A routing method, medium, device and product of a low earth orbit satellite constellation

Abstract

The application discloses a routing method, medium, equipment and product of a low-orbit satellite constellation, relates to the technical field of satellite routing, and comprises the following steps: according to a quality of service parameter, performing snapshot freezing on a current network topology to construct a static topology graph structure; constructing a basic quality of service parameter cost function for each link; performing an offline ant colony optimization algorithm to globally evaluate a satellite topology library constructed in advance, constructing a heuristic function according to a link basic quality of service parameter cost; initializing a pheromone value for each link, and updating the pheromone in an iteration process according to the heuristic function; adjusting the cost function based on the pheromone; taking the cost of the discounted pheromone as the final link weight of the satellite network, constructing a weighted graph structure, and adopting a shortest path search algorithm to calculate a routing path between a source node and a target node, so that an optimal routing path is obtained. The application solves the problems of high-speed time-varying satellite link state adaptability and poor quality of service.

Description

Technical Field

[0001] This invention relates to the field of satellite routing technology, and in particular to a routing method, medium, device and product for a low-Earth orbit satellite constellation. Background Technology

[0002] Low-Earth orbit (LEO) satellite constellation communication systems, due to their wide coverage, low latency, and high bandwidth, have become a key carrier for solving the problem of universal communication coverage. Compared with high-Earth orbit (HEO) satellite communication systems, LEO satellite constellations have advantages such as lower propagation loss, lower terminal power consumption, and shorter communication latency, making them a core infrastructure for realizing integrated space-air-ground Internet of Things (IoT). The development trend of satellite communication networks indicates that LEO satellite constellations will evolve towards large-scale and high-density deployments, with the number of satellites and orbital complexity continuously increasing. This places higher demands on the adaptability and performance of satellite routing systems.

[0003] However, the high-speed orbit of low-Earth orbit (LEO) satellites causes frequent constellation topology reconfigurations, resulting in dynamic fluctuations in link states (propagation delay, load level, link lifetime, and bandwidth margin). Currently widely used traditional satellite routing methods (such as the fixed-weight Dijkstra algorithm) are designed based on static topology assumptions and cannot quickly adapt to topology reconfiguration needs. This leads to problems such as long route reconfiguration times, high end-to-end communication latency, high packet loss rates, and low link lifetime utilization. Furthermore, traditional routing methods do not fully integrate multi-dimensional quality of service (QoS) constraints, making it difficult to balance the optimization requirements of multiple objectives such as latency, load, and link stability. Compared to traditional static routing methods, adopting dynamic routing optimization techniques that can adapt to the topology reconfiguration characteristics of LEO satellites, combining topology snapshots, intelligent optimization algorithms, and multi-constraint QoS decisions, can improve the adaptability of routing to dynamic topology changes and enhance communication performance. This is one of the effective methods to solve the performance bottleneck of LEO satellite constellation routing.

[0004] Dijkstra's algorithm is a classic graph theory algorithm for finding single-source shortest paths. Due to its rigorous path optimization logic, stable convergence, and adaptability to weighted topology solutions, it is widely used as a core routing computation technique in topology reconstruction and on-board routing planning for low-Earth orbit (LEO) satellite constellation networks. However, traditional Dijkstra's algorithm also has several inherent shortcomings in LEO satellite topology reconstruction scenarios. For example, it has extremely poor adaptability to high-speed, time-varying topologies in satellite networks. When the high-speed movement of LEO satellites causes frequent network topology changes, the fixed-weight Dijkstra's algorithm cannot respond to dynamic changes in link states in real time, easily leading to route failures, link congestion, and end-to-end transmission interruptions. To address the routing failure problem of traditional Dijkstra's algorithm under dynamic topologies, existing solutions often employ fixed-weight iterative updates and multi-round topology snapshot full recalculation to optimize algorithm performance. However, frequent full-topology recalculations significantly increase on-board computational overhead and route reconstruction latency. Furthermore, the traditional Dijkstra algorithm suffers from inherent drawbacks such as inability to accommodate multiple constraints on quality of service requirements, tendency to converge to local optima, inability to proactively avoid congested links, and severe route jitter during topology switching. These shortcomings make the traditional Dijkstra algorithm unsuitable for meeting the routing transmission requirements of future integrated space-air-ground networks for low-Earth orbit satellite constellations, which demand low latency, high reliability, high capacity, and strong robustness. Therefore, it is necessary to provide a new routing method for dynamic topology reconstruction of low-Earth orbit satellites. Summary of the Invention

[0005] The purpose of this invention is to address the problem of poor adaptability and service quality of low-Earth orbit (LEO) satellite network topology reconstruction relying on fixed weights for high-speed, time-varying satellite link states. A routing method for LEO satellite constellations is proposed, comprising the following steps: S1. Collect the quality of service parameters of the current satellite network topology links; S2. Based on the quality of service parameters, perform a snapshot freeze on the current network topology and construct a static topology graph structure; S3. Based on the static topology graph structure, construct a basic service quality parameter cost function for each link; S4. Execute the offline ant colony optimization algorithm to perform global path evaluation on the pre-built satellite topology library. Based on the link basic service quality parameter cost, construct the heuristic function in the ant colony optimization algorithm; initialize the pheromone value for each link, and update the pheromone during the iteration process according to the heuristic function. S5. Adjust the cost function of basic service quality parameters based on pheromones to obtain the cost after pheromone discount; S6. Using the cost after pheromone discount as the final link weight of the satellite network, construct a weighted graph structure, and use the shortest path search algorithm to calculate the routing path between the source node and the target node to obtain the optimal routing path.

[0006] Furthermore, the formula for calculating the cost function of the basic service quality parameters is as follows:

[0007] in, This represents the cost of the basic quality of service parameter for the link between the i-th node and the j-th node. This represents the basic propagation delay of the link between the i-th node and the j-th node. This represents the packet loss penalty term between the i-th node and the j-th node. This represents the link lifetime penalty term between the i-th node and the j-th node. This represents a hard penalty term for insufficient link bandwidth between the i-th node and the j-th node. and These are the corresponding weighting coefficients.

[0008] Furthermore, the formula for calculating the cost after pheromone discount is as follows:

[0009] in, This represents the cost of the link between the i-th node and the j-th node after pheromone discount. This represents the cost of the basic quality of service parameter for the link between the i-th node and the j-th node. This represents the pheromone discount factor for the link between the i-th node and the j-th node;

[0010] in, This is the discount factor. The normalized pheromone is the link between the i-th node and the j-th node.

[0011] Furthermore, the basic service quality parameter cost function is calculated by weighting the link propagation delay, packet loss rate penalty, link lifetime penalty, and insufficient bandwidth penalty.

[0012] Furthermore, the heuristic function is calculated as the reciprocal of the underlying quality of service parameters.

[0013] Furthermore, the quality of service parameters include: link propagation delay, link distance, link load, bandwidth margin, and link lifetime.

[0014] The present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described routing method for a low-Earth orbit satellite constellation.

[0015] The present invention also proposes an electronic device, including a processor and a memory, wherein the processor is interconnected with the memory, the memory is used to store a computer program, the computer program including computer-readable instructions, and the processor is configured to invoke the computer-readable instructions to execute the above-described routing method for a low-Earth orbit satellite constellation.

[0016] The present invention also proposes a computer program product, including a computer program / instruction that, when executed by a processor, implements the steps of the above-described routing method for a low-Earth orbit satellite constellation.

[0017] The beneficial effects of the technical solution provided by this invention are: This invention performs topology snapshot acquisition on low-Earth orbit satellite networks, discretizing the continuously changing dynamic topology into an instantaneous static graph structure. It constructs a service quality cost function with equivalent delay as a unified metric, achieving dimensional unification and additive expression of multiple constraint indicators through normalization mapping and penalty term reduction for non-delay indicators. A pheromone multiplication discount fusion mechanism is introduced, fusing the pheromone converged after ant colony optimization iterations with the service quality cost under the current network state to form a pheromone-enhanced comprehensive link cost. During the online routing phase, dynamic parameters such as link load and link lifetime are collected in real time, triggering topology snapshot updates and recalculating the comprehensive link cost. Using this comprehensive cost as the edge weight in the graph, a weighted network model is constructed, and the shortest path algorithm is employed to calculate the optimal path from the source node to the destination node. This invention achieves effective decoupling between computationally intensive global optimization and real-time lightweight decision-making through a two-level processing mechanism combining offline and online methods. By using a service quality cost function with unified dimensions and a pheromone guidance mechanism, it achieves balanced optimization of path performance under multiple constraints. While maintaining end-to-end latency and link lifetime at a level comparable to traditional methods, it effectively reduces packet loss rate and shortens topology reconstruction time. This solves the problems of adaptability and poor service quality of high-speed, time-varying satellite link states, and is suitable for the efficient routing needs of large-scale low-Earth orbit satellite constellations in high-speed dynamic environments and under conditions of limited computing resources. Attached Figure Description

[0018] Figure 1 This is a flowchart of a routing method for a low-Earth orbit satellite constellation according to an example of the present invention; Figure 2 This is a comparison chart of packet loss rates between the present invention and existing traditional routing algorithms; Figure 3 This is a comparison chart of the topology reconstruction time of the present invention and existing traditional routing algorithms; Figure 4 This is a comparison chart of link lifetime between the present invention and existing traditional routing algorithms; Figure 5 A comparison chart of the latency of this invention and existing traditional routing algorithms; Figure 6 This is a block diagram of an electronic device according to an exemplary embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0020] A flowchart of a low-Earth orbit satellite constellation routing method according to an example of the present invention is shown below. Figure 1 Specifically, it includes: S1. Collect the quality of service parameters of the current satellite network topology links.

[0021] During satellite network operation, the current network topology status is collected to obtain service quality (SQM) parameters for each inter-satellite link. These SQM parameters include link propagation delay, link distance, link load, bandwidth margin, and link lifetime. Link propagation delay is calculated based on link distance and signal propagation speed; link load reflects the current data service usage on the link; bandwidth margin represents the proportion of remaining available bandwidth; and link lifetime describes the duration for which the link will remain available in the future. By collecting these parameters, a set of link SQM characteristics is formed, providing the foundational data for subsequent cost calculations.

[0022] S2. Based on the quality of service parameters, perform a snapshot freeze on the current network topology and construct a static topology graph structure.

[0023] Based on the quality of service (QoS) parameters collected in step S1, periodic or event-triggered topology snapshots are taken of the low-Earth orbit (LEO) satellite network, discretizing the continuously changing dynamic topology into an instantaneous static graph structure. This snapshot describes the connectivity between network nodes and the QoS characteristics of the corresponding links at the current moment. By fixing the dynamically changing network state at a certain point in time, real-time computational complexity can be reduced, and a stable topology environment can be provided for subsequent path optimization.

[0024] S3. Based on the static topology graph structure, construct a basic service quality parameter cost function for each link.

[0025] The basic service quality parameter cost function uses link propagation delay as the primary indicator, while also incorporating packet loss rate penalties, link lifetime penalties, and bandwidth insufficiency penalties. These are weighted to form a unified comprehensive cost. The packet loss rate is estimated based on link load variations, the link lifetime penalty reduces the probability of selecting links that are about to fail, and the bandwidth penalty limits links with insufficient bandwidth resources. By unifying the units of measurement, the impact of each penalty is kept on the same order of magnitude as the propagation delay, thus ensuring a reasonable balance among the service quality indicators.

[0026] In an exemplary embodiment of the present invention, the calculation formula for the basic service quality parameter cost function is as follows:

[0027] in, This represents the cost of the basic quality of service parameter for the link between the i-th node and the j-th node. This represents the basic propagation delay of the link between the i-th node and the j-th node. This represents the packet loss penalty term between the i-th node and the j-th node. This represents the link lifetime penalty term between the i-th node and the j-th node. This represents a hard penalty term for insufficient link bandwidth between the i-th node and the j-th node. and These are the corresponding weighting coefficients. , These are adjustable weighting coefficients used to balance the impact of various service quality penalties on routing costs; for example, here we can take... =0.3, =0.3.

[0028] S4. Execute the ant colony optimization algorithm to perform global path evaluation on the pre-built satellite topology library. Based on the link's basic service quality parameter cost, construct the heuristic function in the ant colony optimization algorithm; initialize pheromone values ​​for each link, and update the pheromone during the iteration process according to the heuristic function. Specifically, the ant colony optimization process is executed at ground control stations or on-board nodes with edge computing capabilities. Based on the pre-built large-scale satellite topology library (including satellite orbital parameters, spatial location, and inter-satellite link characteristic parameters), and using a unified dimensional cost function for multi-constraint service quality as heuristic information, the ant colony algorithm performs multiple rounds of iterative search to explore and evaluate global paths, continuously updating the path pheromone distribution to form a converged pheromone distribution under the current network state. Specifically, the reciprocal of the link's basic service quality parameter cost is used as heuristic information, ensuring that links with lower costs have a higher selection probability during the path search process, thereby guiding the search process towards links with low latency and low congestion.

[0029] To avoid the unstable impact of excessively large or small pheromone values ​​on path selection probability, the link pheromone is normalized. The ratio of each link pheromone value to the maximum pheromone value in the current network is calculated to obtain the normalized pheromone coefficient, thus ensuring the comparability of pheromone weights between different links.

[0030] Let the pheromone concentration of the link between the i-th node and the j-th node be . The largest pheromone in the network is Then the normalized pheromone is:

[0031] in, Let ε be the normalized pheromone of the link between the i-th node and the j-th node, where ε is a very small positive number used to prevent the denominator from being 0.

[0032] The ant colony optimization algorithm is used to perform multiple path searches on the topology snapshot. Each ant at the node... Select the next hop node At that time, the following probability rule applies: based on the link pheromone strength. of Power and Heuristic Function Values of Normalizing the product of powers yields the selection probability of each candidate link. Heuristic functions It is the reciprocal of the basic cost of the link; the smaller the cost, the greater the attraction. Controlling the degree of influence of historical pheromone experience, Controlling the degree of influence of local heuristic greedy algorithms, such as desirability. , This approach ensures the algorithm maintains a reasonable balance between historical experience and local greed. Visited nodes are not included in the candidate set to avoid path loops; if no candidate node is available for the current node, the algorithm backtracks to the previous node and reselects. After an ant successfully reaches the target node, the pheromone deposition on the traversed links is updated based on the total path cost. The lower the total path cost, the more pheromone is deposited, thus strengthening the attractiveness of high-quality paths. Simultaneously, the influence of historical paths is gradually reduced through a pheromone evaporation mechanism, maintaining a dynamic balance between exploring new paths and utilizing historically valuable paths.

[0033] S5. Adjust the cost function of the basic service quality parameters based on pheromones to obtain the cost after pheromone discount.

[0034] After obtaining the updated pheromone matrix, normalized pheromone coefficients are introduced into the link cost function, and the basic service quality cost is adjusted through a multiplicative discount mechanism. Specifically, when the pheromone value of a link is high, its corresponding overall cost will be appropriately reduced, making it easier to be selected in subsequent path selection processes; conversely, when the pheromone value is low, its cost is maintained or increased. This mechanism allows historically high-performing paths to be continuously strengthened while preventing pheromone from having an excessively dominant effect on service quality indicators.

[0035] In an exemplary embodiment of the present invention, the formula for calculating the cost after pheromone discount is as follows:

[0036] in, This represents the cost of the link between the i-th node and the j-th node after pheromone discount. This represents the cost of the basic quality of service parameter for the link between the i-th node and the j-th node. This represents the pheromone discount factor of the link between the i-th node and the j-th node.

[0037] The pheromone discount factor is calculated based on normalized pheromones, using the following formula:

[0038] in, This is the discount factor. The normalized pheromone is the link between the i-th node and the j-th node.

[0039] Expanding the cost of basic service quality, with α set to 0.5, we get:

[0040] The higher the pheromone concentration, the lower the corresponding link cost, thus increasing the link's priority in the path selection process.

[0041] S6. In the online routing phase, dynamic parameters such as link load and link lifetime are collected in real time, triggering topology snapshot updates and ant colony optimization iterations. The comprehensive cost after pheromone discount is used as the final link weight of the satellite network to construct a weighted graph structure. The shortest path search algorithm is used to calculate the routing path between the source node and the target node to obtain the optimal routing path.

[0042] In this embodiment, the Dijkstra shortest path algorithm can be used to complete the path search, thereby obtaining the optimal routing path that comprehensively considers propagation delay, link load, packet loss rate and link stability, and realizing topology reconstruction and dynamic routing optimization of the satellite network.

[0043] By combining offline ant colony optimization prior learning with online snapshot shortest path solving, this invention can fully utilize link service quality information and historical path experience to efficiently reconstruct and optimize the topology of dynamically changing satellite networks while ensuring computational efficiency. This effectively reduces network transmission latency and packet loss rate, and improves link stability and overall network performance.

[0044] The method of this invention is compared with the traditional Dijkstra algorithm. The cost function formula of the traditional algorithm is:

[0045] In this embodiment, the value of α in the cost function formula of the traditional algorithm is 0.6. The traditional algorithm is a fixed-weight shortest path routing method commonly used in low-Earth orbit satellite networks. Its implementation process includes: first, topology initialization, constructing the low-Earth orbit satellite network topology, determining the location of each satellite node, the inter-satellite link connection relationship, and the basic link parameters, including link distance and basic transmission delay; second, initial cost calculation, by setting a weighting coefficient α (set to 0.6), constructing a routing cost function based on the basic link delay and link lifetime penalty term, wherein the link lifetime penalty term is negatively correlated with the remaining available time of the link; then, load iterative adjustment, performing multiple iterations to update the link load, each iteration correcting the routing cost function and recalculating the path based on the current link load to reflect the impact of load changes on path selection; finally, based on the updated routing cost function, using the Dijkstra algorithm to calculate the shortest path from the source node to the destination node to complete the route selection. The above methods mainly optimize paths based on single or low-dimensional indicators. They suffer from performance limitations under multi-dimensional service quality constraints and have limited ability to respond to rapid changes in the topology of low-Earth orbit satellite networks, which can easily lead to network congestion and path instability.

[0046] In comparison, the method combining offline ant colony optimization and online snapshots is the approach proposed in this invention. This invention's dynamic topology reconstruction method, based on a combination of network snapshots, quality of service constraints, and ant colony optimization, can achieve superior path selection results under multi-dimensional performance indicators and dynamic environments. For large-scale low-Earth orbit (LEO) satellite scenarios, the experimental cases use a large-scale LEO Walker constellation architecture with 80 orbital planes, 50 satellites per orbital plane, and a total of 4000 satellites. The constellation's size, orbital altitude, maximum inter-satellite link communication distance, and other core parameters are fully consistent with the first-phase commercial deployment plans of current mainstream global commercial LEO satellite communication constellations, completely differentiating it from other technologies that only validate routing schemes on a small-scale simulated constellation of a few hundred satellites. Key simulation parameters are shown in Table 1. This invention highly replicates the operational scenario of real commercial low-Earth orbit (LEO) satellite networks from multiple core dimensions, including topology scale, link dynamic characteristics, network load distribution, and on-board computing power constraints. The algorithm architecture is fully adapted to the sparsity and dynamic update characteristics of network topology under large-scale LEO satellites, and can maintain stable performance advantages in real scenarios. It solves the core pain point of existing routing algorithms being seriously disconnected from small-scale simulation verification and real engineering implementation scenarios, and has engineering practicality that can be directly adapted to the large-scale deployment of commercial LEO satellite constellations.

[0047] Table 1

[0048] Packet loss rate is a core indicator for measuring the reliability and quality of service of satellite network data transmission. The lower the packet loss rate, the better the integrity of data transmission and the higher the quality of network service. It is the core evaluation dimension of low-Earth orbit satellite routing schemes.

[0049] Figure 2 This is a comparison chart of packet loss rates between the present invention and existing traditional routing algorithms. Figure 2 The results show that the proposed solution achieves a relative reduction in packet loss rate exceeding 77% across all tested load scenarios, covering uniform, low, medium, high, and bimodal load conditions without any bottlenecks, thus achieving a significant reduction in packet loss rate across all scenarios. The lowest relative reduction occurred in the low-load scenario, reaching 77.78%; the highest relative reduction occurred in the bimodal load scenario, reaching 79.42%, with an average relative reduction of approximately 78.36% across all scenarios. This proposed solution significantly improves the transmission reliability of low-Earth orbit satellite communication, demonstrating extremely significant performance advantages. The satellite routing solution proposed in this invention, which integrates the offline ant colony optimization algorithm with online snapshots, fundamentally solves the industry pain points of high packet loss rate and poor transmission reliability of traditional routing solutions under fluctuating loads in low-Earth orbit satellite networks through the synergistic optimization of the efficient forwarding mechanism and quality of service guarantee mechanism of the snapshot architecture with the ant colony optimization intelligent routing algorithm. Even under extreme network conditions such as high load, this proposed solution maintains extremely strong anti-packet loss performance, greatly improving the data transmission reliability and quality of service guarantee capabilities of satellite networks, possessing outstanding substantive characteristics.

[0050] Topology reconfiguration time is a core indicator for measuring the response and adaptability of satellite routing to the high-speed dynamic topology and dynamic changes in network load of low Earth orbit satellite constellations. The shorter the topology reconfiguration time, the faster the routing convergence speed, the stronger the adaptability to dynamic changes in satellite networks, and the better the network's anti-interference and robustness. It is a key performance indicator for adapting to the high-speed mobility characteristics of low Earth orbit satellites.

[0051] Figure 3 This is a comparison chart of the topology reconstruction time of the present invention and existing traditional routing algorithms. Figure 3The results show that the solution of this invention significantly reduces topology reconfiguration time in all test load scenarios, with a relative reduction of over 33% across all scenarios, covering uniform, low, medium, high, and bimodal load conditions without any bottlenecks. The lowest relative reduction occurred in the bimodal load scenario, reaching 33.05%; the highest relative reduction occurred in the low load scenario, reaching 53.84%, with an average relative reduction of approximately 45.48% across all scenarios. The solution significantly improves the topology convergence efficiency of low-Earth orbit satellite networks, better adapting to the frequent topology changes caused by the high-speed movement of low-Earth orbit satellites, fundamentally optimizing the industry pain points of traditional routing solutions such as slow topology response and low reconfiguration efficiency. This invention, through the lightweight design of the Snapshot architecture and the fast convergence characteristics of the ant colony-optimized intelligent routing algorithm, effectively solves the core technical problems of slow reconfiguration and low convergence efficiency of existing traditional routing solutions under the high-speed dynamic topology of low-Earth orbit satellites, significantly improving the response speed and adaptability of the routing solution to dynamic changes in satellite networks, demonstrating significant technological advancement.

[0052] Figure 4 This is a comparison chart of link lifetime between the present invention and existing traditional routing algorithms. Figure 5 A comparison chart of the latency of this invention and existing traditional routing algorithms. Figure 4 and Figure 5 This indicates that the satellite routing scheme proposed in this invention, which combines offline ant colony optimization and online snapshots, exhibits slight differences from traditional routing algorithms in terms of end-to-end latency and link lifetime. Regarding end-to-end latency, this scheme only shows a slight increase of less than 1.3ms in low-load, medium-load, high-load, and dual-peak load scenarios, with a maximum absolute increase of 1.211ms and a maximum relative increase of only 3.01%. In uniform load scenarios, the end-to-end latency is reduced by 2.38% compared to the traditional algorithm. Latency remains stable in the 40-42ms range across all scenarios, with minimal overall fluctuation, essentially on par with the latency level of the traditional algorithm, showing no significant degradation and not substantially impacting satellite communication transmission efficiency. Regarding link lifetime, the difference between the improved algorithm and the traditional algorithm is controlled within 6%, maintaining a high stability level of over 14,000 seconds. This duration represents a high level of link stability, effectively ensuring data transmission continuity and avoiding interference from frequent link interruptions, fully meeting the core requirements of large-scale low-Earth orbit satellite networks for link reliability. The slight differences between the two indicators mentioned above represent a controllable trade-off made by this invention to achieve core innovative effects such as significantly reducing packet loss rate and greatly shortening route reconfiguration time. The purpose is to conduct a comprehensive and objective integrated performance analysis of the two algorithms to fully verify the outstanding advantages of this invention in improving network reliability and dynamic adaptability, and to further highlight the innovation and practical value of this invention compared to traditional algorithms.

[0053] This invention addresses the inherent characteristics of low-Earth orbit satellite constellations, including high-speed dynamic topology, large network load fluctuations, and complex transmission environments. Through deep integration of architecture optimization, quality of service (QoS) assurance, and intelligent routing algorithms, it simultaneously ensures real-time data transmission, reliability, network stability, and dynamic topology adaptability. This fundamentally solves the problems of high packet loss rates, slow reconfiguration convergence, and insufficient QoS assurance capabilities in existing traditional satellite routing solutions. The satellite routing solution proposed in this invention, which combines offline ant colony optimization algorithms with online snapshots, achieves a significant performance improvement, with an average relative reduction of approximately 78% in packet loss rate and an average relative reduction of nearly 40% in topology reconstruction time. It demonstrates comprehensive performance far superior to existing traditional solutions under full load scenarios.

[0054] In one exemplary embodiment, a computer-readable storage medium is included, which stores a computer program that, when executed by a processor, implements the routing method for the low-Earth orbit satellite constellation described above.

[0055] Please see Figure 6 In one exemplary embodiment, the device further includes an electronic device including at least one processor, at least one memory, and at least one communication bus.

[0056] The memory stores a computer program, which includes computer-readable instructions. The processor calls the computer-readable instructions stored in the memory through the communication bus to execute the routing method of the low-Earth orbit satellite constellation described above.

[0057] In one exemplary embodiment, a computer program product is proposed, comprising a computer program / instructions that, when executed by a processor, implement the steps of the routing method for the low-Earth orbit satellite constellation described above.

[0058] 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 routing method for a low-Earth orbit satellite constellation, characterized in that, Includes the following steps: S1. Collect the quality of service parameters of the current satellite network topology links; S2. Based on the quality of service parameters, perform a snapshot freeze on the current network topology and construct a static topology graph structure; S3. Based on the static topology graph structure, construct a basic service quality parameter cost function for each link; S4. Execute the offline ant colony optimization algorithm to perform global path evaluation on the pre-built satellite topology library. Based on the link basic service quality parameter cost, construct the heuristic function in the ant colony optimization algorithm; initialize the pheromone value for each link, and update the pheromone during the iteration process according to the heuristic function. S5. Adjust the cost function of basic service quality parameters based on pheromones to obtain the cost after pheromone discount; S6. Using the cost after pheromone discount as the final link weight of the satellite network, construct a weighted graph structure, and use the shortest path search algorithm to calculate the routing path between the source node and the target node to obtain the optimal routing path.

2. The routing method for a low-Earth orbit satellite constellation according to claim 1, characterized in that, The formula for calculating the cost function of basic service quality parameters is as follows: in, This represents the cost of the basic quality of service parameter for the link between the i-th node and the j-th node. This represents the basic propagation delay of the link between the i-th node and the j-th node. This represents the packet loss penalty term between the i-th node and the j-th node. This represents the link lifetime penalty term between the i-th node and the j-th node. This represents a hard penalty term for insufficient link bandwidth between the i-th node and the j-th node. and These are the corresponding weighting coefficients.

3. The routing method for a low-Earth orbit satellite constellation according to claim 1, characterized in that, The formula for calculating the cost after pheromone discount is: in, This represents the cost of the link between the i-th node and the j-th node after pheromone discount. This represents the cost of the basic quality of service parameter for the link between the i-th node and the j-th node. This represents the pheromone discount factor for the link between the i-th node and the j-th node; in, This is the discount factor. The normalized pheromone is the link between the i-th node and the j-th node.

4. The routing method for a low-Earth orbit satellite constellation according to claim 1, characterized in that, The basic service quality parameter cost function is calculated by weighting the link propagation delay, packet loss rate penalty, link lifetime penalty, and insufficient bandwidth penalty.

5. The routing method for a low-Earth orbit satellite constellation according to claim 1, characterized in that, The heuristic function is calculated as the reciprocal of the cost of the basic quality of service parameters.

6. The routing method for a low-Earth orbit satellite constellation according to claim 1, characterized in that, Quality of service parameters include: link propagation delay, link distance, link load, bandwidth margin, and link lifetime.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 6.

8. An electronic device, characterized in that, The device includes a processor and a memory, the processor being interconnected with the memory, wherein the memory is used to store a computer program, the computer program including computer-readable instructions, and the processor is configured to invoke the computer-readable instructions to perform the method as described in any one of claims 1 to 6.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method described in any one of claims 1 to 6.

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