Satellite visible time window calculation method based on visible area

Abstract

The invention relates to the field of satellite communication, in particular to a satellite visible time window calculation method based on a visible area, and the method comprises the steps: constructing an accurate three-dimensional terrain model through real-time volume rendering and a multi-level dynamic modeling technology; thirdly, calculating visual area data by using a high-performance ray tracing algorithm and a self-adaptive precision adjustment mechanism; then, generating corrected predicted orbit data by combining a machine learning algorithm and a multi-factor dynamic correction method; integrating the data by using a multi-objective optimization algorithm, and calculating an optimal satellite visual time window; and finally, efficient and stable satellite switching is ensured through real-time monitoring and feedback adjustment of a multi-satellite cooperation mechanism and a dynamic switching strategy. According to the method, the calculation precision and efficiency are remarkably improved, the resource allocation is optimized, and the stability of the system and the user experience are enhanced.

Description

Technical Field

[0001] The invention relates to the field of satellite communications, and in particular to a method for calculating a satellite visible time window based on a visible area. Background Art

[0002] Due to their high-speed movement and small coverage, low-orbit satellites are only visible to ground users for a short time. This requires the system to efficiently calculate the visible time window of the satellite to ground users in order to perform reasonable satellite switching and resource allocation. Accurate visible time window calculation can not only reduce the cost of frequent switching between different satellites, but also improve communication stability and user experience.

[0003] The existing technology (Chinese invention patent, publication number: CN117421518B, name: A method and system for calculating the time window of ground coverage by a low-orbit satellite) mainly adopts geometric calculation and simplified model methods, such as multi-sensor coverage algorithm, great circle approximation fast algorithm and simplified earth shape model. These methods have the following shortcomings when calculating the visible time window: on the one hand, although the geometric calculation method is accurate, the calculation complexity is high and the resource consumption is large; on the other hand, although the simplified model method improves the calculation efficiency, due to over-simplification, the calculation accuracy is not high and cannot meet the accuracy requirements in practical applications. In addition, the existing technology is difficult to balance the calculation accuracy and calculation time, especially when facing a large number of concurrent calculations, it is easy to have delays, affecting the timeliness of switching control. Summary of the invention

[0004] In view of the many problems existing in the above-mentioned prior art, the present invention provides a satellite visible time window calculation method based on the visible area. The present invention combines a variety of advanced technologies, such as real-time volume rendering, high-performance ray tracing, machine learning and multi-objective optimization, and realizes efficient calculation and optimization of satellite visible time windows through dynamic three-dimensional modeling, precise orbit prediction and real-time monitoring and adjustment. The calculation accuracy and efficiency are significantly improved, the satellite switching overhead is reduced, and the system stability and user experience are enhanced.

[0005] The satellite visible time window calculation method based on the visible area includes the following steps:

[0006] Collect and process satellite position data, ground user position data and real-time atmospheric data to generate a comprehensive data set;

[0007] Based on the comprehensive data set, a dynamic three-dimensional terrain model is constructed using real-time volume rendering and multi-level dynamic modeling technology;

[0008] In the dynamic three-dimensional terrain model, the visible area data between the satellite and the ground user is calculated through a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism;

[0009] Using historical orbit data and real-time environmental data, combined with machine learning algorithms and multi-factor dynamic correction methods, the corrected predicted orbit data is generated;

[0010] Integrating the visible area data and the corrected predicted orbit data, and using a multi-objective optimization algorithm to calculate the optimal satellite visible time window;

[0011] Based on the ground user location data and the satellite visible time window data, a multi-satellite coordination mechanism is established, a dynamic switching strategy is designed and optimized, satellite switching is executed, and real-time monitoring and feedback adjustments are carried out.

[0012] Preferably, the collecting and processing of satellite position data, ground user position data and real-time atmospheric data to generate a comprehensive data set comprises:

[0013] Collect satellite position data, ground user position data and real-time atmospheric data;

[0014] Cleaning and standardizing the satellite position data, ground user position data and real-time atmospheric data;

[0015] The processed data are fused to generate a comprehensive dataset.

[0016] Preferably, the dynamic three-dimensional terrain model construction includes:

[0017] Convert geographic coordinate data and atmospheric data from the integrated dataset into three-dimensional volume data;

[0018] Using multi-level dynamic modeling technology, a multi-level dynamic three-dimensional terrain model is constructed according to different resolutions and time scales;

[0019] The 3D terrain model is updated in real time to reflect topographic and atmospheric changes.

[0020] Preferably, the real-time volume rendering technology used in the step of constructing the three-dimensional terrain model includes:

[0021] Combine geographic data and atmospheric data to generate initial three-dimensional volume data;

[0022] Construct multi-level 3D volume data at different resolutions and time scales;

[0023] Dynamically update multi-level 3D volume data to reflect real-time changes.

[0024] Preferably, the calculating of the visible area data between the satellite and the ground user by using a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism comprises:

[0025] Send rays from the satellite position to the ground user position to simulate the signal propagation path;

[0026] Calculate the refraction effect of rays in the atmosphere and adjust the ray path;

[0027] Determine whether the ray path is blocked by the terrain and calculate the blocking effect;

[0028] Adaptively adjust the calculation accuracy of ray tracing based on the complexity of the area.

[0029] Preferably, the adaptive precision adjustment mechanism includes:

[0030] Assess the topographic and atmospheric complexity of the area through which the ray path passes;

[0031] Adjust the accuracy of the ray tracing calculations based on the complexity assessment results, increasing accuracy in critical areas and reducing accuracy in simple areas.

[0032] Preferably, the machine learning trajectory prediction and multi-factor dynamic correction include:

[0033] Collect historical orbit data and perform cleaning and standardization;

[0034] Extract key features from historical track data and train machine learning models;

[0035] Orbit prediction using real-time orbit data;

[0036] Collect environmental data and calculate the correction factor, and correct the preliminary predicted orbit data according to the following calculation expression:

[0037] R=P+E×F

[0038] Among them, R is the corrected predicted orbit data; P is the preliminary predicted orbit data; E is the environmental data; and F is the correction factor.

[0039] Preferably, the multi-objective optimization algorithm calculates the optimal satellite visible time window including:

[0040] Integrate visible area data with revised predicted orbit data;

[0041] Define optimization objective function and constraints;

[0042] A multi-objective optimization algorithm is used for iterative calculation to generate the optimal satellite visible time window.

[0043] Preferably, the multi-satellite coordination mechanism and dynamic switching strategy include:

[0044] Evaluate satellite switching requirements based on ground user location data and satellite visible time window data;

[0045] Select an optimization algorithm to optimize the switching strategy;

[0046] Execute the optimized switching strategy and conduct real-time monitoring and feedback adjustments.

[0047] Preferably, the real-time monitoring and feedback adjustment of the multi-satellite coordination and dynamic switching strategy includes:

[0048] Collect performance data during the switching process in real time through sensors and monitoring systems;

[0049] Analyze performance data to identify potential failures or efficiency issues during switching;

[0050] Dynamically adjust the switching strategy based on the analysis results to improve system stability and switching efficiency.

[0051] Compared with the prior art, the advantages and beneficial effects of the present invention are:

[0052] The present invention realizes the construction of dynamic three-dimensional terrain model through real-time volume rendering and multi-level dynamic modeling technology, and improves the ability to accurately describe terrain and atmospheric changes;

[0053] The present invention realizes the accurate calculation of the visible area data between the satellite and the ground user through a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism, ensuring the high accuracy and efficiency of the calculation results;

[0054] The present invention achieves high-precision correction of orbit prediction by combining machine learning algorithms and multi-factor dynamic correction methods, effectively improving the accuracy of orbit prediction;

[0055] The present invention integrates the visible area data and the corrected predicted orbit data through a multi-objective optimization algorithm, realizes the calculation of the optimal satellite visible time window, and optimizes resource allocation and coverage time;

[0056] The present invention realizes efficient management of the satellite switching process and improves the stability and switching efficiency of the system through real-time monitoring and feedback adjustment of a multi-satellite coordination mechanism and a dynamic switching strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 Schematic diagram of the calculation process of the method of the present invention;

[0058] Figure 2 It is a flow chart of data collection and processing in the present invention;

[0059] Figure 3 A flow chart for constructing a dynamic three-dimensional terrain model in the present invention;

[0060] Figure 4 A flowchart of ray tracing and adaptive precision adjustment in the present invention;

[0061] Figure 5 This is a flow chart of trajectory prediction and correction in the present invention;

[0062] Figure 6 It is a multi-objective optimization flow chart in the present invention;

[0063] Figure 7 This is a flow chart of the multi-satellite coordination and dynamic switching strategy in the present invention. DETAILED DESCRIPTION

[0064] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present disclosure. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0065] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "include", "comprising", etc. used herein indicate the existence of the features, steps, operations and / or components, but do not exclude the existence or addition of one or more other features, steps, operations or components.

[0066] All terms (including technical and scientific terms) used herein have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification, and should not be interpreted in an idealized or overly rigid manner.

[0067] like Figure 1-Figure 2 As shown, the satellite visible time window calculation method based on the visible area includes the following steps:

[0068] Collect and process satellite position data, ground user position data and real-time atmospheric data to generate a comprehensive data set;

[0069] Preferably, the collecting and processing of satellite position data, ground user position data and real-time atmospheric data to generate a comprehensive data set comprises:

[0070] Collect satellite position data, ground user position data and real-time atmospheric data;

[0071] Cleaning and standardizing the satellite position data, ground user position data and real-time atmospheric data;

[0072] The processed data are fused to generate a comprehensive dataset.

[0073] Satellite position data refers to the specific position of the satellite in orbit, including longitude, latitude and elevation information. These data can be obtained through the satellite's own positioning system or real-time monitoring by ground stations. In order to ensure the accuracy and consistency of the data, the collected satellite position data needs to be cleaned and standardized. The cleaning process includes removing noise data and outliers. Standardization ensures that the data format is unified, which is convenient for subsequent processing and fusion. Ground user position data refers to the specific location of ground users, including longitude, latitude and elevation information. This data can be obtained through channels such as geographic information systems (GIS) or the GPS positioning system of user equipment. It also needs to be cleaned and standardized to ensure the accuracy and consistency of the data. Real-time atmospheric data includes temperature, humidity, air pressure, wind speed, etc. These data have an important impact on the propagation path and refraction effect of rays in the atmosphere. Atmospheric data is usually obtained in real time through meteorological satellites, ground meteorological stations, etc. Cleaning and standardization of atmospheric data ensures the accuracy of the data and provides necessary environmental information for the subsequent construction of three-dimensional terrain models.

[0074] By cleaning and standardizing satellite position data, ground user position data, and real-time atmospheric data, the processed data are integrated to generate a comprehensive data set. This comprehensive data set provides unified and accurate basic data for subsequent dynamic three-dimensional terrain model construction, high-performance ray tracing, and machine learning orbit prediction. Specifically, the comprehensive data set integrates spatial position and environmental variables to ensure the consistency and accuracy of data between each step, thereby improving data processing efficiency and accuracy, and ensuring the reliability and accuracy of subsequent calculations.

[0075] For example, in a certain satellite observation mission, by collecting real-time atmospheric data, it was found that there was high humidity and low pressure in the observation area. After the atmospheric data was standardized, this information was fused with the satellite position data and the ground user position data to generate a comprehensive data set, so that the subsequent three-dimensional terrain model can accurately reflect the actual atmospheric conditions, thereby accurately calculating the visible area between the satellite and the ground user, and finally optimizing the calculation of the satellite visible time window. In this way, the generation of a comprehensive data set not only provides a reliable data basis for subsequent steps, but also significantly improves the accuracy and effectiveness of the overall solution, ensuring that high-precision calculation results can still be achieved under complex environmental conditions.

[0076] like Figure 3 As shown, based on the comprehensive data set, a dynamic three-dimensional terrain model is constructed using real-time volume rendering and multi-level dynamic modeling technology;

[0077] Preferably, the dynamic three-dimensional terrain model construction includes:

[0078] Convert geographic coordinate data and atmospheric data from the integrated dataset into three-dimensional volume data;

[0079] Using multi-level dynamic modeling technology, a multi-level dynamic three-dimensional terrain model is constructed according to different resolutions and time scales;

[0080] The 3D terrain model is updated in real time to reflect topographic and atmospheric changes.

[0081] The geographic coordinate data and atmospheric data in the comprehensive data set are converted into three-dimensional volume data. Through multi-level dynamic modeling technology, a multi-level dynamic three-dimensional terrain model is constructed according to different resolutions and time scales, and the three-dimensional terrain model is updated in real time to reflect terrain and atmospheric changes.

[0082] First, the geographic coordinate data and atmospheric data in the comprehensive data set are converted into three-dimensional volume data. The core of this step is to map the two-dimensional geographic coordinate data and atmospheric data into three-dimensional space through the data conversion algorithm to generate initial three-dimensional volume data. These three-dimensional volume data include information such as the height, slope, surface features of the terrain, and the humidity, temperature, and air pressure of the atmosphere, providing a basis for subsequent modeling.

[0083] Next, a multi-level dynamic modeling technique is used to construct a multi-level dynamic three-dimensional terrain model based on different resolutions and time scales. This step adopts the principle of multi-level dynamic modeling, and achieves a refined description of terrain and atmospheric changes by modeling at different resolutions and time scales. Specifically, multi-level dynamic modeling technology performs detailed modeling of key areas (such as areas with dramatic terrain fluctuations) at high resolution, and simplifies areas with smaller changes at low resolution, thereby improving modeling efficiency and accuracy. At the same time, combined with data at different time scales, the model can be dynamically updated so that it can reflect environmental changes in real time.

[0084] Finally, the 3D terrain model is updated in real time to reflect changes in terrain and atmosphere. This step uses real-time volume rendering technology to continuously update the latest data to the 3D terrain model, so that the model can reflect the current terrain and atmospheric conditions in real time. This not only ensures the accuracy of the model, but also improves the dynamic response ability of the model, enabling it to maintain high-precision performance under complex environmental conditions.

[0085] Through the above steps, the constructed dynamic three-dimensional terrain model provides an accurate environmental foundation for subsequent high-performance ray tracing and adaptive precision calculations, significantly improving the accuracy and efficiency of the visible area calculation between the satellite and ground users. For example, in practical applications, the real-time updated three-dimensional terrain model can accurately reflect the terrain and atmospheric changes in the satellite coverage area at a certain moment, so that in the calculation of the signal propagation path between the satellite and the ground user, factors such as atmospheric refraction and terrain occlusion are accurately considered, and the calculation results of the satellite visible time window are finally optimized. In summary, based on the comprehensive data set, the construction of a dynamic three-dimensional terrain model using real-time volume rendering and multi-level dynamic modeling technology not only improves the accuracy and real-time performance of the calculation, but also provides a solid technical foundation for the efficient execution of the overall solution.

[0086] Preferably, the real-time volume rendering technology used in the step of constructing the three-dimensional terrain model includes:

[0087] Combine geographic data and atmospheric data to generate initial three-dimensional volume data;

[0088] Construct multi-level 3D volume data at different resolutions and time scales;

[0089] Dynamically update multi-level 3D volume data to reflect real-time changes.

[0090] Real-time volume rendering technology is used to combine geographic data and atmospheric data to generate initial three-dimensional volume data, and multi-level three-dimensional volume data is constructed at different resolutions and time scales. Finally, the multi-level three-dimensional volume data is dynamically updated to reflect real-time changes.

[0091] Generate initial 3D volume data: Combine the geographic data and atmospheric data in the comprehensive dataset to generate initial 3D volume data. Geographic data includes the height, slope and surface features of the terrain, while atmospheric data includes information such as humidity, temperature and air pressure. These data are mapped in 3D space through volume rendering algorithms to form initial 3D volume data. This process ensures the comprehensive integration of terrain and atmospheric information, providing a basis for subsequent multi-level modeling.

[0092] Construct multi-level 3D volume data: Construct multi-level 3D volume data at different resolutions and time scales. Multi-level dynamic modeling technology optimizes the use of computing resources by modeling key areas in detail at high resolution and simplifying areas with less changes at low resolution. Specifically, high-resolution modeling is used in areas with dramatic terrain fluctuations to capture subtle terrain changes, while low-resolution modeling is used in relatively flat areas to reduce the amount of computation. In addition, with data at different time scales, short-term and long-term terrain change models can be established, thereby improving the dynamic response capability of the model.

[0093] Dynamically update 3D volume data: Through real-time volume rendering technology, multi-level 3D volume data is dynamically updated to reflect real-time changes. The core of real-time updating is to continuously obtain new geographic and atmospheric data and integrate it into the existing model so that the model can reflect the current environmental conditions in real time. This process ensures the accuracy and timeliness of the model, enabling it to maintain high-precision performance under complex environmental conditions.

[0094] By combining geographic data and atmospheric data to generate initial 3D volume data, the integrity and accuracy of the model can be ensured in the initial stage. Using multi-level dynamic modeling technology, constructing multi-level 3D volume data at different resolutions and time scales not only optimizes the use of computing resources, but also improves the model's detail expression and dynamic response capabilities. Real-time updating of 3D volume data enables the model to reflect actual changes in terrain and atmosphere in a timely manner, ensuring accurate terrain information under various environmental conditions.

[0095] For example, when conducting satellite observations in a complex terrain environment, the system can capture terrain changes such as mountains and rivers in a timely manner through real-time volume rendering technology, and generate a real-time updated three-dimensional terrain model in combination with current atmospheric conditions (such as temperature and humidity changes). This model not only accurately reflects the actual terrain and atmospheric conditions, but also provides reliable environmental data for subsequent high-performance ray tracing, thereby accurately calculating the visible area between the satellite and ground users. In this way, the final optimized satellite visible time window can fully consider real-time terrain and atmospheric changes, improving the accuracy and efficiency of observation and communication.

[0096] like Figure 4 As shown, in the dynamic three-dimensional terrain model, the visible area data between the satellite and the ground user is calculated through a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism;

[0097] Preferably, the calculating of the visible area data between the satellite and the ground user by using a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism comprises:

[0098] Send rays from the satellite position to the ground user position to simulate the signal propagation path;

[0099] Calculate the refraction effect of rays in the atmosphere and adjust the ray path;

[0100] Determine whether the ray path is blocked by the terrain and calculate the blocking effect;

[0101] Adaptively adjust the calculation accuracy of ray tracing based on the complexity of the area.

[0102] The principle of this step is to emit rays from the satellite position to the ground user position, simulate the signal propagation path, calculate the refraction effect of the rays in the atmosphere and adjust the ray path, determine whether the ray path is blocked by the terrain and calculate the blocking effect, and adaptively adjust the calculation accuracy of ray tracing according to the regional complexity.

[0103] Rays are emitted from the satellite position to the ground user position to simulate the signal propagation path. High-performance ray tracing algorithms simulate the path of signal transmission from satellite to ground users by emitting rays in three-dimensional space. Ray tracing algorithms need to consider the real-time positions of satellites and ground users and calculate the path of signal propagation in a three-dimensional terrain model.

[0104] Calculate the refraction effect of rays in the atmosphere and adjust the ray path. Due to the existence of the atmosphere, rays will refract during propagation, which will change the ray path. In order to accurately simulate this effect, it is necessary to calculate the refraction angle based on real-time atmospheric data and adjust the ray path. Specifically, the atmospheric refraction model can be used to calculate the refraction angle of rays in each layer based on the different density and temperature distribution of the atmosphere, thereby adjusting its path.

[0105] Determine whether the ray path is blocked by terrain and calculate the blocking effect. During propagation, the ray may be blocked by terrain features (such as mountains and buildings), causing the signal to fail to reach ground users. By determining whether the ray path intersects the terrain surface in the 3D terrain model, it is possible to determine whether the ray is blocked and calculate the degree and impact of the blockage. This step requires combining the height data of the terrain model and the geometric information of the ray path to accurately determine the blocking effect.

[0106] Adaptively adjust the calculation accuracy of ray tracing according to regional complexity. The terrain and atmospheric complexity of different regions are different, and the calculation accuracy should be adjusted accordingly. In areas with complex terrain or drastic atmospheric changes, the calculation accuracy of ray tracing needs to be improved to ensure the accuracy of the results; in areas with flat terrain or stable atmosphere, the calculation accuracy can be reduced to improve calculation efficiency. The adaptive accuracy adjustment mechanism dynamically adjusts the calculation accuracy and optimizes the use of computing resources by evaluating the complexity of the area through which the ray path passes.

[0107] By applying high-performance ray tracing algorithms and adaptive precision adjustment mechanisms in dynamic three-dimensional terrain models, the visible area data between satellites and ground users can be accurately calculated. The high-performance ray tracing algorithm ensures accurate simulation of the signal propagation path, while the adaptive precision adjustment mechanism dynamically adjusts the calculation accuracy according to the complexity of the area, which not only ensures the accuracy of the calculation results, but also improves the calculation efficiency. The resulting visible area data provides a reliable foundation for subsequent orbit prediction and time window optimization.

[0108] "High-performance ray tracing algorithms" in this invention refer to a class of optimized ray tracing algorithms that can efficiently and accurately calculate the propagation path and visible area of ​​rays in complex environments. Such algorithms include but are not limited to the following optimization methods:

[0109] Basic ray tracing algorithm: The basic ray tracing algorithm calculates the path of the ray from the emission point to the target point, taking into account the refraction and reflection of the ray in different media. This algorithm is usually used to calculate the propagation path of light in three-dimensional space.

[0110] Acceleration data structures: In order to improve the efficiency of ray tracing, acceleration data structures such as Bounding Volume Hierarchies (BVH), Kd-trees or Grid-based methods are usually used. These data structures can quickly determine whether the ray intersects with objects in the scene, thereby reducing unnecessary calculations.

[0111] Adaptive Sampling: Adaptive sampling technology is used to dynamically adjust the sampling density according to the scene complexity during ray tracing. The sampling density is increased in complex areas to improve accuracy, and the sampling density is reduced in simple areas to save computing resources.

[0112] Multi-level details (LOD): Using multi-level detail technology, high-resolution models are used in areas where high precision is required, and low-resolution models are used in areas where low precision is required. This method can effectively balance calculation accuracy and efficiency.

[0113] Parallel computing: Modern high-performance ray tracing algorithms often use parallel computing techniques to significantly increase the computing speed through multi-threading, GPU acceleration, etc. This is especially important when processing a large number of rays.

[0114] In the present invention, the implementation steps of the high-performance ray tracing algorithm are as follows:

[0115] Send rays from the satellite position to the ground user position to simulate the signal propagation path; here the basic ray tracing algorithm is used to calculate the initial path;

[0116] During the ray propagation process, the refraction effect of the ray in the atmosphere is calculated and the ray path is adjusted; based on the atmospheric model, the Snell law is used to calculate the refraction angle in different atmospheric layers and dynamically adjust the ray path;

[0117] Determine whether the ray path is blocked by the terrain; through acceleration data structures (such as BVH or Kd-trees), quickly determine whether the ray intersects with the terrain model and calculate the blocking effect;

[0118] Adaptively adjust the calculation accuracy of ray tracing according to the terrain and atmospheric complexity of the area where the ray path passes; increase the sampling density in areas with complex terrain or drastic atmospheric changes to improve calculation accuracy; reduce the sampling density in areas with flat terrain or stable atmosphere to improve calculation efficiency;

[0119] By using parallel computing technology, a large number of rays can be processed in parallel through multi-threading or GPU acceleration, which significantly improves the calculation speed.

[0120] Through the above high-performance ray tracing algorithm, the present invention can efficiently and accurately calculate the visible area data between the satellite and the ground user. This not only improves the calculation accuracy, but also significantly improves the calculation efficiency, ensuring that the satellite visible time window can still be calculated and optimized in real time and accurately under complex environmental conditions.

[0121] For example, in an actual satellite observation mission, a high-performance ray tracing algorithm is used to emit rays from the satellite position. The ray path adjusted by real-time atmospheric data can accurately calculate the refraction effect of the signal in the atmosphere. When the ray path is blocked by terrain such as mountains, the three-dimensional terrain model is used to determine whether the ray is blocked and calculate the blocking effect. In areas with complex terrain, the adaptive precision adjustment mechanism will improve the calculation accuracy and ensure the accuracy of ray tracing; in areas with simple terrain, the calculation accuracy is reduced to save computing resources. Ultimately, the calculated visible area data can accurately reflect the propagation range of satellite signals under actual terrain and atmospheric conditions, providing accurate data support for optimizing the satellite visible time window.

[0122] Preferably, the adaptive precision adjustment mechanism includes:

[0123] Assess the topographic and atmospheric complexity of the area through which the ray path passes;

[0124] Adjust the accuracy of the ray tracing calculations based on the complexity assessment results, increasing accuracy in critical areas and reducing accuracy in simple areas.

[0125] The principle of this mechanism is to evaluate the complexity of the terrain and atmosphere in the area where the ray path passes, adjust the accuracy of the ray tracing calculation according to the complexity evaluation results, improve the accuracy in key areas, and reduce the accuracy in simple areas.

[0126] The adaptive precision adjustment mechanism determines the required calculation accuracy by evaluating the terrain and atmospheric complexity of the area through which the ray path passes. Terrain complexity refers to the drastic degree of change in terrain height, such as terrain features such as mountains and canyons, while atmospheric complexity includes changes in parameters such as temperature, humidity, and air pressure in the atmosphere. These factors will affect the path and refraction effect of the ray during propagation. Therefore, by comprehensively evaluating the complexity of the terrain and atmosphere, the required level of accuracy for ray tracing calculations can be determined.

[0127] Specifically, to evaluate the terrain complexity of the area through which the ray path passes, the terrain elevation data can be used to calculate the standard deviation of the terrain undulation or other statistics to quantify the complexity of the terrain. For the evaluation of atmospheric complexity, the complexity of the atmosphere can be determined by analyzing the rate of change and fluctuation amplitude of real-time atmospheric data. For example, in areas with drastic temperature changes, the refraction effect of the ray will be more obvious, so higher calculation accuracy is required.

[0128] Based on the complexity evaluation results, the adaptive precision adjustment mechanism dynamically adjusts the calculation accuracy of ray tracing. In areas with complex terrain or drastic atmospheric changes, the calculation accuracy of ray tracing is improved to ensure the accuracy of the calculation results. In areas with flat terrain or stable atmosphere, the calculation accuracy can be reduced to save computing resources and improve computing efficiency. This adaptive adjustment ensures the reasonable allocation of computing resources while ensuring the accuracy of the results.

[0129] Through the adaptive precision adjustment mechanism, the use of computing resources can be optimized while ensuring the accuracy of the calculation results. Specifically, in areas with complex terrain or drastic atmospheric changes, improving the calculation accuracy can capture subtle changes, ensure the accuracy of ray path calculations, and avoid calculation errors caused by insufficient precision. In areas with flat terrain or stable atmosphere, reducing the calculation accuracy can reduce the amount of calculations, improve overall calculation efficiency, and save resources.

[0130] For example, in a satellite observation mission, the system evaluated the area through which the ray path passed through through an adaptive precision adjustment mechanism and found that there were mountains and canyons in a certain area. These terrain features led to high terrain complexity. Therefore, the system improved the calculation accuracy of ray tracing in this area to ensure that the calculation results can accurately reflect the impact of the actual terrain. In another area with flat terrain and stable meteorological conditions, the system reduced the calculation accuracy, reducing calculation time and resource consumption. Ultimately, by dynamically adjusting the calculation accuracy, the system can always maintain efficient and accurate visible area calculation capabilities under different environmental conditions.

[0131] like Figure 5 As shown, the historical orbit data and real-time environmental data are used, combined with machine learning algorithms and multi-factor dynamic correction methods to generate corrected predicted orbit data;

[0132] Preferably, the machine learning trajectory prediction and multi-factor dynamic correction include:

[0133] Collect historical orbit data and perform cleaning and standardization;

[0134] Extract key features from historical track data and train machine learning models;

[0135] Orbit prediction using real-time orbit data;

[0136] Collect environmental data and calculate the correction factor, and correct the preliminary predicted orbit data according to the following calculation expression:

[0137] R=P+E×F

[0138] Among them, R is the corrected predicted orbit data; P is the preliminary predicted orbit data; E is the environmental data; and F is the correction factor.

[0139] The principle of this step is to collect and process historical orbit data, extract key features to train machine learning models, use real-time orbit data for orbit prediction, and combine environmental data for dynamic correction to finally generate accurate predicted orbit data.

[0140] Collect historical orbit data and perform cleaning and standardization. Historical orbit data includes information about the satellite's orbital position and motion status over a period of time. These data can be obtained from satellite databases or ground observation stations. In order to ensure the accuracy and consistency of the data, the data needs to be cleaned, noise and outliers removed, and standardized to unify the data format for subsequent processing.

[0141] Extract key features from historical orbital data and train machine learning models. Key features can include orbital position, velocity, acceleration, orbital inclination, etc. These features can reflect the changing patterns of satellite orbits. Through machine learning algorithms (such as LSTM, GRU, etc.), these key features are used to train models so that the models can capture the patterns and trends of orbital changes and thus perform orbit predictions.

[0142] Use real-time orbit data for orbit prediction. Real-time orbit data is the orbital position and motion status information of the satellite at the current moment. This data is obtained in real time through the satellite's own positioning system or ground station. Input the real-time orbit data into the trained machine learning model. The model will predict the satellite's orbital data in the future based on the orbital change rules learned from historical data, that is, the preliminary predicted orbital data.

[0143] Collect environmental data and calculate correction factors. Environmental data includes external factors that affect satellite orbits, such as solar wind, Earth's magnetic field, gravity disturbances, etc. These data can be obtained through ground observation stations or other satellite observations. Based on the environmental data, the correction factor is calculated. The role of the correction factor is to adjust the preliminary predicted orbit data to make it more accurately reflect the actual situation.

[0144] The preliminary predicted orbit data is corrected according to the above calculation expression. Through the above expression, combined with the preliminary predicted data and environmental influences, more accurate corrected orbit data is calculated.

[0145] By combining machine learning algorithms and multi-factor dynamic correction methods, more accurate predicted orbit data can be generated. The machine learning model can capture the complex laws of orbit changes and improve the accuracy of predictions by learning from historical data. The multi-factor dynamic correction method further adjusts the preliminary prediction results by considering the influence of environmental factors to make them more in line with actual conditions.

[0146] For example, in actual applications, the system first collects satellite orbit data from the past few years, and extracts key features such as orbital position and speed through data cleaning and standardization. These features are used to train the LSTM model so that it can predict the future orbital position of the satellite. In actual operation, the system obtains current orbital data through real-time satellite positioning and inputs it into the model for preliminary prediction. Subsequently, the system collects environmental data such as solar wind and the Earth's magnetic field, calculates correction factors, and applies them to the preliminary prediction data. Finally, the corrected orbital data obtained by calculation can accurately reflect the actual orbital position of the satellite in the future.

[0147] like Figure 6 As shown, the visible area data and the corrected predicted orbit data are integrated, and a multi-objective optimization algorithm is used to calculate the optimal satellite visible time window;

[0148] Preferably, the multi-objective optimization algorithm calculates the optimal satellite visible time window including:

[0149] Integrate visible area data with revised predicted orbit data;

[0150] Define optimization objective function and constraints;

[0151] A multi-objective optimization algorithm is used for iterative calculation to generate the optimal satellite visible time window.

[0152] The principle of this step is to integrate multi-source data, define optimization objective functions and constraints, use multi-objective optimization algorithms for iterative calculations, and finally generate the optimal satellite visible time window. The following is a detailed description of this step, including the principle and effect.

[0153] Integrate visible area data and corrected predicted orbit data. Visible area data is calculated through high-performance ray tracing algorithms and adaptive precision adjustment mechanisms, including visible area information between satellites and ground users. Corrected predicted orbit data is generated through machine learning algorithms and multi-factor dynamic correction methods, including satellite orbital position and motion state information in the future. These two types of data are the basis for calculating the satellite visible time window. By integrating them, accurate information about the satellite's coverage of ground users within a specific time can be obtained.

[0154] Define the optimization objective function and constraints. The definition of the optimization objective function needs to consider multiple factors, such as maximizing satellite coverage time, minimizing the number of satellite switching times, and improving communication quality. Constraints include satellite orbit restrictions, ground user locations and needs, atmospheric conditions, etc. By reasonably defining the optimization objective function and constraints, it can be ensured that the optimization results meet actual needs and technical limitations.

[0155] Use a multi-objective optimization algorithm for iterative calculations. The multi-objective optimization algorithm can be a genetic algorithm, particle swarm optimization, etc. These algorithms can search for the optimal solution in a multi-dimensional target space. Through iterative calculations, the algorithm continuously adjusts the satellite's visible time window to gradually approach the optimal solution. In each iteration, the algorithm evaluates the quality of the current solution and adjusts it according to the optimization objectives and constraints, ultimately generating the optimal satellite visible time window.

[0156] By integrating the visible area data and the corrected predicted orbit data, and using a multi-objective optimization algorithm for calculation, the optimal satellite visible time window can be generated. This not only improves the efficiency of satellite coverage of ground users, but also meets the needs of different users and optimizes resource allocation. For example, in a satellite communication mission, the system first calculates the visible area data through a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism, and combines it with the corrected predicted orbit data generated by a machine learning algorithm. After integrating these data, the system defines the optimization objective function, including maximizing coverage time and minimizing the number of switching times. Then, the system uses a genetic algorithm for iterative calculations to gradually adjust the satellite's visible time window. After multiple iterations, the algorithm finally generates the optimal time window, ensuring that the satellite can maximize coverage of ground users and reduce unnecessary switching, thereby improving communication quality and system stability.

[0157] like Figure 7 As shown, based on the ground user location data and the satellite visible time window data, a multi-satellite coordination mechanism is established, a dynamic switching strategy is designed and optimized, satellite switching is executed, and real-time monitoring and feedback adjustment are performed.

[0158] Preferably, the multi-satellite coordination mechanism and dynamic switching strategy include:

[0159] Evaluate satellite switching requirements based on ground user location data and satellite visible time window data;

[0160] Select an optimization algorithm to optimize the switching strategy;

[0161] Execute the optimized switching strategy and conduct real-time monitoring and feedback adjustments.

[0162] The principle of this step is to evaluate the satellite switching requirements, select the optimization algorithm to optimize the switching strategy, execute the optimized switching strategy, and conduct real-time monitoring and feedback adjustment to achieve efficient multi-satellite coordination and dynamic switching. The following is a detailed description of this step, including the principle and effect.

[0163] Satellite switching needs are evaluated based on ground user location data and satellite visible time window data. Ground user location data provides the user's specific location, including latitude, longitude and altitude information, while satellite visible time window data indicates the coverage area of ​​each satellite at a specific time. By integrating these two types of data, it can be determined whether the current satellite can continue to cover ground users or whether satellite switching is required. The core of evaluating switching needs lies in identifying the end time of the current satellite coverage and the start time of the next satellite coverage, as well as the gap between these time points.

[0164] Select an optimization algorithm to optimize the switching strategy. The optimization algorithm can be a genetic algorithm, particle swarm optimization, etc. These algorithms can search for the optimal solution in a multi-dimensional target space. The goal of the optimization is to ensure that the satellite coverage time and communication quality are maximized while minimizing the number of switching times. Through iterative calculations, the algorithm continuously adjusts the switching timing and strategy to ensure the smoothness and efficiency of satellite switching. In each iteration, the algorithm evaluates the effect of the current strategy and adjusts it according to the optimization goal and constraints to finally generate the optimal switching strategy.

[0165] Execute the optimized switching strategy and conduct real-time monitoring and feedback adjustment. When executing the switching strategy, it is necessary to ensure that the communication between the satellite and the ground user is not interrupted and the switching process is smooth. The real-time monitoring system continuously tracks the performance data during the switching process, such as signal strength, delay, and error rate, through sensors and monitoring equipment. The feedback adjustment mechanism dynamically adjusts the switching strategy based on the real-time monitoring data to respond to sudden environmental changes or technical problems and ensure the stable operation of the system.

[0166] By establishing a multi-satellite coordination mechanism, designing and optimizing dynamic switching strategies, executing satellite switching, and conducting real-time monitoring and feedback adjustments, efficient and smooth multi-satellite coordination can be achieved. This not only improves the continuity and communication quality of satellite coverage of ground users, but also effectively utilizes satellite resources, reduces the number of switching times and communication interruptions. For example, in a satellite communication mission, the system first evaluates the coverage capability of the current satellite through ground user location data and satellite visible time window data, and identifies the time point when switching is required. Then, the system uses genetic algorithms to optimize the switching strategy to ensure that the communication quality is maximized while minimizing the number of switching times. During the actual execution of the optimized switching strategy, the real-time monitoring system tracks performance data such as signal strength and error rate, and timely feedback adjustments are made to ensure a smooth switching process and uninterrupted communication. Finally, the system achieves efficient multi-satellite coordination and dynamic switching, significantly improving user experience and system stability.

[0167] Preferably, the real-time monitoring and feedback adjustment of the multi-satellite coordination and dynamic switching strategy includes:

[0168] Collect performance data during the switching process in real time through sensors and monitoring systems;

[0169] Analyze performance data to identify potential failures or efficiency issues during switching;

[0170] Dynamically adjust the switching strategy based on the analysis results to improve system stability and switching efficiency.

[0171] The principle of this step is to collect performance data of the switching process in real time through sensors and monitoring systems, analyze the performance data to identify potential failures or efficiency issues, and dynamically adjust the switching strategy based on the analysis results to improve the stability and switching efficiency of the system. The following is a detailed description of this step, including the principle and effect.

[0172] The performance data of the switching process is collected in real time through sensors and monitoring systems. The sensors and monitoring systems are deployed on satellites and ground stations, and can monitor the operating status of satellites and communication performance data in real time, such as signal strength, delay, bit error rate, and switching time. These performance data provide a basis for subsequent analysis and adjustments.

[0173] Analyze performance data to identify potential failures or efficiency issues during the switching process. Performance data is transmitted to the data analysis center in real time through the monitoring system. The data analysis center uses data mining and machine learning techniques to analyze the performance data and identify possible problems in the switching process. For example, if the bit error rate increases significantly during a period of time, it may indicate that there is interference or signal attenuation during the switching process; if the switching time is too long, it may indicate that the switching strategy needs to be optimized.

[0174] Dynamically adjust the switching strategy based on the analysis results to improve system stability and switching efficiency. The core of dynamic adjustment is to respond to problems identified during the switching process in real time and optimize the switching strategy. Specifically, you can adjust the switching timing, optimize the switching path, increase signal compensation, etc. to ensure a smooth switching process, reduce communication interruptions, and improve system stability and efficiency.

[0175] Through real-time monitoring and feedback adjustment, the switching efficiency and stability of the multi-satellite system can be significantly improved. Real-time monitoring ensures that the system can promptly detect and respond to problems that arise during the switching process, and the feedback adjustment mechanism ensures that the system can dynamically optimize the switching strategy and maintain efficient operation. For example, in actual applications, the system collects signal strength and delay data during the switching process in real time through sensors and monitoring systems, and finds that the signal strength drops significantly during a certain period of time. The data analysis center determined through analysis that the problem was excessive signal interference during the switching process. The system then adjusted the switching strategy, optimized the switching path, avoided interference sources, and increased signal compensation. After the adjustment, the signal strength during the switching process returned to normal, the communication quality was guaranteed, and the system stability and switching efficiency were significantly improved.

[0176] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware.

[0177] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A satellite visible time window calculation method based on a visible area, characterized in that: The following steps are involved: Collect and process satellite position data, ground user position data and real-time atmospheric data to generate a comprehensive data set; Based on the comprehensive data set, a dynamic three-dimensional terrain model is constructed using real-time volume rendering and multi-level dynamic modeling technology; In the dynamic three-dimensional terrain model, the visible area data between the satellite and the ground user is calculated through a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism; Using historical orbit data and real-time environmental data, combined with machine learning algorithms and multi-factor dynamic correction methods, the corrected predicted orbit data is generated; Integrating the visible area data and the corrected predicted orbit data, and using a multi-objective optimization algorithm to calculate the optimal satellite visible time window; Based on the ground user location data and the satellite visible time window data, a multi-satellite coordination mechanism is established, a dynamic switching strategy is designed and optimized, satellite switching is executed, and real-time monitoring and feedback adjustments are carried out.

2. The satellite visible time window calculation method based on the visible area according to claim 1 is characterized in that: The collecting and processing of satellite position data, ground user position data and real-time atmospheric data to generate a comprehensive data set includes: Collect satellite position data, ground user position data and real-time atmospheric data; Cleaning and standardizing the satellite position data, ground user position data and real-time atmospheric data; The processed data are fused to generate a comprehensive dataset.

3. The satellite visible time window calculation method based on the visible area according to claim 1 is characterized in that: The dynamic three-dimensional terrain model construction includes: Convert geographic coordinate data and atmospheric data from the integrated dataset into three-dimensional volume data; Using multi-level dynamic modeling technology, a multi-level dynamic three-dimensional terrain model is constructed according to different resolutions and time scales; The 3D terrain model is updated in real time to reflect topographic and atmospheric changes.

4. The satellite visible time window calculation method based on the visible area according to claim 3 is characterized in that: The real-time volume rendering technology used in the step of constructing the three-dimensional terrain model includes: Combine geographic data and atmospheric data to generate initial three-dimensional volume data; Construct multi-level 3D volume data at different resolutions and time scales; Dynamically update multi-level 3D volume data to reflect real-time changes.

5. The satellite visible time window calculation method based on the visible area according to claim 1 is characterized in that: The method of calculating the visible area data between the satellite and the ground user by using a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism includes: Send rays from the satellite position to the ground user position to simulate the signal propagation path; Calculate the refraction effect of rays in the atmosphere and adjust the ray path; Determine whether the ray path is blocked by the terrain and calculate the blocking effect; Adaptively adjust the calculation accuracy of ray tracing based on the complexity of the area.

6. The satellite visible time window calculation method based on the visible area according to claim 5 is characterized in that: The adaptive precision adjustment mechanism includes: Assess the topographic and atmospheric complexity of the area through which the ray path passes; Adjust the accuracy of the ray tracing calculations based on the complexity assessment results, increasing accuracy in critical areas and reducing accuracy in simple areas.

7. The satellite visible time window calculation method based on the visible area according to claim 1 is characterized in that: The machine learning orbit prediction and multi-factor dynamic correction include: Collect historical orbit data and perform cleaning and standardization; Extract key features from historical track data and train machine learning models; Orbit prediction using real-time orbit data; Collect environmental data and calculate the correction factor, and correct the preliminary predicted orbit data according to the following calculation expression: R=P+E×F Among them, R is the corrected predicted orbit data; P is the preliminary predicted orbit data; E is the environmental data; and F is the correction factor.

8. The satellite visible time window calculation method based on the visible area according to claim 1 is characterized in that: The multi-objective optimization algorithm calculates the optimal satellite visible time window including: Integrate visible area data with revised predicted orbit data; Define optimization objective function and constraints; A multi-objective optimization algorithm is used for iterative calculation to generate the optimal satellite visible time window.

9. The satellite visible time window calculation method based on the visible area according to claim 1 is characterized in that: The multi-satellite coordination mechanism and dynamic switching strategy include: Evaluate satellite switching requirements based on ground user location data and satellite visible time window data; Select an optimization algorithm to optimize the switching strategy; Execute the optimized switching strategy and conduct real-time monitoring and feedback adjustments.

10. The satellite visible time window calculation method based on the visible area according to claim 1 is characterized in that: The real-time monitoring and feedback adjustment of the multi-satellite coordination and dynamic switching strategy includes: Collect performance data during the switching process in real time through sensors and monitoring systems; Analyze performance data to identify potential failures or efficiency issues during switching; Dynamically adjust the switching strategy based on the analysis results to improve system stability and switching efficiency.

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