A node importance-based proactive low-orbit satellite network multi-path routing method

Abstract

This invention relates to a look-ahead multipath routing method for low-Earth orbit (LEO) satellite networks based on node importance. The method includes: collecting data from several homogeneous satellites in the LEO network to construct an undirected graph; calculating the overall quality score of each node in the undirected graph; using the overall quality score of each node as a basis, employing a look-ahead greedy algorithm for path planning, and selecting multiple non-intersecting paths through virtual pruning and recursive look-ahead mechanisms; and triggering route recalculation or dynamic optimization of traffic allocation based on whether the initial traffic sharing weight of each non-intersecting path triggers route maintenance conditions. This method achieves deep integration and collaborative optimization of long-term link stability and global node importance, effectively overcoming the short-sightedness of traditional routing methods and significantly improving the overall performance, reliability, and adaptability of multipath transmission in dynamic LEO satellite networks.

Description

Technical Field

[0001] This invention belongs to the field of wireless communication technology, specifically relating to a forward-looking multipath routing method for low-Earth orbit satellite networks based on node importance. Background Technology

[0002] With the continued growth of global communication demands and the development of 6G technology, building a ubiquitous, integrated space-air-ground network has become an important direction. Among these, low Earth orbit (LEO) satellite networks, due to their advantages such as low latency and wide coverage, have become a key component for achieving seamless global communication. However, LEO satellite networks have unique dynamic characteristics: the high-speed movement of satellite nodes causes continuous time-varying network topology changes, inter-satellite links are frequently established and interrupted, and the transmission quality of these links fluctuates dramatically due to distance, obstructions, and the space environment.

[0003] Existing routing methods for low-Earth orbit satellite networks still have significant limitations when facing the aforementioned highly dynamic and resource-constrained environments. Traditional routing schemes mostly rely on static or periodically updated topology snapshots for path calculation, often using a single metric (such as shortest hop count or minimum instantaneous propagation delay) as the link cost. Their routing results lack consideration for the long-term stability and anti-interference capabilities of the links. Although some studies have introduced multipath transmission to improve throughput and reliability, common greedy multipath algorithms (such as iterative K-shortest path) often prioritize key nodes and high-quality links in the network when selecting paths one by one. This results in subsequent paths having to choose lower-quality detours from the fragmented remaining topology. While this "short-sighted" resource contention strategy can guarantee the optimization of a single path, it sacrifices the overall performance of the multipath set, making it difficult to achieve global optimal allocation of network resources.

[0004] Furthermore, existing methods typically treat satellite nodes as homogeneous forwarding units during routing decisions, neglecting the actual impact of nodes' different locations within the network topology and variations in their connection quality. In situations of uneven network load or partial node failure, the lack of a mechanism to quantitatively assess node importance and incorporate it into routing decisions can easily lead to overload of critical nodes, localized network congestion, and negatively impact the overall stability and efficiency of transmission.

[0005] In short, existing technologies in low-Earth orbit satellite dynamic networks lack a coordinated consideration of the long-term stability of links and the global importance of nodes, resulting in short-sighted routing decisions and a decline in the overall performance of multipaths. Summary of the Invention

[0006] To address the aforementioned problems in existing technologies, this invention provides a forward-looking multipath routing method for low-Earth orbit satellite networks based on node importance. The technical problem to be solved by this invention is achieved through the following technical solution: This invention provides a forward-looking multipath routing method for low-Earth orbit satellite networks based on node importance, comprising: Data information from several homogeneous satellites operating in low Earth orbit and with a polar orbit configuration is collected to construct an undirected graph with real-time link state attributes. For each node in the undirected graph, its intrinsic performance is calculated based on the time-varying stability of its adjacent links, and the importance of the node in the network topology is integrated to obtain the corresponding comprehensive quality score of the node. Based on the overall quality score of each node, a look-ahead greedy algorithm is used for path planning. Through virtual pruning and recursive look-ahead mechanism, the impact of the current path selection on the availability of future path resources is simulated, and multiple non-intersecting paths are selected. Based on the judgment result of whether the initial traffic sharing weight of each node's non-intersecting path triggers the route maintenance condition, route recalculation or dynamic optimization allocation of traffic is triggered.

[0007] Compared with the prior art, the beneficial effects of the present invention are as follows: To address the problem that existing technologies in low-Earth orbit (LEO) satellite dynamic networks lack a coordinated consideration of long-term link stability and the global importance of nodes, leading to short-sighted routing decisions and degraded overall multi-path performance, this invention provides a forward-looking multi-path routing method for LEO satellite networks based on node importance. This method constructs a systematic routing decision-making framework through the following steps: First, a dynamic topology model is constructed based on real-time satellite position and communication constraints, forming an undirected network graph with real-time link state attributes. On this basis, time... The spatial fusion node comprehensive quality assessment mechanism: In the time dimension, it analyzes the historical behavior of links through a sliding window to quantify their survival rate and quality volatility to assess stability; in the spatial dimension, it introduces a risk penalty factor based on the geographical region where the link is located to perceive environmental heterogeneity; then it aggregates the quality of all adjacent links of a node, and combines the global importance level of the node in the network topology, and generates a comprehensive quality score that fully reflects the node's forwarding potential and structural influence through gravity model normalization.

[0008] Then, using the overall quality score of nodes as the path cost basis, a greedy algorithm that integrates virtual pruning and recursive look-ahead mechanisms is employed for multi-path planning. In each round of path selection, this algorithm not only evaluates the immediate quality of candidate paths but also predicts the overall impact of the current choice on the subsequent path planning space by simulating recursive look-ahead of resource consumption. This allows for collaborative optimization globally, selecting multiple high-quality transmission paths that do not intersect.

[0009] Finally, the initial traffic sharing weights are calculated based on path quality, and a hybrid route maintenance strategy is deployed. By monitoring multiple triggering conditions such as path connectivity, quality degradation rate, and periodic timers in parallel, dynamic decisions are made to perform route recalculation or simply adjust traffic weights, achieving rapid response to changes in network status and preventative correction of chronic drift.

[0010] This invention achieves deep integration and collaborative optimization of long-term link stability and global node importance through the closed-loop process of modeling, evaluation, planning and maintenance, effectively overcoming the short-sightedness of traditional routing methods and significantly improving the overall performance, reliability and adaptability of multipath transmission in low-Earth orbit satellite dynamic networks. Attached Figure Description

[0011] Figure 1 This is a flowchart illustrating the forward-looking low-Earth orbit satellite network multi-path routing method based on node importance provided in an embodiment of the present invention. Figure 2 This is an execution flowchart of S3 provided in an embodiment of the present invention; Figure 3 This is a flowchart of the routing maintenance process of S4 provided in an embodiment of the present invention. Detailed Implementation

[0012] The present invention will be further described in detail below with reference to specific embodiments, but the implementation of the present invention is not limited thereto.

[0013] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. In addition, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

[0014] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art will understand and implement other variations of the disclosed embodiments by reviewing the accompanying drawings, disclosure, and appended claims in carrying out the claimed invention. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0015] The present invention will now be described in detail with reference to the accompanying drawings, a forward-looking multipath routing method for low-Earth orbit satellite networks based on node importance.

[0016] Figure 1 This is a flowchart illustrating the forward-looking low-Earth orbit satellite network multi-path routing method based on node importance provided in an embodiment of the present invention. Figure 1 As shown, the method includes: S1. Collect data from several homogeneous satellites operating in low Earth orbit with a polar orbit configuration to construct an undirected graph with real-time link state attributes.

[0017] This describes a low-Earth orbit (LEO) satellite network with a polar orbit configuration. The constellation consists of T homogeneous satellites evenly distributed across P orbital planes, each containing F homogeneous satellites. Homogeneous satellites refer to individual satellites within the same LEO network that possess the same or highly similar technical specifications, hardware configurations, and functional performance. From a network routing and resource scheduling perspective, these satellites can be functionally considered interchangeable nodes. Their differences primarily lie in the instantaneous connectivity and link quality resulting from variations in real-time position and motion, rather than differences in their inherent capabilities.

[0018] Based on this, the undirected graph in S1 is obtained in the following way: S1.1: Obtain the orbital parameters and real-time position data of each homogeneous satellite.

[0019] S1.2: Determine the real-time geometric positional relationship between satellites based on orbital parameters.

[0020] For example, based on the satellite position data obtained in S1.1, spatial geometric calculations are used to determine the real-time distance, relative azimuth, and visibility relationship (i.e., whether there is a direct communication path that is not blocked by the Earth) between any two satellites. This relationship is the geometric basis for determining whether an inter-satellite link exists and for calculating its transmission characteristics.

[0021] S1.3: Based on the preset maximum communication distance and real-time geometric position relationship, determine the existence of inter-satellite links, thereby establishing the topological connection relationship at the current moment and forming the structural skeleton of an undirected graph, in which each node corresponds to a homogeneous satellite and each link connects two nodes.

[0022] In the dynamic environment of low-Earth orbit satellite networks, determining the existence of inter-satellite links is an essential prerequisite for constructing an accurate network model. This is because the high-speed motion of satellites causes continuous changes in inter-satellite distances and relative azimuths. Two satellites are not always in a communicable state; it is necessary to dynamically determine whether a link truly exists based on the current geometric relationship. Some satellites may be geometrically visible, but their distance exceeds communication capabilities, thus no valid link exists. Failure to perform this determination and incorrectly including such a link in the topology may result in data packets being routed to that "link" and lost. Including non-existent links in the model will lead to incorrect resource allocation and routing decision failures.

[0023] S1.4: Calculate the propagation delay of each link based on the real-time location data of all nodes on each link.

[0024] S1.5: Obtain the current remaining bandwidth of the link, and use it together with the corresponding propagation delay as the real-time status attribute of the link.

[0025] Here, because satellite network service traffic is non-uniform in time and space, and multiple data streams in multi-path routing will compete to share the same link, using historical or average bandwidth values ​​will not reflect the current real carrying capacity of the link. Therefore, it is necessary to periodically obtain the current remaining bandwidth of the link at high frequency.

[0026] The final undirected graph is used This is represented by V, where V is the set of all satellite nodes and E is the set of all inter-satellite links.

[0027] After completing the undirected graph modeling of the dynamic topology of the low-Earth orbit satellite network, this invention enters the core technology stage—node comprehensive quality assessment (S2). This step aims to transform the dynamically changing link physical attributes and network topology information in the above model into a unified quantitative evaluation of the forwarding capability and network importance of each satellite node, thereby laying a precise decision-making foundation for subsequent intelligent path planning.

[0028] S2. For each node in the undirected graph, calculate its intrinsic performance based on the time-varying stability of its adjacent links, and combine the importance of the node in the network topology to obtain the corresponding comprehensive quality score of the node.

[0029] Specifically, S2 includes: S2.1: At the current evaluation time, the corresponding comprehensive link quality score is calculated using the historical data of each link in the undirected graph within a sliding window of a preset time length; wherein, the historical data attached to a single link includes the instantaneous link quality sequence and the connectivity state sequence.

[0030] Here, it is necessary to introduce both historical reputation factors and geographical risk penalty factors to comprehensively evaluate the link quality, in order to screen out high-quality links that not only have good current quality, but also have stable past performance and low future risk. This stems from the two fundamental characteristics inherent in low-Earth orbit satellite networks that are intertwined: the dynamic time-varying nature of the network status and the non-uniform heterogeneity of the space environment.

[0031] (1) Dynamic time-varying characteristics are manifested as follows: the continuous high-speed movement of satellite nodes causes the physical connection relationship, transmission distance and signal quality of inter-satellite links to fluctuate rapidly on the order of seconds or even sub-seconds. The current excellent performance of a link may be fleeting, and its stability trend and reliability cannot be judged by instantaneous snapshots alone.

[0032] (2) Spatial heterogeneity is manifested in the fact that, constrained by orbital configuration and Earth's geometry, there are specific spatial regions in the network, such as reverse gaps and polar regions, where communication performance is inherently insufficient or the risk of interruption is significantly increased. The transmission reliability of a link is closely related to its location, and a uniform assessment will ignore such location-determined inherent risks.

[0033] By leveraging historical reputation factors and geographical risk penalty factors, and by mining behavioral patterns over time and perceiving environmental risks in a spatial dimension, key information that traditional static or instantaneous indicators cannot capture can be obtained. This effectively addresses the short-sightedness in reliability assessment caused by dynamic time-varying factors and the lack of risk perception due to spatial heterogeneity, jointly ensuring that link quality assessment remains accurate, forward-looking, and robust in the face of rapidly changing and non-uniform network environments.

[0034] Furthermore, this instantaneous link quality sequence was obtained by calculating the following: S2.1.1: Obtain the propagation delay and current remaining bandwidth of a single link at time t within the sliding window, 1 ≤ t ≤ M, where M is the preset time length. It should be noted that time t refers to the current evaluation time.

[0035] S2.1.2: The instantaneous mass value at time t is calculated using the following formula: ; in, As a time-delay sensitive factor, As a bandwidth-sensitive factor, ; Refers to nodes and nodes At any moment The normalized value of the delay, Refers to nodes and nodes At any moment The normalized value of available remaining bandwidth.

[0036] S2.1.3: Based on the instantaneous quality value, historical reputation factor, and geographical risk penalty factor at time t, the corresponding comprehensive link quality assessment value is calculated; whereby the historical reputation factor at time t is calculated using the link survival rate and quality volatility at the corresponding time, the link survival rate at time t is calculated using the connectivity state sequence at time t, and the quality volatility at time t is calculated using the instantaneous quality value at time t.

[0037] For example, the expression for calculating the link survival rate at time t is: ; in, It is the sampling window size (unit: time step). Refers to nodes and nodes At any moment The link status is a binary indicator variable; a value of 1 indicates that the link is in good condition. At any moment The system is physically connected; a value of 0 indicates that the link is interrupted. It refers to the average link quality within the sliding window.

[0038] It should be noted that the connectivity state sequence is obtained by continuously monitoring and recording each link in the undirected graph within a sliding window of a preset time length. For example, assume the sliding window is 5 time steps and the monitoring frequency is once per second. For a certain link, the monitoring results in the past 5 seconds are as follows: connected (1), connected (1), disconnected (0), connected (1), connected (1). Then, its connectivity state sequence at the current moment is [1, 1, 0, 1, 1].

[0039] Here, the expression for calculating the mass fluctuation at time t is: ; Where k is the summation variable, representing the k-th time step within the sliding window.

[0040] Here, the expression for calculating the historical reputation factor at time t is: ; in, This is the weighting factor.

[0041] Here, the expression for calculating the geographical risk penalty factor at time t is: ; in, The link at time t The maximum geographic dimension of the satellite nodes at both ends. If the link crosses a reverse gap, the risk value is set to 1.0.

[0042] Furthermore, the link at time t The formula for calculating the comprehensive link quality assessment value is: .

[0043] S2.1.4: Arrange all the comprehensive link quality assessment values ​​within the sliding window in chronological order to form the instantaneous link quality sequence of a single link.

[0044] S2.2: For each node, a multi-dimensional aggregation function is used to aggregate the comprehensive link quality scores of all its adjacent links to obtain the intrinsic performance score of that node.

[0045] In this step, aggregation is performed from three dimensions: capacity expectation (average connection quality), bottleneck effect (bottleneck connection quality), and balance (uniformity of connection distribution).

[0046] It should be understood that in low-Earth orbit satellite networks, each satellite typically establishes inter-satellite links (such as forward, backward, and lateral links) with multiple other satellites. Therefore, in an undirected graph, a node naturally has multiple neighboring nodes. Evaluating the performance of this node essentially means evaluating its overall ability to send and receive data through all these existing connections. Furthermore, the "intrinsic performance" defined in this invention aims to quantify a node's potential as a data forwarding hub. This requires identifying all of the node's neighbors (adjacent nodes), obtaining a comprehensive link quality assessment (QL) value for the links between the node and each neighboring node, and aggregating these QL values ​​from multiple neighbors (e.g., calculating the average, minimum, and uniformity).

[0047] Furthermore, define Time Node The neighbor set is Its degree is (Note: This specifically refers to undirected graphs.) Time and Node The number of other directly connected nodes), and the formula for calculating the intrinsic performance score of each node is: ; ; ; in, It is the node at time t. Endogenous performance score, It is the node at time t. The average connection quality of the link. It is the node at time t. The bottleneck connection quality, It is the node at time t. The uniformity of the connection distribution of nodes The standard deviation of link quality , and All are preset weighting factors. , It is the node at time t. and nodes The overall link quality assessment value of the constructed links.

[0048] S2.3: Decompose the network topology represented by the undirected graph and determine the topological importance level of each node in the network.

[0049] It should be noted that the network topology decomposition is performed only once to obtain the topological importance level of all nodes.

[0050] S2.4: Integrate the intrinsic performance score and topological importance level of each node, and perform normalization to obtain the corresponding comprehensive quality score of the node.

[0051] Specifically, including: S2.4.1: Based on historical data within the sliding window, perform the following steps for each node at the current evaluation time: S2.4.2: For each node, its intrinsic performance score and topological importance level are weighted and fused to obtain the initial comprehensive quality value of the node; wherein, the topological importance level is determined by fusing the K-Shell index and degree centrality index of the node; S2.4.3: Based on the connection relationships between nodes in an undirected graph, calculate the link cost between any two nodes; S2.4.4: For each node, determine the set of neighboring nodes within the preset effective gravitational radius; S2.4.5: For each neighboring node in the set of neighboring nodes, calculate the importance gravity value from that neighboring node to the node; S2.4.6: Sum the importance attraction values ​​of all neighboring nodes in the neighboring node set to obtain the total importance attraction value of the node; S2.4.7: Normalize the total importance gravity value of the node to obtain the corresponding comprehensive node quality score. .

[0052] In one possible implementation, the expression for calculating the overall node quality score for each node is: ; ; ; ; ; in, It is the node at time t. The topological importance level, The weights are defined as [0,1]. It is the node at time t. K-Shell normalized value, It is the normalized value of degree centrality. It is the node at time t. The initial overall quality value, It is an emphasis factor, with a parameter range of [0,1]. It is the link cost at time t for the link formed from node u to node v. It is the effective gravitational radius The set of neighboring nodes corresponding to node u within the range. It is the gravity value of node v pointing to node u at time t; It is the effective gravitational radius The sum of the importance gravity values ​​of all adjacent nodes to this node; Through the After performing normalization, the overall node quality score of node u at time t is obtained.

[0053] S3. Based on the overall quality score of each node, a look-ahead greedy algorithm is used for path planning. Through virtual pruning and recursive look-ahead mechanism, the impact of the current path selection on the availability of future path resources is simulated, and multiple non-intersecting paths are selected.

[0054] For ease of understanding, Figure 2 This is an execution flowchart of S3 provided in an embodiment of the present invention. Figure 3 As shown, S3 includes: Set the number of paths to be found P and the look-ahead depth L. Initialize the available node set to all nodes in the network, and the currently selected path set to be empty. In the p-th iteration (1≤p≤P), perform the following steps: (1) Based on the current set of available nodes, generate the current link cost weight according to the node comprehensive quality score, and use the path search algorithm to generate Q candidate paths from the source node to the destination node; (2) For each candidate path, perform virtual pruning and recursive look-ahead evaluation: a. Create a replica of the currently available set of nodes as a simulated node set; b. Remove all intermediate nodes, except for the source and destination nodes, from the simulation node set in the currently evaluated candidate paths; c. On the set of simulated nodes after removal, the cost weight of all links in the set of simulated nodes is dynamically calculated based on the overall quality score of the nodes. Based on the regenerated link cost weights, subsequent paths are recursively searched until the look-ahead depth L is reached. The total number of paths searched is limited by the number of remaining paths to be searched, and the recursive look-ahead evaluation results are obtained. (3) Based on the recursive look-ahead evaluation results, calculate the total expected score for each candidate path; (4) Select the candidate path with the highest total expected score as the p-th official path, add it to the set of selected paths, and remove all intermediate nodes in the path except the source node and the destination node from the set of available nodes; After completing P iterations, the output contains P non-intersecting paths between nodes.

[0055] Here, the expression for calculating the link cost weights to generate or update them is: ,in, This is a local minimum value, used to prevent the logarithm from being negative infinity.

[0056] S4. Based on the judgment result of whether the initial traffic sharing weight of each non-intersecting path triggers the route maintenance condition, trigger route recalculation or execute dynamic optimization allocation of traffic.

[0057] After completing multi-path planning, this invention enters the adaptive route maintenance and traffic allocation phase. This phase first calculates the initial traffic sharing weight based on the overall quality retention rate of each path, achieving load balancing based on path quality. Subsequently, the system employs a hybrid route maintenance strategy that integrates event-driven and periodic correction: it continuously monitors the network status by parallelly monitoring three core triggers: the path connectivity failure rate, the degree of overall path quality degradation, and a preset periodic timer.

[0058] Specifically, Figure 3 This is a flowchart of the routing maintenance process for S4 provided in an embodiment of the present invention. For example... Figure 3 As shown, the route maintenance conditions include: (1) The path connectivity failure rate of any non-intersecting path of any node is lower than a preset threshold. (2) The overall quality retention rate of any non-intersecting path is lower than the preset quality threshold for that path; (3) The preset periodic route maintenance time is reached.

[0059] Furthermore, S4 includes: during the route maintenance phase, continuously monitoring the current status of each node's non-intersecting path and monitoring the system runtime; if the monitoring results indicate that any node's non-intersecting path triggers any of the route maintenance conditions, then route recalculation is triggered; route recalculation includes re-executing the aforementioned look-ahead greedy multi-path route planning steps to plan a new set of node non-intersecting paths and update the traffic sharing weights; otherwise, based on the current overall quality retention rate of each node's non-intersecting path, recalculating and executing dynamic optimization allocation of traffic.

[0060] It should be noted that during the route maintenance phase, the current state of each node's disjoint path includes the path connectivity failure rate and the overall quality retention rate. Let's first explain these two calculation methods.

[0061] First, define what a source node is. to the destination node path The total cost is Its calculation expression is: ; Furthermore, path quality is defined as the reciprocal of its cost, and its calculation expression is: ; Let the size of the path set after the last route establishment be K. At the start of each time slice, monitor the number of surviving paths in real time. Define the path survival rate. Its calculation expression is: ; When the number of survival paths If the value is below the preset threshold, it indicates that the redundancy of the multi-path system has been severely compromised, and rerouting will be triggered immediately.

[0062] Record the total quality of the multipath after the last route was established. This is the sum of the qualities of multiple paths. At runtime... Reassess the total real-time quality of the current survival paths. Define the quality retention rate as... Its calculation expression is: ; When the quality retention rate is When the value is below the threshold, it indicates that although the path is still physically accessible, its performance has significantly degraded, triggering rerouting optimization.

[0063] Here, the traffic sharing weight is a normalized proportional value that determines how the total traffic should be distributed across multiple parallel transmission paths (paths with non-intersecting nodes); and the specific expression for calculating or updating the traffic sharing weight is as follows: .

[0064] Thus, this invention constructs a complete routing decision-making and maintenance system for low-Earth orbit satellite dynamic networks through the closed-loop execution of four core steps, S1 to S4. This method starts with high-fidelity dynamic topology modeling and, through time-lapse fusion... This invention employs a comprehensive quality assessment of nodes with empty characteristics to achieve multi-path collaborative planning based on forward-looking resource game theory. Ultimately, through adaptive traffic scheduling and a hybrid triggering maintenance mechanism, it ensures that the routing strategy maintains high reliability, efficiency, and stability in a constantly changing network environment. This invention fundamentally overcomes the shortcomings of traditional methods in terms of dynamism, global perspective, and forward-looking capabilities, providing a systematic, scalable, and high-performance routing solution for low-Earth orbit satellite networks.

[0065] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of the present invention, and all such modifications and substitutions should be considered within the scope of protection of the present invention.

Claims

1. A forward-looking multi-path routing method for low-Earth orbit satellite networks based on node importance, characterized in that, include: Data information from several homogeneous satellites operating in low Earth orbit and with a polar orbit configuration is collected to construct an undirected graph with real-time link state attributes. For each node in the undirected graph, its intrinsic performance is calculated based on the time-varying stability of its adjacent links, and the importance of the node in the network topology is integrated to obtain the corresponding comprehensive quality score of the node. Based on the overall quality score of each node, a look-ahead greedy algorithm is used for path planning. Through virtual pruning and recursive look-ahead mechanism, the impact of the current path selection on the availability of future path resources is simulated, and multiple non-intersecting paths are selected. Based on the judgment result of whether the initial traffic sharing weight of each node's non-intersecting path triggers the route maintenance condition, route recalculation or dynamic optimization allocation of traffic is triggered.

2. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 1, characterized in that, The undirected graph was obtained in the following way: Obtain the orbital parameters and real-time position data of each homogeneous satellite; Based on the orbital parameters, the real-time geometric positional relationship between the satellites is determined; Based on the preset maximum communication distance and the real-time geometric position relationship, the existence of inter-satellite links is determined, thereby establishing the topological connection relationship at the current moment and forming the structural skeleton of the undirected graph, wherein each node corresponds to a homogeneous satellite and each link connects two nodes; Calculate the propagation delay of each link based on the real-time location data of all nodes on each link; Obtain the current remaining bandwidth of the link, and use it together with the corresponding propagation delay as the real-time status attribute of the link.

3. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 1, characterized in that, For each node in the undirected graph, its intrinsic performance is calculated based on the time-varying stability of its adjacent links, and the node's importance in the network topology is integrated to obtain a corresponding comprehensive node quality score, including: At the current evaluation moment, the corresponding comprehensive link quality score is calculated using the historical data of each link in the undirected graph within a sliding window of a preset time length; wherein, the historical data accompanying each link includes an instantaneous link quality sequence and a connectivity state sequence; For each node, a multi-dimensional aggregation function is used to aggregate the comprehensive link quality scores of all its adjacent links to obtain the intrinsic performance score of that node. The network topology represented by the undirected graph is decomposed to determine the topological importance level of each node in the network; The intrinsic performance score and topological importance level of each node are combined and normalized to obtain the corresponding comprehensive quality score of the node.

4. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 3, characterized in that, The instantaneous link quality sequence is obtained by calculating the following method: Obtain the propagation delay and current remaining bandwidth of the single link at time t within the sliding window, 1≤t≤M, where M is the preset time length; The instantaneous mass value at time t is calculated using the following formula: ； in, As a time-delay sensitive factor, As a bandwidth-sensitive factor, ; Refers to nodes and nodes At any moment The normalized value of the delay, Refers to nodes and nodes At any moment The normalized value of available remaining bandwidth; Based on the instantaneous quality value, historical reputation factor, and geographical risk penalty factor at time t, the corresponding comprehensive link quality assessment value is calculated; wherein, the historical reputation factor at time t is calculated using the link survival rate and quality volatility at the corresponding time, the link survival rate at time t is calculated using the connectivity state sequence at time t, and the quality volatility at time t is calculated using the instantaneous quality value at time t. All the comprehensive link quality assessment values ​​within the sliding window are arranged in chronological order to form the instantaneous link quality sequence of the single link.

5. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 3, characterized in that, The formula for calculating the intrinsic performance score of each node is as follows: ； ； ； in, It is the node at time t. Endogenous performance score, It is the node at time t. The average connection quality of the link. It is the node at time t. The bottleneck connection quality, It is the node at time t. The uniformity of the connection distribution of the nodes is the basis for the node's connection distribution. The standard deviation of link quality , and All are preset weighting factors. , It is the node at time t. and nodes The overall link quality assessment value of the constructed links.

6. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 3, characterized in that, The intrinsic performance score and topological importance level of each node are fused and normalized to obtain the corresponding comprehensive node quality score, including: Based on the historical data within the sliding window, perform the following steps for each node at the current evaluation time: For each node, its intrinsic performance score and topological importance level are weighted and fused to obtain the initial comprehensive quality value of the node; wherein, the topological importance level is determined by fusing the K-Shell index and degree centrality index of the node; Based on the connection relationships between nodes in the undirected graph, calculate the link cost between any two nodes; For each node, determine the set of neighboring nodes of that node within a preset effective gravitational radius; For each neighboring node in the set of neighboring nodes, calculate the importance gravity value from that neighboring node to the node; The total importance gravity value of the node is obtained by summing the importance gravity values ​​of all neighboring nodes in the set of neighboring nodes. The total importance gravity value of the node is normalized to obtain the corresponding comprehensive quality score of the node.

7. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 6, characterized in that, The formula for calculating the overall quality score of each node is as follows: ； ； ； ； ； in, It is the node at time t. The topological importance level, The weights are defined as [0,1]. It is the node at time t. K-Shell normalized value, It is the normalized value of degree centrality. It is the node at time t. The initial overall quality value, It is an emphasis factor, with a parameter range of [0,1]. It is the link cost at time t for the link formed from node u to node v. It is the effective gravitational radius The set of neighboring nodes corresponding to node u within the specified range. It is the importance gravity value of node v pointing to node u at time t; It is the effective gravitational radius. The sum of the importance gravity values ​​of all adjacent nodes to this node; By the above Normalization is performed to obtain the overall node quality score of node u at time t.

8. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 1, characterized in that, Based on the comprehensive quality score of each node, a look-ahead greedy algorithm is used for path planning. Virtual pruning and recursive look-ahead mechanisms are employed to simulate the impact of the current path selection on future path resource availability, thereby filtering out multiple non-intersecting paths, including: Set the number of paths to be found P and the look-ahead depth L. Initialize the available node set to all nodes in the network, and the currently selected path set to be empty. In the p-th iteration (1≤p≤P), perform the following steps: (1) Based on the current set of available nodes, generate the current link cost weight according to the comprehensive quality score of the nodes, and generate Q candidate paths from the source node to the destination node using the path search algorithm; (2) For each candidate path, perform virtual pruning and recursive look-ahead evaluation: a. Create a replica of the currently available set of nodes as a simulated node set; b. Remove all intermediate nodes, except for the source and destination nodes, from the simulation node set in the currently evaluated candidate paths; c. On the set of simulated nodes after removal, based on the comprehensive quality score of the nodes, the cost weight of all links in the set of simulated nodes is dynamically calculated, and based on the regenerated link cost weight, subsequent paths are recursively searched until the look-ahead depth L is reached, and the total number of paths searched is limited by the number of remaining paths to be searched, so as to obtain the recursive look-ahead evaluation result. (3) Based on the recursive look-ahead evaluation results, calculate the total expected score for each candidate path; (4) Select the candidate path with the highest total expected score as the p-th official path, add it to the set of selected paths, and remove all intermediate nodes in the path except the source node and the destination node from the set of available nodes; After completing P iterations, the output contains P non-intersecting paths between nodes.

9. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 1, characterized in that, The routing maintenance conditions include: (1) The path connectivity failure rate of any non-intersecting path of any node is lower than a preset threshold. (2) The overall quality retention rate of any non-intersecting path is lower than the preset quality threshold for that path; (3) The preset periodic route maintenance time is reached.

10. The forward-looking low-Earth orbit satellite network multi-path routing method based on node importance according to claim 8 or 9, characterized in that, The step of triggering route recalculation or dynamic traffic optimization allocation based on the judgment result of whether the initial traffic sharing weight of each node's disjoint path triggers the route maintenance condition includes: During the route maintenance phase, the current status of each node's non-intersecting path is continuously monitored, and the system uptime is also monitored. If the monitoring results determine that any non-intersecting path of any node triggers any of the route maintenance conditions, then route recalculation is triggered; the route recalculation includes re-executing the look-ahead greedy multi-path routing planning steps of claim 8 to plan a new set of non-intersecting paths of nodes and update the traffic sharing weights. If not, recalculate and perform dynamic optimization of traffic allocation based on the overall quality retention rate of non-intersecting paths at each node.

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