Method for calculating satellite visibility time window based on visible area

Abstract

The present application relates to the field of satellite communication, and more particularly to a satellite visible time window calculation method based on visible areas, which constructs an accurate 3D terrain model through real-time volume rendering and multi-level dynamic modeling techniques; then, uses a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism to calculate visible area data; then, combines machine learning algorithms and multi-factor dynamic correction methods to generate corrected predicted orbit data; then, uses a multi-objective optimization algorithm to integrate data and calculate the optimal satellite visible time window; finally, through real-time monitoring and feedback adjustment of the multi-satellite coordination mechanism and dynamic switching strategy, efficient and stable satellite switching is ensured; the present application significantly improves the calculation accuracy and efficiency, optimizes resource allocation, and enhances the stability and user experience of the system.

Description

Technical Field

[0001] This invention relates to the field of satellite communications, and more particularly to a method for calculating the visible time window of a satellite based on the visible area. Background Technology

[0002] Due to their high-speed movement and limited coverage, low-Earth orbit (LEO) satellites offer very short visibility times for ground users. This necessitates that the system efficiently calculates the visible time window for ground users to enable rational satellite switching and resource allocation. Accurate calculation of the visible time window not only reduces the overhead of frequent switching between different satellites but also improves communication stability and user experience.

[0003] Existing technologies (Chinese Invention Patent, Publication No.: CN117421518B, Title: A Method and System for Calculating the Time Window of Low-Earth Orbit Satellite Coverage of the Ground) mainly employ geometric calculations and simplified model methods, such as multi-sensor coverage algorithms, fast algorithms for approximating great circles, and simplified Earth shape models. These methods have the following shortcomings when calculating the visible time window: Firstly, while geometric calculation methods are accurate, they are computationally complex and resource-intensive; secondly, while simplified model methods improve computational efficiency, their excessive simplification leads to low accuracy, failing to meet the precision requirements of practical applications. Furthermore, existing technologies struggle to balance computational accuracy and computation time, especially when facing a large number of concurrent calculations, easily resulting in delays that affect the timeliness of switching control. Summary of the Invention

[0004] To address the numerous problems existing in the prior art, this invention provides a method for calculating satellite visibility time windows based on the visible area. This invention combines multiple advanced technologies, such as real-time volumetric rendering, high-performance ray tracing, machine learning, and multi-objective optimization. Through dynamic 3D modeling, accurate orbit prediction, and real-time monitoring and adjustment, it achieves efficient calculation and optimization of satellite visibility time windows. This significantly improves calculation accuracy and efficiency, reduces satellite switching overhead, and enhances system stability and user experience.

[0005] The method for calculating the satellite visibility time window 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 dataset;

[0007] Based on the comprehensive dataset, a dynamic 3D terrain model is constructed using real-time volumetric rendering and multi-level dynamic modeling techniques.

[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 accuracy adjustment mechanism.

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

[0010] By integrating the visible area data and the corrected predicted orbit data, a multi-objective optimization algorithm is used to calculate the optimal satellite visibility time window;

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

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

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

[0014] The satellite position data, ground user position data, and real-time atmospheric data are cleaned and standardized.

[0015] The merged data is used to generate a comprehensive dataset.

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

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

[0018] Multi-level dynamic modeling technology is used to construct multi-level dynamic three-dimensional terrain models according to different resolutions and time scales;

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

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

[0021] Initial three-dimensional volume data are generated by combining geographic and atmospheric data;

[0022] Constructing multi-level three-dimensional volumetric data at different resolutions and time scales;

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

[0024] Preferably, the calculation of the visible area data between the satellite and the ground user through a high-performance ray tracing algorithm and an adaptive accuracy adjustment mechanism includes:

[0025] A ray is emitted from the satellite location to the ground user location to simulate the signal propagation path;

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

[0027] Determine whether the ray path is obstructed by the terrain and calculate the obstruction effect;

[0028] The computational accuracy of ray tracing is adaptively adjusted based on the complexity of the region.

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

[0030] Assess the topographic and atmospheric complexity of the area traversed by the ray path;

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

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

[0033] Collect historical orbital data and perform cleaning and standardization processes;

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

[0035] Use real-time orbit data for orbit prediction;

[0036] Collect environmental data and calculate correction factors. Correct the initially predicted orbital data according to the following calculation expression:

[0037] R = P + E × F

[0038] Where R represents the corrected predicted orbit data; P represents the preliminary predicted orbit data; E represents the environmental data; and F represents the correction factor.

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

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

[0041] Define the optimization objective function and constraints;

[0042] Iterative calculations using a multi-objective optimization algorithm are performed to generate the optimal satellite visibility time window.

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

[0044] Assess satellite handover requirements based on ground user location data and the satellite visibility time window data;

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

[0046] Implement the optimized switching strategy and perform real-time monitoring and feedback adjustments.

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

[0048] Performance data during the switching process is collected in real time through sensors and monitoring systems;

[0049] Analyze performance data to identify potential faults or efficiency issues during the switchover process;

[0050] The switching strategy is dynamically adjusted 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 as follows:

[0052] This invention utilizes real-time volumetric rendering and multi-layered dynamic modeling techniques to construct dynamic 3D terrain models, thereby improving the ability to accurately describe terrain and atmospheric changes.

[0053] This invention achieves accurate calculation of visible area data between satellites and ground users through a high-performance ray tracing algorithm and an adaptive precision adjustment mechanism, ensuring high accuracy and efficiency of the calculation results;

[0054] This 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] This invention integrates visible area data and corrected predicted orbit data through a multi-objective optimization algorithm, thereby calculating the optimal satellite visibility time window and optimizing resource allocation and coverage time.

[0056] This invention achieves efficient management of the satellite handover process through real-time monitoring and feedback adjustment of a multi-satellite collaborative mechanism and dynamic handover strategy, thereby improving system stability and handover efficiency. Attached Figure Description

[0057] Figure 1 This is a schematic diagram of the calculation process of the method of the present invention;

[0058] Figure 2 This is a flowchart of the data collection and processing process in this invention;

[0059] Figure 3 This is a flowchart of the dynamic three-dimensional terrain model construction process in this invention;

[0060] Figure 4 This is a flowchart of the ray tracing and adaptive precision adjustment process in this invention;

[0061] Figure 5 This is a flowchart of the orbit prediction and correction process in this invention;

[0062] Figure 6 This is a flowchart of the multi-objective optimization process in this invention;

[0063] Figure 7 This is a flowchart of the multi-satellite collaboration and dynamic switching strategy in this invention. Detailed Implementation

[0064] The embodiments of the present disclosure will now 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 disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0065] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

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

[0067] like Figures 1-2 As shown, the method for calculating the satellite visibility time window 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 dataset;

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

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

[0071] The satellite position data, ground user position data, and real-time atmospheric data are cleaned and standardized.

[0072] The merged data is used to generate a comprehensive dataset.

[0073] Satellite position data refers to the specific location of a satellite in its orbit, including latitude, longitude, and elevation information. This data can be obtained in real time through the satellite's own positioning system or ground station monitoring. 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 and outliers, while standardization ensures a uniform data format, facilitating subsequent processing and fusion. Ground user position data refers to the specific location of ground users, including latitude, longitude, and elevation information. This data can be obtained through Geographic Information Systems (GIS) or the GPS positioning system of user equipment. It also needs to undergo cleaning and standardization to ensure accuracy and consistency. Real-time atmospheric data includes temperature, humidity, air pressure, and wind speed. These data have a significant impact on the propagation path and refraction of rays in the atmosphere. Atmospheric data is usually acquired in real time through meteorological satellites and ground meteorological stations. Cleaning and standardization of atmospheric data ensures data accuracy and provides necessary environmental information for subsequent 3D terrain model construction.

[0074] By cleaning and standardizing satellite position data, ground user position data, and real-time atmospheric data, a comprehensive dataset is generated through fusion processing. This comprehensive dataset provides unified and accurate foundational data for subsequent dynamic 3D terrain model construction, high-performance ray tracing, and machine learning orbit prediction. Specifically, the comprehensive dataset integrates spatial location and environmental variables, ensuring data consistency and accuracy across each step, thereby improving data processing efficiency and precision, and ensuring the reliability and accuracy of subsequent calculations.

[0075] For example, in a satellite observation mission, by collecting real-time atmospheric data, high humidity and low pressure were found in the observation area. This information, after standardizing the atmospheric data, was fused with satellite position data and ground user position data to generate a comprehensive dataset. This enabled the subsequent 3D terrain model to accurately reflect the actual atmospheric conditions, thereby precisely calculating the visible area between the satellite and ground users, and ultimately optimizing the calculation of the satellite's visible time window. In this way, the generation of the comprehensive dataset not only provides a reliable data foundation for subsequent steps but also significantly improves the accuracy and effectiveness of the overall solution, ensuring high-precision calculation results even under complex environmental conditions.

[0076] like Figure 3 As shown, a dynamic 3D terrain model is constructed based on the comprehensive dataset using real-time volumetric rendering and multi-level dynamic modeling techniques.

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

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

[0079] Multi-level dynamic modeling technology is used to construct multi-level dynamic three-dimensional terrain models according to different resolutions and time scales;

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

[0081] The geographic coordinate data and atmospheric data in the comprehensive dataset 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 changes in terrain and atmosphere.

[0082] First, the geographic coordinate data and atmospheric data in the integrated dataset are converted into three-dimensional volumetric data. The core of this step is to use data transformation algorithms to map the two-dimensional geographic coordinate data and atmospheric data into three-dimensional space, generating initial three-dimensional volumetric data. This three-dimensional volumetric data includes information such as terrain height, slope, surface features, and atmospheric humidity, temperature, and air pressure, providing a foundation for subsequent modeling.

[0083] Next, multi-level dynamic modeling technology is used to construct a multi-level dynamic 3D terrain model based on different resolutions and time scales. This step employs the principle of multi-level dynamic modeling, achieving 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 undulations) at high resolution, while simplifying areas with less change at low resolution, thereby improving modeling efficiency and accuracy. Simultaneously, by combining data from different time scales, the model can be dynamically updated to 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 volumetric rendering technology to continuously update the 3D terrain model with the latest data, enabling the model to reflect current terrain and atmospheric conditions in real time. This not only ensures the accuracy of the model but also improves its dynamic response capability, allowing it to maintain high precision even under complex environmental conditions.

[0085] Through the above steps, the constructed dynamic 3D terrain model provides a precise environmental foundation for subsequent high-performance ray tracing and adaptive accuracy calculations, significantly improving the accuracy and efficiency of visible area calculations between satellites and ground users. For example, in practical applications, the real-time updated 3D terrain model can accurately reflect the terrain and atmospheric changes within the satellite coverage area at a given moment. This allows for precise consideration of atmospheric refraction and terrain occlusion factors in the signal propagation path calculation between satellites and ground users, ultimately optimizing the calculation results for the satellite's visible time window. In summary, based on the aforementioned comprehensive dataset, constructing a dynamic 3D terrain model using real-time volumetric rendering and multi-level dynamic modeling techniques not only improves the accuracy and real-time performance of calculations but also provides a solid technical foundation for the efficient execution of the overall solution.

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

[0087] Initial three-dimensional volume data are generated by combining geographic and atmospheric data;

[0088] Constructing multi-level three-dimensional volumetric data at different resolutions and time scales;

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

[0090] The initial 3D volume data is generated by combining geographic and atmospheric data using real-time volume rendering technology. Multi-level 3D volume data is then constructed at different resolutions and time scales. Finally, the multi-level 3D volume data is dynamically updated to reflect real-time changes.

[0091] Generating initial 3D volumetric data: Geographic and atmospheric data from the integrated dataset are combined to generate initial 3D volumetric data. Geographic data includes terrain elevation, slope, and surface features, while atmospheric data includes information such as humidity, temperature, and air pressure. This data is mapped in 3D space using a volumetric rendering algorithm to form the initial 3D volumetric data. This process ensures comprehensive integration of terrain and atmospheric information, providing a foundation for subsequent multi-level modeling.

[0092] Constructing Multi-Level 3D Volumetric Data: Multi-level 3D volumetric data is constructed at different resolutions and time scales. Multi-level dynamic modeling techniques optimize computational resource utilization by performing detailed modeling of key areas at high resolution and simplifying less variable areas at low resolution. Specifically, high-resolution modeling is used for areas with dramatic terrain undulations to capture subtle terrain changes, while low-resolution modeling is used for relatively flat areas to reduce computational load. Furthermore, by using data at different time scales, short-term and long-term terrain change models can be established, thereby improving the model's dynamic response capability.

[0093] Dynamically updated 3D volume data: Through real-time volume rendering technology, multi-layered 3D volume data is dynamically updated to reflect real-time changes. The core of real-time updates lies in continuously acquiring new geographic and atmospheric data and integrating it into the existing model, enabling the model to reflect the current environmental conditions in real time. This process ensures the accuracy and timeliness of the model, allowing it to maintain high-precision performance under complex environmental conditions.

[0094] By combining geographic and atmospheric data to generate initial 3D volumetric data, the integrity and accuracy of the model in its initial stages can be ensured. Utilizing multi-level dynamic modeling techniques to construct multi-level 3D volumetric data at different resolutions and time scales not only optimizes the use of computational resources but also improves the model's detail representation and dynamic response capabilities. Real-time updates to the 3D volumetric data enable 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, during satellite observations in complex terrain environments, real-time volumetric rendering technology allows the system to capture terrain changes such as mountains and rivers in a timely manner. Combined with current atmospheric conditions (such as temperature and humidity variations), it generates a real-time updated 3D terrain model. 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 visibility window fully considers 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 accuracy adjustment mechanism.

[0097] Preferably, the calculation of the visible area data between the satellite and the ground user through a high-performance ray tracing algorithm and an adaptive accuracy adjustment mechanism includes:

[0098] A ray is emitted from the satellite location to the ground user location to simulate the signal propagation path;

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

[0100] Determine whether the ray path is obstructed by the terrain and calculate the obstruction effect;

[0101] The computational accuracy of ray tracing is adaptively adjusted based on the complexity of the region.

[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 complexity of the area.

[0103] Ray tracing algorithms simulate signal propagation paths by projecting rays from a satellite's location to a ground user's location. A high-performance ray tracing algorithm simulates the path of a signal transmitted from a satellite to a ground user by projecting rays in three-dimensional space. The algorithm needs to consider the real-time positions of both the satellite and the ground user and calculate the signal propagation path within a three-dimensional terrain model.

[0104] The refraction of rays in the atmosphere is calculated, and the ray path is adjusted. Due to the presence of the atmosphere, rays are refracted during propagation, which alters their path. To accurately simulate this effect, the refraction angle needs to be calculated based on real-time atmospheric data, and the ray path adjusted accordingly. Specifically, an atmospheric refraction model can be used to calculate the refraction angle of the ray in each layer based on the different density and temperature distributions of the atmosphere, thereby adjusting its path.

[0105] Determine if the ray path is obstructed by terrain and calculate the obstruction effect. Rays may be obstructed by terrain features (such as mountains and buildings) during propagation, preventing the signal from reaching ground users. By determining whether the ray path intersects with the terrain surface in the 3D terrain model, it is possible to determine if the ray is obstructed and calculate the degree and impact of the obstruction. This step requires combining the height data of the terrain model and the geometric information of the ray path to accurately determine the obstruction effect.

[0106] The computational accuracy of ray tracing is adaptively adjusted based on regional complexity. Different regions exhibit varying terrain and atmospheric complexity, necessitating adjustments to the computational accuracy accordingly. In regions with complex terrain or drastic atmospheric changes, increased ray tracing accuracy is required to ensure accurate results; conversely, in flat terrain or atmospherically stable regions, reduced accuracy can improve computational efficiency. This adaptive accuracy adjustment mechanism dynamically adjusts the computational accuracy by assessing the complexity of the regions traversed by the ray path, thereby optimizing the use of computational resources.

[0107] By applying a high-performance ray tracing algorithm and an adaptive accuracy adjustment mechanism to a dynamic 3D terrain model, the visible area data between the satellite and ground users can be accurately calculated. The high-performance ray tracing algorithm ensures accurate simulation of the signal propagation path, while the adaptive accuracy adjustment mechanism dynamically adjusts the calculation accuracy according to the complexity of the region, ensuring both the accuracy of the calculation results and improving computational efficiency. The resulting visible area data provides a reliable foundation for subsequent orbit prediction and time window optimization.

[0108] In this invention, "high-performance ray tracing algorithm" refers 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. These 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 a ray from its emission point to its target point, taking into account refraction and reflection of the ray in different media. This algorithm is commonly used to calculate the propagation path of light in three-dimensional space.

[0110] Accelerated Data Structures: To improve the efficiency of ray tracing, accelerated data structures are typically used, such as BoundingVolume Hierarchies (BVH), Kd-trees, or grid-based methods. These data structures can quickly determine whether a ray intersects with an object in the scene, thereby reducing unnecessary computation.

[0111] Adaptive sampling: Adaptive sampling techniques are used to dynamically adjust the sampling density based on scene complexity during ray tracing. Sampling density is increased in complex regions to improve accuracy, while sampling density is reduced in simple regions to save computational resources.

[0112] Level of Detail (LOD): This technique uses a high-resolution model in areas requiring high precision and a low-resolution model in areas requiring low precision. This approach effectively balances computational accuracy and efficiency.

[0113] Parallel computing: Modern high-performance ray tracing algorithms typically employ parallel computing techniques, significantly improving computational speed through multithreading, GPU acceleration, and other methods. This is especially important when processing large numbers of rays.

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

[0115] A ray is emitted from the satellite position to the ground user's position to simulate the signal propagation path; the basic ray tracing algorithm is used to calculate the initial path.

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

[0117] Determine if the ray path is obstructed by the terrain; quickly determine if the ray intersects with the terrain model and calculate the obstruction effect by accelerating the data structure (such as BVH or Kd-trees);

[0118] The computational accuracy of ray tracing is adaptively adjusted based on the terrain and atmospheric complexity of the area traversed by the ray path; the sampling density is increased in areas with complex terrain or drastic atmospheric changes to improve computational accuracy; and the sampling density is reduced in areas with flat terrain or stable atmosphere to improve computational efficiency.

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

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

[0121] For example, in a real-world satellite observation mission, a high-performance ray tracing algorithm is used to emit rays from the satellite's location. The ray path, adjusted using real-time atmospheric data, allows for precise calculation of the signal's refraction within the atmosphere. When the ray path encounters terrain obstruction, such as mountains, a 3D terrain model is used to determine if the ray is blocked and to calculate the obstruction effect. In areas with complex terrain, an adaptive accuracy adjustment mechanism improves calculation precision to ensure accurate ray tracing; in areas with simple terrain, the calculation precision is reduced to conserve computational resources. Ultimately, the calculated visible area data accurately reflects the satellite signal's propagation range under actual terrain and atmospheric conditions, providing precise data support for optimizing the satellite's visible time window.

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

[0123] Assess the topographic and atmospheric complexity of the area traversed by the ray path;

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

[0125] The principle behind this mechanism is to assess the complexity of the terrain and atmosphere in the area through which the ray path passes, and then adjust the accuracy of the ray tracing calculation based on the complexity assessment results, increasing accuracy in critical areas and decreasing accuracy in simple areas.

[0126] The adaptive accuracy adjustment mechanism determines the required computational accuracy by assessing the topographic and atmospheric complexity of the area traversed by the ray path. Topographic complexity refers to the degree of variation in terrain altitude, such as mountains and valleys, while atmospheric complexity includes changes in atmospheric parameters such as temperature, humidity, and air pressure. These factors all affect the ray's path and refraction during propagation. Therefore, by comprehensively assessing the complexity of the terrain and atmosphere, the required level of accuracy for ray tracing calculations can be determined.

[0127] Specifically, assessing the topographic complexity of the area traversed by a ray path can be done by calculating the standard deviation of topographic relief or other statistics using topographic elevation data, thereby quantifying the complexity of the terrain. For assessing atmospheric complexity, it can be determined by analyzing the rate of change and amplitude of fluctuations in real-time atmospheric data. For example, in regions with drastic temperature variations, the refraction effect of rays is more pronounced, thus requiring higher computational precision.

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

[0129] An adaptive precision adjustment mechanism can optimize the use of computing resources while ensuring the accuracy of calculation results. Specifically, in areas with complex terrain or drastic atmospheric changes, increasing the calculation precision can capture subtle changes, ensure the accuracy of ray path calculations, and avoid calculation errors caused by insufficient precision. Conversely, in areas with flat terrain or stable atmospheres, reducing the calculation precision can reduce the amount of computation, improve overall computational efficiency, and save resources.

[0130] For example, in a satellite observation mission, the system assessed the area traversed by the ray path using an adaptive accuracy adjustment mechanism. It discovered that a certain area contained mountains and valleys, resulting in high terrain complexity. Therefore, the system increased the computational accuracy of ray tracing in this area to ensure that the calculation results accurately reflected the actual terrain. In another area with flat terrain and stable weather conditions, the system reduced the computational accuracy, decreasing computation time and resource consumption. Ultimately, by dynamically adjusting the computational accuracy, the system can maintain efficient and accurate computational capabilities across visible areas under various environmental conditions.

[0131] like Figure 5 As shown, by using historical orbit data and real-time environmental data, combined with machine learning algorithms and multi-factor dynamic correction methods, corrected predicted orbit data is generated.

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

[0133] Collect historical orbital data and perform cleaning and standardization processes;

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

[0135] Use real-time orbit data for orbit prediction;

[0136] Collect environmental data and calculate correction factors. Correct the initially predicted orbital data according to the following calculation expression:

[0137] R = P + E × F

[0138] Where R represents the corrected predicted orbit data; P represents the preliminary predicted orbit data; E represents the environmental data; and F represents the correction factor.

[0139] The principle behind this step is to collect and process historical orbit data, extract key features to train a machine learning model, use real-time orbit data to predict orbits, and combine environmental data for dynamic correction, ultimately generating accurate predicted orbit data.

[0140] Historical orbital data is collected and cleaned and standardized. This data includes the satellite's orbital position and motion status over a past period, obtainable from satellite databases or ground-based observation stations. To ensure accuracy and consistency, the data needs to be cleaned to remove noise and outliers, and standardized to ensure a consistent format for easier subsequent processing.

[0141] Key features are extracted from historical orbital data to train a machine learning model. These key features can include orbital position, velocity, acceleration, and orbital inclination, reflecting the patterns of orbital changes. Machine learning algorithms (such as LSTM and GRU) are then used to train the model, enabling it to capture patterns and trends in orbital changes and thus predict orbits.

[0142] Orbit prediction is performed using real-time orbit data. Real-time orbit data refers to the satellite's current orbital position and motion status, which is acquired in real time through the satellite's own positioning system or ground stations. This real-time orbit data is then input into a trained machine learning model. Based on orbital change patterns learned from historical data, the model predicts the satellite's orbital data for a future period, i.e., preliminary orbital prediction.

[0143] Environmental data is collected and correction factors are calculated. Environmental data includes external factors affecting satellite orbits, such as solar wind, Earth's magnetic field, and gravitational disturbances. This data can be obtained through ground-based observation stations or other satellite observations. Based on the environmental data, correction factors are calculated. These correction factors adjust the initial predicted orbit data to more accurately reflect the actual situation.

[0144] The preliminary predicted orbit data is corrected based on the above calculation expression. Using this expression, combined with the preliminary predicted data and environmental impacts, a 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. Machine learning models, through learning from historical data, can capture the complex patterns of orbital changes, improving prediction accuracy. Meanwhile, multi-factor dynamic correction methods, by considering the influence of environmental factors, further adjust the initial prediction results to better reflect reality.

[0146] For example, in practical applications, the system first collects satellite orbit data from the past few years and extracts key features such as orbital position and velocity through data cleaning and standardization. These features are then used to train an LSTM model capable of predicting the satellite's future orbital position. In actual operation, the system acquires 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 Earth's magnetic field, calculates correction factors, and applies them to the preliminary prediction data. Finally, the corrected orbital data is calculated, accurately reflecting the satellite's actual orbital position over a future period.

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

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

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

[0150] Define the optimization objective function and constraints;

[0151] Iterative calculations using a multi-objective optimization algorithm are performed to generate the optimal satellite visibility time window.

[0152] The principle behind this step is to integrate multi-source data, define the optimization objective function and constraints, and use a multi-objective optimization algorithm for iterative calculations to ultimately generate the optimal satellite visibility time window. The following is a detailed description of this step, including its principles and effects.

[0153] The system integrates visible area data and corrected predicted orbit data. Visible area data, calculated using a high-performance ray tracing algorithm and adaptive accuracy adjustment mechanism, includes information on the visible area between the satellite and ground users. Corrected predicted orbit data, generated using machine learning algorithms and multi-factor dynamic correction methods, includes information on the satellite's orbital position and motion status over a future period. These two types of data form the basis for calculating the satellite's visible time window; by integrating them, precise information on the satellite's coverage of ground users within a specific timeframe can be obtained.

[0154] Define the optimization objective function and constraints. Defining the objective function requires considering multiple factors, such as maximizing satellite coverage time, minimizing satellite handover frequency, and improving communication quality. Constraints include satellite orbital limitations, the location and needs of ground users, and atmospheric conditions. By appropriately defining the objective function and constraints, it can be ensured that the optimization results meet practical needs and technical limitations.

[0155] Iterative calculations are performed using a multi-objective optimization algorithm. This algorithm can be a genetic algorithm, particle swarm optimization, or similar methods, which can search for the optimal solution in a multi-dimensional objective space. Through iterative calculations, the algorithm continuously adjusts the satellite's visible time window, gradually approaching the optimal solution. In each iteration, the algorithm evaluates the quality of the current solution and adjusts it according to the optimization objective and constraints, ultimately generating the optimal satellite visible time window.

[0156] By integrating visible area data and corrected predicted orbit data, and using a multi-objective optimization algorithm, the optimal satellite visibility 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 using a high-performance ray tracing algorithm and an adaptive accuracy adjustment mechanism. This data is then combined with corrected predicted orbit data generated by a machine learning algorithm. After integrating this data, the system defines an optimization objective function, including maximizing coverage time and minimizing the number of handovers. Next, the system uses a genetic algorithm for iterative calculations, gradually adjusting the satellite's visibility 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 handovers, thereby improving communication quality and system stability.

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

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

[0159] Assess satellite handover requirements based on ground user location data and the satellite visibility time window data;

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

[0161] Implement the optimized switching strategy and perform real-time monitoring and feedback adjustments.

[0162] The principle behind this step is to assess satellite handover requirements, select an optimization algorithm to optimize the handover strategy, execute the optimized handover strategy, and perform real-time monitoring and feedback adjustments to achieve efficient multi-satellite coordination and dynamic handover. The following is a detailed description of this step, including its principles and effects.

[0163] Satellite handover needs are assessed based on ground user location data and satellite visibility time window data. Ground user location data provides the user's specific location, including latitude, longitude, and altitude information, while satellite visibility time window data indicates the coverage area of ​​each satellite within a specific time period. By integrating these two types of data, it can be determined whether the current satellite can continuously cover the ground user or whether a satellite handover is necessary. The core of assessing handover 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 intervals between these time points.

[0164] An optimization algorithm is selected to optimize the handover strategy. Optimization algorithms such as genetic algorithms and particle swarm optimization can be chosen, as these algorithms can search for optimal solutions in a multi-dimensional objective space. The optimization objective is to maximize satellite coverage time and communication quality while minimizing the number of handovers. Through iterative calculations, the algorithm continuously adjusts the handover timing and strategy to ensure the smoothness and efficiency of satellite handover. In each iteration, the algorithm evaluates the effectiveness of the current strategy and adjusts it according to the optimization objective and constraints, ultimately generating the optimal handover strategy.

[0165] The optimized handover strategy is implemented, with real-time monitoring and feedback adjustments. During handover, uninterrupted communication between the satellite and ground users must be ensured, and the handover process must be smooth. The real-time monitoring system continuously tracks performance data during the handover process, such as signal strength, latency, and error rate, using sensors and monitoring equipment. The feedback adjustment mechanism dynamically adjusts the handover strategy based on real-time monitoring data to address unexpected environmental changes or technical problems, ensuring stable system operation.

[0166] By establishing a multi-satellite collaborative mechanism, designing and optimizing dynamic handover strategies, executing satellite handovers, and performing real-time monitoring and feedback adjustments, efficient and stable multi-satellite collaborative operation can be achieved. This not only improves the continuity and communication quality of satellite coverage for ground users but also effectively utilizes satellite resources, reducing the number of handovers and communication interruptions. For example, in a satellite communication mission, the system first assesses the current satellite coverage capability using ground user location data and satellite visibility time window data to identify the time points requiring handover. Next, the system uses a genetic algorithm to optimize the handover strategy, ensuring that communication quality is maximized while minimizing the number of handovers. During the actual execution of the optimized handover strategy, the system tracks performance data such as signal strength and error rate in real-time, providing timely feedback adjustments to ensure a smooth handover process and uninterrupted communication. Ultimately, the system achieves efficient multi-satellite collaboration and dynamic handover, significantly improving user experience and system stability.

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

[0168] Performance data during the switching process is collected in real time through sensors and monitoring systems;

[0169] Analyze performance data to identify potential faults or efficiency issues during the switchover process;

[0170] The switching strategy is dynamically adjusted based on the analysis results to improve system stability and switching efficiency.

[0171] The principle behind this step is to collect performance data in real time during the handover process using sensors and monitoring systems, analyze the performance data to identify potential faults or efficiency issues, and dynamically adjust the handover strategy based on the analysis results, thereby improving system stability and handover efficiency. The following is a detailed description of this step, including its principles and effects.

[0172] Performance data during the handover process is collected in real time through sensors and monitoring systems. These systems, deployed at both the satellite and ground stations, monitor the satellite's operational status and communication performance data in real time, such as signal strength, latency, bit error rate, and handover time. This performance data provides the basis for subsequent analysis and adjustments.

[0173] Performance data is analyzed to identify potential faults or efficiency issues during the handover process. Performance data is transmitted in real-time to the data analysis center via a monitoring system. The data analysis center uses data mining and machine learning techniques to analyze the performance data and identify potential problems during the handover. For example, a significant increase in the bit error rate over a certain period may indicate interference or signal attenuation during the handover; an excessively long handover time may indicate that the handover strategy needs optimization.

[0174] The handover strategy is dynamically adjusted based on the analysis results to improve system stability and handover efficiency. The core of dynamic adjustment lies in real-time response to problems identified during the handover process and optimization of the handover strategy. Specifically, this can involve adjusting the handover timing, optimizing the handover path, and adding signal compensation to ensure a smooth handover process, reduce communication interruptions, and improve system stability and efficiency.

[0175] Real-time monitoring and feedback adjustments can significantly improve the handover efficiency and stability of multi-satellite systems. Real-time monitoring ensures the system can promptly detect and respond to problems during handover, while the feedback adjustment mechanism ensures the system can dynamically optimize handover strategies and maintain efficient operation. For example, in practical applications, the system collects signal strength and delay data in real time during handover using sensors and monitoring systems. It detects a significant drop in signal strength during a certain period. The data analysis center determines that the problem stems from excessive signal interference during handover. The system then adjusts its handover strategy, optimizes the handover path, avoids interference sources, and increases signal compensation. After the adjustment, the signal strength during handover returns to normal, communication quality is guaranteed, and system stability and handover efficiency are significantly improved.

[0176] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0177] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for calculating the satellite visibility time window based on the visible area, characterized in that, Includes the following steps: Collect and process satellite position data, ground user position data, and real-time atmospheric data to generate a comprehensive dataset; Based on the comprehensive dataset, a dynamic 3D terrain model is constructed using real-time volumetric rendering and multi-level dynamic modeling techniques. 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 accuracy adjustment mechanism. The calculation of visible area data between the satellite and the ground user through a high-performance ray tracing algorithm and an adaptive accuracy adjustment mechanism includes: emitting rays from the satellite position to the ground user position and simulating the signal propagation path; calculating the refraction effect of the rays in the atmosphere and adjusting the ray path; determining whether the ray path is blocked by terrain and calculating the blocking effect; and adaptively adjusting the calculation accuracy of ray tracing according to the complexity of the area. The adaptive accuracy adjustment mechanism includes: assessing the terrain and atmospheric complexity of the area traversed by the ray path; adjusting the accuracy of the ray tracing calculation based on the complexity assessment results, increasing accuracy in critical areas and decreasing accuracy in simple areas; By utilizing historical orbit data and real-time environmental data, combined with machine learning algorithms and multi-factor dynamic correction methods, corrected predicted orbit data is generated. By integrating the visible area data and the corrected predicted orbit data, a multi-objective optimization algorithm is used to calculate the optimal satellite visibility time window; Based on ground user location data and satellite visibility time window data, a multi-satellite collaborative mechanism is established, a dynamic switching strategy is designed and optimized, satellite switching is executed, and real-time monitoring and feedback adjustments are performed.

2. The satellite visibility time window calculation method based on the visible area according to claim 1, characterized in that, The process of collecting and processing satellite position data, ground user position data, and real-time atmospheric data to generate a comprehensive dataset includes: Collect satellite position data, ground user position data, and real-time atmospheric data; The satellite position data, ground user position data, and real-time atmospheric data are cleaned and standardized. The merged data is used to generate a comprehensive dataset.

3. The method for calculating the satellite visibility time window based on the visible area according to claim 1, characterized in that, The construction of the dynamic three-dimensional terrain model includes: Convert the geographic coordinate data and atmospheric data in the integrated dataset into three-dimensional volume data; Multi-level dynamic modeling technology is used to construct multi-level dynamic three-dimensional terrain models according to different resolutions and time scales; The 3D terrain model is updated in real time to reflect changes in terrain and atmosphere.

4. The satellite visibility time window calculation method based on the visible area according to claim 3, characterized in that, The real-time volumetric rendering technology used in the construction steps of the 3D terrain model includes: Initial three-dimensional volume data are generated by combining geographic and atmospheric data; Constructing multi-level three-dimensional volumetric data at different resolutions and time scales; Dynamically update multi-level three-dimensional volume data to reflect real-time changes.

5. The method for calculating the satellite visibility time window based on the visible area according to claim 1, characterized in that, The machine learning algorithm and multi-factor dynamic correction method include: Collect historical orbital data and perform cleaning and standardization processes; Extract key features from historical orbital data and train machine learning models; Use real-time orbit data for orbit prediction; Collect environmental data and calculate correction factors. Correct the initially predicted orbital data according to the following calculation expression: R = P + E × F in, R The corrected predicted orbit data; P For preliminary orbital data; E For environmental data; F This is a correction factor.

6. The method for calculating the satellite visibility time window based on the visible area according to claim 1, characterized in that, The multi-objective optimization algorithm calculates the optimal satellite visibility time window by including: Integrate visible area data with corrected predicted orbit data; Define the optimization objective function and constraints; Iterative calculations using a multi-objective optimization algorithm are performed to generate the optimal satellite visibility time window.

7. The method for calculating the satellite visibility time window based on the visible area according to claim 1, characterized in that, The establishment of a multi-satellite collaborative mechanism and the design and optimization of dynamic handover strategies include: Assess satellite handover requirements based on ground user location data and the satellite visibility time window data; Select an optimization algorithm to optimize the switching strategy; Implement the optimized switching strategy and perform real-time monitoring and feedback adjustments.

8. The method for calculating the satellite visibility time window based on the visible area according to claim 1, characterized in that, The real-time monitoring and feedback adjustment includes: Performance data during the switching process is collected in real time through sensors and monitoring systems; Analyze performance data to identify potential faults or efficiency issues during the switchover process; The switching strategy is dynamically adjusted based on the analysis results to improve system stability and switching efficiency.

Images