Laser load autonomous pointing calibration optimization method and system

Abstract

The invention provides a laser load autonomous pointing calibration optimization method and system. Belongs to the technical field of space aurora communication. The method comprises the steps that according to the position and speed of a satellite under a J2000 coordinate system, an orbital normal vector is solved, and an orbital coordinate system is established; carrying out target star screening; according to the screened star catalogue, sequentially pointing to the target star in an open-loop manner; and when the target star is in the view field range of the star-sensitive camera, calculating miss distance x and y according to the position of the mass center of the light spot of the target star and the deviation of the tracking point, and then inversely calculating the theoretical direction of the motor. According to the autonomous calibration scheme provided by the invention, an algorithm for autonomously screening the target stars is provided, fixed star data does not need to be injected on the ground, and the calibration efficiency and the data reliability are greatly improved.

Description

Technical Field

[0001] The present invention proposes a laser payload autonomous pointing calibration optimization method and system, belonging to the technical field of space laser communication. Background Art

[0002] Laser terminal pointing error calibration includes three calibration methods: star calibration, star-to-satellite calibration, and inter-satellite calibration, using stars, ground calibration stations, or calibrated other-satellite companion laser terminals as calibration light sources.

[0003] Stellar calibration offers advantages over other methods, including high precision, practicality, independence, and long-term stability. The positions of stars in the universe are known and relatively stable, making them suitable for precise calibration. Stellar calibration does not rely on external equipment and is relatively immune to environmental influences, allowing for independent calibration testing.

[0004] In "A Ground Star Calibration Test System and Test Method for Satellite-borne Laser Communication Payload" (202211316762.3), the test system provided by the invention includes an on-board part and a ground part. It can conduct ground tests in advance on the star calibration of the laser communication payload to verify the feasibility of calibration, select the calibrated stars in advance, solidify the calibration process on the ground, and ensure the smooth progress of the on-orbit calibration process. The method provided by the invention first requires the selection of the target star, input the position information of the star into the simulation center, and then frame it and send it to the laser communication payload. After the payload points to the target star based on the input position, it calculates the azimuth and pitch angles that the turntable needs to rotate according to the calibration principle, and uses the actual pointing angle after stable tracking to calculate the correction matrix. The correction matrix is ​​added and the star calibration is performed again. Summary of the Invention

[0005] The present invention provides a method and system for optimizing the autonomous pointing calibration of a laser payload to solve the problems mentioned in the above background technology:

[0006] The present invention proposes a laser payload autonomous pointing calibration optimization method, which is characterized in that the method includes:

[0007] S1. Based on the position and velocity of the local satellite in the J2000 coordinate system, calculate the orbital normal vector and establish the orbital coordinate system; and select the target satellite;

[0008] S2. According to the selected star catalog, open the loop and point to the target star in order;

[0009] S3. When the target star is within the field of view of the star-sensing camera, the miss distances x and y are calculated based on the deviation between the center of mass of the target star spot and the tracking point, and then the theoretical direction of the motor is calculated.

[0010] S4. Calculate the error installation correction matrix using the actual and theoretical directions of the motor;

[0011] S5. Repeat steps S1-S4 until all target stars are calibrated.

[0012] S6. Average all error installation matrices to obtain a correction matrix;

[0013] S7. Add the correction matrix and calibrate the previously calibrated target star again. If the target star falls on the tracking point of the star-sensitive camera after pointing to it, the calibration is considered successful. If the error is large, repeat the calibration process.

[0014] Furthermore, the S1 includes:

[0015] S11. Obtain the precise position data and velocity vector of the satellite in the J2000 coordinate system; the position and velocity include longitude, latitude, and altitude;

[0016] S12. Use ephemeris data and navigation algorithms to update the satellite's position and velocity information in real time to ensure the accuracy and timeliness of the data;

[0017] S13. Based on the position and velocity of the local satellite, use the principles of orbital mechanics to calculate the normal vector of the orbital plane, and determine the direction of the orbital normal vector as the negative Z-axis direction of the orbital coordinate system;

[0018] S14. Taking the local satellite as the coordinate origin, select the X-axis and Y-axis to construct the orbital coordinate system;

[0019] S15. Verify the correctness of the coordinate system to ensure that the axes are perpendicular to each other and satisfy the right-hand screw rule;

[0020] S16. Call the star database to obtain the position information of all the stars to be screened in the J2000 coordinate system, and calculate the angle between the vector of each star and the -Z direction of the orbital coordinate system;

[0021] S17. Calculate the angle between the star vector and the sun vector to ensure that the star is not blocked by the Earth but is far enough away from the sun to avoid light interference.

[0022] S18. Add stars that meet the requirements to the target star list as calibration targets.

[0023] Furthermore, the S2 includes:

[0024] S21. Sort the target star list according to relevant factors of the stars, including brightness and observation angle;

[0025] S22. The control system calculates and outputs a pointing instruction based on the position information of the target star. After receiving the instruction, the laser payload adjusts its attitude so that the optical axis points to the target star.

[0026] S23. Monitor the posture adjustment process in real time.

[0027] Furthermore, the S23 includes:

[0028] S231. Before attitude adjustment, accurately measure the current attitude of the laser payload using a high-precision gyroscope and accelerometer to obtain initial attitude data;

[0029] S232, comparing the initial posture data with the preset ideal posture, calculating the posture deviation, and performing preliminary calibration of the posture based on the deviation result;

[0030] S233, dynamically tracking the attitude of the laser payload through a Kalman filter or an extended Kalman filter, and fusing data from multiple sensors in real time;

[0031] S234, based on the star's trajectory and the current attitude of the laser payload, use the celestial navigation algorithm to predict the target star's position in the future;

[0032] S235. Dynamically adjust the attitude adjustment strategy based on the predicted star position and the current attitude deviation, adaptively optimize the attitude adjustment strategy through a fuzzy logic algorithm, and dynamically adjust the control parameters based on the real-time feedback attitude data and changes in the external environment;

[0033] S236. During the posture adjustment process, a closed-loop control strategy is adopted to compare the deviation between the actual posture and the target posture in real time, and the posture adjustment is controlled by a PID controller;

[0034] S237. During the attitude adjustment process, the various performance indicators of the laser payload are continuously monitored. If any abnormality is found, the abnormality handling mechanism is immediately triggered.

[0035] Furthermore, the S3 includes:

[0036] S31. Capturing a target star image using a star-sensitive camera; extracting the centroid position of the target star spot using an image processing algorithm;

[0037] S32, extract the center of mass position coordinates (X c ,Y c ) and the preset tracking point coordinates (X t ,Y t ) to compare and calculate the preliminary horizontal direction (X axis) deviation ΔX=X c -X t Deviation from the vertical direction (Y axis) ΔY=Yc -Y t ;

[0038] S33. Preset a deviation threshold, such as σx, σy, and use the deviation threshold to determine whether the deviation is within an acceptable range. If |ΔX|≤σx and |ΔY|≤σy, it is preliminarily considered that the miss distance is small, and the next step is directly entered;

[0039] S34. If the deviation exceeds the threshold, further judgment and processing are performed;

[0040] If the deviation is too large, check whether there is any external interference factor and take corresponding measures, such as waiting for the interference to disappear and adjusting the camera exposure;

[0041] If there is no external interference or the interference has been dealt with, but the deviation is still large, dynamic adjustment is performed;

[0042] S35, analyzing the movement trend of the target star and predicting the position of the target star in the next frame of image;

[0043] S36, dynamically adjusting the tracking points and exposure parameters of the camera according to the predicted position, capturing the image again and extracting the centroid position;

[0044] S37, repeat the above steps until the deviation is within an acceptable range;

[0045] S38. After the deviation is acceptable, calculate the miss distances x and y based on the final extracted center of mass position and tracking point coordinates; miss distance x = final ΔX, miss distance y = final ΔY;

[0046] S39: Record the miss distances x and y calculated this time, as well as any abnormalities or special processing situations encountered during the process, and feed the miss distance data back to the control system.

[0047] Furthermore, the S31 includes:

[0048] Calibrate the star-sensing camera and initialize the camera parameters; formulate a search strategy within the field of view of the star-sensing camera based on the known target star position prediction;

[0049] Start the camera, capture the target star image according to the preset parameters, and apply image enhancement technology in real time to improve the image quality;

[0050] Image stability analysis algorithms detect minor jitter or motion blur in image sequences and use image registration techniques to remove or compensate for these effects;

[0051] Image segmentation and morphological processing techniques are used to identify and remove obstructions in the image. The degree of obstruction is determined by setting a threshold, and the recapture mechanism or adjustment of the observation angle is triggered based on the preset threshold.

[0052] According to the light spot characteristics of the target star, the feature extraction algorithm is optimized to locate the outline and center of mass of the target star light spot.

[0053] Furthermore, the S4 includes:

[0054] S41. Detect the current state of the laser terminal, including the real-time position and speed of the motor and whether there are any abnormal alarms; if there are any abnormalities, enter the fault handling process, record the error, and attempt automatic or manual repair;

[0055] S42, extracting the centroid position of the target star spot captured by the star-sensitive camera; and at the same time, reading the coordinates of the pre-set tracking point;

[0056] S43, comparing the center of mass position with the tracking point coordinates, calculating the miss distances in azimuth and pitch directions, and recording them;

[0057] S44, calculating the rotation amount that needs to be compensated for the azimuth motor and the pitch motor respectively according to the miss distances x and y and the mechanical characteristics of the laser terminal;

[0058] S45. Using the calculated motor compensation amount, update the theoretical direction of the laser terminal; compare the theoretical direction with the actual direction to evaluate the difference between the two;

[0059] S46. Convert the actual pointing direction and the theoretical pointing direction into corresponding direction cosine matrices; the matrix corresponding to the actual pointing direction is recorded as err_co, and the matrix corresponding to the theoretical pointing direction is recorded as co;

[0060] S47, based on the direction cosine matrices err_co and co, using the principle of matrix transformation, calculate the PAT installation matrix;

[0061] S48, verifying the calculated PAT installation matrix. If the verification result does not meet the requirements, return to the above steps to recalculate and adjust until a satisfactory installation matrix is ​​obtained;

[0062] S49. Repeat the above steps for all selected target stars. Update the correction matrix every time the calibration of a target star is completed. During the entire calibration process, the status and performance parameters of the laser terminal are continuously monitored.

[0063] Furthermore, the S6 includes:

[0064] S61. The error installation correction matrix calculated during each calibration process is stored. When all selected target stars have completed calibration, all error installation correction matrices are averaged to obtain a preliminary average correction matrix.

[0065] S62. Use the preliminary average correction matrix to recalibrate some or all of the calibrated target stars; compare the recalibration results with the initial calibration results to evaluate the accuracy and stability of the average correction matrix;

[0066] If the recalibration result meets the preset accuracy requirements, the average correction matrix is ​​considered valid and the process proceeds to the next step. Otherwise, the calibration process is rechecked, the cause is analyzed, and the screening conditions or correction matrix calculation method are adjusted as needed.

[0067] S64. If the average correction matrix is ​​found to be insufficient during the verification process, the correction matrix is ​​optimized, and after optimization, verification is performed again until an average correction matrix that meets the accuracy requirements is obtained;

[0068] S65. The average correction matrix that has finally passed the verification is stored, and the relevant verification information is marked, and the average correction matrix is ​​applied to the subsequent laser payload pointing control.

[0069] Furthermore, the S7 includes:

[0070] S71. Use the correction matrix noted above to recalibrate the previously calibrated target star. Observe the centroid of the target star's spot with the star-sensitive camera to determine whether it accurately falls on the tracking point.

[0071] If the target star's spot centroid position coincides with the tracking point or the error is within an acceptable range, the calibration is considered successful.

[0072] If the calibration result has a large error, that is, the deviation between the target star's spot centroid position and the tracking point exceeds the acceptable range, an error analysis is performed;

[0073] S72, analyzing error sources, including errors in stellar position data, orbital parameter errors, camera tracking accuracy, and motor control accuracy; adjusting the correction matrix or re-performing relevant steps in the calibration process based on the error analysis results;

[0074] S73. After the error adjustment is completed, repeat steps S2-S6 to recalibrate the target star and re-verify the calibration results until the predetermined accuracy requirements are met.

[0075] S74. Record the results of each calibration, error analysis, adjustment measures and final verification results.

[0076] A system proposed in the present invention is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0077] Memory for storing computer programs;

[0078] A processor, configured to implement the method steps described in any one of claims 1 to 9 when executing a program stored in a memory.

[0079] The present invention provides a beneficial effect: The autonomous calibration solution, which features an algorithm for autonomously selecting target stars, eliminates the need for ground-based star data annotation, significantly improving calibration efficiency and data reliability. Furthermore, the selection process ensures the feasibility of the calibration process while also ensuring the safety of the laser terminal during the calibration process. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] Figure 1 A diagram showing the steps of the method of the present invention;

[0081] Figure 2 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0082] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0083] One embodiment of the present invention, as Figure 1 and Figure 2 A laser payload autonomous pointing calibration optimization method is shown, the method comprising:

[0084] S1. Based on the position and velocity of the local star in the J2000 coordinate system, calculate the orbital normal vector and establish the orbital coordinate system. Select stars whose star vectors have an angle less than 90 degrees with the -z direction of the orbital coordinate system and an angle greater than 90 degrees with the sun vector as target stars, that is, stars that are not blocked by the Earth and whose optical axes are far away from the sun;

[0085] S2. According to the selected star catalog, open the loop and point to the target star in order;

[0086] S3. When the target star is within the field of view of the star-sensing camera, the miss distances x and y are calculated based on the deviation between the center of mass of the target star spot and the tracking point, and then the theoretical direction of the motor is calculated.

[0087] S4. Calculate the error installation correction matrix using the actual and theoretical directions of the motor;

[0088] S5. Repeat steps S1-S4 until all target stars are calibrated.

[0089] S6. Average all error installation matrices to obtain a correction matrix;

[0090] S7. Add the correction matrix and calibrate the previously calibrated target star again. If the target star falls on the tracking point of the star-sensing camera after pointing to the target star, the calibration is considered successful. If the error is large, the calibration process is repeated. The specific calibration principle is as follows:

[0091] In one embodiment, the position of the local star is first obtained. Speed, positions of other stars to be screened and the sun vector Position in the J2000 coordinate system.

[0092] Find the angle between the star and the orbital coordinate system in the -z direction

[0093] Find the angle between the star and the sun vector

[0094] Stars that satisfy θ1<90° and θ2>90° are added to the calibration data source.

[0095] According to the calibration data source, after the laser terminal is open-loop pointed at the target star, the star-sensitive camera can detect the target star's light spot and center of mass position. The camera's azimuth miss distance x and pitch miss distance y are obtained by the center of mass position and the tracking point. The movement amount that the laser terminal motor needs to compensate can be calculated as:

[0096] Azimuth motor compensation:

[0097] Pitch motor compensation amount:

[0098] The theoretical direction of the laser terminal is obtained based on the azimuth motor compensation, the pitch motor compensation, and the current direction of the laser terminal. The actual and theoretical directions are converted into corresponding direction cosine matrices. The direction cosine matrix corresponding to the actual direction of the motor is errco, and the direction cosine matrix corresponding to the theoretical direction of the motor is co. Next, the PAT installation matrix is ​​calculated:

[0099] Rotation around the z-axis ψ = azimuth motor compensation ΔAZ

[0100] Rotation around the y-axis θ = pitch motor compensation ΔEL

[0101] Calculate the rotation φ around the x-axis according to the 312 rotation sequence

[0102] R y (θ)R x (φ)R z (ψ)errco=co

[0103] R z (ψ)=R y (θ) -1·co·errco -1 ·R z (ψ) -1

[0104] The working principle of the above technical solution is as follows: Based on the satellite's position and velocity information in J2000 (a commonly used inertial reference coordinate system), the satellite's orbital normal vector is calculated. Based on this, an orbital coordinate system is established to determine the satellite's precise position and attitude in space. By screening a star database, stars whose vectors form an angle less than 90 degrees with the -z direction of the orbital coordinate system (usually pointing toward the center of the Earth) (i.e., the stars are located above or below the orbital plane and are not easily obscured by the Earth) and whose angles with the sun vector are greater than 90 degrees (i.e., the star's optical axis is pointed away from the sun to avoid sunlight interference) are selected as target stars. Based on the selected target star list, the laser payload begins to attempt to point at these stars in a certain order (e.g., brightness, position, etc.). This step performs preliminary alignment, and the payload's attitude is adjusted to approximately align with the target star. When the target star enters the field of view of a star-sensing camera (a high-precision astronomical camera used to observe and locate stars), the deviation (miss distance x and y) between the target star's spot center of mass and the camera's preset tracking point is calculated to infer the theoretical pointing angle that the motor (the mechanism that controls the payload's attitude) should achieve. Next, the difference between the actual pointing of the motor and the theoretical pointing is compared, and the error installation correction matrix is ​​calculated. The correction matrix reflects the deviation between the current payload attitude and the ideal attitude, which is used for subsequent correction. Repeat the above steps (S1-S4) to calibrate all selected target stars. Through multiple iterations, the error of the payload attitude can be gradually reduced and the pointing accuracy can be improved. Take the average of all error installation matrices to obtain the final correction matrix. The correction matrix contains the error information accumulated during all calibration processes and is a key parameter for optimizing the pointing accuracy of the payload. Upload the correction matrix to the control system of the payload, and verify the pointing of the previously calibrated target star again. If the target star can accurately fall on the tracking point of the star-sensitive camera, it means that the calibration is successful and the pointing accuracy of the payload has been effectively improved. If the verification result shows that the error amount is still large, it may be necessary to redo the calibration process until the accuracy requirements are met.

[0105] The above technical solution significantly improves the pointing accuracy of laser payloads by selecting suitable target stars (i.e., stars that are not obscured by the Earth and whose optical axes point away from the Sun) and performing precise calibration on them. This method enables autonomous calibration of laser payloads, reducing reliance on ground-based measurement and control stations. In space missions, where ground-based measurement and control resources are limited and expensive, autonomous calibration significantly enhances mission flexibility and reliability. Through an iterative calibration process, errors can be gradually reduced and the correction matrix optimized, effectively improving the payload's pointing accuracy. Furthermore, by reducing reliance on the ground, the calibration cycle is shortened, improving mission efficiency. This method dynamically selects suitable target stars for calibration based on real-time satellite position and velocity information. It can operate at different orbital altitudes, inclinations, and velocities, offering strong adaptability and flexibility. Through multiple calibration and verification processes, the laser payload's pointing accuracy is ensured to meet predetermined requirements. Furthermore, averaging the error correction matrix further improves the stability and reliability of the correction matrix. Autonomous calibration reduces the demand on ground-based measurement and control resources, thereby lowering mission costs. Furthermore, by improving pointing accuracy and efficiency, other costs during mission execution (such as fuel consumption and time costs) can also be indirectly reduced. The application and continuous optimization of this technical solution will promote the development and advancement of autonomous laser payload pointing technology. As the technology matures and its application becomes more widespread, it will provide a more reliable and efficient means for future space exploration and scientific research.

[0106] In one embodiment of the present invention, the S1 includes:

[0107] S11. Obtain the precise position data and velocity vector of the satellite in the J2000 coordinate system; the position and velocity include longitude, latitude, and altitude;

[0108] S12. Use ephemeris data and navigation algorithms to update the satellite's position and velocity information in real time to ensure the accuracy and timeliness of the data;

[0109] S13. Based on the position and velocity of the local satellite, use the principles of orbital mechanics to calculate the normal vector of the orbital plane, and determine the direction of the orbital normal vector as the negative Z-axis direction of the orbital coordinate system;

[0110] S14. With the local star as the coordinate origin, select appropriate X-axis and Y-axis (for example, the X-axis points to a fixed star, and the Y-axis is determined according to the right-hand rule) to construct the orbital coordinate system;

[0111] S15. Verify the correctness of the coordinate system to ensure that the axes are perpendicular to each other and satisfy the right-hand screw rule;

[0112] S16. Call the star database to obtain the position information of all the stars to be screened in the J2000 coordinate system, and calculate the angle between the vector of each star and the -Z direction of the orbital coordinate system (i.e., the direction of the Earth);

[0113] S17. Calculate the angle between the star vector and the sun vector to ensure that the star is neither blocked by the Earth (angle < 90°) nor far enough away from the sun to avoid light interference (angle > 90°).

[0114] S18. Add stars that meet the requirements to the target star list as potential calibration targets.

[0115] The working principle of the above technical solution is: the system obtains the precise position data and velocity vector of the satellite (i.e., the "local satellite") in the J2000 coordinate system. The data includes longitude, latitude, and altitude (in the space environment, altitude usually refers to the distance relative to a reference ellipsoid, such as the WGS-84 ellipsoid), but more accurately, the data should be the coordinates (X, Y, Z) and velocity vector (V in three-dimensional space). x ,V y ,V z ). Using ephemeris data and navigation algorithms (such as algorithms of satellite navigation systems such as GPS, GLONASS, Galileo, or autonomous navigation algorithms based on star maps), the system updates the satellite's position and velocity information in real time. Based on the satellite's real-time position and velocity, the system uses the principles of orbital mechanics to calculate the normal vector of the satellite's orbital plane. This normal vector is perpendicular to the orbital plane and points outside the orbital plane. When constructing the orbital coordinate system, the direction of this normal vector is defined as the negative direction of the Z axis of the orbital coordinate system (because it is usually customary to regard the direction of the earth as downward). With the satellite as the coordinate origin, appropriate X-axis and Y-axis are selected to construct a complete orbital coordinate system. The choice of X-axis can be arbitrary, and it is usually chosen to point to a fixed star or a specific direction (such as a point on the earth's equatorial plane). The Y-axis is determined according to the right-hand rule, that is, the Y-axis is perpendicular to the X-axis and the Z-axis, and the three axes form a right-hand spiral system. After the coordinate system is constructed, the system needs to verify the correctness of the coordinate system. This includes checking whether the axes are perpendicular to each other and whether the right-hand spiral rule is satisfied. The system accesses the star database to obtain the position information of all stars to be screened in the J2000 coordinate system. For each star, the system calculates the angle between its vector and the -Z direction of the orbital coordinate system (i.e., the direction of the Earth), as well as the angle between the star vector and the Sun vector. This method selects stars that are neither obscured by the Earth (angle < 90°) nor far enough from the Sun to avoid light interference (angle > 90°). Stars that meet these conditions are added to the target list as potential calibration targets.

[0116] The effect of the above technical solution is: by obtaining the precise position data and velocity vector of the local satellite in the J2000 coordinate system, including longitude, latitude, and altitude (although three-dimensional coordinates and velocity vectors are usually used in space), the basic data for subsequent calculations is ensured to be highly accurate. This is crucial for constructing a precise orbital coordinate system and selecting suitable target satellites. Using ephemeris data and advanced navigation algorithms to update the local satellite's position and velocity information in real time not only ensures the timeliness of the data, but also improves the accuracy and reliability of the subsequent calibration process. Real-time updates can cope with minor changes and disturbances in the satellite's orbit, ensuring the continuity and accuracy of the calibration process. The normal vector of the orbital plane is accurately calculated using the principles of orbital mechanics, and the orbital coordinate system is constructed based on this. This construction method based on physical principles ensures the scientific nature and accuracy of the coordinate system. At the same time, selecting appropriate X-axis and Y-axis directions and following the right-hand rule make the coordinate system more standardized and easy to understand. By accessing the star database and calculating the angle between each star and the -Z direction (i.e., Earth direction) of the orbital coordinate system, as well as the angle between the star and the Sun vector, the system ensures that the selected target stars are neither obscured by the Earth nor positioned away from the Sun to avoid optical interference. This stringent selection process significantly improves the success rate and reliability of the calibration process. By selecting suitable target stars as potential calibration targets, the number and time of ineffective calibrations are reduced, improving the efficiency of the calibration process. Furthermore, because the target stars are selected based on precise calculations and selection criteria, the calibration results are more accurate and reliable. This technical solution enables autonomous pointing calibration of the laser payload, reducing reliance on ground measurement and control stations. It enables the system to perform calibration independently without ground support, enhancing its autonomy and flexibility. Furthermore, because the calibration process can be iterated and optimized multiple times based on actual conditions, the system is highly adaptable and scalable. The application of autonomous pointing calibration technology reduces the demand on ground measurement and control resources, thereby lowering mission costs. Furthermore, by improving calibration accuracy and reliability, the risk of mission failure due to pointing errors is reduced, ensuring smooth and successful mission execution.

[0117] In one embodiment of the present invention, the S2 includes:

[0118] S21. Sort the target star list based on star-related factors, including brightness and observation angle, and prioritize stars that are easy to observe.

[0119] S22. The control system calculates and outputs a pointing instruction based on the position information of the target star. After receiving the instruction, the laser payload adjusts its attitude so that the optical axis points to the target star.

[0120] S23. Monitor the posture adjustment process in real time.

[0121] The working principle of the above technical solution is as follows: stars are sorted according to relevant factors, including their brightness and observation angle. Among them, brightness determines the visibility of stars in the night sky, while the observation angle affects the difficulty and accuracy of the laser payload in observing stars; priority is given to stars that are easy to observe. During the sorting process, stars with higher brightness and more favorable observation angles are given priority. The control system calculates and generates pointing instructions based on the position information of the sorted target stars. The instructions contain the attitude information that the laser payload needs to adjust to ensure that the optical axis can accurately point to the target star; after receiving the pointing instruction, the laser payload will adjust its own attitude according to the instruction content. This includes adjusting the satellite's pointing mechanism, stabilization platform, etc., so that the optical axis can be accurately aligned with the target star; the purpose of real-time monitoring of the attitude adjustment process is to ensure that the laser payload can accurately adjust its attitude according to the predetermined instructions and ultimately point the optical axis to the target star; the monitoring content includes but is not limited to the speed, accuracy, stability, etc. of the attitude adjustment. Through real-time monitoring, any possible deviations or problems can be discovered and corrected in a timely manner to ensure smooth attitude adjustment. If deviations or problems are found in the attitude adjustment during the monitoring process, the control system will provide timely feedback and may recalculate the pointing instructions or adjust the attitude adjustment strategy to ensure that the final optical axis pointing meets the requirements.

[0122] The above technical solution achieves this by sorting the target star list according to brightness and observation angle, allowing the system to prioritize easy-to-observe stars. This significantly improves observation efficiency and reduces time and resources wasted on difficult-to-observe stars. Prioritizing stars with higher brightness and better observation angles helps the laser payload more accurately lock onto its target and perform calibration. Brighter stars have a higher signal-to-noise ratio when observed, while optimized observation angles reduce the influence of atmospheric interference and other factors, thereby improving calibration accuracy. This technical solution enables autonomous pointing control of the laser payload, reducing the need for manual intervention. The control system automatically calculates and outputs pointing commands based on the target star's position information, enabling the laser payload to autonomously adjust its attitude, enhancing the system's autonomy and intelligence. Real-time monitoring of the attitude adjustment process promptly identifies and corrects any potential deviations or problems, ensuring the laser payload maintains stable pointing direction towards the target star. This real-time monitoring mechanism helps improve system stability and reliability, reducing errors and failures caused by improper attitude adjustment. This technical solution can adapt to varying observation conditions and changing stellar properties. Whether dealing with dim stars or limited viewing angles, the system can adapt through flexible sorting and pointing strategies to ensure a smooth calibration process. By optimizing observation efficiency and calibration accuracy, this technical solution helps increase the success rate of missions such as laser communications and astronomical observations. This improved observation efficiency and calibration accuracy also helps reduce operating costs. Reducing investment in ineffective observations and duplicate calibrations allows for more efficient use of limited resources, thereby improving overall economic benefits.

[0123] In one embodiment of the present invention, the step S23 includes:

[0124] S231. Before attitude adjustment, accurately measure the current attitude of the laser payload using a high-precision gyroscope and accelerometer to obtain initial attitude data;

[0125] S232. Compare the initial posture data with the preset ideal posture, calculate the posture deviation, and perform preliminary posture calibration based on the deviation result; wherein the posture deviation involves comparing the current posture quaternion (or rotation matrix) with the ideal posture quaternion (or rotation matrix), and the posture deviation calculation method based on the quaternion is as follows:

[0126] Let the current quaternion be Q current =[q w ,q x ,q y ,q z ], the quaternion of the ideal posture is Q desired =[q wd ,q xd ,q yd,q zd ], the quaternion Q of the attitude deviation error Calculated by the following formula:

[0127]

[0128] in, It is the inverse of the current attitude quaternion. The inverse of the quaternion is calculated by the following formula:

[0129]

[0130] Dot product of quaternions Calculated using the following formula:

[0131]

[0132] The norm (modulus) of the quaternion ‖Q current ‖Calculated by the following formula:

[0133]

[0134] S233, dynamically tracking the attitude of the laser payload through a Kalman filter or an extended Kalman filter, and fusing data from multiple sensors (such as a gyroscope, a star-sensing camera, etc.) in real time;

[0135] S234, based on the star's trajectory and the current attitude of the laser payload, use the celestial navigation algorithm to predict the target star's position in the future;

[0136] S235. Dynamically adjust the attitude adjustment strategy based on the predicted star position and the current attitude deviation, including adjusting the speed, acceleration, and optimizing the adjustment path. Adaptively optimize the attitude adjustment strategy using a fuzzy logic algorithm. Dynamically adjust control parameters based on real-time feedback of attitude data and external environmental changes (such as wind speed and temperature).

[0137] S236. During the posture adjustment process, a closed-loop control strategy is adopted to compare the deviation between the actual posture and the target posture in real time, and the posture adjustment is finely controlled through the PID controller;

[0138] S237. During the attitude adjustment process, the various performance indicators of the laser payload (such as attitude accuracy, motor current, temperature, etc.) are continuously monitored. If an abnormality is found (such as excessive attitude deviation, motor overheating, etc.), the abnormality handling mechanism is immediately triggered; including automatic adjustment of control parameters, issuing an alarm to notify the operator, or executing an emergency shutdown.

[0139] The working principle of the above technical solution is as follows: Using high-precision sensors such as gyroscopes and accelerometers, the current attitude of the laser payload is accurately measured to obtain initial attitude data. This data is used for subsequent attitude adjustment. The initial attitude data is compared with a preset ideal attitude to calculate the attitude deviation. The attitude deviation reflects the difference between the current and desired attitudes of the laser payload. Based on the calculated attitude deviation, the laser payload undergoes preliminary attitude calibration to reduce the initial deviation, laying the foundation for subsequent dynamic tracking and adjustment. A Kalman filter or an extended Kalman filter is used to fuse data from multiple sensors, such as gyroscopes and star cameras, in real time. Based on the stellar motion trajectory and the current attitude of the laser payload, a celestial navigation algorithm is used to predict the target star position within a certain period of time. Based on the predicted star position and the current attitude deviation, an attitude adjustment strategy is formulated. This adjustment strategy includes determining the adjustment speed, acceleration, and optimizing the adjustment path. A fuzzy logic algorithm is used to adaptively optimize the attitude adjustment strategy. This optimization method dynamically adjusts control parameters based on real-time feedback of attitude data and external environmental changes (such as wind speed and temperature), making the attitude adjustment process more accurate and efficient. During the attitude adjustment process, a closed-loop control strategy is adopted. By comparing the deviation between the actual attitude and the target attitude in real time, a PID controller is used to finely control the attitude adjustment. This control strategy ensures that the laser payload always remains on the desired trajectory during the adjustment process. Various performance indicators of the laser payload are continuously monitored, including attitude accuracy, motor current, temperature, etc. Once an anomaly is detected (such as excessive attitude deviation, motor overheating, etc.), the exception handling mechanism is immediately triggered. The exception handling mechanism may include automatically adjusting control parameters, issuing an alarm to notify the operator, or executing an emergency shutdown to ensure the safety and stability of the laser payload.

[0140] The above technical solution achieves the following effects: High-precision gyroscopes and accelerometers accurately measure the laser payload's current attitude, compare it with a preset ideal attitude, calculate attitude deviations, and perform preliminary calibration, ensuring the accuracy of the starting point for attitude adjustment and laying a solid foundation for subsequent adjustments. A Kalman filter or extended Kalman filter is used to fuse data from multiple sensors in real time, improving the accuracy and robustness of attitude estimation. The comprehensive utilization of multi-source data effectively reduces the impact of single sensor errors on the overall attitude estimation. Based on the star's motion trajectory and the laser payload's current attitude, a celestial navigation algorithm is used to predict the target star's position within a certain period of time. This predictive capability enables the laser payload to plan attitude adjustment strategies in advance, enhancing the system's dynamic adaptability. A fuzzy logic algorithm is used to adaptively optimize the attitude adjustment strategy, dynamically adjusting control parameters based on real-time feedback of attitude data and external environmental changes (such as wind speed and temperature). This adaptive mechanism enables the system to flexibly respond to various complex situations and ensure smooth attitude adjustment. A closed-loop control strategy compares the deviation between the actual and target attitudes in real time, and a PID controller is used to precisely control attitude adjustments. This control method can rapidly respond to attitude deviations and make corresponding adjustments, ensuring that the laser payload always remains on the desired trajectory. It continuously monitors various performance indicators of the laser payload and immediately triggers an exception handling mechanism upon detection of an anomaly. This monitoring and handling mechanism effectively ensures system stability and safety, avoiding attitude adjustment failures or equipment damage due to abnormal conditions. The attitude adjustment strategy is dynamically adjusted based on the predicted star positions and current attitude deviations, including adjusting speed, acceleration, and optimizing the adjustment path. This optimization strategy reduces unnecessary attitude adjustment actions and time consumption, improving overall performance and efficiency. By enhancing the accuracy, stability, and flexibility of attitude adjustment, this technical solution helps to increase the success rate of tasks such as laser communications and astronomical observations. Accurate attitude pointing and stable tracking capabilities provide strong guarantees for the smooth progress of these tasks.

[0141] In one embodiment of the present invention, the step S233 includes:

[0142] First, ensure that the multi-source sensor data, including high-precision gyroscopes, accelerometers, magnetometers, and (optional) GPS receivers, are strictly synchronized in time. High-precision clock synchronization technology ensures that the sensor data has a consistent time base during fusion processing.

[0143] Preprocessing of the collected multi-source sensor data, including noise filtering, outlier removal, and data interpolation, and using statistical methods and machine learning algorithms to identify and remove outliers;

[0144] Initializing key parameters of the Kalman filter according to the system model and sensor characteristics, the key parameters including the state vector, covariance matrix, process noise, and measurement noise;

[0145] Based on the system model, the current state vector and its covariance matrix are predicted according to the estimated value at the previous moment. The measured values ​​of multiple sensors are combined and weighted fusion is performed through the Kalman gain matrix to update the state vector and covariance matrix, thus performing real-time fusion of multi-source data.

[0146] For systems with highly nonlinear dynamics (such as the attitude changes of laser payloads in complex space environments), the extended Kalman filter (EKF) is introduced. In the EKF, the nonlinear model is linearized through Taylor series expansion.

[0147] By monitoring the statistical characteristics of sensor data in real time, the covariance matrix of process noise and measurement noise is dynamically adjusted. Through Bayesian reasoning or model probability update mechanism, the optimal model is selected or model switching is performed based on real-time data.

[0148] Establish a performance evaluation mechanism for attitude estimation to evaluate the accuracy and stability of the fusion results regularly or on demand; based on the evaluation results, dynamically adjust the filter parameters, model selection strategy or data preprocessing method to form a closed-loop optimization loop.

[0149] The working principle of the above technical solution is as follows: high-precision clock synchronization technology is used to ensure strict time consistency during data acquisition from multiple sensors, including gyroscopes, accelerometers, magnetometers, and (if available) GPS receivers. The collected multi-source sensor data is then preprocessed, including noise filtering (such as low-pass filtering), outlier removal (identifying and removing outliers that significantly deviate from the normal range), and data interpolation (filling in missing data points). Statistical methods and machine learning algorithms are then used to further identify and remove potential outliers. Key parameters of the Kalman filter are initialized based on the system model and sensor characteristics. These key parameters include the state vector (a variable representing the system state), the covariance matrix (representing the uncertainty of the state estimate), process noise (representing the uncertainty of the system dynamics), and measurement noise (representing the uncertainty of sensor measurements). Based on the system model, the Kalman filter uses the estimated value at the previous moment to predict the current state vector and its covariance matrix. The measurements from multiple sensors are then combined and weighted using the Kalman gain matrix. For systems with highly nonlinear dynamics, traditional Kalman filters may not be directly applicable. At this point, an extended Kalman filter (EKF) is introduced to linearize the nonlinear model through Taylor series expansion. By monitoring the statistical characteristics of sensor data in real time, the covariance matrix of process noise and measurement noise is dynamically adjusted. Bayesian reasoning or a model probability update mechanism is used to select the optimal model or switch models based on real-time data. A performance evaluation mechanism for attitude estimation is established to regularly or on-demand assess the accuracy and stability of the fusion results. Based on the evaluation results, filter parameters, model selection strategies, and data preprocessing methods are dynamically adjusted to form a closed-loop optimization circuit.

[0150] The above technical solution achieves the following results: High-precision clock synchronization ensures strict temporal consistency of multi-source sensor data. Time synchronization eliminates errors caused by data time differences, improving the accuracy of attitude estimation. Preprocessing steps such as noise filtering, outlier removal, and data interpolation, as well as the use of statistical methods and machine learning algorithms to identify and eliminate outliers, improves data reliability and accuracy, providing a high-quality data source for subsequent fusion processing. A Kalman filter (and its extended version, the EKF) is used to perform real-time fusion processing on multi-source sensor data. The Kalman filter updates the state vector and covariance matrix in real time based on the estimated value at the previous moment and the measured value at the current moment, enabling continuous tracking and prediction of the system state. For nonlinear systems, the EKF linearizes the nonlinear model through Taylor series expansion, ensuring real-time performance and accuracy in nonlinear environments. By monitoring the statistical characteristics of sensor data in real time, dynamically adjusting the covariance matrix of process noise and measurement noise, and selecting the optimal model or performing model switching based on real-time data, the system can adapt to different environments and operating conditions, improving the adaptability and robustness of attitude estimation. A performance evaluation mechanism for attitude estimation is established to regularly or on-demand evaluate the accuracy and stability of the fusion results. This allows for timely detection and correction of potential problems, ensuring the continued high quality of attitude estimation. Based on the evaluation results, filter parameters, model selection strategies, or data preprocessing methods are dynamically adjusted to form a closed-loop optimization loop. This closed-loop control mechanism continuously optimizes attitude estimation performance and improves the overall efficiency of the system. This technical solution supports the fusion processing of multiple sensors, including gyroscopes, accelerometers, magnetometers, and (optionally) GPS receivers, allowing for flexible sensor selection and configuration based on actual needs.

[0151] In one embodiment of the present invention, the step S235 includes:

[0152] Conduct a comprehensive analysis of initial attitude deviations and star position prediction errors, and evaluate the contributions of different factors (such as sensor noise, environmental interference, and star motion model accuracy) to these deviations and errors using statistical methods and machine learning models (such as neural networks or random forests).

[0153] Based on the comprehensive analysis results, a dynamic adjustment strategy generation mechanism is used. This mechanism can automatically generate or select the optimal attitude adjustment strategy based on different scenarios (such as tracking highly dynamic stars, crossing radiation belts, etc.) and real-time deviation / error conditions; the adjustment strategy includes adjustment rate, acceleration limit, and path planning;

[0154] The generated attitude adjustment strategy is further optimized through fuzzy logic algorithm. Fuzzy logic can handle uncertainty and ambiguity. By defining appropriate fuzzy sets and fuzzy rules, complex nonlinear relationships are mapped into easy-to-handle fuzzy reasoning processes. Fuzzy rules and control parameters are dynamically adjusted based on real-time feedback of attitude data, changes in the external environment (such as solar wind intensity, Earth's magnetic field disturbances), and prediction errors.

[0155] The working principle of the above technical solution is as follows: It collects multiple data sources such as initial attitude deviation, star position prediction error, sensor noise, environmental interference, and star motion model accuracy. It then uses statistical methods and machine learning models (such as neural networks and random forests) to comprehensively analyze these data and assess the contribution of different factors to deviation and error. It then identifies the current scene based on real-time data and pre-defined scene modes (such as tracking high-dynamic stars and crossing radiation belts). Based on the results of scene identification and comprehensive analysis, a dynamic adjustment strategy generation mechanism automatically generates or selects the optimal attitude adjustment strategy. This strategy includes adjustment rate, acceleration limit, and path planning. It then fuzzifies factors such as real-time feedback attitude data, external environmental changes (such as solar wind intensity and Earth's magnetic field disturbances), and prediction errors to define appropriate fuzzy sets and fuzzy rules. It then uses fuzzy logic algorithms to perform reasoning, mapping complex nonlinear relationships into easily manageable fuzzy reasoning processes. Based on the results of fuzzy reasoning, it dynamically adjusts fuzzy rules and control parameters.

[0156] The effects of the above technical solutions are as follows: through comprehensive analysis of the initial attitude deviation and star position prediction error, the impact of multiple factors on system performance can be fully considered; statistical methods and machine learning models (such as neural networks, random forests) are used to evaluate the contribution of different factors, thereby improving the accuracy and reliability of the evaluation; the mechanism can automatically generate or select the optimal attitude adjustment strategy according to different scenarios (such as tracking high-dynamic stars, crossing radiation belts, etc.) and real-time deviation / error conditions; the fuzzy logic algorithm can handle the uncertainty and ambiguity in the system, and by defining appropriate fuzzy sets and fuzzy rules, complex nonlinear relationships are mapped into easy-to-handle fuzzy reasoning processes; based on real-time feedback of attitude data, external environment changes (such as solar wind The system dynamically adjusts fuzzy rules and control parameters based on factors such as the intensity of the Earth's magnetic field and prediction errors, thereby improving the response speed and stability of the system. Through comprehensive analysis and dynamic adjustment strategies, the impact of initial attitude deviation and star position prediction error on system performance is effectively reduced. The optimized attitude adjustment strategy further improves the tracking accuracy and stability of the system, ensuring reliable operation in highly dynamic and complex environments. This technical solution can cope with the challenges of various complex environments (such as radiation belts and strong magnetic fields) and ensure the normal operation of the system under these extreme conditions. Through the optimization of dynamic adjustment strategies and fuzzy logic algorithms, the system has a certain fault tolerance and can tolerate the influence of adverse factors such as sensor noise and environmental interference to a certain extent.

[0157] In one embodiment of the present invention, the step S236 includes:

[0158] During the attitude adjustment process, the current attitude data of the laser payload is obtained in real time through high-precision sensors (such as gyroscopes, accelerometers, etc.), and accurately compared with the target attitude to calculate the real-time attitude deviation;

[0159] According to the specific dynamic characteristics of the laser payload and the external environmental conditions, intelligent optimization algorithms (such as genetic algorithm, particle swarm optimization, etc.) are used to optimize the proportional (P), integral (I), and differential (D) parameters of the PID controller offline or online;

[0160] Establish a dynamic adjustment mechanism for PID parameters, and dynamically adjust PID parameters based on real-time feedback of posture data, environmental parameters, and system performance indicators;

[0161] On the basis of PID control, a predictive control strategy is integrated. Based on the future star position information predicted by the celestial navigation algorithm and the dynamic model of the laser payload, the attitude change trend in the future period is predicted and the control input is adjusted in advance.

[0162] The working principle of the above technical solution is as follows: using high-precision sensors such as gyroscopes and accelerometers to obtain the current attitude data of the laser payload in real time; accurately comparing the real-time attitude data with the target attitude to calculate the real-time attitude deviation; based on the specific dynamic characteristics and external environmental conditions of the laser payload, using genetic algorithms, particle swarm optimization and other intelligent optimization algorithms to optimize the proportional (P), integral (I), and differential (D) parameters of the PID controller offline or online; adjusting the PID parameters through the optimization algorithm to reduce the attitude deviation; establishing a dynamic adjustment mechanism for PID parameters, and dynamically adjusting the PID parameters according to real-time feedback attitude data, environmental parameters (such as temperature, vibration, etc.) and system performance indicators (such as attitude stability, tracking accuracy, etc.); predicting future star position information based on the celestial navigation algorithm to provide an accurate reference for attitude adjustment; combining the dynamic model of the laser payload to predict the attitude change trend in the future; adjusting the control input in advance according to the prediction results to cope with the upcoming attitude change.

[0163] The above technical solution achieves the following benefits: Using high-precision sensors to acquire the laser payload's current attitude data in real time and accurately compare it with the target attitude, it can promptly detect and correct minor attitude deviations, thereby improving attitude adjustment accuracy. Using an intelligent optimization algorithm to optimize the PID controller parameters offline or online, it automatically adjusts the control parameters based on the laser payload's specific dynamic characteristics and external environmental conditions, making the control system more adaptable to complex and changing environments and improving attitude adjustment stability. A dynamic PID parameter adjustment mechanism is established to dynamically adjust the PID parameters based on real-time feedback of attitude data, environmental parameters, and system performance indicators, ensuring that the control system maintains optimal control performance under varying conditions. The integrated application of a predictive control strategy enables the system to predict future attitude trends based on future stellar position information predicted by a celestial navigation algorithm and the laser payload's dynamic model, and to proactively adjust control inputs. This improves system robustness and reduces the risk of attitude loss of control due to sudden changes in the external environment or internal system failures. The acquisition and comparison of real-time attitude data, intelligent optimization of PID parameters, and the establishment of a dynamic adjustment mechanism collectively enhance the system's response speed and adjustment accuracy, enabling the laser payload to more quickly reach its target attitude and maintain stability. The application of predictive control strategies enables the system to plan control inputs in advance, reducing energy consumption and mechanical wear caused by frequent posture adjustments, thereby improving the system's overall performance and operational efficiency. Due to the system's high adaptability and robustness, it can automatically respond to a variety of complex and changing scenarios and external environmental conditions, thereby reducing the frequency and difficulty of maintenance required due to system failures or performance degradation. Furthermore, the application of intelligent optimization algorithms and dynamic adjustment mechanisms reduces reliance on manual intervention, making system maintenance more convenient and efficient.

[0164] In one embodiment of the present invention, S3 includes:

[0165] S31, capturing a target star image using a star-sensitive camera, and extracting the centroid position of the target star spot using an image processing algorithm (such as a centroid method);

[0166] S32, extract the center of mass position coordinates (X c ,Y c ) and the preset tracking point coordinates (X t ,Y t ) to compare and calculate the preliminary horizontal direction (X axis) deviation ΔX=X c -X t Deviation from the vertical direction (Y axis) ΔY=Y c -Y t ;

[0167] S33. Preset a deviation threshold, such as σx, σy, and use the deviation threshold to determine whether the deviation is within an acceptable range. If |ΔX|≤σx and |ΔY|≤σy, it is preliminarily considered that the miss distance is small, and the next step is directly entered;

[0168] S34. If the deviation exceeds the threshold, further judgment and processing are performed;

[0169] If the deviation is too large, check whether there are external interference factors (such as cloud cover, solar radiation interference, etc.) and take appropriate measures, such as waiting for the interference to disappear and adjusting the camera exposure;

[0170] If there is no external interference or the interference has been dealt with, but the deviation is still large, dynamic adjustment is performed;

[0171] S35, analyzing the motion trend of the target star (based on the position and velocity information in the star database) and predicting the possible position of the target star in the next frame of image;

[0172] S36, dynamically adjusting the tracking points and exposure parameters of the camera according to the predicted position, capturing the image again and extracting the centroid position;

[0173] S37, repeat the above steps until the deviation is within an acceptable range;

[0174] S38. After the deviation is acceptable, calculate the miss distances x and y based on the final extracted center of mass position and tracking point coordinates; miss distance x = final ΔX, miss distance y = final ΔY;

[0175] S39. Record the miss distances x and y calculated this time, as well as any abnormalities or special processing situations encountered during the process, and feed the miss distance data back to the control system.

[0176] The working principle of the above technical solution is as follows: the target star image is captured by a star-sensitive camera, ensuring that the image is clear and unobstructed. As a high-precision sensor, the star-sensitive camera can capture the weak light signals in the starry sky; the captured image is processed using an image processing algorithm (such as the centroid method) to extract the centroid position of the target star spot. The centroid method determines the centroid position by calculating the weighted average brightness of the pixels within the spot; the extracted centroid position coordinates (X c ,Y c ) and the preset tracking point coordinates (X t ,Y t ) to compare and calculate the preliminary horizontal direction (X axis) deviation ΔX=X c -Y t Deviation from the vertical direction (Y axis) ΔY=Y c -Y t These two deviation values ​​reflect the difference between the current tracking state and the target state. A deviation threshold (such as σx, σy) is preset to determine whether the deviation is within an acceptable range. If |ΔX|≤σx and |ΔY|≤σy, it is preliminarily considered that the miss distance is small and the next step can be directly processed. If the deviation exceeds the threshold, further judgment and processing are performed:

[0177] If the deviation is too large, first check whether there are external interference factors (such as cloud cover, solar radiation interference, etc.) and take corresponding measures (such as waiting for the interference to disappear, adjusting the camera exposure, etc.).

[0178] If there is no external interference or the interference has been processed, but the deviation is still large, dynamic adjustment is performed.

[0179] Analyze the target star's motion trend and predict the target star's possible position in the next frame based on the position and velocity information in the star database. Dynamically adjust the camera's tracking point and exposure parameters according to the predicted position, capture the image again, and extract the center of mass position. By continuously iterating this process, the deviation value is gradually reduced. Repeat the above steps (S32 to S36) until the deviation is within an acceptable range. This process is a closed-loop control process that optimizes the tracking effect through continuous feedback and adjustment. After the deviation is acceptable, the miss distances x and y are calculated based on the final extracted center of mass position and tracking point coordinates. Miss distance x = final ΔX, miss distance y = final ΔY. Record the miss distances x and y calculated this time, as well as any abnormalities or special processing situations encountered during the process. Feedback the miss distance data to the control system for subsequent motor pointing adjustment and error correction matrix calculation. By continuously optimizing the control parameters and algorithms, the tracking accuracy and stability can be further improved.

[0180] The above technical solution achieves the following: Using high-precision image processing algorithms (such as the centroid method), the center of mass of the target star's spot is extracted and compared with the preset tracking point coordinates to calculate a precise deviation value. This ensures the foundation for tracking accuracy and provides reliable data support for subsequent adjustments. When the deviation exceeds a preset threshold, the system automatically makes dynamic adjustments, including checking for external interference, adjusting camera exposure parameters, and predicting the target star's position based on its motion. This dynamic adjustment mechanism enables the system to adapt to environmental changes in real time, maintaining tracking stability and accuracy. The system can automatically detect and address external interference factors such as cloud cover and solar radiation. By waiting for interference to disappear or adjusting camera exposure parameters, the system can maintain stable tracking of the target star in complex environments. The entire technical solution is designed to account for various possible abnormal situations and provide corresponding handling strategies. This robust design ensures the system maintains a certain level of stability and reliability in the face of unforeseen challenges. The technical solution employs a closed-loop control strategy that continuously compares the actual deviation with a preset threshold and automatically adjusts tracking parameters to achieve accurate tracking of the target star. This automated control method reduces the need for manual intervention and improves work efficiency. Based on the position and velocity information in the star database, the system intelligently predicts the target star's motion and adjusts the camera's tracking points and exposure parameters accordingly. This intelligent prediction and adjustment mechanism enables the system to prepare in advance, ensuring continuous and accurate tracking. The system records detailed information about each calculated miss distance and any anomalies or special handling situations encountered during the process. This data is crucial for subsequent analysis and optimization, helping to identify potential problems and develop appropriate improvement measures. Miss distance data is promptly fed back to the control system for subsequent motor pointing adjustments and error correction matrix calculations. This feedback mechanism forms a closed-loop optimization process, enabling continuous improvement in system performance.

[0181] In one embodiment of the present invention, the step S31 includes:

[0182] Perform precise calibration of the star-sensing camera, including lens distortion correction, focus adjustment, and sensor pixel alignment, and initialize camera parameters such as exposure time, gain setting, and white balance. Based on known target star position predictions (e.g., from a star database or previous observation data), develop an efficient search strategy within the star-sensing camera's field of view, such as using spiral search or grid scanning, to quickly locate the target star region.

[0183] Start the camera, capture the target star image according to the preset parameters, and apply image enhancement techniques such as histogram equalization, contrast stretching or noise suppression algorithms in real time to improve the image quality;

[0184] Image stability analysis algorithms detect minor jitter or motion blur in image sequences and use image registration techniques to remove or compensate for these effects;

[0185] Image segmentation and morphological processing techniques are used to identify and remove obstructions in the image, such as clouds, satellite shadows, or cosmic dust belts. The degree of obstruction is determined by setting a threshold, and the recapture mechanism or observation angle adjustment is triggered based on the preset threshold.

[0186] According to the light spot characteristics of the target star, the feature extraction algorithm is optimized to locate the outline and center of mass of the target star light spot.

[0187] The working principle of the above technical solution is: correct the distortion of the camera lens through mathematical models to ensure that the geometric shapes in the image are accurate and without distortion; adjust the focal length of the camera according to observation requirements to obtain the best image clarity and resolution. Ensure that pixels on the camera sensor are aligned to minimize image quality issues caused by pixel misalignment. Parameters such as exposure time, gain, and white balance are set to adapt to different observing environments and lighting conditions. Predict the approximate position of the target star within the star-sensing camera's field of view based on a star database or previous observation data. Use efficient algorithms such as spiral search or grid scanning to quickly locate the target star region within the field of view, reducing search time and resource consumption. Start the camera according to preset parameters and capture the target star image. Apply algorithms such as histogram equalization, contrast stretching, or noise suppression to improve image quality and enhance visibility and clarity. Use image stability analysis algorithms to monitor for minor jitter or motion blur in the image sequence. Use image registration techniques to remove or compensate for these effects, ensuring the stability and consistency of the image sequence. Use image segmentation techniques to separate different objects or regions in the image. Use techniques such as morphological processing to identify obstructions (such as clouds, shadows, dust lanes, etc.) in the image and set a threshold to determine the degree of obstruction. If the degree of obstruction exceeds a preset threshold, trigger a recapture mechanism or adjust the observation angle to avoid the obstruction. Optimize feature extraction algorithms based on the target star's spot characteristics. In addition to the traditional centroid method, it also combines advanced technologies such as edge detection, Hough transform or deep learning algorithms (such as convolutional neural network CNN) to accurately locate the outline and centroid position of the target star spot.

[0188] The effects of the above technical solutions are as follows: by correcting the lens distortion, adjusting the focal length and aligning the sensor pixels of the star-sensitive camera, the high precision and consistency of the camera are ensured, thereby improving the quality of the captured image; real-time application of image enhancement technologies, such as histogram equalization, contrast stretching or noise suppression algorithms, effectively improves the visibility and clarity of the image, making subsequent processing more accurate; based on the known target star position prediction, an efficient search strategy (such as spiral search or grid scanning method) is formulated, which can quickly locate the target star area and reduce search time and resource consumption; by setting thresholds and triggering mechanisms, the system can automatically determine the degree of occlusion and adjust the observation angle or trigger the recapture mechanism, thereby improving the flexibility and efficiency of the search; through image stability analysis algorithms and image registration technology, it can monitor and remove or compensate for the obstructions in the image sequence. It can eliminate slight jitter or motion blur to ensure the stability and consistency of the image; it uses image segmentation and morphological processing technology to effectively identify and remove obstructions in the image, such as clouds, satellite structure shadows or cosmic dust belts, reducing the impact of external factors on the observation results; according to the light spot characteristics of the target star, the feature extraction algorithm is optimized, and a comprehensive analysis is carried out by combining multiple technologies (such as edge detection, Hough transform or deep learning algorithm), which can more accurately locate the outline and center of mass position of the target star light spot, thereby improving the accuracy and reliability of tracking; this technical solution achieves a high degree of automation, from camera calibration, image capture to feature extraction, reducing the need for manual intervention and improving work efficiency; by introducing advanced technologies such as deep learning, the system can intelligently process and analyze image data, improving the accuracy and intelligence level of processing.

[0189] In one embodiment of the present invention, the S4 includes:

[0190] S41. Detect the current state of the laser terminal, including the real-time position and speed of the motor and whether there are any abnormal alarms (such as overheating, overcurrent, etc.); if there is an abnormality, enter the fault handling process, record the error, and attempt automatic or manual repair;

[0191] S42, accurately extracting the centroid position of the target star spot captured by the star-sensitive camera; at the same time, reading the coordinates of the pre-set tracking point;

[0192] S43, comparing the center of mass position with the tracking point coordinates, calculating the miss distances in azimuth and pitch (i.e., deviations in the x and y directions), and recording them;

[0193] The miss distances in azimuth and elevation are obtained as follows:

[0194] Assume: (x centroid ,y centroid ) is the coordinate of the extracted spot centroid position;

[0195] (xtrack ,y track ) are the preset tracking point coordinates;

[0196] The azimuth miss distance ΔAz and the elevation miss distance ΔEl are calculated using the following formula:

[0197] ΔAz=x centroid -x track

[0198] ΔEl=y centroid -y track

[0199] S44. Calculate the rotation amounts that need to be compensated for the azimuth motor and the pitch motor, respectively, based on the miss distances x and y, and the mechanical characteristics of the laser terminal (such as the gear ratio, motor resolution, etc.). The compensation rotation amounts of the azimuth motor and the pitch motor are obtained as follows:

[0200] ΔAz and ΔE1 are the miss distances in azimuth and elevation, respectively (obtained from step S43);

[0201] M az and M el are the optical system magnifications in azimuth and elevation directions (unit: angle / pixel);

[0202] R az and R el are the angular resolutions of the azimuth and pitch motors (unit: angle / step);

[0203] Azimuth motor compensation rotation Δθ az and the pitch motor compensation rotation Δθ el Calculated by the following formula:

[0204]

[0205] S45. Using the calculated motor compensation amount, update the theoretical direction of the laser terminal; compare the theoretical direction with the actual direction (i.e., the actual position of the current motor) to evaluate the difference between the two;

[0206] S46. Convert the actual pointing direction and the theoretical pointing direction into corresponding direction cosine matrices; the matrix corresponding to the actual pointing direction is recorded as err_co, and the matrix corresponding to the theoretical pointing direction is recorded as co;

[0207] S47. Based on the direction cosine matrices err_co and co, using the principle of matrix transformation, calculate the PAT (Pointing Adjustment and Testing) installation matrix; wherein the PAT installation matrix is ​​obtained as follows:

[0208] Assume: Cactual is the direction cosine matrix of the actual direction;

[0209] C desired is the direction cosine matrix of the theoretical orientation;

[0210] PAT Installation Matrix M PAT Calculated by the following formula:

[0211]

[0212] in, Indicates C desired The inverse matrix of the direction cosine matrix is ​​a 3×3 matrix that represents the rotation from one coordinate system to another in three-dimensional space;

[0213] The direction cosine matrix C is expressed as:

[0214]

[0215] Among them, C xx , C xy , C xz , C yx , C yy , C yz , C zx , C zy , C zz is the direction cosine, which represents the cosine of the angle between the axes of the carrier coordinate system and the axes of the inertial coordinate system;

[0216] Calculate the inverse matrix Obtain it through the following steps:

[0217] A1. Calculate matrix C desired The determinant value of ;

[0218] A2. Calculate matrix C desired The adjoint matrix of

[0219] A3. Divide each element of the adjoint matrix by the determinant value;

[0220] Obtained through the following formula:

[0221]

[0222] Among them, det(C desired ) is C desired The determinant of adj(C desired ) is C desired The adjoint matrix of .

[0223] Calculate M by matrix multiplication PATIn practical applications, numerical computing libraries (such as NumPy) are usually used to handle these matrix operations.

[0224] S48, verifying the calculated PAT installation matrix. If the verification result does not meet the requirements, return to the above steps to recalculate and adjust until a satisfactory installation matrix is ​​obtained;

[0225] S49. Repeat the above steps for all selected target stars. After completing the calibration of each target star, update the correction matrix (which can be the weighted average of all previous correction matrices or the latest value). During the entire calibration process, continuously monitor the status and performance parameters of the laser terminal.

[0226] The working principle of the above technical solution is as follows: First, the system detects the current state of the laser terminal, which includes the real-time position and speed of the motor and whether there are any abnormal alarms (such as overheating, overcurrent, etc.). If an abnormality is detected, the fault handling process is entered, the error is recorded, and automatic or manual repair is attempted; the target star spot is captured by the star-sensitive camera, and the center of mass position of the spot is accurately extracted. At the same time, the preset tracking point coordinates are read; the center of mass position is compared with the tracking point coordinates, and the azimuth and pitch miss amounts (i.e., deviations in the x and y directions) are calculated and recorded respectively. Based on the miss amounts x and y, as well as the mechanical characteristics of the laser terminal (such as gear ratio, motor resolution, etc.), the rotation amount that needs to be compensated for the azimuth motor and pitch motor is calculated. The calculated motor compensation amount is used to update the theoretical pointing of the laser terminal. Then, the theoretical pointing is compared with the actual pointing (i.e., the actual position of the current motor) to evaluate the difference between the two. The actual pointing and theoretical pointing are converted into corresponding direction cosine matrices. The construction of the direction cosine matrix must be accurate to multiple decimal places; based on the direction cosine matrices err_co and co, the PAT installation matrix is ​​calculated using the principle of matrix transformation. During the calculation process, the symmetry and orthogonality of the installation matrix are taken into consideration, and matrices that do not meet the conditions are corrected or recalculated. The calculated PAT installation matrix is ​​verified to ensure that it can effectively reduce the miss amount. If the verification result does not meet the requirements, return to the above steps to recalculate and adjust until a satisfactory installation matrix is ​​obtained; repeat the above steps for all selected target stars, and update the correction matrix once each target star is calibrated (it can be the weighted average of all previous correction matrices or the latest value). During the entire calibration process, the status and performance parameters of the laser terminal are continuously monitored to ensure the stability and reliability of the system.

[0227] The above technical solution has the following effects: by calculating the rotation compensation required for the azimuth and pitch motors respectively and taking into account the physical limitations of the motors, the accuracy and achievability of the compensation amount are ensured, thereby significantly improving the pointing accuracy of the laser terminal; throughout the calibration process, the status and performance parameters of the laser terminal are continuously monitored to ensure stable system operation and reduce pointing errors caused by equipment failure or performance degradation; the system can automatically detect the current status of the laser terminal and, when an anomaly is detected, enter the fault handling process and attempt automatic or manual repairs, thereby improving the system's automation level and fault response speed; using the direction cosine matrix and matrix transformation principles, the PAT installation matrix is ​​intelligently calculated, reducing human intervention and calculation errors, and improving the intelligence level of the system; the calibration steps are repeated for all selected target stars, and the correction matrix is ​​updated after each target star is calibrated, allowing the system to adapt to different target stars and observation requirements and improving the system's flexibility; this technical solution has good scalability, and new functional modules can be added or existing algorithms optimized as needed to meet future higher-precision pointing requirements; the calculated PAT installation matrix is ​​rigorously verified to ensure that it can effectively reduce misses. Through various verification methods (such as application to other calibrated target satellites and simulation verification using simulation software), the system's reliability and accuracy have been improved. By recording and analyzing data such as the difference between actual and theoretical pointing and the amount of miss, strong data support has been provided for system optimization and improvement. Through the automated and intelligent calibration process, manual intervention and repetitive work have been reduced, improving the efficiency of calibration work. Equipment failures have been discovered and repaired in a timely manner, reducing downtime and maintenance costs caused by equipment failures. At the same time, by improving pointing accuracy and stability, the waste of resources and increased costs caused by pointing errors have been reduced.

[0228] In one embodiment of the present invention, the S6 includes:

[0229] S61. The error installation correction matrix calculated during each calibration process is stored. When all selected target stars have completed calibration, all error installation correction matrices are averaged to obtain a preliminary average correction matrix.

[0230] S62. Use the preliminary average correction matrix to recalibrate some or all of the calibrated target stars; compare the recalibration results with the initial calibration results to evaluate the accuracy and stability of the average correction matrix;

[0231] If the recalibration result meets the preset accuracy requirements (e.g., the pointing error is less than a certain threshold), the average correction matrix is ​​considered valid and the process proceeds to the next step. Otherwise, the calibration process is rechecked to analyze possible causes, such as the accuracy of the star position data and sensor errors, and the screening conditions or correction matrix calculation method are adjusted as needed.

[0232] S64. If it is found during the verification process that some aspects of the average correction matrix are deficient, such as poor correction effects in certain specific directions, the correction matrix is ​​optimized. The optimization includes adjusting the calculation weights of the correction matrix, introducing a more complex mathematical model (such as the least squares method, Kalman filtering, etc.) to optimize the solution process of the correction matrix, or performing local fine-tuning of the correction matrix based on the recalibration results. After optimization, re-verification is performed until an average correction matrix that meets the accuracy requirements is obtained.

[0233] S65. The average correction matrix that has finally passed the verification is stored, and related verification information (such as verification time, verification result, etc.) is marked, and the average correction matrix is ​​applied to the subsequent laser payload pointing control.

[0234] The working principle of the above technical solution is as follows: during each calibration process, the system will calculate an error installation correction matrix for the current target satellite. When all the selected target satellites have completed calibration, the system will average these correction matrices to obtain a preliminary average correction matrix. By combining the calibration results of all target satellites, a correction matrix that can reflect the overall error characteristics is obtained. The averaging calculation can be a weighted average or a simple arithmetic average, depending on the characteristics of the correction matrix and actual needs; using the preliminary average correction matrix, the system recalibrates some or all of the calibrated target satellites. The purpose of recalibration is to evaluate the accuracy and stability of the average correction matrix by comparing the differences between the recalibration results and the initial calibration results. If the recalibration results can significantly reduce the pointing error, and this reduction is stable (that is, the performance is consistent on different target satellites), the average correction matrix is ​​preliminarily considered to be valid; the system will verify the accuracy of the recalibration results to check whether they meet the preset accuracy requirements (such as the pointing error is less than a certain threshold). If the requirements are met, the average correction matrix is ​​considered valid and can proceed to the next application. If not, the calibration process needs to be re-examined and possible causes, such as the accuracy of the stellar position data and sensor errors, need to be analyzed. Based on the analysis results, the screening conditions or the correction matrix calculation method may need to be adjusted, and calibration and averaging calculations must be repeated. If the average correction matrix is ​​found to be deficient in certain aspects during the verification process (such as poor correction in certain directions), the correction matrix needs to be optimized. Optimization methods may include adjusting the calculation weights of the correction matrix, introducing more complex mathematical models (such as the least squares method or Kalman filtering) to optimize the correction matrix solution process, or making local fine-tuning of the correction matrix based on the recalibration results. The optimized correction matrix is ​​re-verified until the average correction matrix meets the accuracy requirements. The final verified average correction matrix is ​​stored and annotated with relevant verification information (such as the verification time and results).

[0235] The above technical solution achieves this by averaging the error correction matrices obtained during the calibration of all selected target stars, generating a preliminary average correction matrix that comprehensively reflects the overall error characteristics of the system. This comprehensive correction approach more comprehensively accounts for the errors under different target stars and observation conditions, thereby improving pointing accuracy and stability. The preliminary average correction matrix is ​​then used to recalibrate some or all of the calibrated target stars, and the accuracy and stability of the average correction matrix are evaluated by comparing the recalibration results with the initial calibration results. This ensures the validity and reliability of the correction matrix in practical applications. During the verification process, if the average correction matrix is ​​found to have deficiencies in certain aspects, such as poor correction performance in a specific direction, the system will optimize it. This optimization includes adjusting the calculation weights of the correction matrix and introducing more complex mathematical models to improve the adaptability and accuracy of the correction matrix. The system can locally fine-tune or recalculate the correction matrix based on the recalibration results and verification information to adapt to different observation conditions and error conditions. This adaptive adjustment capability enables the system to maintain high pointing accuracy and stability in practical applications. Through an automated calibration and verification process, the system can efficiently process large amounts of data and calculate an accurate correction matrix. It reduces manual intervention and repetitive work, and improves work efficiency; the entire calibration and verification process is repeatable, which means that the system can be recalibrated and verified when needed in the future to ensure the accuracy and effectiveness of the correction matrix. Through a rigorous verification process, the system can ensure that the average correction matrix that passes the final verification is reliable and effective. Reliability verification reduces pointing errors and potential risks caused by inaccurate correction matrices; when applying the correction matrix for laser payload pointing control, the system will continuously monitor the accuracy and stability of the correction matrix to ensure that the system always operates in a safe and reliable state; the system's storage and annotation of the correction matrix makes subsequent maintenance and management simple and convenient. When the correction matrix needs to be updated or adjusted, the system can quickly find and replace the old correction matrix.

[0236] In one embodiment of the present invention, the S7 includes:

[0237] S71. Use the correction matrix noted above to recalibrate the previously calibrated target star. Observe the centroid of the target star's spot with the star-sensitive camera to determine whether it accurately falls on the tracking point.

[0238] If the target star's spot centroid position coincides with the tracking point or the error is within an acceptable range, the calibration is considered successful.

[0239] If the calibration result has a large error, that is, the deviation between the target star's spot centroid position and the tracking point exceeds the acceptable range, an error analysis is performed;

[0240] S72. Analyze error sources, including errors in star position data, orbital parameter errors, camera tracking accuracy, and motor control accuracy. Based on the error analysis results, adjust the correction matrix or re-perform relevant steps in the calibration process, such as re-selecting target stars and recalculating motor compensation.

[0241] S73. After the error adjustment is completed, repeat steps S2-S6 to recalibrate the target star and re-verify the calibration results until the predetermined accuracy requirements are met.

[0242] S74. Record the results of each calibration, error analysis, adjustment measures and final verification results.

[0243] The working principle of the above technical solution is as follows: First, the correction matrix obtained in step S6 is used to recalibrate the previously calibrated target star. The target star's spot center of mass is observed using a star-sensing camera and compared with the preset tracking point to determine whether the spot center of mass accurately falls on the tracking point or whether the deviation is within an acceptable range. If the spot center of mass coincides with the tracking point or the error is within an acceptable range, the calibration is considered successful and the correction matrix is ​​valid. If the calibration result has a large error, that is, there is a significant deviation between the target star's spot center of mass and the tracking point, an error analysis is required to determine the root cause of the problem. During the error analysis phase, the system considers various possible error sources, including the accuracy of the star position data, errors in orbital parameters, camera tracking accuracy, and motor control accuracy. By comprehensively analyzing these factors, the system can identify the main cause of the calibration error. Based on the results of the error analysis, the system will take appropriate adjustment measures, including adjusting certain parameters of the correction matrix, re-screening the target stars to ensure uniform and representative distribution, and recalculating motor compensation to optimize control performance. After error adjustment is complete, the system repeats steps S2 through S6, re-selecting the target satellite, calibrating, and calculating and verifying the correction matrix. Through multiple iterations of calibration and verification, the system gradually approaches optimal performance, ensuring the laser payload pointing control system maintains high accuracy and stability in practical applications. Throughout the calibration process, the system records each calibration result, error analysis, adjustment measures taken, and final verification results in detail.

[0244] The above technical solution achieves the following benefits: By retesting a calibrated target star using the previously used correction matrix, the accuracy and effectiveness of the correction matrix can be double-verified. This double-verification mechanism significantly improves calibration accuracy and reliability, reducing the risk of calibration failure due to single-test errors. When significant errors are detected in the calibration results, the error analysis in step S72 accurately identifies the source of the errors, such as errors in stellar position data, orbital parameter errors, camera tracking accuracy, or motor control accuracy. Targeted adjustments based on the error analysis results, such as adjusting the correction matrix, reselecting target stars, or recalculating motor compensation, can effectively reduce errors and improve calibration accuracy. The iterative calibration process in step S73 allows the system to gradually approach optimal performance through continuous adjustment and optimization. By repeatedly executing steps S2 through S6, the system can make necessary adjustments to the correction matrix and calibration process based on the results of the previous calibration and error analysis, thereby gradually reducing calibration errors and improving pointing accuracy. The system can adaptively adjust the correction matrix and related parameters and steps in the calibration process based on actual test results and error analysis. The system's adaptive adjustment capabilities enable it to cope with varying observation conditions and error conditions, maintaining high pointing accuracy and stability. Step S74 requires detailed documentation of each calibration, including error analysis, adjustment measures, and final verification results. These records not only provide valuable reference information for subsequent maintenance and upgrades, but also facilitate continuous optimization and improvement of system performance. By recording and summarizing the calibration process, the system can identify common error patterns and areas for improvement, providing valuable insights and guidance for future calibration efforts. This continuous improvement cycle helps continuously enhance the system's pointing accuracy and reliability. Through a rigorous calibration and verification process, the system ensures high pointing accuracy and stability in practical applications. This stability is crucial for laser payload pointing control systems, ensuring they function properly in a variety of complex environments. Detailed documentation and summaries make system maintenance easier and more efficient. Maintenance personnel can quickly identify problems based on the recorded information and take appropriate measures to repair and optimize the system. This enhanced maintainability reduces system maintenance costs and downtime, improving overall system efficiency.

[0245] One embodiment of the present invention provides a system, characterized in that it includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0246] Memory for storing computer programs;

[0247] A processor, configured to implement the method steps described in any one of claims 1 to 9 when executing a program stored in a memory.

[0248] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A laser payload autonomous pointing calibration optimization method, characterized in that: The method comprises: S1. According to the position and velocity of the satellite in the J2000 coordinate system, the orbital normal vector is calculated and the orbital coordinate system is established; and the target satellite is screened; S2. According to the selected star catalog, open the loop and point to the target star in order; S3, when the target star is within the field of view of the star-sensitive camera, the miss distances x and y are calculated based on the deviation between the centroid position of the target star spot and the tracking point, and then the theoretical direction of the motor is calculated; S4, calculating the error installation correction matrix using the actual orientation and theoretical orientation of the motor; S5, repeat steps S1-S4 until all target stars are calibrated; S6, averaging all error installation matrices to obtain a correction matrix; S7. Add the correction matrix and calibrate the previously calibrated target star again. If the target star falls on the tracking point of the star-sensitive camera after pointing to it, the calibration is considered successful. If the error is large, repeat the calibration process.

2. According to claim 1, a laser payload autonomous pointing calibration optimization method is characterized in that: Said S1 comprises: S11, obtaining the precise position data and velocity vector of the satellite in the J2000 coordinate system; the position velocity includes longitude, latitude and altitude; S12. Use ephemeris data and navigation algorithms to update the satellite's position and velocity information in real time to ensure the accuracy and timeliness of the data; S13. According to the position and velocity of the satellite, the normal vector of the orbital plane is calculated using the principle of orbital mechanics, and the direction of the orbital normal vector is determined as the negative direction of the Z axis of the orbital coordinate system; S14, taking the local satellite as the coordinate origin, selecting the X-axis and the Y-axis, and constructing the orbital coordinate system; S15. Verify the correctness of the coordinate system to ensure that the axes are perpendicular to each other and satisfy the right-hand screw rule; S16, calling the star database, obtaining the position information of all the stars to be screened in the J2000 coordinate system, and calculating the angle between each star vector and the -Z direction of the orbital coordinate system; S17, calculating the angle between the star vector and the sun vector; S18. Add the stars that meet the conditions to the target star list as calibration targets.

3. According to claim 1, a laser payload autonomous pointing calibration optimization method is characterized in that: The S2 comprises: S21. Sort the target star list according to the relevant factors of the stars; S22, the control system calculates and outputs a pointing instruction according to the position information of the target star, and after receiving the instruction, the laser payload adjusts its attitude so that the optical axis points to the target star; S23. Monitor the posture adjustment process in real time.

4. According to claim 3, a laser payload autonomous pointing calibration optimization method is characterized in that: The S23 comprises: S231. Before adjusting the attitude, accurately measure the current attitude of the laser payload through a high-precision gyroscope and an accelerometer to obtain initial attitude data; S232, comparing the initial posture data with the preset ideal posture, calculating the posture deviation, and performing preliminary calibration of the posture based on the deviation result; S233, dynamically tracking the attitude of the laser payload through a Kalman filter or an extended Kalman filter, and fusing data from multiple sensors in real time; S234, based on the motion trajectory of the star and the current attitude of the laser payload, the celestial navigation algorithm is used to predict the position of the target star in the future; S235, dynamically adjusting the attitude adjustment strategy according to the predicted star position and the current attitude deviation, adaptively optimizing the attitude adjustment strategy through the fuzzy logic algorithm, and dynamically adjusting the control parameters according to the real-time feedback attitude data and external environment changes; S236. During the posture adjustment process, a closed-loop control strategy is adopted to compare the deviation between the actual posture and the target posture in real time, and the posture adjustment is controlled by a PID controller; S237. During the attitude adjustment process, various performance indicators of the laser payload are continuously monitored. If any abnormality is found, the abnormality handling mechanism is immediately triggered.

5. According to claim 1, a laser payload autonomous pointing calibration optimization method is characterized in that: The S3 includes: S31, capturing a target star image by a star-sensitive camera; and extracting the centroid position of the target star spot by an image processing algorithm; S32, extract the centroid position coordinates (X c ,Y c ) and the preset tracking point coordinates (X t ,Y t ) to compare and calculate the preliminary horizontal direction (X axis) deviation ΔX = X c -X t and the vertical direction (Y axis) deviation ΔY=Y c -Y t ; S33, preset a deviation threshold, such as σx, σy, and judge whether the deviation is within an acceptable range by the deviation threshold. If |ΔX|≤σx and |ΔY|≤σy, it is preliminarily considered that the miss amount is small, and directly proceed to the next step; S34, if the deviation exceeds the threshold, further judgment and processing are performed; If the deviation is too large, check whether there is an external interference factor and perform corresponding processing, which includes waiting for the interference to disappear and adjusting the camera exposure; If there is no external interference or the interference has been dealt with, but the deviation is still large, dynamic adjustment is performed; S35, analyzing the movement trend of the target star and predicting the position of the target star in the next frame of image; S36, dynamically adjusting the tracking point and exposure parameters of the camera according to the predicted position, capturing the image again and extracting the centroid position; S37, repeat the above steps until the deviation is within an acceptable range; S38, after the deviation is acceptable, the miss distances x and y are calculated according to the final extracted centroid position and tracking point coordinates; the miss distance x = final ΔX, the miss distance y = final ΔY; S39, recording the miss distances x and y calculated this time, as well as the abnormalities or special processing situations encountered in the process, and feeding back the miss distance data to the control system.

6. According to claim 5, a laser payload autonomous pointing calibration optimization method is characterized in that: The S31 includes: Calibrate the star-sensing camera and initialize the camera parameters; formulate a search strategy within the field of view of the star-sensing camera based on the known target star position prediction; Start the camera, capture the target star image according to the preset parameters, and apply image enhancement technology in real time to improve the image quality; Through image stability analysis algorithms, the image sequence is monitored for small jitters or motion blurs, and image registration techniques are used to remove or compensate for these effects; Image segmentation and morphological processing technology are used to identify and remove occlusions in the image, the degree of occlusion is determined by setting a threshold, and the recapture mechanism is triggered or the observation angle is adjusted based on the preset threshold; According to the light spot characteristics of the target star, the feature extraction algorithm is optimized to locate the outline and center of mass of the target star light spot.

7. According to claim 1, a laser payload autonomous pointing calibration optimization method is characterized in that: The S4 comprises: S41, detecting the current state of the laser terminal, the current state including the real-time position and speed of the motor and whether there is any abnormal alarm; if there is an abnormality, entering the fault handling process, recording the error and attempting automatic or manual repair; S42, extracting the centroid position of the target star spot captured by the star-sensitive camera; at the same time, reading the preset tracking point coordinates; S43, comparing the centroid position with the tracking point coordinates, calculating the miss distances in azimuth and elevation, and recording them; S44, according to the miss distances x and y, and the mechanical characteristics of the laser terminal, calculating the rotation amounts that the azimuth motor and the pitch motor need to compensate for respectively; S45, using the calculated motor compensation amount, updating the theoretical orientation of the laser terminal; comparing the theoretical orientation with the actual orientation, and evaluating the difference between the two; S46, converting the actual pointing direction and the theoretical pointing direction into corresponding direction cosine matrices; S47, based on the direction cosine matrix, using the principle of matrix transformation, calculate the PAT installation matrix; S48, verifying the calculated PAT installation matrix. If the verification result does not meet the requirements, return to the above steps to recalculate and adjust until a satisfactory installation matrix is ​​obtained; S49. Repeat the above steps for all selected target stars. After each target star is calibrated, the correction matrix is ​​updated. During the entire calibration process, the status and performance parameters of the laser terminal are continuously monitored.

8. According to claim 1, a laser payload autonomous pointing calibration optimization method is characterized in that: The S6 comprises: S61, storing the error installation correction matrix calculated in each calibration process, and when all the selected target stars have completed the calibration, averaging all the error installation correction matrices to obtain a preliminary average correction matrix; S62. Use the preliminary average correction matrix to recalibrate some or all of the calibrated target stars; compare the difference between the recalibration results and the initial calibration results to evaluate the accuracy and stability of the average correction matrix; S63. If the recalibration result meets the preset accuracy requirement, the average correction matrix is ​​considered to be valid and the next step is entered; otherwise, the calibration process is rechecked, the reasons are analyzed, and the screening conditions or the correction matrix calculation method are adjusted as needed; S64, if it is found that the average correction matrix is ​​insufficient during the verification process, the correction matrix is ​​optimized, and after optimization, verification is performed again until an average correction matrix that meets the accuracy requirements is obtained; S65, storing the average correction matrix that has passed the final verification, marking the relevant verification information, and applying the average correction matrix to the subsequent laser payload pointing control.

9. The method for optimizing the autonomous pointing calibration of a laser payload according to claim 1, characterized in that: The S7 comprises: S71. Use the correction matrix noted above to perform calibration test on the previously calibrated target star again, and observe the centroid position of the target star's spot through the star-sensitive camera to determine whether it accurately falls on the tracking point; If the centroid position of the target star's spot coincides with the tracking point or the error is within an acceptable range, the calibration is considered successful; If the calibration result has a large error, that is, the deviation between the target star's spot centroid position and the tracking point exceeds the acceptable range, an error analysis is performed; S72, analyzing the error sources, wherein the error sources include star position data errors, orbit parameter errors, camera tracking accuracy, and motor control accuracy; adjusting the correction matrix or re-performing the relevant steps in the calibration process according to the error analysis results; S73, after completing the error adjustment, repeat the steps of S2-S6, recalibrate the target star, and re-verify the calibration result until the predetermined accuracy requirement is met; S74. Record the results of each calibration, error analysis, adjustment measures and final verification results.

10. A system, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 9 when executing a program stored in a memory.

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