High and low orbit satellite communication resource scheduling method and system

Abstract

The invention relates to the technical field of satellite communication, in particular to a high-orbit and low-orbit satellite communication resource scheduling method and system, which collects real-time state information of high-orbit and low-orbit satellites, performs digital simulation of a satellite network based on the real-time information, constructs a satellite network management model and predicts satellite trajectory change. The method comprises the following steps: performing multi-dimensional evaluation on a satellite network management model by using a pre-constructed network evaluation framework, generating cross-orbit link evaluation information, analyzing parameters such as link quality and bandwidth, obtaining current service demand information, analyzing a communication resource allocation strategy in combination with a cross-orbit link evaluation result, and formulating a resource scheduling strategy according to strategy analysis. And a high-orbit and low-orbit satellite communication network is constructed through execution of a hierarchical network management mechanism and a cross-orbit data transmission protocol.

Description

Technical Field

[0001] This invention relates to the field of satellite communication technology, and more specifically, to a method and system for scheduling high and low orbit satellite communication resources. Background Technology

[0002] High-Earth orbit (GEO) satellites and low-Earth orbit (LEO) satellites each play important roles in modern satellite communication networks. GEO satellites, due to their high orbit, wide coverage area, and long-term stability, are usually used for large-scale communication services, while LEO satellites, due to their lower orbit, smaller coverage area, and lower latency, are usually used for fast and efficient communication services. The status (such as position and velocity) of GEO and LEO satellites changes over time, and the topology of the satellite network needs to be dynamically adjusted accordingly. Traditional fixed topology management methods are difficult to cope with the impact of satellite position changes on communication links. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a method and system for scheduling high and low orbit satellite communication resources, so as to dynamically adjust the satellite network to adapt to changes in satellite position and service requirements.

[0004] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions: According to one aspect of the present invention, a method for scheduling high and low orbit satellite communication resources is provided, comprising: Real-time status information of the high-orbit and low-orbit levels participating in the satellite network is obtained to perform digital simulation and trajectory prediction on the satellite network, so as to obtain a satellite network management model. Based on a pre-built network evaluation framework, the satellite network management model is evaluated for cross-orbit data transmission paths to generate cross-orbit link evaluation information for the satellite network. Obtain the current service demand information, and perform a strategy analysis on the communication resource allocation of the service demand based on the cross-track link evaluation information to obtain a resource scheduling strategy; The resource scheduling strategy is executed through a hierarchical network management mechanism and a cross-orbit data transmission protocol to construct a high- and low-orbit satellite communication network.

[0005] According to another aspect of the present invention, a high- and low-Earth orbit satellite communication resource scheduling system is provided, comprising: The model building module is used to obtain real-time status information of the high-orbit and low-orbit levels participating in the satellite network, so as to perform digital simulation and trajectory prediction on the satellite network to obtain a satellite network management model. The path evaluation module is used to evaluate the cross-orbit data transmission path of the satellite network based on the pre-built network evaluation framework, so as to generate cross-orbit link evaluation information of the satellite network. The resource allocation module is used to obtain the current business demand information and perform communication resource allocation strategy analysis on the business demand based on the cross-track link evaluation information to obtain the resource scheduling strategy. The network link module is used to execute the resource scheduling strategy through a hierarchical network management mechanism and cross-orbit data transmission protocol to construct a high- and low-orbit satellite communication network.

[0006] As can be seen from the above technical solutions, the high and low orbit satellite communication resource scheduling method provided by the present invention has the following beneficial effects: This invention acquires real-time status information of high-Earth orbit (GEO) and low-Earth orbit (LEO) satellites and performs dynamic topology management, enabling flexible allocation of GEO and LEO satellite resources according to actual needs. This maximizes the utilization of satellite resources in different communication scenarios, avoids resource waste and inefficient use, and improves the overall efficiency of the satellite communication system.

[0007] This invention uses dynamic simulation and trajectory prediction to adjust the topology of the satellite network in real time, ensuring the stability of satellite links. Even if the relative positions between satellites change, the system can still quickly adjust resource allocation, avoiding network interruptions or performance degradation caused by orbital changes or link quality deterioration, thereby improving the reliability and stability of the network.

[0008] By acquiring real-time service demand information and combining it with cross-orbit evaluation results, this invention enables precise allocation of communication resources, ensuring that every service demand is met in a timely manner. Whether it is a change in bandwidth demand or an adjustment in latency requirements, the system can intelligently allocate high and low orbit satellite resources through strategy analysis, achieving efficient resource management.

[0009] This invention utilizes dynamic topology management and intelligent resource scheduling strategies to achieve intelligent management of satellite communication networks. Through automated real-time evaluation and strategy adjustment, it can significantly reduce manual intervention, improve the automation and intelligence level of network management, and thus enhance the network's autonomous optimization capabilities. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort: Figure 1 This is a schematic diagram illustrating the steps of the high and low orbit satellite communication resource scheduling method provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of a high- and low-orbit satellite communication resource scheduling system provided in an embodiment of the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] High-Earth orbit (GEO) satellites and low-Earth orbit (LEO) satellites each play important roles in modern satellite communication networks. GEO satellites, due to their high orbit, wide coverage area, and long-term stability, are usually used for large-scale communication services, while LEO satellites, due to their lower orbit, smaller coverage area, and lower latency, are usually used for fast and efficient communication services. The status (such as position and velocity) of GEO and LEO satellites changes over time, and the topology of the satellite network needs to be dynamically adjusted accordingly. Traditional fixed topology management methods are difficult to cope with the impact of satellite position changes on communication links.

[0013] In view of this, the present invention provides a method for scheduling high and low orbit satellite communication resources, the steps of which are as follows: Figure 1 As shown, it includes: The first step is to obtain real-time status information of the high-orbit and low-orbit levels participating in the satellite network in order to perform digital simulation and trajectory prediction on the satellite network and obtain a satellite network management model. The second step is to evaluate the cross-orbit data transmission path of the satellite network based on the pre-built network evaluation framework, so as to generate cross-orbit link evaluation information of the satellite network. The third step is to obtain the current business demand information and, based on the cross-track link evaluation information, perform a strategy analysis on the communication resource allocation of the business demand to obtain a resource scheduling strategy. The fourth step involves implementing the resource scheduling strategy through a hierarchical network management mechanism and a cross-orbit data transmission protocol to construct a high- and low-orbit satellite communication network.

[0014] Specifically, in the first step of the embodiments provided by this invention, real-time status sensing devices or mechanisms (such as satellite sensors, satellite positioning systems, etc.) are deployed to acquire real-time orbital data, velocity information, position information, and satellite health status from high-orbit and low-orbit satellites. Combined with existing satellite communication protocols and monitoring systems, resource usage and communication load data of satellites at different levels are acquired in real time. The satellite trajectory and status data are dynamically changing. Accurate real-time data is the foundation for generating an effective topology management model. If it cannot be updated in real time, the effectiveness and accuracy of the prediction model will be greatly reduced. Acquiring multi-dimensional satellite status information (including orbit, position, velocity, communication load, etc.) provides comprehensive basic data support for subsequent model construction and resource allocation. This step ensures that the true state of each unit (satellite) in the satellite network is accurately grasped at any given time, enabling subsequent network topology simulation, link evaluation, and resource scheduling to be based on real data.

[0015] More specifically, based on real-time satellite status data, trajectory prediction algorithms (such as Kalman filtering, particle filtering, or other numerical simulation methods) are used to simulate and predict the future trajectory of satellites. During the prediction process, the satellite's orbital dynamics model, environmental factors (such as climate influences, Earth's gravity, etc.), and existing historical data in the network are combined to simulate the changes in the satellite's trajectory and position over a future period. A dynamic digital model is used to update and correct the prediction results to reflect possible changes and uncertainties. In multi-satellite networks, satellite trajectories are dynamically changing, and the orbits of high-orbit and low-orbit satellites are different. Therefore, predicting the future position and status of satellites is crucial. This helps to anticipate changes in communication links and data transmission paths between satellites over a future period. The purpose of using digital simulation and trajectory prediction is to enable the network management system not only to adapt to the current state but also to anticipate future changes and take preventative measures for adjustment and optimization. This step achieves accurate prediction of satellite trajectories and provides reliable future data support for building a dynamic satellite network topology, enabling network scheduling to be more forward-looking and flexible, and avoiding network congestion or communication disconnections in the short term.

[0016] More specifically, based on the acquired real-time status information and predicted trajectory data, a satellite network topology model is established. Considering factors such as the position, orbit, and velocity of each satellite, a topology construction algorithm connects the satellite nodes into a complete network, forming a dynamic and time-varying topology structure. This structure is continuously updated as satellite positions and communication status change. A topology optimization algorithm is used to optimize the topology structure, ensuring that the links within the network maintain optimal communication status at different times. The topology of the satellite communication network is constantly changing because satellite movement can cause the interruption or change of existing links. A dynamic topology model can reflect these changes in a timely manner, supporting real-time network management and resource scheduling. Through optimization algorithms, the efficiency of the network topology at specific times can be ensured, avoiding problems such as link overload and signal interference, thereby improving the stability and communication quality of the entire network. The satellite network management model generated in this step can effectively reflect the actual situation of the satellite communication network and can automatically adjust the topology structure at different points in time. This provides necessary support for subsequent resource allocation, link selection, and cross-orbit coordination.

[0017] More specifically, the network topology model is updated in real time based on changes in satellite status and feedback from link quality assessments. Whenever there are significant changes in satellite trajectory, position, load, or other information, the topology model is automatically updated based on the new data to ensure that the network topology is always consistent with the actual state of the satellite network. An automated mechanism is used to monitor topology changes and provide real-time feedback to ensure that the matching degree between the topology model and the satellite network is optimal. The satellite network is in a dynamically changing environment, and the topology model must be able to quickly adjust according to new data and environmental changes to adapt to frequent changes between high and low orbit satellites. The network topology cannot remain static. The real-time update and feedback mechanism helps the network continuously optimize during actual operation, avoiding resource waste or network interruptions under sudden events or abrupt changes. This step ensures that the satellite network topology model is always in the most suitable state, thereby optimizing the use of satellite resources and improving the network's anti-interference capability and stability.

[0018] Understandably, through these four steps, the entire process ensures that the satellite network maintains a dynamic and accurate network topology and efficient resource management when high- and low-Earth orbit satellites work together. The real-time satellite status information provides the most reliable data foundation for network topology management. Combined with digital simulation and trajectory prediction, changes in the satellite network can be anticipated in advance, providing support for subsequent network optimization and resource scheduling. Through dynamic topology management and real-time updates, the structure of the satellite network can be continuously adjusted to ensure efficient resource allocation and stable communication quality. Through the dynamic topology model, cross-orbit resource collaborative scheduling can be achieved, and the links and resource allocation between satellites can be optimized.

[0019] Specifically, in the second step of the embodiment provided by this invention, evaluation dimensions are determined, including link quality (e.g., latency, packet loss rate, bandwidth), link stability (e.g., link volatility, bit error rate), network load (e.g., traffic, bandwidth utilization), and reliability of cross-orbit data transmission (e.g., link recovery capability, communication interference). Specific evaluation indicators are defined for each dimension, and a suitable weighting system is designed to enable comprehensive evaluation based on actual needs during the evaluation process. To more accurately evaluate the performance of satellite links, a comprehensive consideration from multiple dimensions (e.g., quality, stability, load, etc.) is necessary. A single indicator may not fully reflect the actual performance of the satellite link. The selection and weighting of each dimension can be customized according to different application scenarios and needs, making the evaluation framework more flexible and adaptable. This step, by defining and quantifying evaluation indicators across multiple dimensions, ensures that the evaluation results of cross-orbit links comprehensively and objectively reflect the various performance aspects of the satellite network, thereby providing a basis for subsequent path optimization.

[0020] More specifically, satellite communication systems (such as ground stations or sensors built into satellites) are used to collect data streams, link performance, and system load information between high-Earth orbit (HEO) and low-Earth orbit (LEO) satellites. Real-time monitoring of inter-orbit links involved in data transmission (e.g., relay links between ground stations and LEO satellites, or HEO satellites) is conducted, collecting information such as link latency, packet loss rate, and bandwidth usage. Since satellite network performance can change at any time, real-time monitoring and data collection are essential to ensure that the link data used in the evaluation process is up-to-date and accurate. The link quality and performance between HEO and LEO satellites are affected by various factors, including satellite position, orbital altitude, and communication frequency. Therefore, these characteristics must be monitored in real-time to ensure that the link quality reflects the actual situation during the evaluation. Through real-time monitoring and data collection, the evaluation process is ensured to be based on the latest link performance data, avoiding misassessments due to data delays or outdated data, thereby ensuring that the evaluation results of inter-orbit links are more accurate and timely.

[0021] More specifically, a pre-built network evaluation framework is used. Real-time collected link data is input into the framework, which analyzes real-time data from multiple dimensions such as link quality, bandwidth utilization, transmission latency, and bit error rate to evaluate the performance of each cross-track link. Algorithms (such as weighted scoring, decision tree analysis, or machine learning models) are used to comprehensively evaluate the multi-dimensional data of the links and generate evaluation scores for the cross-track links. A single-dimensional evaluation may not be able to fully describe the performance of cross-track links. Using multi-dimensional evaluation can comprehensively and accurately evaluate the overall performance of the links. Through the network evaluation framework, multiple evaluation algorithms can be combined to accurately measure link performance, avoiding the single perspective or bias that may occur in traditional evaluation methods. This step, through comprehensive evaluation, quantifies the various performance aspects of cross-track links, thereby providing data support for subsequent link optimization, resource scheduling, and path planning, and helping to adjust resource allocation strategies in real time.

[0022] More specifically, based on the link evaluation results, a detailed cross-orbit link evaluation report is automatically generated. The report includes detailed data on link quality, load, network performance, and potential bottlenecks. The evaluation report will include the advantages and disadvantages of each link and sort them by link quality score to facilitate subsequent optimization by decision-makers. Generating a detailed evaluation report helps to better understand the current performance of the satellite network, thereby supporting subsequent decision-making and optimization. Through data analysis in the report, bottlenecks and performance gaps in cross-orbit links can be effectively identified, providing guidance for the next step of link optimization. This step, by automatically generating the link evaluation report, provides an easy-to-understand view, enabling rapid identification of problems and action to improve link performance and overall network efficiency.

[0023] More specifically, based on cross-orbit link assessment information and considering factors such as service requirements and network load, cross-orbit links are optimized. Optimization methods include adjusting link selection, optimizing bandwidth allocation, and selecting the best path. In-depth analysis of link assessment information is conducted to select the optimal path to ensure efficient and stable cross-orbit data transmission. Link assessment identifies and optimizes underperforming links or resource allocations in the current satellite network to improve resource utilization and reduce network congestion. Cross-orbit link optimization ensures the stability and reliability of data transmission, preventing issues such as bandwidth overload and link instability. This step, through precise link optimization and resource scheduling, improves the overall efficiency of the satellite network, ensuring that cross-orbit data transmission maintains excellent performance even under high load.

[0024] Understandably, these five steps enable the assessment of cross-orbit data transmission paths in satellite networks and generate cross-orbit link assessment information. Through a multi-dimensional assessment system, comprehensive coverage of link quality, bandwidth, latency, and stability is achieved, ensuring that the assessment results reflect the actual network conditions. Real-time data acquisition and the assessment framework ensure the accuracy of the assessment process, enabling the optimization of cross-orbit links to be based on the latest and most reliable data. Through optimization algorithms and the generation of assessment reports, the accuracy of link selection and resource scheduling is improved, resulting in a significant improvement in the performance of satellite networks.

[0025] Specifically, in the third step of the embodiment provided by this invention, information on various service requirements is acquired in real time through a satellite network management system or ground control station. These service requirements include data transmission volume, latency requirements, bandwidth requirements, communication quality requirements, and service priorities. The service requirement information is categorized according to real-time traffic, latency sensitivity, etc., and key parameters such as the start time, end time, and data volume of each task are recorded. To ensure that the resource allocation strategy reflects the actual situation of the current network load and service requirements, real-time service requirement information must be acquired. This helps avoid resource waste or overloading, and allows for dynamic adjustment of network resources based on the timeliness of different services (e.g., real-time data transmission has different priorities than batch transmission), thereby improving resource utilization efficiency. By acquiring service requirements in real time, resource allocation decisions are ensured to be based on the current service load and requirements, avoiding the use of outdated data for resource scheduling and improving the system's flexibility and adaptability.

[0026] More specifically, an in-depth analysis of cross-track link evaluation information is conducted. The evaluation includes latency, packet loss rate, bandwidth utilization, network load, and link stability. The link evaluation information is then ranked according to performance scoring criteria to identify which links have high reliability and stability, and which links require optimization. The quality and stability of cross-track links directly impact resource allocation efficiency and communication quality. The evaluation information provides a comprehensive understanding of link status, helping to determine the strengths and weaknesses of each link. The results of the evaluation information analysis will serve as the basis for subsequent communication resource allocation, helping to optimize resource utilization and ensure that critical tasks are transmitted through the optimal path. Through comprehensive analysis of link evaluation information, the advantages and bottlenecks of different links can be clearly identified, ensuring that business needs are met while avoiding performance degradation caused by unstable links.

[0027] More specifically, based on real-time business demand information and cross-orbit link evaluation information, a communication resource allocation strategy is designed. This strategy includes: allocating bandwidth, adjusting communication priorities, and selecting the optimal transmission path. For latency-sensitive services (such as video calls and real-time data transmission), high-quality links and bandwidth are prioritized; for services with high bandwidth requirements (such as big data transmission), more stable links are allocated. Traffic engineering algorithms (such as shortest path algorithms and bandwidth allocation models) are used to determine cross-orbit link resource allocation. Different services have different resource requirements, and the allocation strategy must ensure that critical tasks receive sufficient resources, especially prioritizing latency-sensitive and high-bandwidth-demand services. Link performance (such as latency and stability) is closely related to the task type; prioritizing high-quality links ensures smooth transmission of high-priority services. Through reasonable resource allocation, the overall performance of the satellite network can be improved, resource waste avoided, the transmission quality of latency-sensitive services improved, and network stability ensured.

[0028] More specifically, resources across different orbital layers (LEO, MEO, GEO) are coordinated and scheduled. Considering the characteristics of each orbit, such as bandwidth, transmission distance, and signal delay, scheduling algorithms (e.g., dynamic traffic scheduling, load balancing algorithms) are used to coordinately optimize resources across different orbital layers. This ensures reasonable resource allocation across multiple orbital layers. Scheduling strategies are dynamically adjusted based on link quality, service priority, and expected transmission results for each orbital layer to achieve efficient multi-orbit resource scheduling. Since resources and communication capabilities vary across orbits, reasonable cross-orbit resource coordination ensures network load balancing, improves link utilization, and reduces resource conflicts between orbits. Through coordinated scheduling, resource usage across orbital layers is optimized, preventing overload in one layer and idle resources in others, thus improving the overall performance of the satellite network. By generating reasonable resource scheduling strategies, the overall resource utilization of the satellite network in multi-orbit scenarios can be significantly improved, resource allocation optimized, and all service requirements fully met.

[0029] More specifically, during implementation, real-time monitoring of service demands and cross-orbit link performance is conducted, and resource allocation strategies are dynamically adjusted. If bottlenecks or high loads occur on certain links, adjustments are made immediately to optimize resource allocation. A feedback mechanism is set up to continuously evaluate the effectiveness of the resource allocation strategy and adjust the strategy based on feedback to ensure that network resources are always in an optimal configuration state. The load and link status of the satellite network may change dynamically. The dynamic adjustment and feedback mechanism can ensure that network resources are always in an optimal state. Through real-time feedback, problems in resource allocation can be identified and corrected in a timely manner, avoiding maladaptive problems caused by fixed strategies. Implementing a dynamic adjustment and feedback mechanism can improve the system's adaptability, ensure that network resource allocation always matches actual needs and network conditions, and improve system stability and performance.

[0030] Understandably, these five steps enable the analysis of communication resource allocation strategies based on real-time service demand information and cross-orbit link evaluation information, thereby obtaining effective resource scheduling strategies. By acquiring service demands in real time and analyzing cross-orbit link performance, resources are ensured to be allocated on demand, improving the overall transmission capacity of the satellite network. The cross-orbit resource collaborative scheduling strategy can effectively reduce resource conflicts and imbalances, optimize resource use, and ensure that service demands are prioritized. The dynamic adjustment and feedback mechanism enhances the network's adaptive capabilities, enabling the network to continuously adjust its strategies according to real-time conditions and maintain stable and efficient operation.

[0031] Specifically, in the fourth step of the embodiment provided by this invention, the satellite network is divided into different layers: a ground layer, a low-Earth orbit (LEO) satellite layer, a medium-Earth orbit (MEO) satellite layer, and a high-Earth orbit (HEO) satellite layer. Different network management modules are set up at each layer, and management is carried out according to the functional requirements of each layer. The ground layer is responsible for scheduling and monitoring, while the satellite layer performs data routing and processing according to mission requirements. Appropriate network management tools are deployed in each layer to ensure that data transmission and resource scheduling between different layers are coordinated and consistent. The layered network management mechanism can help simplify the management of the entire satellite network, allowing for more granular optimization of resources at each layer. The management system of each layer focuses on the resource scheduling and monitoring of its own layer, reducing resource competition and interference between different layers. HEO satellites are mainly responsible for global communication management and control, MEO satellites provide relatively stable link services, and LEO satellites provide high-bandwidth services with lower latency. Through layered management, collaborative work between different satellite orbits can be achieved. Layered management allows each layer of the satellite network to effectively focus on its specific mission, thereby avoiding the management complexity caused by excessive centralization or excessive decentralization, improving the maintainability and scalability of the network, enhancing the resource management efficiency of the satellite network, and realizing flexible mission scheduling.

[0032] More specifically, it involves determining cross-orbit data transmission protocols, designing data transmission formats, protocol stacks, and interfaces to ensure efficient and secure data exchange between different orbital levels. The protocols need to consider latency differences, bandwidth limitations, and network load balancing between different orbits. For example, low-Earth orbit (LEO) satellites can relay data to high-Earth orbit (HEO) satellites via medium-Earth orbit (MEO) satellites, while HEO satellites can communicate with other satellites via ground stations. Different transmission control strategies should be designed based on the link characteristics of different orbits (such as latency, bandwidth, and packet loss rate) to ensure data transmission quality and stability. Since the link characteristics of satellites in different orbits vary significantly, designing appropriate data transmission protocols can compensate for these differences, ensuring seamless data transmission between different orbits. Through reasonable protocol design, bandwidth can be allocated rationally, avoiding bandwidth overload or idleness on a particular orbit, ensuring efficient use of network resources. Cross-orbit transmission protocols enable smooth data transmission between orbital levels, reducing data loss and latency, improving the stability and reliability of satellite communication networks. Optimizing bandwidth usage through protocol design avoids resource waste and improves overall network efficiency.

[0033] More specifically, satellite resources at each orbital level are scheduled according to a pre-defined resource scheduling strategy to ensure that communication resources at each level are rationally allocated. During scheduling, factors such as link quality, bandwidth utilization, and latency requirements of different orbits need to be considered. For example, when low-Earth orbit (LEO) satellites have high bandwidth requirements, some traffic can be forwarded through medium-Earth orbit (MEO) satellites to avoid overloading the LEO satellite network. During data transmission, the resource usage of each orbit is monitored in real time, and resources that do not conform to the scheduling strategy are dynamically adjusted to ensure load balancing across the entire network. Satellite networks in different orbits differ in resource usage, coverage, and communication latency. Coordinated scheduling ensures that resources on each orbit are rationally allocated and adjusted according to demand. Dynamic scheduling avoids resource conflicts or overload on a particular orbit, ensuring optimized workload on each orbit and improving network efficiency. Multi-orbit resource coordinated scheduling balances the load between orbits, avoiding resource bottlenecks at a particular orbital level and improving the overall performance of the satellite communication network. The dynamic adjustment strategy enables the satellite communication network to cope with sudden load changes, ensuring efficient and stable network operation.

[0034] More specifically, during the operation of high- and low-Earth orbit satellite communication networks, a monitoring system is deployed to monitor key parameters such as network load, link quality, latency, and bandwidth at each orbital level in real time. Based on the monitoring data, a feedback mechanism is implemented to promptly identify and adjust problems in resource scheduling. For example, when the link quality of low-Earth orbit satellites deteriorates, the communication path can be dynamically switched or bandwidth allocation adjusted according to the feedback mechanism. Regular performance evaluation and optimization are conducted, and strategies are adjusted based on actual operational results to improve the long-term performance and stability of the entire network. Dynamic changes in network status and service demands require timely monitoring and feedback to ensure that resource scheduling strategies can adapt to changes in network conditions. Through monitoring and feedback mechanisms, deficiencies in network resource allocation can be identified, and corresponding adjustments can be implemented to ensure the long-term efficient operation of the network. By implementing real-time monitoring and feedback mechanisms, adjustments can be made in a timely manner when network bottlenecks or instability occur, improving the network's adaptability. Dynamic optimization strategies can further improve the utilization rate of network resources while ensuring communication quality.

[0035] More specifically, following the aforementioned steps, a resource scheduling strategy is implemented, and the satellite communication network is initially deployed. In the initial stage of network operation, overall performance is monitored, network operation data is collected, and strategies are optimized based on problems encountered during actual operation. The scheduling strategy and resource allocation scheme are updated regularly based on constantly changing business needs and network load. The satellite communication network may face some unforeseen problems in the initial stage, and incremental optimization can gradually improve network performance. The operating status and business needs of the satellite network will change over time, and regular strategy adjustments can ensure the network's continuous adaptability. Through iterative optimization, network performance can be continuously improved based on actual operational feedback, enhancing system adaptability. The optimized scheduling strategy and resource allocation scheme can effectively improve the overall efficiency and stability of the satellite communication network.

[0036] Understandably, through the above steps, the hierarchical network management mechanism and cross-orbit data transmission protocol can effectively execute resource scheduling strategies, construct high and low orbit satellite communication networks, avoid resource waste and overload through hierarchical management and cross-orbit resource scheduling, improve the overall performance of the satellite network, and enhance the adaptive capability of the satellite network through real-time monitoring and feedback mechanisms. The network resources can be adjusted according to the actual situation to ensure communication quality. Through precise data transmission protocols and optimized resource scheduling, data transmission can be carried out efficiently and stably between different orbits, ensuring that the needs of low latency and high bandwidth services are met.

[0037] Furthermore, the steps of acquiring real-time status information of each network layer participating in the satellite network to perform digital simulation and trajectory prediction on the satellite network to obtain a satellite network management model include: The first step involves using a real-time status awareness mechanism pre-deployed in each satellite unit to monitor the position, velocity, and available resources of each satellite unit at both the high-orbit and low-orbit levels participating in the satellite network, thereby collecting high-orbit and low-orbit satellite information sets.

[0038] Specifically, a real-time status awareness mechanism is deployed on each satellite unit. This mechanism monitors parameters such as the satellite's position, velocity, and available resources to achieve real-time awareness at both high-orbit and low-orbit satellite levels. Each satellite, through built-in sensors or communication modules, periodically feeds back its current position, velocity, and various resource statuses (such as bandwidth, storage, and power) to the ground or central control system. Deploying this awareness mechanism ensures real-time monitoring of each satellite unit in the satellite network and enables the immediate collection of critical operational data. Real-time awareness provides dynamic satellite information, including real-time satellite position, velocity, and resource status, laying the data foundation for subsequent digital simulations and trajectory predictions. Through the real-time awareness mechanism, dynamic status information of high-orbit and low-orbit satellites can be accurately collected, ensuring that calculations in subsequent digital simulations are based on accurate data. This improves the response speed to real-time changes in the satellite network and helps to adjust the network topology or resource allocation strategy in a timely manner.

[0039] Specifically, real-time information from high-orbit and low-orbit satellites transmitted from each satellite unit is collected. This information includes satellite positions, velocities, and available resources, ultimately forming two datasets: a high-orbit satellite information set and a low-orbit satellite information set. By collecting detailed status information from satellites at different orbital levels, a comprehensive understanding of the operational status and resource availability of each satellite unit in the network can be achieved. This information is stored in an integrated form, facilitating subsequent digital simulations and trajectory prediction. The real-time information sets from high-orbit and low-orbit satellites ensure the accuracy of subsequent models, resulting in a satellite network simulation model with high real-world fit, improving the completeness and consistency of the dataset, and facilitating subsequent data fusion and model calculations.

[0040] The second step involves performing digital simulations on the high-orbit and low-orbit levels of the satellite network based on the high-orbit satellite information set and the low-orbit satellite information set, respectively, to generate high-orbit simulation models and low-orbit simulation models. The high-orbit simulation models and low-orbit simulation models are then combined to generate a satellite network simulation model.

[0041] Specifically, based on the collected real-time information sets of high-orbit and low-orbit satellites, digital simulations are performed on satellite networks at both the high-orbit and low-orbit levels. For high-orbit satellites, their orbits, communication ranges, and network connections are simulated; for low-orbit satellites, their speeds, paths, and communication links are simulated, generating two simulation models: a high-orbit satellite simulation model and a low-orbit satellite simulation model. Digital simulations can accurately reproduce the operational behavior of satellites and their performance within the network, simulating resource usage and potential performance bottlenecks. Simulating both high-orbit and low-orbit satellites separately allows for analysis of their independent operation and further research into their synergistic effects, optimizing overall network scheduling. Through the simulations of high-orbit and low-orbit satellites, the performance of different levels of the satellite network under various conditions can be derived, providing data support for network management and resource scheduling. The simulation results provide a theoretical basis for subsequent topology model construction and optimization, contributing to improved satellite network capacity and efficiency.

[0042] Specifically, by combining high-orbit satellite simulation models with low-orbit satellite simulation models, a complete satellite network simulation model is formed. Simulation data from the two orbital levels are mapped to each other, creating an overall network structure encompassing all satellite units. This determines the connection methods, data transmission paths, and collaborative working strategies between satellites, generating a complete satellite network topology. Combining high-orbit and low-orbit simulation data, the overall satellite network topology can be presented, demonstrating the connection relationships and data flows between different satellites. Combining satellite models at different levels creates a more complete network view, reflecting the synergistic effect of multi-orbit networks. The generated satellite network simulation model realistically reflects the interdependence and cooperation between different orbital levels, helping to identify potential network bottlenecks and optimization opportunities. The topology model provides a theoretical basis for satellite network resource scheduling, path planning, and communication management, contributing to improved network efficiency and stability.

[0043] The third step involves predicting the trajectories of satellite units in the high-orbit satellite information set and the low-orbit satellite information set, respectively, and then adding the predicted trajectory information to the satellite network simulation model based on the trajectory prediction results to obtain the satellite network management model.

[0044] Specifically, based on real-time information sets of high-orbit and low-orbit satellites, the trajectories of these two types of satellites are predicted. For high-orbit satellites, their future trajectories are predicted based on their orbital parameters. For low-orbit satellites, their future trajectories are predicted based on parameters such as velocity and acceleration. The results of these trajectory predictions are then applied to a satellite network simulation model, incorporating the predicted trajectory information. By predicting satellite trajectories, we can anticipate future changes in satellite position and status, helping to optimize resource scheduling and communication path planning. Trajectory prediction provides sufficient predictive information before satellites actually move, allowing for advance deployment and optimization adjustments to avoid network congestion or performance degradation. Trajectory prediction enables satellite networks to dynamically respond to changes. For example, when data is transmitted between satellites, predicted trajectories can help anticipate potential connection interruptions or delays. In dynamic topology management, incorporating predicted trajectory information can further improve network reliability and optimize path selection.

[0045] Specifically, based on trajectory prediction results, the predicted information is added to the satellite network simulation model to form a satellite network management model. The satellite network management model can update the satellite's position, velocity, and connectivity over time, enabling real-time monitoring and adjustment of the satellite network. By incorporating trajectory prediction data, the network topology can be adjusted promptly according to changes in satellite position, avoiding potential communication interruptions and network overload. Dynamic topology management can cope with constantly changing network environments, automatically adjusting resource allocation and path selection to improve network stability and service quality. The satellite network management model enables the satellite network to make adaptive adjustments based on real-time changes, avoiding the limitations of static network topologies, improving the robustness of the satellite network, and enhancing its adaptability to environmental changes.

[0046] Furthermore, the steps of predicting the trajectories of satellite units in the high-orbit satellite information set and the low-orbit satellite information set, respectively, and adding predicted trajectory information to the satellite network simulation model based on the trajectory prediction results to obtain the satellite network management model include: The first step is to analyze the real-time information of each satellite unit in the satellite network at each time based on the high-orbit satellite information set and the low-orbit satellite information set, so as to obtain the historical real-time sequence of each satellite unit.

[0047] Specifically, based on high-orbit and low-orbit satellite information sets, the real-time data of each satellite unit at different points in time is analyzed to form a historical real-time information sequence of the satellite unit. The changes in key parameters such as satellite position, velocity, and resource status over time will form a time-series dataset. The real-time data of each satellite unit at each moment is analyzed, and the correlation between these data at historical moments is constructed. By analyzing the real-time data at historical moments, we can understand the past behavior and status of the satellite unit, providing basic data for future predictions. The historical data sequence can reveal the operating modes, patterns, and abnormal fluctuations of the satellite unit, thus providing an important basis for subsequent trajectory prediction and pattern identification. The complete historical real-time sequence helps to improve the understanding of satellite unit behavior, ensuring that subsequent trajectory prediction and topology management are based on accurate data, providing detailed time-series data support for subsequent prediction and optimization, and helping to reveal potential operating modes or abnormal situations.

[0048] The second step is to analyze the changes in satellite real-time information at each moment in the historical real-time sequence of each satellite unit in order to discover the real-time change patterns of each satellite unit, and to assign corresponding real-time pattern characteristics to the simulation units corresponding to each satellite unit in the satellite network simulation model based on the real-time change patterns of each satellite unit.

[0049] Specifically, the historical data sequence of each satellite unit is analyzed to identify the changing patterns of satellite data at various times, uncover the patterns of change, analyze the trends of satellite changes over different time periods, and identify periodic or non-periodic patterns. Based on these patterns, real-time pattern characteristics are assigned to the corresponding satellite units in the satellite network simulation model. By mining patterns in historical data, more accurate predictions can be provided for the model. Especially in complex dynamic environments, the behavior of satellite units often follows certain patterns. By assigning unique real-time pattern characteristics to each satellite unit, the topology model can better adapt to the dynamic behavior of different units in the satellite network. Clear real-time pattern characteristics help improve the accuracy of the prediction model. In particular, in dynamic topology management, the network behavior of satellites can be adjusted in real time. Through pattern analysis, possible operating modes can be discovered in the satellite network, helping to optimize network design and resource allocation strategies.

[0050] The third step is to perform trajectory prediction processing on each simulation unit based on the real-world pattern characteristics assigned to each simulation unit, so as to generate the predicted trajectory of each simulation unit.

[0051] Specifically, based on the historical changes of each satellite unit, the trajectory of the satellite unit is predicted, and a predicted trajectory for the simulated unit is generated. Appropriate trajectory prediction algorithms, such as Kalman filtering or neural networks, are used to infer the future trajectory of the satellite unit based on patterns and historical data. Specific prediction models are formed for different satellite units, and predicted trajectory data is generated. Trajectory prediction is the core of the satellite network management model. It can predict the position, velocity, and resource status changes of satellites at a future point in time. Empowering the prediction model with real-time patterns helps improve the accuracy of future trajectory predictions, especially regarding motion and status changes within the satellite network. Trajectory prediction provides crucial information for satellite network management, particularly in resource planning, path optimization, and network fault prevention, where it has direct application value. High-precision trajectory prediction can optimize coordination and resource sharing among satellites, improving the overall efficiency of the satellite network.

[0052] The fourth step involves performing a consistency analysis of the data patterns of the predicted trajectory based on the historical real-time sequences of each simulation unit, and then vectorizing the consistency analysis to generate a pattern deviation matrix.

[0053] Specifically, based on the historical data sequence of the simulation unit, a consistency analysis is performed on the predicted trajectory to ensure that the predictive data and historical data maintain the same regularity. The historical data and the predicted trajectory are compared to analyze the consistency of their patterns, and a pattern deviation matrix is ​​generated through a vectorization method. The pattern deviation matrix reflects the deviation and inconsistency between the predicted trajectory and the historical trajectory. Consistency analysis can ensure that the predicted trajectory is consistent with the actual historical data and avoid prediction deviations caused by model errors. By generating the pattern deviation matrix, the differences between the prediction and historical data can be found, and the prediction model can be corrected and optimized. Consistency analysis helps to improve the accuracy and stability of trajectory prediction and ensures that the satellite network simulation model can effectively adapt to actual changes. The generation of the pattern deviation matrix provides a quantitative basis for subsequent pattern correction and feedback mechanisms.

[0054] The fifth step involves feeding back information on the real-world pattern characteristics of each simulation unit based on the pattern deviation matrix, adjusting the real-world pattern characteristics, and configuring several possible future data and corresponding pattern correction characteristics for the adjusted real-world pattern characteristics to obtain a satellite network management model. The possible future data refers to the real-world information that the satellite unit may exhibit in the future, and the pattern correction characteristics are the methods used to correct the real-world pattern characteristics based on the possible future data.

[0055] Specifically, based on the deviation matrix, information feedback is provided on the actual characteristics of the simulated units, adjusting their characteristics. For deviations in the predicted trajectory, the model's characteristics are corrected to more closely reflect the actual behavior of satellite units. Multiple future possibility data are configured to consider different future states, and the pattern corrections are adjusted to reflect the potential behavior of satellite units. Through feedback mechanisms and pattern corrections, the model can be continuously optimized to more accurately reflect the dynamic changes of satellite units. In satellite networks, future state changes are uncertain; configuring multiple future possibility data enhances the model's adaptability and flexibility. Dynamic feedback and pattern correction characteristics greatly improve the model's adaptability to changes in the actual satellite network and reduce prediction errors. By considering future possibility data, the satellite network management model can better cope with sudden changes and complex situations in the satellite network.

[0056] Specifically, based on all trajectory predictions, pattern deviation adjustments, and multi-possibility data, a complete satellite network management model is generated. This model can automatically adjust the satellite network topology and resource allocation according to real-time data changes, prediction results, and pattern corrections. The satellite network management model can continuously optimize the network structure and resource allocation based on real-time data and future prediction results of satellite units, ensuring efficient network operation. By considering multiple future possibilities, the model can flexibly respond to different scenarios and changes, ensuring network stability and service quality. The satellite network management model provides an efficient management solution for satellite networks, ensuring network performance and stability in complex and rapidly changing environments. The model's real-time adjustment capability enables the satellite network to adapt to external interference or internal changes, optimize network resource allocation, and improve overall efficiency.

[0057] Furthermore, the step of evaluating the cross-orbit data transmission paths of the satellite network based on a pre-built network evaluation framework to generate cross-orbit link evaluation information for the satellite network includes: The first step involves invoking the distributed routing evaluation algorithm, link quality evaluation algorithm, and traffic load balancing evaluation algorithm contained in the pre-built network evaluation framework to evaluate the cross-orbit data transmission path of the satellite network at the current time, thereby obtaining several feasible link forms and corresponding link evaluation information for the cross-orbit links between the high-orbit and low-orbit levels of the satellite network at the current time.

[0058] Specifically, using a pre-built network evaluation framework, the distributed routing evaluation algorithm, link quality evaluation algorithm, and traffic load balancing evaluation algorithm are invoked to evaluate the cross-orbit data transmission path of the satellite network at the current time point. This analyzes the communication paths between various satellite units in the network, assesses the feasibility and efficiency of cross-orbit data transmission, evaluates the link quality between high-Earth orbit and low-Earth orbit, considers factors such as signal attenuation, bandwidth, and latency, and evaluates the load balancing of data traffic based on the current traffic situation. The data transmission path is then optimized. The evaluation of cross-orbit links does not rely on a single factor but is a comprehensive analysis of multiple dimensions, helping to fully understand the transmission capacity and bottlenecks of the satellite network. Through this evaluation, multiple feasible link forms can be selected for the satellite network at the current time point to ensure the stability and efficiency of data transmission. It provides cross-orbit link evaluation information, reflecting the performance of the current satellite network under different conditions, and provides a quantitative basis for subsequent dynamic adjustments and optimizations, enabling the satellite network to make corresponding adjustments based on real-time data.

[0059] The second step involves continuously monitoring the real-time information of the satellite network over a certain period of time. Based on the real-time pattern characteristics of each simulation unit in the satellite network management model, as well as several possible future data and corresponding pattern correction characteristics, the real-time pattern of each satellite unit in the satellite network is corrected. Then, based on the corrected real-time pattern characteristics, the future time of the satellite network is predicted to generate several possible network forms of the satellite network and their corresponding possibility weights.

[0060] Specifically, the system continuously monitors the real-time information of the satellite network over a certain period of time, and performs pattern corrections based on the real-time pattern characteristics of each satellite unit in the satellite network management model. Based on historical data and current information, the system corrects the pattern characteristics of each simulated unit, adjusts the state changes in the prediction model, configures future possibility data and corresponding pattern correction characteristics, and predicts the behavior of the satellite network at future points in time. The state of the satellite network changes over time, and pattern corrections help adjust the network model in real time, ensuring the accuracy of the prediction results. Based on future possibility data, the future state of the satellite network can be effectively predicted, providing multiple possible network forms to choose from. Through real-time pattern corrections and future predictions, more accurate dynamic information can be provided for satellite network optimization, supporting the adaptive adjustment of the satellite network in complex environments and optimizing network stability and performance.

[0061] Specifically, based on the corrected real-time patterns, the satellite network is predicted for future times, generating several possible network forms. The behavior of satellite units at future moments is predicted, and corresponding probability weights are generated. By combining multiple possible network forms, the dynamic performance of the satellite network in the future is predicted. The future state of the satellite network is influenced by various factors; therefore, by considering multiple possible network forms, the possible future network conditions can be comprehensively reflected. By assigning weights to different network forms, the scheduling and resource allocation strategies of the satellite network can be further optimized. This step provides multiple possible paths for the future evolution of the satellite network, supporting flexible scheduling and planning. The generated multiple possible network forms provide stronger predictive capabilities for satellite network management and reduce potential risks.

[0062] The third step involves using the distributed routing evaluation algorithm, link quality evaluation algorithm, and traffic load balancing evaluation algorithm included in the network evaluation framework to conduct multi-dimensional evaluations of various possible network forms and their corresponding possible weights of the satellite network, so as to obtain several feasible link forms and corresponding link evaluation information for the cross-orbit links of the satellite network in the future.

[0063] Specifically, the distributed routing evaluation algorithm, link quality evaluation algorithm, and traffic load balancing evaluation algorithm in the network evaluation framework are used to conduct multi-dimensional evaluations of various possible network forms of the satellite network and their corresponding possible weights. Cross-orbit link evaluations are performed for each possible network form to obtain the corresponding link quality, routing performance, and load balancing status. Through multi-dimensional evaluations of different possible network forms, the optimal network configuration scheme can be provided for the satellite network under different scenarios. This evaluation provides decision-makers with reliable data support to help them make appropriate decisions when facing different future scenarios. Multi-dimensional evaluation provides performance analysis under different scenarios for the future development of the satellite network, helps optimize future network configurations, provides more detailed evaluation information, and helps ensure that the satellite network can operate efficiently in the future.

[0064] The fourth step is to combine the various feasible link configurations of the satellite network in the current and future times with the corresponding link evaluation information to generate the cross-orbit link evaluation information of the satellite network.

[0065] Specifically, by combining all feasible link configurations and their corresponding link evaluation information for the satellite network in the current and future times, cross-orbit link evaluation information is generated. By integrating the link evaluation information from the current and future times, a complete link evaluation report is formed. Combining the current and future evaluation results, a comprehensive understanding of the satellite network's performance can be obtained, thus providing a basis for subsequent network optimization and management. Through cross-time dimension link evaluation, the topology and resource allocation of the satellite network can be dynamically adjusted. The generated cross-orbit link evaluation information provides detailed performance evaluation data for satellite network optimization, helping decision-makers make reasonable management and scheduling decisions. This step ensures that the satellite network can maintain efficient and stable operation across different time dimensions.

[0066] Furthermore, the steps of obtaining the current service demand information and performing communication resource allocation strategy analysis on the service demand based on the cross-track link evaluation information to obtain the resource scheduling strategy include: The first step is to obtain the current business demand information and parse the business demand information to convert it into the target node feature distribution at the surface level of the satellite network.

[0067] Specifically, the system acquires current-moment business demand information, including traffic requirements, latency requirements, and bandwidth requirements. This information is then parsed and converted into a target node feature distribution at the satellite network's surface layer. The parsing process involves analyzing the type, location, load requirements, and communication protocols of the ground target nodes. Transforming business demands into satellite network node characteristics provides a clear demand model for subsequent network resource allocation. Through the target node feature distribution, a more accurate mapping to the satellite network topology can be achieved, ensuring a good match between demand and network requirements. This provides a clear model of business demands and data support for the formulation of communication resource allocation and scheduling strategies for the satellite network.

[0068] The second step involves linking the target node feature distribution using the satellite network management model relative to the Earth's surface level network topology to generate several overall network link forms.

[0069] Specifically, based on the satellite network management model, the target node characteristic distribution is linked at the ground-level topology of the satellite network to generate several overall network link forms. In this step, considering the current topology of the satellite network, the target nodes are dynamically connected to ensure that each target node can communicate through the satellite link. The network topology will change dynamically with time, node status, and demand. Therefore, it is necessary to link the target nodes in real time and adjust the network links. Through dynamic topology modeling, multiple potential network links can be configured for each target node, providing flexible link selection and generating a variety of possible network link forms that adapt to current business needs. This satisfies business requirements while providing a flexible network resource allocation scheme.

[0070] The third step is to evaluate the link value of various overall network link forms based on the cross-track link evaluation information, so as to generate link value parameters for various overall network link forms.

[0071] Specifically, based on cross-track link evaluation information, link value assessment is performed on various overall network link types, generating link value parameters for each link type. The link value assessment considers factors such as link quality, bandwidth, latency, and traffic load. By assessing link quality, a value parameter can be assigned to each link, prioritizing higher-quality links for data transmission. The link value assessment provides a scientific basis for subsequent network optimization decisions, providing value parameters for each link type and supporting reasonable weighting of multiple links to optimize network resource allocation.

[0072] The fourth step is to perform a difference analysis on the various overall network link forms to generate structural difference characteristics of the various overall network link forms. Based on the link value parameters and the structural difference characteristics, the link structure of the various overall network link forms is decomposed and the structural value is evaluated to generate several unit links of the satellite network and their corresponding reference values.

[0073] Specifically, a differential analysis is conducted on various overall network link forms to generate structural difference characteristics for each link form. By comparing these characteristics, the differences between link forms are analyzed, including differences in bandwidth, latency, and reliability. Based on link value parameters and structural difference characteristics, the link forms are decomposed and their structural value is assessed, generating unit links of the satellite network and their corresponding reference values. By analyzing the structural differences of the links, the advantages and disadvantages of each link form can be identified, which helps to accurately adjust resource allocation. The structural differences and value assessment of the links provide detailed decision-making basis for subsequent optimization, assigning structural difference characteristics and reference values ​​to each link form, which helps to make reasonable choices among multiple links and optimize network configuration.

[0074] The fifth step involves reconstructing the connection of each unit link in the satellite network based on the reference value of each unit link, thereby obtaining several optimized network link forms and their reference values. Simultaneously, communication resource allocation simulation is performed on the target node feature distribution based on each optimized network link form to obtain the communication resource allocation effect of each optimized network link form.

[0075] Specifically, based on the reference value of each unit link in the satellite network, the network link connection is reconstructed to obtain several optimized network link forms and their reference value. Based on the optimized network link forms, communication resource allocation simulation is performed on the target node characteristic distribution to evaluate the communication resource allocation effect. Link connection reconstruction helps to eliminate bottlenecks in the network, optimize link utilization, and improve the overall network performance. By simulating resource allocation for optimized network link forms, the actual effect of different link configurations can be evaluated in advance, achieving optimized network link configuration, ensuring the best network performance under different service requirements, and improving the overall resource utilization.

[0076] The sixth step is to perform feature encoding on each of the optimized network link forms, convert the optimized network link forms into feature encoding sets, and extract optimization factors from each of the feature encoding sets based on the link reference value and the communication resource allocation effect, so as to perform deep optimization on each of the optimized network link forms according to the optimization factors, so as to obtain a resource scheduling strategy.

[0077] Specifically, each optimized network link form is feature-encoded and converted into a feature code set. Based on the link reference value and communication resource allocation effect, optimization factors are extracted from the feature code set, and the network links are deeply optimized according to these optimization factors to obtain a resource scheduling strategy. By converting the optimized network link form into a feature code set, key optimization factors can be extracted from the code set for further deep optimization. Deep optimization not only optimizes individual links but also coordinates resource allocation between different links, ultimately forming a collaborative scheduling strategy. Through deep optimization, efficient collaborative scheduling of multi-orbit resources is achieved, improving the response capability and resource utilization of the satellite network under different service requirements.

[0078] Furthermore, the steps of performing a difference analysis on various overall network link forms to generate structural difference characteristics of various overall network link forms, and based on the link value parameters and the structural difference characteristics, performing link structure decomposition and structural value assessment on various overall network link forms to generate several unit links of the satellite network and their corresponding reference values ​​include: The first step is to perform multidimensional tensor decomposition on the various overall network link forms to obtain the multidimensional tensor feature matrices corresponding to the various overall network link forms.

[0079] Specifically, multidimensional tensor decomposition is performed on all overall network link types to obtain multidimensional tensor feature matrices for each link type. Multidimensional tensor decomposition extracts various features (such as bandwidth, latency, reliability, etc.) of each link through high-dimensional analysis of the link data and converts them into tensor form. The feature matrix of each link can provide rich information to help subsequent analysis of the internal structural differences of the links. Multidimensional tensor decomposition can extract multi-dimensional features from complex datasets, making network link analysis not limited to traditional linear models, but capable of handling more complex data patterns. Through multidimensional tensor form, the multi-level and multi-dimensional features of the links can be clearly expressed, which is convenient for subsequent analysis. Multidimensional feature matrices of each overall network link type are generated, providing basic data for subsequent difference analysis and link structure evaluation.

[0080] The second step is to perform a difference analysis based on the multidimensional tensor feature matrices, and to extract the difference elements of the overall network link form according to the difference analysis results, so as to generate the structural difference features.

[0081] Specifically, based on the obtained multidimensional tensor feature matrix, a difference analysis is performed on each type of network link. By calculating the feature differences between different link types, the differential features of each link type are extracted. The results of the difference analysis will help identify the key differences of the links, such as differences in bandwidth, latency, load capacity, etc. Through the difference analysis, the key feature differences of different link types can be identified, thereby enabling targeted evaluation of link performance. The results of the difference analysis help to extract the elements of the links more accurately, which is helpful for subsequent link evaluation and optimization. It provides the differential features of each link type, which is helpful for subsequent link decomposition and structural value assessment.

[0082] The third step is to perform an interactive analysis on the structural difference features based on the link value parameters of each of the overall network link forms that include the structural difference features, so as to obtain the reference value of each of the structural difference features.

[0083] Specifically, based on the structural differences in links and their corresponding value parameters, an interaction analysis is performed on these structural differences to generate a reference value for each feature. The analysis also examines the interactions between different link features and assesses the value of each feature in different contexts. This interaction analysis helps reveal the interdependencies in link performance, thus enabling a more accurate assessment of the overall link value. Different features of a link typically interact with each other; analyzing individual features may overlook these relationships. Interaction analysis facilitates a more comprehensive link evaluation. By analyzing the interactions between links, the value of each feature can be more accurately assessed, helping to further optimize resource allocation and scheduling strategies. This process generates a reference value for the structural differences in each link form, providing a quantitative reference for subsequent link decomposition and consistency structural feature analysis.

[0084] The fourth step involves decomposing the consistent structural features of each overall network link form based on the structural differences and conducting interactive analysis of the link value parameters to obtain several consistent structural features and their corresponding reference values.

[0085] Specifically, the structural differences and consistency features of each overall network link are decomposed and interactively analyzed to generate several consistency features and their corresponding reference values. During the analysis, the consistent parts of the link structure are identified, decomposed to obtain consistency features, and the impact of these consistency features on link value parameters is analyzed to evaluate their value under different network configurations. Consistency features in the link have a significant impact on overall performance. By decomposing the consistency structure, we can further understand which features play an important role in link performance. By decomposing and analyzing consistency features, we can discover which structural features contribute the most to network performance, providing support for subsequent resource optimization. A series of consistency features and their reference values ​​are obtained, providing practical data support for network optimization and link resource allocation.

[0086] The fifth step involves quantifying and evaluating the structural differences and consistency features into unit elements to generate several unit links of the satellite network and their corresponding reference values.

[0087] Specifically, the structural difference and consistency features are quantitatively decomposed and value-assessed as unit elements, ultimately generating several unit links of the satellite network and their corresponding reference values. The difference and consistency features are transformed into unit elements, and each element is quantitatively evaluated. The evaluation results generate the reference value of each unit link, providing a specific basis for resource allocation. By decomposing structural difference and consistency features into unit elements, the specific contribution of each link can be evaluated more meticulously, ensuring the accuracy of the optimization process. Through quantitative decomposition and value assessment, operable unit links are ultimately formed, providing a clear direction for the optimal allocation of network resources. Several unit links of the satellite network are generated, and each unit link is assigned a specific reference value, ensuring the efficiency and accuracy of the network optimization process.

[0088] Furthermore, the steps of executing the resource scheduling strategy through a hierarchical network management mechanism and cross-orbit data transmission protocol to construct a high- and low-orbit satellite communication network include: The first step is to analyze the resource scheduling strategy through a hierarchical network management mechanism to obtain the intra-level satellite link execution schemes for the high-orbit and low-orbit levels, and then perform intra-level signal links between the high-orbit and low-orbit levels of the satellite network according to the intra-level satellite link execution schemes.

[0089] Specifically, a hierarchical network management mechanism is used to analyze resource scheduling strategies, resulting in intra-level satellite link execution schemes for both high-Earth orbit (HEO) and low-Earth orbit (LEO) levels. This step aims to ensure effective management of HEO and LEO satellite link resources and to conduct reasonable link planning. The HEO and LEO satellite link execution schemes will indicate how to achieve signal transmission and resource coordination within their respective levels. Satellite networks have different hierarchical structures; hierarchical management can effectively allocate and schedule resources, ensuring optimal execution of links at each level. By effectively analyzing and executing satellite links within each level, network performance within each level can be maximized, improving resource utilization. The HEO and LEO satellite link execution schemes have been determined, and internal signal links between HEO and LEO levels have been established, providing a foundation for subsequent cross-level communication and resource scheduling.

[0090] The second step involves parsing the resource scheduling strategy using a cross-orbit data transmission protocol to obtain the cross-level satellite link execution scheme between the high-orbit and low-orbit levels, and the satellite resource allocation scheme between the high-orbit and low-orbit levels relative to the surface level.

[0091] Specifically, the resource scheduling strategy is parsed using the inter-orbit data transmission protocol to obtain the cross-level satellite link execution scheme between the high-orbit and low-orbit levels. This step needs to ensure that the link between high-orbit and low-orbit satellites can effectively transmit data and optimize cross-level communication. At the same time, based on the protocol, a satellite resource allocation scheme for the high-orbit and low-orbit levels relative to the Earth's surface level is generated. Data transmission between high-orbit and low-orbit satellites requires cross-level cooperation. A specialized protocol can optimize the data transmission efficiency between different orbital levels. Through the inter-orbit data transmission protocol, the rational allocation and scheduling of resources between different orbital levels can be achieved, ensuring that the data needs of the Earth's surface level are met. The resulting satellite link execution scheme after parsing the inter-orbit data transmission protocol, as well as the satellite resource allocation scheme for the Earth's surface, provide support for cross-level signal links and resource scheduling.

[0092] The third step involves establishing cross-level signal links between the high-orbit and low-orbit levels of the satellite network according to the cross-level satellite link execution scheme, and driving the low-orbit satellite units to establish signal links towards the signal terminals on the Earth's surface according to the satellite resource allocation scheme, thereby constructing a high- and low-orbit satellite communication network.

[0093] Specifically, according to the cross-level satellite link execution scheme, signals from the high-orbit and low-orbit levels are linked. This cross-level signal link ensures effective data exchange between high-orbit and low-orbit systems, avoiding signal transmission problems caused by differences in orbital distribution. This step also needs to consider the stability and bandwidth requirements of the signal link to ensure the efficiency and stability of cross-level communication. Cross-level signal links between high- and low-orbit satellites are crucial for achieving multi-orbit resource collaborative scheduling, ensuring data interoperability and information sharing between different orbital levels. By optimizing the cross-level link execution scheme, signal attenuation and loss can be reduced, improving the overall stability and reliability of the communication system. Establishing cross-level signal links between high- and low-orbit systems ensures effective communication between satellites at different levels.

[0094] Specifically, according to the satellite resource allocation scheme, low-Earth orbit (LEO) satellite units are driven to establish signal links with ground-based signal terminals. In this step, LEO satellites need to adjust their orbits and attitudes to ensure they can establish stable communication links with ground-based signal terminals. This step requires precise positioning and scheduling of LEO satellites to ensure they can provide high-quality signals when communicating with ground-based terminals. LEO satellites are typically used for direct communication with ground-based terminals; therefore, a stable link with ground-based signal terminals is crucial for the success of multi-orbit communication networks. By precisely scheduling LEO satellites according to the satellite resource allocation scheme, the communication quality and stability between LEO satellites and ground-based terminals can be improved, successfully establishing signal links between LEO satellites and ground-based signal terminals, thus completing the full communication link between high- and low-Earth orbit satellites and ground-based terminals.

[0095] Specifically, by combining the above steps, a high-Earth orbit (HEO) and low-Earth orbit (LEO) satellite communication network is ultimately constructed to ensure communication links between satellites and ground terminals at all levels. Through intra-level satellite link execution schemes, cross-level signal links, and ground signal links between LEO satellites, a complete HEO and LEO satellite communication network architecture is formed. Constructing a complete HEO and LEO satellite communication network can ensure interconnection and interoperability between satellites and ground terminals at different levels, improve network coverage and transmission efficiency, and achieve wider coverage and higher transmission efficiency through the collaborative work of HEO and LEO satellites, providing support for global communication. The construction of the HEO and LEO satellite communication network ensures the efficient utilization and coordinated scheduling of multi-orbit resources and improves the overall performance of the satellite communication system.

[0096] Based on the technical content of the high and low orbit satellite communication resource scheduling method described in the above-disclosed embodiments, the present invention provides a high and low orbit satellite communication resource scheduling system, the structure of which is as follows: Figure 2 The method for scheduling high and low orbit satellite communication resources as described in any one of the first aspects includes: The model building module is used to obtain real-time status information of the high-orbit and low-orbit levels participating in the satellite network, so as to perform digital simulation and trajectory prediction on the satellite network to obtain a satellite network management model. The path evaluation module is used to evaluate the cross-orbit data transmission path of the satellite network based on the pre-built network evaluation framework, so as to generate cross-orbit link evaluation information of the satellite network. The resource allocation module is used to obtain the current business demand information and perform communication resource allocation strategy analysis on the business demand based on the cross-track link evaluation information to obtain the resource scheduling strategy. The network link module is used to execute the resource scheduling strategy through a hierarchical network management mechanism and cross-orbit data transmission protocol to construct a high- and low-orbit satellite communication network.

[0097] In this embodiment, the specific implementation of each module in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0098] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0099] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein can be implemented directly by hardware, a software module executed by a processor, or a combination of both. The software module can be located in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art.

[0100] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for scheduling high- and low-orbit satellite communication resources, characterized in that, include: Real-time status information of the high-orbit and low-orbit levels participating in the satellite network is obtained to perform digital simulation and trajectory prediction on the satellite network, so as to obtain a satellite network management model. Based on a pre-built network evaluation framework, the satellite network management model is evaluated for cross-orbit data transmission paths to generate cross-orbit link evaluation information for the satellite network. Obtain the current service demand information, and perform a strategy analysis on the communication resource allocation of the service demand based on the cross-track link evaluation information to obtain a resource scheduling strategy; The resource scheduling strategy is executed through a hierarchical network management mechanism and a cross-orbit data transmission protocol to construct a high- and low-orbit satellite communication network.

2. The high and low orbit satellite communication resource scheduling method as described in claim 1, characterized in that, The steps of acquiring real-time status information of each network layer participating in the satellite network, and performing digital simulation and trajectory prediction on the satellite network to obtain a satellite network management model include: By pre-deploying a real-time status awareness mechanism in each satellite unit, the real-time status information of each satellite unit at the high-orbit and low-orbit levels participating in the satellite network is perceived to collect high-orbit satellite information sets and low-orbit satellite information sets; wherein, the real-time status information includes position, velocity and available resources; Based on the high-orbit satellite information set and the low-orbit satellite information set, digital simulations are performed on the high-orbit and low-orbit levels of the satellite network to generate high-orbit simulation models and low-orbit simulation models, and the high-orbit simulation models and low-orbit simulation models are combined to generate a satellite network simulation model. Based on the high-orbit satellite information set and the low-orbit satellite information set, trajectory prediction of satellite units is performed on the high-orbit simulation model and the low-orbit simulation model respectively. Based on the trajectory prediction results, the predicted trajectory information is added to the satellite network simulation model to obtain the satellite network management model.

3. The high and low orbit satellite communication resource scheduling method as described in claim 2, characterized in that, The steps of predicting the trajectories of satellite units in the high-orbit satellite information set and the low-orbit satellite information set, respectively, and then appending the predicted trajectory information to the satellite network simulation model based on the trajectory prediction results to obtain the satellite network management model include: Based on the high-orbit satellite information set and the low-orbit satellite information set, the real-time information of each satellite unit of the satellite network at each time is analyzed to obtain the historical real-time sequence of each satellite unit; The historical real-time sequence of each satellite unit is analyzed to reveal the real-time change patterns of each satellite unit. Based on these patterns, the simulation units corresponding to each satellite unit in the satellite network simulation model are assigned corresponding real-time pattern characteristics. Based on the real-world characteristics assigned to each simulation unit, trajectory prediction processing is performed on each simulation unit to generate the predicted trajectory of each simulation unit. Based on the historical real-time sequences of each simulation unit, a consistency analysis of the data patterns of the predicted trajectory is performed, and the consistency analysis is vectorized to generate a pattern deviation matrix. The actual situation characteristics of each simulation unit are fed back based on the deviation matrix to adjust the actual situation characteristics. At the same time, several possible future data and corresponding pattern correction characteristics are configured for the adjusted actual situation characteristics to obtain a satellite network management model. The possible future data are the actual situation information that the satellite unit may exhibit in the future, and the pattern correction characteristics are the ways to correct the actual situation characteristics according to the possible future data.

4. The high and low orbit satellite communication resource scheduling method as described in claim 3, characterized in that, The steps for evaluating the cross-orbit data transmission paths of the satellite network based on a pre-built network evaluation framework to generate cross-orbit link evaluation information for the satellite network include: The pre-built network evaluation framework includes distributed routing evaluation algorithms, link quality evaluation algorithms, and traffic load balancing evaluation algorithms. These algorithms are used to evaluate the cross-orbit data transmission paths of the satellite network management model at the current time, so as to obtain several feasible link forms and corresponding link evaluation information for the cross-orbit links between the high-orbit and low-orbit levels of the satellite network at the current time. The system continuously monitors the real-time information of the satellite network over a certain period of time, and based on the real-time pattern characteristics of each simulation unit in the satellite network management model, as well as the configured future possibility data and corresponding pattern correction characteristics, it corrects the real-time patterns of each satellite unit in the satellite network, and then predicts the future time of the satellite network based on the corrected real-time pattern characteristics, so as to generate several possible network forms of the satellite network and corresponding possibility weights. The distributed routing evaluation algorithm, link quality evaluation algorithm, and traffic load balancing evaluation algorithm included in the network evaluation framework are used to evaluate the various possible network forms and corresponding possible weights of the satellite network in multiple dimensions, so as to obtain several feasible link forms and corresponding link evaluation information of the cross-orbit links of the satellite network in the future. The various feasible link configurations of the satellite network in the current and future times are combined with the corresponding link evaluation information to generate cross-orbit link evaluation information for the satellite network.

5. The high and low orbit satellite communication resource scheduling method as described in claim 1, characterized in that, The steps of obtaining the current service demand information and performing communication resource allocation strategy analysis on the service demand based on the cross-track link evaluation information to obtain the resource scheduling strategy include: Obtain the current business demand information and parse the business demand information to convert it into the target node feature distribution at the surface level of the satellite network; Based on the satellite network management model, the target node feature distribution is linked by the satellite network relative to the ground level network topology to generate several overall network link forms. Based on the cross-track link evaluation information, the link value of various overall network link forms is evaluated to generate link value parameters for various overall network link forms; A difference analysis is performed on various overall network link forms to generate structural difference characteristics of various overall network link forms. Based on the link value parameters and the structural difference characteristics, the link structure of various overall network link forms is decomposed and the structural value is evaluated to generate several unit links of the satellite network and their corresponding reference values. Based on the reference value of each unit link of the satellite network, the satellite network is reconnected and reconstructed to obtain several optimized network link forms and their reference values. At the same time, based on each of the optimized network link forms, the target node feature distribution is simulated to obtain the communication resource allocation effect of each of the optimized network link forms. Each optimized network link form is feature-encoded and converted into a feature code set. Based on the link reference value and the communication resource allocation effect, optimization factors are extracted from each feature code set to perform deep optimization on each optimized network link form according to the optimization factors, so as to obtain a resource scheduling strategy.

6. The high- and low-orbit satellite communication resource scheduling method as described in claim 5, characterized in that, The steps of performing a difference analysis on various overall network link forms to generate structural difference characteristics of various overall network link forms, and then, based on the link value parameters and the structural difference characteristics, performing link structure decomposition and structural value assessment on various overall network link forms to generate several unit links of the satellite network and their corresponding reference values ​​include: Multidimensional tensor decomposition is performed on various overall network link forms to obtain multidimensional tensor feature matrices corresponding to various overall network link forms; Based on the multidimensional tensor feature matrices, a difference analysis is performed to extract the difference elements of the overall network link form according to the difference analysis results, so as to generate the structural difference features. Based on the link value parameters of each of the overall network link forms that include the structural difference features, an interactive analysis is performed on the structural difference features to obtain the reference value of each of the structural difference features. Based on the structural differences described, the consistency structural features of each of the overall network link forms are decomposed and the interaction of link value parameters is analyzed to obtain several consistent structural features and their corresponding reference values. The structural difference features and the consistent structural features are quantitatively decomposed and value-assessed as unit elements to generate several unit links of the satellite network and their corresponding reference values.

7. The high and low orbit satellite communication resource scheduling method as described in claim 1, characterized in that, The steps for constructing a high- and low-Earth orbit satellite communication network by executing the resource scheduling strategy through a hierarchical network management mechanism and a cross-orbit data transmission protocol include: The resource scheduling strategy is analyzed through a hierarchical network management mechanism to obtain the intra-level satellite link execution scheme of the high-orbit and low-orbit levels, and the intra-level signal link of the satellite network is performed on the high-orbit and low-orbit levels according to the intra-level satellite link execution scheme. The resource scheduling strategy is analyzed by the cross-orbit data transmission protocol to obtain the cross-level satellite link execution scheme between the high-orbit and low-orbit levels, and the satellite resource allocation scheme between the high-orbit and low-orbit levels relative to the surface level. According to the cross-level satellite link execution scheme, cross-level signal links are established between the high-orbit and low-orbit levels of the satellite network, and according to the satellite resource allocation scheme, the satellite units of the low-orbit level are driven to establish signal links towards the signal terminals on the ground, so as to construct a high- and low-orbit satellite communication network.

8. A high- and low-orbit satellite communication resource scheduling system, characterized in that, include: The model building module is used to obtain real-time status information of the high-orbit and low-orbit levels participating in the satellite network, so as to perform digital simulation and trajectory prediction on the satellite network to obtain a satellite network management model. The path evaluation module is used to evaluate the cross-orbit data transmission path of the satellite network based on the pre-built network evaluation framework, so as to generate cross-orbit link evaluation information of the satellite network. The resource allocation module is used to obtain the current business demand information and perform communication resource allocation strategy analysis on the business demand based on the cross-track link evaluation information to obtain the resource scheduling strategy. The network link module is used to execute the resource scheduling strategy through a hierarchical network management mechanism and cross-orbit data transmission protocol to construct a high- and low-orbit satellite communication network.

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