A method and device for routing in a mega constellation network based on topology and flow critical node control

Abstract

The application discloses a kind of based on topology and flow key node control's giant constellation network routing method and device, belong to satellite network routing control technical field, the method includes: giant constellation network is equally divided into multiple areas, and obtains controller node in each area;According to ER random network theory, the number of core topology driver nodes is determined, according to the complex network control theory, the corresponding number of core topology driver nodes is selected from giant constellation network, and the corresponding auxiliary flow driver node is selected from the surrounding node of controller node according to the flow through the node;Each controller node updates the link weight of the link connected by each driver node, and is sent to all controlled nodes in the region, and the controlled node recalculates routing.The application can greatly reduce the burden of routing state interaction in giant constellation, shorten routing convergence time, meet the requirements of giant constellation network space large scale and high dynamic on routing method.

Description

Technical Field

[0001] This invention relates to the field of satellite network routing control technology, and in particular to a routing method and apparatus for a mega-constellation network based on topology and traffic-critical node control. Background Technology

[0002] Satellite constellation networks are a crucial component of integrated space-air-ground information networks. In recent years, numerous mega-constellation projects have emerged, such as Iridium Next, OneWeb, and Starlink, which have garnered significant attention and become a research hotspot due to their crucial roles in various high-profile events. The topology and dynamic changes of mega-constellation networks are far more complex than those of traditional constellations, with the network topology evolving over time, posing challenges to routing method design. Typically, existing routing methods periodically acquire link-state information and calculate routes accordingly. However, frequent link-state interactions and route updates consume substantial link bandwidth resources, impacting satellite lifespan. Furthermore, the large-scale spatial characteristics of mega-constellation networks make it even more difficult for routing methods to obtain real-time network status information. In addition, the high dynamism of the network places higher demands on the convergence time of routing methods. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a routing method and apparatus for mega-constellation networks based on topology and traffic critical node control, thereby solving the technical problems of high routing overhead and high convergence time requirements in mega-constellation networks.

[0004] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0005] In a first aspect, the present invention provides a routing method for a mega-constellation network based on topology and traffic-critical node control, comprising:

[0006] The giant constellation network is divided into multiple regions, and the controller nodes in each region are obtained.

[0007] The number of core topology driving nodes is determined according to ER random network theory, and a corresponding number of core topology driving nodes are selected from the giant constellation network according to complex network restraint control theory.

[0008] Each controller node is controlled to obtain the flow of surrounding nodes from its surrounding nodes, and one or more nodes with the largest flow are used as corresponding auxiliary flow driving nodes.

[0009] The core topology driver node and the auxiliary traffic driver node in the same area are used as the driver nodes of the controller node in the same area;

[0010] Each controller node obtains the status information of its driver node from its driver node, updates the link weight of the link connected to each driver node according to the status information, and sends the updated link weight to all controlled nodes in the area where each controller node is located.

[0011] Each controlled node is controlled to recalculate the route based on the updated link weights.

[0012] Optionally, each of the regions includes the same number of orbits and satellites; in each of the regions, the node that is closest to each controlled node is selected as the controller node.

[0013] Optionally, determining the number of core topology driving nodes based on ER random network theory includes:

[0014] Determine the upper bound of the quantity based on ER random network theory. And thus determine the number of driver nodes. :

[0015]

[0016]

[0017] In the formula, The average degree of nodes in a giant constellation network. It is the first proportionality constant;

[0018] The number of core topology driver nodes for:

[0019]

[0020] In the formula, This is the second proportionality constant.

[0021] Optionally, selecting a corresponding number of core topology driving nodes from the mega-constellation network based on complex network restraint control theory includes:

[0022] According to the complex network constraint control theory, the number of nodes traversed is... Calculate the control efficiency of each node combination. :

[0023]

[0024] In the formula, The maximum eigenvalue representation of the principal submatrix after removing nodes from the node combination in the Laplace matrix of a giant constellation network; The number of core topology driving nodes;

[0025] The node in the node combination with the highest control efficiency is selected as the core topology driving node.

[0026] Optionally, the step of designating one or more nodes with the highest flow as corresponding auxiliary flow-driving nodes further includes:

[0027] Each controller node selects an auxiliary traffic driver node one by one, and after selecting an auxiliary traffic driver node, it sets the neighboring nodes of the auxiliary traffic driver node as follower nodes and exits the selection of auxiliary traffic driver nodes.

[0028] Optionally, the mega-constellation network routing method further includes:

[0029] The operation time of the mega-constellation network is divided into equally spaced snapshots. At the beginning of each snapshot, if the topology of the mega-constellation network changes, the core topology driving nodes and auxiliary traffic driving nodes are redefined; if the topology of the mega-constellation network does not change, only the auxiliary traffic driving nodes are redefined.

[0030] Secondly, the present invention provides a giant constellation network routing device based on topology and traffic-critical node control, comprising:

[0031] The region division module is configured to divide the giant constellation network into multiple regions and obtain the controller nodes in each region;

[0032] The topology driving node selection module is configured to determine the number of core topology driving nodes according to ER random network theory, and select a corresponding number of core topology driving nodes from the giant constellation network according to complex network restraint control theory.

[0033] The flow-driven node selection module is configured to control each controller node to obtain the flow of surrounding nodes from its surrounding nodes, and select one or more nodes with the largest flow as the corresponding auxiliary flow-driven nodes.

[0034] The driver node binding module is configured to use the core topology driver node and the auxiliary traffic driver node in the same area as the driver node of the controller node in the same area;

[0035] The link weight update module is configured to control each controller node to obtain the status information of its driver node from its driver node, update the link weight of the link connected to each driver node according to the status information, and send the updated link weight to all controlled nodes in the area where each controller node is located.

[0036] The routing update module is configured to control each of the controlled nodes to recalculate routes based on the updated link weights.

[0037] Thirdly, the present invention provides an electronic device, including a processor and a storage medium;

[0038] The storage medium is used to store instructions;

[0039] The processor is configured to operate according to the instructions to perform the steps according to the method described above.

[0040] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0041] Fifthly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the above-described method.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0043] This invention provides a routing method and apparatus for mega-constellation networks based on topology and traffic-critical node control. By controlling a small number of key driving nodes in the mega-constellation network, scalable routing is achieved through the inherent connectivity and coupling of the network topology. These key driving nodes include core topology driving nodes selected based on topology theory, ensuring the basic controllability of the constellation network, and auxiliary traffic-driven nodes based on traffic demand, thereby further optimizing network performance. This method can significantly reduce the burden of routing state interactions in mega-constellations, shorten routing convergence time, and meet the requirements of the large spatial scale and high dynamics of mega-constellation networks for routing methods. This invention can be widely applied to various types of mega-constellation networks. Attached Figure Description

[0044] Figure 1 This is a flowchart illustrating the routing method for a giant constellation network based on topology and traffic critical node control provided in an embodiment of the present invention. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0046] Example 1

[0047] like Figure 1 As shown, this embodiment of the invention provides a routing method for a mega-constellation network based on topology and traffic-critical node control, including the following steps:

[0048] Step S1: Divide the giant constellation network into multiple regions and obtain the controller nodes in each region.

[0049] Due to the large spatial scale of the mega-constellation network, this embodiment adopts a distributed control framework when selecting routes between controller nodes and drive nodes. Considering the periodic changes in constellation operation and ground service distribution, the entire mega-constellation is divided into multiple regions, each containing the same number of orbits and satellites. Controller nodes are determined in each region based on distance centrality, such as finding the node with the smallest distance from each controlled node in each region as the controller node.

[0050] Step S2: Determine the number of core topology driving nodes based on ER random network theory, and select the corresponding number of core topology driving nodes from the giant constellation network based on complex network restraint control theory.

[0051] In a network, increasing the number of driver nodes does not necessarily lead to improved performance. Generally, the proportion of driver nodes increases monotonically with increasing network degree heterogeneity. For example, in a single-layer megacons (such as shell 1 of the Starlink constellation), since all satellite nodes have a degree of 4, its degree heterogeneity is 0. Therefore, theoretically, a single driver node can control the entire system. However, for multi-shell megacons, due to the higher degree heterogeneity of nodes compared to single-layer constellations, the number of driver nodes will change significantly.

[0052] This embodiment considers that an ER network can be formed by adding random connection edges to a regular network. The proportion of driving nodes in the ER network can be used as an upper bound reference for the giant constellation network. Therefore, the upper bound of the number is determined according to the ER random network theory. And thus determine the number of driver nodes. :

[0053]

[0054]

[0055] In the formula, The average degree of nodes in a giant constellation network. This is the first proportionality constant, typically taken as 90%.

[0056] Number of core topology driver nodes for:

[0057]

[0058] In the formula, This is the second proportionality constant, typically taken as 10%.

[0059] In a giant constellation network, the core topology driving nodes are selected based on the complex network constraint control theory to ensure the basic controllability of the constellation network.

[0060] According to the complex network constraint control theory, the effect of constraining certain nodes in a network can be expressed by the network's Laplace matrix. ( Let i be an N-order identity matrix, and let the diagonal elements be the degree of node i. , The largest eigenvalue of the principal submatrix after removing these nodes from the connection matrix of the constellation network. The smaller the eigenvalue, the more efficient the control. Therefore, the specific selection steps include:

[0061] According to the complex network constraint control theory, the number of nodes traversed is... Calculate the control efficiency of each node combination. :

[0062]

[0063] In the formula, The maximum eigenvalue representation of the principal submatrix after removing nodes from the node combination in the Laplace matrix of a giant constellation network; The number of core topology driving nodes;

[0064] The node in the combination of nodes with the highest control efficiency is selected as the core topology driving node.

[0065] Step S3: Control each controller node to obtain the flow of surrounding nodes from its surrounding nodes, and use one or more nodes with the largest flow as the corresponding auxiliary flow driving nodes.

[0066] In order to establish links between the driving nodes, the selection process follows this procedure: each controller node selects an auxiliary traffic driving node one by one, and after selecting an auxiliary traffic driving node, the neighboring nodes of the auxiliary traffic driving node are designated as follower nodes, and the selection of auxiliary traffic driving nodes is terminated.

[0067] Step S4: Use the core topology driver node and auxiliary traffic driver node in the same area as the driver node of the controller node in the area.

[0068] Step S5: Control each controller node to obtain the status information of its driver node, update the link weight of the link connected to each driver node according to the status information, and send the updated link weight to all controlled nodes in the area where each controller node is located.

[0069] Status information such as link connection status and traffic load is updated as follows:

[0070]

[0071] In the formula, To adjust the coefficient, , The weights of the links connected to the driver nodes before and after the update.

[0072] This is equivalent to increasing the link weight. During routing calculation, the path with the lower weight will be selected. Increasing the link weight will reduce the traffic flowing through that link. When packets are forwarded, they will try to avoid these nodes with high traffic and be distributed to other nodes, thereby achieving traffic balancing and avoiding congestion and packet loss.

[0073] Step S6: Control each controlled node to recalculate the route according to the updated link weight, thereby realizing the update of the route in the network.

[0074] During the execution of the routing method for the mega-constellation network, the running time of the mega-constellation network is divided into equally spaced snapshots. At the beginning of each snapshot, if the topology of the mega-constellation network changes, the core topology driving node and the auxiliary traffic driving node are re-determined (i.e., steps S2 to S6 are executed); if the topology of the mega-constellation network does not change, only the auxiliary traffic driving node is re-determined (i.e., steps S3 to S6 are executed).

[0075] In summary, this invention proposes a routing method for mega-constellation networks. By selecting a small number of key nodes in the mega-constellation network in terms of topology and traffic for routing control, and leveraging the network's inherent connectivity and coupling relationships for state synchronization, it achieves efficient and scalable routing, addressing the problems of high routing overhead and long convergence time requirements in mega-constellation networks. This method categorizes satellite nodes in the mega-constellation into two types: driving nodes and follower nodes. Driving nodes are further divided into topology-driven nodes and traffic-driven nodes. Topology-driven nodes reflect the network's topological characteristics and are called core driving nodes; traffic-driven nodes reflect the network's importance and controllable efficiency in terms of traffic flow and are called traffic-assisted driving nodes. As key nodes in the constellation network, the number of driving nodes is determined using constraint control theory and selected based on the theory of node importance in complex networks. The remaining satellite nodes are follower nodes, affected by the state changes of the driving nodes. The constellation network collects link and traffic information from the driving nodes and updates their link weights. Other nodes, due to their connections with these driving nodes, automatically update their routes when selecting paths, thus being indirectly controlled and achieving routing updates for the entire network.

[0076] Example 2

[0077] This invention provides a giant constellation network routing device based on topology and traffic-critical node control, comprising:

[0078] The region division module is configured to divide the giant constellation network into multiple regions and obtain the controller nodes in each region;

[0079] The topology-driven node selection module is configured to determine the number of core topology-driven nodes based on ER random network theory, and select the corresponding number of core topology-driven nodes from the giant constellation network based on complex network restraint control theory.

[0080] The flow-driven node selection module is configured to control each controller node to obtain the flow of surrounding nodes from its surrounding nodes, and select one or more nodes with the largest flow as the corresponding auxiliary flow-driven nodes.

[0081] The driver node binding module is configured to use the core topology driver node and auxiliary traffic driver node in the same area as the driver node of the controller node in the area.

[0082] The link weight update module is configured to control each controller node to obtain the status information of its driver node, update the link weight of the link connected to each driver node according to the status information, and send the updated link weight to all controlled nodes in the area where each controller node is located.

[0083] The routing update module is configured to control each controlled node to recalculate routes based on the updated link weights.

[0084] Example 3

[0085] Based on the giant constellation network routing method provided in Embodiment 1, this embodiment of the invention provides an electronic device, including a processor and a storage medium;

[0086] Storage media are used to store instructions;

[0087] The processor is used to perform operations according to instructions to execute the steps according to the method described above.

[0088] Example 4

[0089] Based on the giant constellation network routing method provided in Embodiment 1, this embodiment of the invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above method.

[0090] Example 5

[0091] Based on the giant constellation network routing method provided in Embodiment 1, this embodiment of the invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above method.

[0092] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A routing method for mega-constellation networks based on topology and traffic-critical node control, characterized in that, include: The giant constellation network is divided into multiple regions, and the controller nodes in each region are obtained. The number of core topology driving nodes is determined according to ER random network theory, and a corresponding number of core topology driving nodes are selected from the giant constellation network according to complex network restraint control theory. Each controller node is controlled to obtain the flow of surrounding nodes from its surrounding nodes, and one or more nodes with the largest flow are used as corresponding auxiliary flow driving nodes. The core topology driver node and the auxiliary traffic driver node in the same area are used as the driver nodes of the controller node in the same area; Each controller node obtains the status information of its driver node from its driver node, updates the link weight of the link connected to each driver node according to the status information, and sends the updated link weight to all controlled nodes in the area where each controller node is located. Each controlled node is controlled to recalculate the route based on the updated link weights; The determination of the number of core topology driving nodes based on ER random network theory includes: Determine the upper bound of the quantity based on ER random network theory. And thus determine the number of driver nodes. : ； ； In the formula, The average degree of nodes in a giant constellation network. It is the first proportionality constant; The number of core topology driver nodes for: ； In the formula, It is the second proportionality constant; The step of selecting a corresponding number of core topology driving nodes from the giant constellation network based on complex network restraint control theory includes: According to the complex network constraint control theory, the number of nodes traversed is... Calculate the control efficiency of each node combination. : ； In the formula, The maximum eigenvalue representation of the principal submatrix after removing nodes from the node combination in the Laplace matrix of a giant constellation network; The number of core topology driving nodes; The node in the node combination with the highest control efficiency is selected as the core topology driving node.

2. The routing method for mega-constellation networks based on topology and traffic-critical node control according to claim 1, characterized in that, Each region includes the same number of orbits and satellites; in each region, the node that is closest to each controlled node is selected as the controller node.

3. The routing method for mega-constellation networks based on topology and traffic-critical node control according to claim 1, characterized in that, The step of using one or more nodes with the largest flow as corresponding auxiliary flow-driving nodes also includes: Each controller node selects an auxiliary traffic driver node one by one, and after selecting an auxiliary traffic driver node, it sets the neighboring nodes of the auxiliary traffic driver node as follower nodes and exits the selection of auxiliary traffic driver nodes.

4. The routing method for mega-constellation networks based on topology and traffic-critical node control according to claim 1, characterized in that, The mega-constellation network routing method also includes: The operation time of the mega-constellation network is divided into equally spaced snapshots. At the beginning of each snapshot, if the topology of the mega-constellation network changes, the core topology driving nodes and auxiliary traffic driving nodes are redefined; if the topology of the mega-constellation network does not change, only the auxiliary traffic driving nodes are redefined.

5. A giant constellation network routing device based on topology and traffic-critical node control, characterized in that, include: The region division module is configured to divide the giant constellation network into multiple regions and obtain the controller nodes in each region; The topology driver node selection module is configured to determine the number of core topology driver nodes based on ER random network theory, and to select a corresponding number of core topology driver nodes from the mega-constellation network based on complex network control theory; wherein, determining the number of core topology driver nodes based on ER random network theory includes: Determine the upper bound of the quantity based on ER random network theory. And thus determine the number of driver nodes. : ； ； In the formula, The average degree of nodes in a giant constellation network. It is the first proportionality constant; The number of core topology driver nodes for: ； In the formula, It is the second proportionality constant; The step of selecting a corresponding number of core topology driving nodes from the giant constellation network based on complex network restraint control theory includes: According to the complex network constraint control theory, the number of nodes traversed is... Calculate the control efficiency of each node combination. : ； In the formula, The maximum eigenvalue representation of the principal submatrix after removing nodes from the node combination in the Laplace matrix of a giant constellation network; The number of core topology driving nodes; The node in the node combination with the highest control efficiency is selected as the core topology driving node. The flow-driven node selection module is configured to control each controller node to obtain the flow of surrounding nodes from its surrounding nodes, and select one or more nodes with the largest flow as the corresponding auxiliary flow-driven nodes. The driver node binding module is configured to use the core topology driver node and the auxiliary traffic driver node in the same area as the driver node of the controller node in the same area; The link weight update module is configured to control each controller node to obtain the status information of its driver node from its driver node, update the link weight of the link connected to each driver node according to the status information, and send the updated link weight to all controlled nodes in the area where each controller node is located. The routing update module is configured to control each of the controlled nodes to recalculate routes based on the updated link weights.

6. An electronic device, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1-4.

8. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method described in any one of claims 1-4.

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