the earth position vector (i.e., the satellite position ) in the above calculation can be replaced by the position vector of the sun in the J2000 coordinate system at the target time point The remaining calculation steps are the same, and the corresponding attitude quaternion can also be generated.

[0105] In the above manner, the application provides a mechanism for calculating an attitude quaternion in combination with an orbit state and a preset attitude mode, ensuring the continuity, physical rationality, and renderability of the attitude information, supporting the high-reliability implementation of multi-time attitude and orbit joint visualization.

[0106] In actual space missions, due to factors such as telemetry delay, attitude sensor failure, or ground station reception blind area, attitude telemetry data (such as attitude quaternion) at the target time point may not be available. The application provides a "backup path" for attitude calculation through step S23, that is, even if the telemetry data is not available, the attitude quaternion can be derived in combination with the orbit position and velocity, ensuring the integrity and continuity of the attitude trajectory in three-dimensional visualization, and avoiding model frame breaking or rendering failure. Attitude is not an isolated parameter, and often has coupling with the orbit state (position, velocity) in the task logic (such as earth pointing, flight direction stabilization). This step explicitly takes the "orbit position + velocity + preset pointing rule" as input, establishes a reversible logical chain from the orbit state to the attitude state, and improves the physical consistency and model credibility of the attitude estimation result.

[0107] In addition, the embodiment of the present application also proposes a computer device, which comprises a processor and a memory, and the processor is used to execute instructions stored in the memory, so that the computer device executes the satellite multi-temporal visualization method based on the SGP4 orbit model and the database described in the above embodiment.

[0108] Finally, the embodiment of the present application also proposes a computer readable storage medium comprising computer program instructions, which, when executed by a processor, implement the satellite multi-temporal visualization method based on the SGP4 orbit model and the database described in the above embodiment.

[0109] In summary, the satellite multi-temporal visualization method based on the SGP4 orbit model and the database provided by the embodiment of the present application breaks through the key problems of the traditional satellite visualization method, such as strong dependence on telemetry data at the target time point, missing attitude and interrupted trajectory, by constructing a fusion mechanism of “telemetry data + TLE data + visualization rendering”, and has significant engineering practicability and visualization intelligence. On the one hand, when the telemetry data at the target time point is missing, the present application method backtracks the historical GNSS data within a certain time window, and uses a high-order Lagrange interpolation algorithm to reconstruct the initial state of the orbit at the target time point, and then converts it into the orbital elements required by the SGP4 orbit model, to realize the accurate completion and subsequent orbit recursion of the missing orbit data. Compared with the low-order interpolation scheme, this method has higher accuracy and time domain consistency when dealing with high dynamic orbit changes, and improves the continuity and accuracy of trajectory visualization. On the other hand, for the problem of missing attitude telemetry, the present application introduces a mechanism coupling the orbit state and the designed attitude, calculates the attitude quaternion based on the orbit position, velocity and preset attitude strategy at the target time point, so as to ensure the continuity and physical rationality of the attitude model in three-dimensional visualization, effectively avoid the problem of attitude fault or display abnormality, and improve the overall realism and user interaction experience. At the same time, with the help of the high-performance rendering capability of the Cesium open-source three-dimensional geographic engine, the present application can intuitively display the satellite attitude and orbit information at the target time point in a three-dimensional dynamic manner, providing a powerful visualization support tool for spacecraft operation monitoring, task situation analysis, early warning judgment and trajectory control plan formulation. The system can support users to call back the orbit and attitude state at any time, perform trajectory demonstration, attitude deduction, interactive comparison and other operations, and has good universality, adaptability and deployment flexibility. Further, the embodiment also combines the data communication mechanism of the telemetry database and the ground station to realize automatic parsing and storage of telemetry data from file format to table format, providing a solid data foundation for multi-temporal query and deduction of orbit and attitude data. Whether in online operation monitoring or in task simulation and offline verification scenarios, the integrity and practicability of the orbit and attitude rendering process can be maintained.

[0110] Therefore, the embodiments of the present application have constructed a satellite orbit and attitude visualization method with high-precision orbit prediction, high-tolerance attitude recovery, real-time three-dimensional visualization, multi-source data fusion processing, and cross-temporal continuous display capabilities, which significantly improves the visualization integrity under conditions of discontinuous or partially missing telemetry data.

[0111] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise specifically defined.

[0112] In this application, unless otherwise specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they can refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in this application based on the specific circumstances.

[0113] In this application, unless otherwise expressly specified or limited, when a first feature is “on” or “below” a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Moreover, when a first feature is “above”, “above”, or “above” a second feature, it may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is “below”, “below”, or “below” a second feature, it may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0114] In the description of this specification, the description of the terms "one embodiment", "some embodiments", "embodiment", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0115] Although the embodiments of the present application have been shown and described above, it is to be understood that the above-described embodiments are merely exemplary, and are not to be understood as limiting the present application, and modifications, substitutions, replacements and variations of the above-described embodiments can be made by those skilled in the art within the scope of the present application.

Claims

1. A satellite multi-temporal visualization method based on the SGP4 orbit model and database, characterized in that: include: S1. Receive a target time input by a user, and determine whether a preset database stores telemetry data in a tabular form corresponding to the target time, and obtain a determination result; S2. If the judgment result is that there is no telemetry data in tabular form corresponding to the target time stored in the pre-set database, then the telemetry data in tabular form of the specified time period in the pre-set database is obtained, and the attitude and orbit of the satellite at the target time are displayed based on the telemetry data in tabular form of the specified time period in the pre-set database, the pre-acquired TLE data and the specified position and orientation of the satellite at the target time.

2. The satellite multi-temporal visualization method based on the SGP4 orbit model and database according to claim 1, characterized in that: The telemetry data includes: Sampling time of the star sensor; The satellite attitude quaternion corresponding to the sampling time of the star sensor; Sampling time of GNSS receiver; The orbital position and velocity of the satellite corresponding to the sampling time of the GNSS receiver.

3. The satellite multi-temporal visualization method based on the SGP4 orbit model and database according to claim 2, characterized in that: The S2 specifically includes: S21. If the determination result is that the predetermined database does not store telemetry data in a tabular form corresponding to the target time, obtaining telemetry data in a tabular form for a specified time period from the predetermined database, and converting the telemetry data in the tabular form for the specified time period from the predetermined database into a corresponding orbital square root number; S22. Based on the orbital mean root number and aerodynamic parameters in the pre-acquired TLE data, the SGP4 orbit model is used to perform orbit recursion to obtain the position and velocity of the satellite corresponding to the target time; S23. Acquire an attitude quaternion corresponding to the target time based on the position and velocity of the satellite corresponding to the target time and the specified posture of the target time; S24. Input the satellite position and velocity corresponding to the target time, the attitude quaternion corresponding to the target time, and the target time into the Cesium open source library to display the attitude and orbit of the satellite at the target time.

4. The satellite multi-temporal visualization method based on the SGP4 orbit model and database according to claim 3 is characterized in that: The specified time period is: [t-300 seconds, t]; Wherein, t is the time corresponding to the last recorded telemetry data in tabular form before the target time stored in the preset database; The pre-acquired TLE data is the latest released TLE data collected at a preset time interval.

5. The satellite multi-temporal visualization method based on the SGP4 orbit model and database according to claim 4 is characterized in that: The S21 specifically includes: If the judgment result is that the telemetry data corresponding to the target time is not stored in the preset database, the telemetry data of the specified time period in the preset database is obtained, and the orbital position and velocity of the satellite corresponding to the GNSS receiver in the specified time period are subjected to 7th-order Lagrange interpolation to generate initial orbital position values ​​and initial velocity values, and the initial orbital position values ​​and initial velocity values ​​are converted into orbital mean root numbers based on the initial orbital position values ​​and initial velocity values.

6. The satellite multi-temporal visualization method based on the SGP4 orbit model and database according to claim 5, characterized in that: in, The designated posture at the target time is: a pre-set standby posture or a preset design posture corresponding to the satellite at the target time.

7. The satellite multi-temporal visualization method based on the SGP4 orbit model and database according to claim 6, characterized in that: The method further comprises: S3. If the judgment result is that the telemetry data corresponding to the target time is stored in the pre-set database, the telemetry data corresponding to the target time and the pre-acquired TLE data corresponding to the target time are input into the Cesium open source library to display the attitude and orbit of the satellite at the target time.

8. The satellite multi-temporal visualization method based on the SGP4 orbit model and database according to claim 6, characterized in that: The preset database is in communication with the ground station; The ground station parses the telemetry data in the form of a file into telemetry data in the form of a table and stores the data in the pre-set database; Among them, the telemetry data in file form is acquired by the satellite after entering orbit and is transmitted to the ground station through a data transmission station that is connected to the satellite and the ground station respectively.

9. A computer device, characterized in that: The computer device includes a processor and a memory, and the processor is used to execute instructions stored in the memory, so that the computer device performs the satellite multi-temporal visualization method based on the SGP4 orbit model and database according to any one of claims 1-8.

10. A computer-readable storage medium, characterized in that The method comprises computer program instructions, and when the computer program instructions are executed by a processor, the method realizes the satellite multi-temporal visualization method based on the SGP4 orbit model and database according to any one of claims 1 to 8.

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