Low-orbit communication constellation hierarchical resilience routing planning method and system

Abstract

This invention discloses a hierarchical resilient routing planning method and system for low-Earth orbit (LEO) communication constellations, belonging to the field of satellite communication network routing planning technology. To address the problems of insufficient adaptation between satellite packetization and ground demand distribution, inadequate service capacity in hotspot areas, high network-wide path search overhead, and insufficient continuity of routing structure under dynamic topology, this invention constructs a ground demand heatmap, divides and merges demand regions, selects a leader satellite, grows regional-level satellite blocks, aggregates routing domains, and performs hierarchical path planning to form a satellite resource organization and routing structure adapted to the distribution of ground demand. This invention can improve service capacity in hotspot areas and satellite resource utilization efficiency, reduce the overhead of large-scale satellite-level path search, and enhance the continuity of routing structure and communication service resilience under dynamic topology.

Description

Technical Field

[0001] This invention belongs to the field of satellite communication network routing planning technology, and particularly relates to a hierarchical resilient routing planning method and system for low-Earth orbit communication constellations. Background Technology

[0002] Low Earth Orbit (LEO) mega-satellite constellations, characterized by their low orbital altitude, low propagation latency, wide coverage, and low dependence on ground infrastructure, have become an important component of wide-area interconnection, emergency communication, access to remote areas, and integrated space-ground information networks. With the development of large-scale LEO satellite internet, the size of communication constellations has expanded from hundreds of satellites to thousands or even more. Inter-satellite links, satellite-to-ground coverage relationships, and available routing paths continuously change with the high-speed movement of satellites, resulting in a highly dynamic and complex network topology.

[0003] In low-Earth orbit satellite constellations, traditional network-wide shortest path search requires maintaining complete link states and performing frequent path calculations at the satellite-level topology. As the constellation size increases, the number of service requests grows, or link capacity dynamically changes with service usage, network-wide routing faces challenges such as high computational complexity, long planning times, and significant state update overhead. To reduce routing complexity, existing technologies typically employ satellite grouping, virtual nodes, virtual topologies, or hierarchical routing to abstract large-scale satellite networks into a coarser-grained logical structure, thereby reducing the scope of route state maintenance and path search.

[0004] However, existing satellite grouping or routing area division methods primarily rely on orbital planes, geometric locations, topological connections, latency, or link stability. They typically assume that ground service demand is spatially approximately uniform, or adjust it only during the routing phase based on short-term traffic load. In reality, ground service demand is closely related to population distribution, urban activities, economic activities, and geographical environment, exhibiting significant spatial non-uniformity. Hotspot urban clusters, coastal economic zones, or high-population-density areas tend to generate higher communication requests, while low-demand areas consume fewer satellite resources. If satellite grouping continues according to simple orbital or geometric rules, a mismatch between satellite resource organization and ground service demand can easily occur: high-demand areas may not receive sufficiently dense, stable, and redundant satellite service resources, while low-demand areas may be over-segmented, leading to decreased constellation resource utilization efficiency.

[0005] Furthermore, satellite coverage, inter-satellite link distances, route availability, and remaining link capacity in low-Earth orbit (LEO) satellite networks all change over time. For route planning within finite geographical areas, boundary truncation issues may also exist; that is, demand points at the edge of the region could actually be served by nearby satellites outside the region, but if satellites are selected only within the original region, the available paths will be artificially weakened. Current technologies lack a unified hierarchical planning method that can simultaneously consider the spatial heterogeneity of ground demand, dynamic satellite topology, cross-regional route scalability, and boundary buffering effects.

[0006] Therefore, there is an urgent need for a hierarchical resilient routing planning method and system for low-Earth orbit communication constellations. By constructing a stable geographical demand layer, the distribution of ground demand can be explicitly mapped to the constellation resource organization and routing domain construction process, so that satellite grouping matches the distribution of ground services, and the routing planning complexity of large-scale constellation networks can be reduced while maintaining a high routing acceptance rate. Summary of the Invention

[0007] The purpose of this invention is to propose a hierarchical resilient routing planning method and system for low-Earth orbit communication constellations to solve the problems of insufficient adaptation between satellite grouping and ground demand distribution, insufficient service capacity in hotspot areas, large network-wide path search overhead, and insufficient continuity of routing structure under dynamic topology. This invention aims to improve the matching degree between satellite resources and regional demand, reduce routing planning overhead, and enhance the continuity of routing structure and the resilience of communication services.

[0008] To achieve the above objectives, the present invention adopts the following technical solution.

[0009] A hierarchical resilient routing planning method for low-Earth orbit communication constellations includes the following steps: Obtain long-term ground service demand proxy data and preprocess the long-term ground service demand proxy data to obtain a ground demand heat map. The ground demand heatmap is discretized based on a geographic grid, and demand regions are divided and merged according to grid demand to obtain a set of demand regions and demand characterization information for each demand region. The demand characterization information includes representative demand points, demand weights, and regional demand intensity. Based on the demand characterization information of each demand area, the coverage relationship of low-Earth orbit satellites and the time continuity constraints, low-Earth orbit satellites are allocated one-to-one at the regional level to obtain the corresponding leader satellite for each demand area. Using the leader satellite corresponding to each demand region as the seed, satellite block growth and local repair are carried out under connectivity and diameter constraints based on the topological connection relationship of low-Earth orbit satellites and the demand matching relationship between low-Earth orbit satellites and corresponding demand regions to obtain the regional-level satellite blocks corresponding to each demand region. Based on the bridging link quality between adjacent regional satellite blocks, the continuity of historical routing domains, and the demand similarity between corresponding demand areas, the regional satellite blocks are aggregated under connectivity, size, and diameter constraints to obtain a routing domain set. Based on the set of routing domains, a domain-level topology map is constructed. Then, according to the routing domains to which the source and destination satellites corresponding to the communication request belong, domain-level path search, cross-domain boundary link selection, and intra-domain satellite-level path reconstruction are performed under link availability and capacity constraints to obtain end-to-end hierarchical routing paths.

[0010] Further, long-term ground service demand proxy data is acquired, and the long-term ground service demand proxy data is preprocessed to obtain a ground demand heatmap, including: Obtain demand intensity data that reflects the spatial distribution of long-term ground service needs; Invalid data in the demand intensity data are removed, and the valid demand intensity data is cropped, normalized, and nonlinearly compressed to obtain the ground demand heat map.

[0011] Furthermore, invalid data is removed from the demand intensity data, and the valid demand intensity data is cropped, normalized, and nonlinearly compressed to obtain a ground demand heat map, including: Invalid data and data marked as low quality are filtered out from the demand intensity data, and a pruning threshold is determined based on the distribution of valid demand intensity data; Based on the trimming threshold, the range of values ​​for the effective demand intensity data is limited to obtain the trimming demand intensity data; The cutting demand intensity data is normalized and logarithmically compressed, and the demand intensity contrast is adjusted to obtain a ground demand heat map.

[0012] Furthermore, the ground demand heatmap is discretized based on a geographic grid, and demand regions are divided and merged according to grid demand to obtain a set of demand regions and demand representation information for each region, including: The effective pixels in the ground demand heatmap are mapped to geographic grid cells, and the pixel-level demand proxy values ​​in each geographic grid cell are aggregated to obtain the grid demand value of each geographic grid cell. The high-demand geographic grid units and low-demand geographic grid units are divided according to the grid demand value. The grid granularity of the high-demand geographic grid units is retained, and the low-demand geographic grid units are combined according to the geographic adjacency relationship to obtain the initial demand area. Based on geographical adjacency and regional size constraints, low-demand regions in the initial demand regions are merged to obtain a set of demand regions; Based on the grid demand value and spatial location of the geographic grid units in each demand area, representative demand points, demand weights, and regional demand intensity are determined to obtain demand characterization information for each demand area.

[0013] Furthermore, based on geographical adjacency and regional size constraints, low-demand regions within the initial demand regions are merged to obtain a set of demand regions, including: Adjacent low-demand regions are determined based on the shared boundaries between the low-demand regions, and these adjacent low-demand regions are identified as candidate merged region pairs. Based on the number of shared boundaries from largest to smallest, candidate regions are selected from the candidate regions that satisfy the region size constraint and compactness constraint for merging, and the candidate regions are updated. Repeat the selection and merging of the candidate merged region pairs until there are no candidate merged region pairs that satisfy the region size constraint and the compactness constraint. Then, merge the low-demand regions with a region size smaller than the preset size with the adjacent low-demand regions that satisfy the region size constraint and the compactness constraint to obtain a set of demand regions.

[0014] Furthermore, based on the grid demand values ​​and spatial locations of geographic grid cells within each demand region, representative demand points, demand weights, and regional demand intensity are determined to obtain demand characterization information for each demand region, including: Aggregate the grid demand values ​​of geographic grid cells within each demand region to obtain the total regional demand and average regional demand for each demand region; Based on the grid demand value and spatial location of the geographic grid unit in each demand area, the demand weighted centroid and hotspot representative points are determined, and the demand weighted centroid and the hotspot representative points are determined as representative demand points. The demand weight of the representative demand point is determined based on the ratio between the grid demand value of the geographic grid unit in each demand area and the total demand of the corresponding area, and the regional demand intensity is determined based on the average demand of the corresponding area, thus obtaining the demand characterization information of each demand area.

[0015] Furthermore, based on the demand characterization information of each demand region, the coverage relationship of low-Earth orbit (LEO) satellites, and temporal continuity constraints, LEO satellites are allocated one-to-one at the regional level to obtain the corresponding leader satellites for each demand region, including: Based on the representative demand points of each demand area, the coverage relationship of low-orbit satellites to the representative demand points, and the extended candidate window corresponding to each demand area, determine the feasible candidate satellite set for each demand area. The allocation cost of the leader satellite corresponding to each feasible candidate satellite is determined based on the demand weight of each demand region, the relative position of the representative demand point and the feasible candidate satellite, and the coverage relationship. Based on the leader satellite mapping of the previous planning cycle, the cost ranking of each feasible candidate satellite, and the cost difference between each feasible candidate satellite and the optimal feasible candidate satellite, the frozen leader satellite allocation result that satisfies the time continuity constraint is determined. For unfrozen demand regions, the candidate allocation relationship between demand regions that meet the sorting constraints and feasible candidate satellites is retained, and the candidate allocation relationship is allocated one-to-one with minimum cost to obtain the leader satellite corresponding to each demand region.

[0016] Further, based on the demand weight of each demand region, the relative position between the representative demand point and the feasible candidate satellites, and the coverage relationship, the leader satellite allocation cost corresponding to each feasible candidate satellite is determined, including: Based on the demand weight of each representative demand point and the distance between each representative demand point and the ground point of the feasible candidate satellite, determine the demand weighted average distance and the maximum distance. Based on the coverage status of each feasible candidate satellite to the representative demand point and the corresponding demand weight, the demand weighted coverage ratio is determined, and the positional relationship between the sub-satellite point of each feasible candidate satellite and the corresponding extended candidate window is determined. The weighted average distance of the demand, the maximum distance, the weighted coverage ratio of the demand, and the location relationship are weighted and combined to obtain the leader satellite allocation cost.

[0017] Further, based on the leader satellite mapping of the previous planning period, the cost ranking of each feasible candidate satellite, and the cost difference between each feasible candidate satellite and the optimal feasible candidate satellite, the frozen leader satellite allocation result that satisfies the aforementioned time continuity constraint is determined, including: The feasible candidate satellites for each demand area are sorted according to the leader satellite allocation cost, and the sorting offset of the leader satellite in the previous planning period relative to the best feasible candidate satellite is determined. The normalized cost difference is determined based on the allocation cost of the leader satellite corresponding to the leader satellite in the previous planning cycle and the allocation cost of the leader satellite corresponding to the optimal feasible candidate satellite. If the leader satellite of the previous planning period belongs to the current feasible candidate satellite set, and the sorting offset and the normalization cost difference satisfy the corresponding thresholds, the leader satellite of the previous planning period is determined as the frozen leader satellite, and the frozen leader satellite allocation result is obtained.

[0018] Furthermore, for unfrozen demand regions, the candidate allocation relationship between demand regions satisfying the ranking constraints and feasible candidate satellites is retained, and a one-to-one minimum cost allocation is performed on the candidate allocation relationship to obtain the leader satellite corresponding to each demand region, including: Based on the matching relationship between the unfrozen demand areas and feasible candidate satellites, determine the minimum sorting offset threshold that enables the unfrozen demand areas to achieve a complete match, and retain candidate allocation relationships whose sorting offset is not greater than the minimum sorting offset threshold. The matching cost of each candidate allocation relationship is determined based on the sorting offset corresponding to each candidate allocation relationship, the normalized cost difference, and the consistency between the leader satellite corresponding to each candidate allocation relationship and the leader satellite of the previous planning cycle. Based on the matching cost, a one-to-one minimum cost matching is performed between the unfrozen demand regions and feasible candidate satellites, and combined with the frozen leader satellite allocation results, the leader satellite corresponding to each demand region is obtained.

[0019] Furthermore, using the leader satellite corresponding to each demand region as a seed, and based on the low-Earth orbit (LEO) satellite topology connections and the demand matching relationship between LEO satellites and their corresponding demand regions, satellite block growth and local repair are performed under connectivity and diameter constraints to obtain regional-level satellite blocks corresponding to each demand region, including: Initialize the corresponding satellite block with the leader satellite corresponding to each demand area, and mark the leader satellite as an allocated satellite; Based on the low-Earth orbit satellite topology connection relationship, the adjacent satellites of each satellite block are determined, faulty satellites and assigned satellites are excluded from the adjacent satellites, and satellites that satisfy the connectivity and diameter constraints after absorption are retained to obtain the set of absorbable satellites. The demand-aware satellite block score is determined based on the demand affinity between the absorbable satellite and the corresponding demand area, the internal connection scale of the absorbed satellite block, and the distribution of external interface directions. Add the absorbable satellite with the highest score in the corresponding demand-aware satellite block to the satellite block, and update the satellite allocation status and the set of absorbable satellites until there are no absorbable satellites that satisfy the connectivity and diameter constraints, thus obtaining the initial regional-level satellite block. Local repairs are performed on initial regional-level satellite blocks that have faulty satellites, interrupted connections, or do not meet the diameter constraints to obtain regional-level satellite blocks corresponding to each required region.

[0020] Furthermore, based on the demand affinity between the absorbable satellites and the corresponding demand areas, the internal connectivity scale of the absorbed satellite block, and the distribution of external interface directions, the demand-aware satellite block score after absorbing each absorbable satellite is determined, including: Based on the distance attenuation relationship between the sub-satellite point of each absorbable satellite and the representative demand point of the corresponding demand area, the demand affinity between each absorbable satellite and the corresponding demand area is determined. The demand affinity between each satellite in the absorbed satellite block and its corresponding demand region is aggregated, and the aggregation result is compressed and mapped to obtain the demand utility of the satellite block. The internal connection scale is determined based on the number of internal links of the absorbed satellite block and its complement, and the external interface direction concentration is determined based on the cut direction between the absorbed satellite block and its complement. The demand-aware satellite block score is determined based on the satellite block demand utility, the internal connectivity scale, and the external interface direction concentration.

[0021] Furthermore, local repairs are performed on initial region-level satellite blocks that have faulty satellites, interrupted connections, or do not meet the diameter constraints to obtain region-level satellite blocks corresponding to each required region, including: Identify affected satellite blocks from the initial regional satellite blocks that contain faulty satellites, have connectivity interruptions, or do not meet the diameter constraints, and remove faulty satellites from the affected satellite blocks; From the affected satellite block after the removal of the faulty satellite, retain the connected portion that satisfies the connectivity and diameter constraints, and release the satellites outside the connected portion as unassigned satellites; Based on the connected portion, unassigned satellites that satisfy the connectivity and diameter constraints and the demand matching relationship are absorbed from the boundary of the connected portion to obtain the regional-level satellite blocks corresponding to each demand area.

[0022] Furthermore, based on the bridging link quality between adjacent regional satellite blocks, the continuity of historical routing domains, and the demand similarity between corresponding demand areas, the regional satellite blocks are aggregated under connectivity, size, and diameter constraints to obtain a routing domain set, including: Using each regional-level satellite block as a node, block adjacency relationships are established based on the cross-block satellite links between the regional-level satellite blocks, and the set of cross-block satellite links between adjacent regional-level satellite blocks is determined to obtain a block adjacency graph. The quality of bridging links between adjacent regional satellite blocks is determined based on the number of links and the link distance in each cross-block satellite link set; Each regional satellite block is initialized as a routing domain, and candidate routing domain pairs that satisfy the constraints of block adjacency connectivity, number of regional satellite blocks, number of satellites, and diameter of satellite-level subgraphs are determined from the block adjacency graph. The merging gain of the candidate route domain pairs is determined based on the internal bridging link quality, historical route domain continuity, and demand similarity between corresponding demand areas. Select the candidate routing domain pair with the largest positive gain for merging, and update the candidate routing domain pair and its merging gain until there are no more positive gain candidate routing domain pairs, thus obtaining the routing domain set.

[0023] Furthermore, based on the number of links and link distances in each cross-block satellite link set, the bridging link quality between adjacent regional-level satellite blocks is determined, including: The number of links in each cross-block satellite link set is normalized, and the corresponding link quality is determined based on the link distance of each cross-block satellite link. For each cross-block satellite link set, the highest link quality is determined, and the average link quality is determined based on a preset number of cross-block satellite links with the highest quality in the corresponding cross-block satellite link set. The normalized number of links, the highest link quality, and the average link quality are weighted and combined to obtain the bridging link quality between adjacent regional satellite blocks.

[0024] Further, based on the internal bridging link quality of the candidate route domain pair, the continuity of historical route domains, and the demand similarity between corresponding demand areas, the merging gain of the candidate route domain pair is determined, including: The internal interface gain is obtained by aggregating the bridging link quality between each regional-level satellite block within the merged routing domain of the candidate routing domain pairs. The historical continuity gain is determined based on the routing domain affiliation of the candidate routing domain to the associated demand area in the previous planning period, and the demand consistency gain is determined based on the regional demand intensity differences of the demand area. The internal interface gain, the historical continuity gain, and the demand consistency gain are weighted and combined to obtain the overall quality of the merged routing domain, and the merging gain is determined based on the difference between the overall quality of the merged routing domain and the overall quality of each routing domain before merging.

[0025] Furthermore, a domain-level topology map is constructed based on the set of routing domains. Then, according to the routing domains of the source and destination satellites corresponding to the communication request, domain-level path search, cross-domain boundary link selection, and intra-domain satellite-level path reconstruction are performed under link availability and capacity constraints to obtain end-to-end hierarchical routing paths, including: Using each routing domain as a domain-level node, domain-level edges are established based on the available cross-domain satellite links between the routing domains, and the domain-level edge weights are determined based on the link distance, remaining capacity, and link availability of the cross-domain satellite links to obtain a domain-level topology graph. Obtain the source satellite, destination satellite, and bandwidth requirement corresponding to the communication request, and determine the source routing domain to which the source satellite belongs and the destination routing domain to which the destination satellite belongs; If the source routing domain is the same as the destination routing domain, a satellite-level path that satisfies the link availability and capacity constraints is searched within the corresponding routing domain to obtain candidate end-to-end paths; When the source routing domain is different from the destination routing domain, a domain-level path search is performed based on the domain-level topology map, and cross-domain boundary links and intra-domain satellite-level paths are selected along the searched domain-level paths to obtain candidate end-to-end paths; If the candidate end-to-end path satisfies the link availability and capacity constraints, the candidate end-to-end path is determined as an end-to-end hierarchical routing path, and the path link capacity is occupied and released according to the service duration of the communication request. If the candidate end-to-end path does not satisfy the link availability and capacity constraints, the communication request is rejected.

[0026] Furthermore, when the source routing domain and the destination routing domain are different, a domain-level path search is performed based on the domain-level topology map, and cross-domain boundary links and intra-domain satellite-level paths are selected along the searched domain-level paths to obtain candidate end-to-end paths, including: Based on the domain-level edge weights, a domain-level path from the source routing domain to the destination routing domain is searched in the domain-level topology graph; For each pair of adjacent routing domains in the domain-level path, select a cross-domain boundary link from the corresponding cross-domain satellite links that meets the link availability and capacity constraints; Within each routing domain traversed by the domain-level path, search for intra-domain satellite-level paths that connect inbound and outbound satellites and satisfy the link availability and capacity constraints. By connecting the cross-domain boundary link and the intra-domain satellite-level path according to the traversal order of the domain-level path, candidate end-to-end paths are obtained.

[0027] A hierarchical resilient routing planning system for low-Earth orbit communication constellations includes the following steps: The demand heatmap construction module is used to acquire long-term ground business demand proxy data and preprocess the long-term ground business demand proxy data to obtain a ground demand heatmap. The demand gridding and region merging module is used to discretize the ground demand heat map based on the geographic grid, and divide and merge demand regions according to the grid demand to obtain a set of demand regions and demand representation information of each demand region. The demand representation information includes representative demand points, demand weights and regional demand intensity. The leader satellite selection module is used to perform a one-to-one allocation of low-Earth orbit satellites at the regional level based on the demand characterization information of each demand area, the coverage relationship of low-Earth orbit satellites, and time continuity constraints, so as to obtain the leader satellite corresponding to each demand area. The satellite block construction module is used to grow satellite blocks and perform local repairs under connectivity and diameter constraints, based on the leader satellites corresponding to each demand region as seeds, according to the topological connection relationship of low-Earth orbit satellites and the demand matching relationship between low-Earth orbit satellites and the corresponding demand regions. The routing domain aggregation module is used to aggregate the regional satellite blocks under connectivity, size, and diameter constraints based on the bridging link quality between adjacent regional satellite blocks, the continuity of historical routing domains, and the demand similarity between corresponding demand areas, to obtain a routing domain set. The hierarchical routing planning module is used to construct a domain-level topology map based on the set of routing domains, and to perform domain-level path search, cross-domain boundary link selection, and intra-domain satellite-level path reconstruction under link availability and capacity constraints according to the routing domains to which the source satellite and destination satellite corresponding to the communication request belong, so as to obtain an end-to-end hierarchical routing path.

[0028] The present invention has achieved the following beneficial effects.

[0029] 1. This invention constructs a ground demand heatmap by acquiring long-term ground service demand proxy data, and divides and merges demand areas based on geographic grids, introducing the distribution of ground service demand into the resource organization process of low-Earth orbit satellite constellations; by retaining the grid granularity of high-demand geographic grid units and merging adjacent low-demand geographic grid units, hotspot areas participate in satellite resource allocation with finer granularity, and low-demand areas participate in routing planning with compact areas, thereby alleviating the mismatch between satellite grouping and regional service demand, and improving the service capability of hotspot areas and the efficiency of satellite resource utilization.

[0030] 2. This invention determines the allocation cost of the leader satellite based on the representative demand points, demand weights, satellite coverage relationships, and extended candidate windows of the demand area, and performs one-to-one allocation by combining the cost ranking, cost differences, and leader satellite mapping of the previous planning period. This improves the matching degree between the leader satellite and the demand area, takes into account the allocation fairness of different demand areas, and reduces the frequent changes in leader satellite mapping between consecutive planning periods.

[0031] 3. This invention uses the leader satellite as a seed. Under connectivity and diameter constraints, satellite blocks are grown according to the demand affinity between the satellite and the demand area, the scale of internal connections of the satellite block, and the distribution of external interface directions. This makes the resulting regional satellite blocks take into account regional demand adaptability, topological connectivity, and routing and forwarding conditions. When there is a satellite failure, connection interruption, or diameter constraint is not met, only the affected satellite blocks are locally repaired, reducing the overhead of regrouping the entire constellation caused by dynamic topology changes.

[0032] 4. This invention determines the quality of bridging links based on the number and quality of bridging links between adjacent regional satellite blocks, and combines the continuity of historical routing domains and the similarity of demands between demand areas to aggregate routing domains under constraints of connectivity, number of regional satellite blocks, number of satellites, and diameter of satellite-level subgraphs; thereby improving the quality of interfaces within the routing domain, cross-block connection redundancy, demand consistency, and routing domain stability during continuous planning.

[0033] 5. This invention constructs a domain-level topology map based on a set of routing domains. For cross-routing domain communication requests, a domain-level path search is first performed, then cross-domain boundary links are selected and satellite-level paths within each routing domain are reconstructed. At the same time, the acceptance result of the communication request is determined based on link availability and remaining capacity. This avoids continuously performing a full-network-level path search on all satellite nodes, reducing the routing planning overhead of large-scale low-Earth orbit satellite constellations while meeting service bandwidth constraints.

[0034] 6. This invention constructs a hierarchical resource organization and routing planning structure through demand areas, regional satellite blocks, and routing domains. It introduces time continuity or local repair mechanisms in the process of leader satellite selection, satellite block construction, and routing domain aggregation. It can adapt to scenarios of uneven regional demand, dynamic changes in satellite topology, and sudden service disturbances. It is suitable for low-orbit satellite internet, integrated space-ground networks, emergency communication networks, and multi-hop networks with dynamic topology and uneven spatial demand. Attached Figure Description

[0035] Figure 1 This is a flowchart illustrating the hierarchical resilient routing planning method for low-Earth orbit communication constellations in this invention.

[0036] Figure 2 This is a schematic diagram illustrating the specific implementation process of hierarchical resilient routing planning for low-orbit communication constellations in this embodiment of the invention.

[0037] Figure 3 This is a block diagram of the hierarchical resilient routing planning system for low-orbit communication constellations in an embodiment of the present invention. Detailed Implementation

[0038] To make the various technical features, advantages, or effects of the present invention more apparent and understandable, detailed descriptions are provided below through embodiments.

[0039] This invention provides a hierarchical resilient routing planning method for low-Earth orbit (LEO) communication constellations. This method performs routing planning based on long-term ground service demand proxy data for a given geographical area, the current LEO satellite set, satellite positions, satellite coverage relationships, LEO satellite topology connections, and inter-satellite link status. When there is a leader satellite mapping or routing domain partitioning from the previous planning period, the corresponding historical planning results are used as the basis for time continuity processing. The LEO satellite topology connections are determined by currently available inter-satellite links, and the inter-satellite link status includes link availability, link distance, and remaining capacity. The steps of this method are as follows: Figure 1 As shown, one specific implementation process of this method is as follows: Figure 2 As shown.

[0040] Step S1: Obtain long-term ground business demand proxy data and preprocess the long-term ground business demand proxy data to obtain a ground demand heat map.

[0041] In an optional embodiment of the present invention, step S1 may include: Step S11: Obtain demand intensity data that reflects the spatial distribution of long-term ground service needs.

[0042] Specifically, demand intensity data, serving as proxy data for long-term ground service demand, can be nighttime light data, population activity data, historical communication request data, or other data that can reflect the spatial distribution of long-term ground service demand. In one embodiment, nighttime light raster data within a given geographical area is used as demand intensity data, where nighttime light intensity characterizes the intensity of human activity, urbanization, and economic activity at the corresponding ground location.

[0043] Step S12: Remove invalid data from the demand intensity data, and trim, normalize, and nonlinearly compress the valid demand intensity data to obtain the ground demand heat map.

[0044] In an optional embodiment of the present invention, step S12 may include: Step S121: Filter out invalid data and data marked as low quality from the demand intensity data, and determine the pruning threshold based on the distribution of valid demand intensity data.

[0045] Specifically, when the demand intensity data is nighttime light grid data, invalid and low-quality pixels are removed from the nighttime light grid data, and the high quantile clipping threshold is determined based on the original demand intensity distribution of the remaining pixels.

[0046] Step S122: Based on the pruning threshold, limit the range of effective demand intensity data to obtain pruning demand intensity data.

[0047] Specifically, the original demand intensity above the pruning threshold is limited to the pruning threshold, while the original demand intensity not exceeding the pruning threshold is retained, in order to limit the range of values ​​for extremely high demand intensity data.

[0048] Step S123: Normalize and logarithmically compress the cutting demand intensity data, and adjust the demand intensity contrast to obtain a ground demand heat map.

[0049] Specifically, for ground pixels Calculate the pixel-level demand proxy value according to the following formula. : in, Represents ground pixels; Represents ground pixels The intensity of the original demand; This indicates the high quantile pruning threshold; Indicates the required contrast adjustment parameters; Represents ground pixels The pixel-level demand surrogate values. A normalized ground demand heatmap is constructed from the pixel-level demand surrogate values ​​of each effective surface pixel within a given geographic area.

[0050] Step S2: Discretize the ground demand heat map based on the geographic grid, and divide and merge demand regions according to the grid demand to obtain a set of demand regions and demand characterization information for each demand region. The demand characterization information includes representative demand points, demand weights, and regional demand intensity.

[0051] In an optional embodiment of the present invention, step S2 may include: Step S21: Map the valid pixels in the ground demand heatmap to geographic grid cells, and aggregate the pixel-level demand proxy values ​​in each geographic grid cell to obtain the grid demand value of each geographic grid cell.

[0052] Specifically, a hexagonal geographic grid can be used as the basic geographic discrete unit. In one embodiment, an H3 hexagonal geographic grid is used to divide a given geographic region, and each valid pixel in the ground demand heatmap is mapped to the corresponding hexagonal geographic grid unit. For geographic grid units... Grid demand value Represented as: in, Represents a geographic grid cell; Indicates falling into a geographic grid cell The set of valid pixels within; Represents the set of valid pixels The number of pixels in; Indicates the number of valid pixels; Indicates valid pixels Pixel-level demand proxy values; Represents geographic grid cells The grid requirement value.

[0053] Step S22: Divide the geographic grid cells into high-demand and low-demand units based on the grid demand value, retain the grid granularity of the high-demand units, and combine the low-demand units according to their geographic adjacency to obtain the initial demand area.

[0054] Specifically, geographic grid units are classified into demand levels based on their grid demand values. High-demand geographic grid units are formed by combining individual geographic grid units or finer-grained geographic grid units to create initial demand areas, while low-demand geographic grid units are combined based on geographic adjacency relationships formed by shared boundaries.

[0055] Step S23: Merge the low-demand regions in the initial demand region according to geographical adjacency and regional size constraints to obtain the demand region set.

[0056] In an optional embodiment of the present invention, step S23 may include: Step S231: Determine adjacent low-demand areas based on the shared boundaries between low-demand areas, and identify adjacent low-demand areas as candidate merged area pairs.

[0057] Specifically, when the geographic grid cells in two low-demand regions have a shared boundary, the two low-demand regions are identified as adjacent low-demand regions, and the degree of geographic adjacency between the two adjacent low-demand regions is characterized by the number of shared boundaries.

[0058] Step S232: Select candidate merged region pairs from those that meet the region size constraint and compactness constraint according to the number of shared boundaries from large to small, and update the candidate merged region pairs.

[0059] Specifically, the region size constraint can limit the maximum number of geographic grid cells contained in the merged demand region. After each merge, candidate merged region pairs are re-determined based on the shared boundaries between the merged demand region and other low-demand regions, and the number of shared boundaries for each candidate merged region pair is updated.

[0060] Step S233: Repeat the selection and merging of candidate merged region pairs until there are no candidate merged region pairs that meet the region size constraint and compactness constraint. Then, merge the low-demand regions with a region size smaller than the preset size with the adjacent low-demand regions that meet the region size constraint and compactness constraint to obtain the demand region set.

[0061] Specifically, when a low-demand region reaches the maximum number of geographic grid cells or there are no adjacent low-demand regions that can be merged, the region growth for that low-demand region is stopped. For low-demand regions whose size is smaller than the preset size, adjacent low-demand regions are selected for merging based on the number of shared boundaries and compactness constraints. After completing the division and merging of demand regions, the set of demand regions is represented as follows: in, Represents a set of demand regions; Indicates the area of ​​demand; Indicates the number of areas in demand.

[0062] Step S24: Based on the grid demand value and spatial location of the geographic grid unit in each demand area, determine the representative demand point, demand weight, and regional demand intensity to obtain the demand characterization information of each demand area.

[0063] In an optional embodiment of the present invention, step S24 may include: Step S241: Aggregate the grid demand values ​​of geographic grid cells in each demand area to obtain the total regional demand and average regional demand for each demand area.

[0064] Specifically, for the demand area Regional aggregate demand and regional average demand They are represented as follows: in, Indicates the area of ​​demand; Indicates the demand area Geographic grid cells within; Represents geographic grid cells The grid requirement value; Indicates the demand area The number of geographic grid cells included; Indicates the demand area Total regional demand; Indicates the demand area The average regional demand.

[0065] Step S242: Based on the grid demand value and spatial location of the geographic grid unit in each demand area, determine the demand weighted centroid and hotspot representative point, and determine the demand weighted centroid and hotspot representative point as representative demand points.

[0066] Specifically, the demand-weighted centroid is determined by the spatial location and grid demand value of each geographic grid unit within the demand region, and is used to characterize the overall demand center of the demand region. Hotspot representative points are determined based on the local demand intensity within the demand region, and are used to characterize ground locations with high local demand. For the demand region... Geographic grid cells within It can extract the corresponding hotspot coordinates. .

[0067] Step S243: Determine the demand weight of the representative demand point based on the ratio between the grid demand value of the geographic grid unit in each demand area and the total demand of the corresponding area, and determine the regional demand intensity based on the average demand of the corresponding area to obtain the demand characterization information of each demand area.

[0068] Specifically, for the demand area Geographic grid cells within Normalized demand weights and the set of representative demand points Represented as: in, Indicates the demand area The first One geographic grid unit; Represents geographic grid cells The grid requirement value; Indicates the demand area Total regional demand; Represents geographic grid cells The hotspots represent coordinates; Hotspots represent coordinates The corresponding normalized demand weights; Indicates the demand area The representative set of demand points. Regional demand intensity is determined by normalizing the regional average demand.

[0069] Step S3: Based on the demand characterization information of each demand area, the coverage relationship of low-Earth orbit satellites, and the time continuity constraints, perform a one-to-one allocation of low-Earth orbit satellites at the regional level to obtain the corresponding leader satellite for each demand area.

[0070] In an optional embodiment of the present invention, step S3 may include: Step S31: Determine the set of feasible candidate satellites for each demand area based on the representative demand points of each demand area, the coverage relationship of low-orbit satellites to the representative demand points, and the extended candidate windows corresponding to each demand area.

[0071] Specifically, acquiring a low-orbit satellite ensemble within the current planning cycle. A feasible candidate satellite set is constructed for each demand area. The feasible candidate satellite set can be determined based on one or more of the following: the positional relationship between the nadir point of a low-Earth orbit (LEO) satellite and the extended candidate window; the visibility of the LEO satellite to the representative demand point; whether the elevation angle of the LEO satellite relative to the representative demand point meets the minimum coverage condition; and the upper limit on the number of candidate satellites. The extended candidate window is obtained by expanding the latitude and longitude circumscribed range of the demand area.

[0072] Step S32: Determine the allocation cost of the leader satellite corresponding to each feasible candidate satellite based on the demand weight of each demand area, the relative position between the representative demand point and the feasible candidate satellite, and the coverage relationship.

[0073] In an optional embodiment of the present invention, step S32 may include: Step S321: Determine the demand weighted average distance and maximum distance based on the demand weight of each representative demand point and the distance between each representative demand point and the ground point of the feasible candidate satellite.

[0074] Specifically, for the demand area and feasible candidate satellites Demand weighted average distance and maximum distance They are represented as follows: in, Indicates the area of ​​demand; Indicates feasible candidate satellites; Indicates the demand area The set of representative demand points; This indicates the point of demand; This represents the demand point. Corresponding demand weights; This represents the demand point. With feasible candidate satellites The great circle distance between the sub-star points; Indicates feasible candidate satellites Relative to demand area Demand-weighted average distance; Indicates feasible candidate satellites Relative to demand area The maximum distance.

[0075] Step S322: Based on the coverage status of each feasible candidate satellite to the representative demand point and the corresponding demand weight, determine the demand-weighted coverage ratio, and determine the positional relationship between the sub-satellite point of each feasible candidate satellite and the corresponding extended candidate window.

[0076] Specifically, feasible candidate satellites Relative to demand area Demand-weighted coverage ratio Represented as: in, Indicates feasible candidate satellites For demand areas Demand-weighted coverage ratio; Indicates an indicator function; when representing a demand point Viable candidate satellites When overriding, the indicator function takes the value 1; otherwise, it takes the value 0.

[0077] Step S323: The demand-weighted average distance, maximum distance, demand-weighted coverage ratio, and location relationship are weighted and combined to obtain the leader satellite allocation cost.

[0078] Specifically, feasible candidate satellites For demand areas The cost of allocating leadership satellites Represented as: in, , , and Indicates non-negative weighting coefficients; Indicates the demand-weighted average distance; Indicates the maximum distance; Indicates the demand-weighted coverage ratio; Indicates feasible candidate satellites The sub-star point; Indicates the demand area Expanded candidate window; Indicates the sub-star point Not in the expanded candidate window The value is 1 if the time is within the range, and 0 otherwise.

[0079] Step S33: Based on the leader satellite mapping of the previous planning period, the cost ranking of each feasible candidate satellite, and the cost difference between each feasible candidate satellite and the optimal feasible candidate satellite, determine the frozen leader satellite allocation result that satisfies the time continuity constraint.

[0080] Specifically, if there is no leader satellite mapping for the previous planning period, the leader satellite freeze is not executed. If there is a leader satellite mapping for the previous planning period, a feasibility and cost continuity assessment is performed on the leader satellite for the previous planning period.

[0081] In an optional embodiment of the present invention, step S33 may include: Step S331: Sort the feasible candidate satellites for each demand area according to the leader satellite allocation cost, and determine the sorting offset of the leader satellite in the previous planning period relative to the best feasible candidate satellite.

[0082] Specifically, the demand area The set of feasible candidate satellites is ordered in ascending order of the cost of allocating the leader satellite. Candidate satellites Ranking offset relative to the best feasible candidate satellites Represented as: in, Indicates candidate satellites In demand areas The ranking of the feasible candidate satellites; Indicates candidate satellites The sorting offset relative to the best feasible candidate satellite.

[0083] Step S332: Determine the normalized cost difference based on the leader satellite allocation cost corresponding to the leader satellite in the previous planning cycle and the leader satellite allocation cost corresponding to the best feasible candidate satellite.

[0084] Specifically, demand areas Optimal feasible candidate satellite Represented as: Candidate satellites Normalization cost difference Represented as: in, Indicates the demand area A set of feasible candidate satellites; Indicates the demand area The optimal feasible candidate satellite; Indicates candidate satellites For demand areas The cost of allocating leadership satellites; Indicates the optimal feasible candidate satellite The corresponding cost of allocating leadership satellites; Represents a small positive constant; Indicates candidate satellites The difference in normalization costs.

[0085] Step S333: If the leader satellite of the previous planning period belongs to the current feasible candidate satellite set, and the sorting offset and normalization cost difference meet the corresponding thresholds, the leader satellite of the previous planning period is determined as the frozen leader satellite, and the frozen leader satellite allocation result is obtained.

[0086] Specifically, the demand areas in the previous planning cycle The corresponding leadership satellite is represented as .when The demand area will be frozen if it still belongs to the set of feasible candidate satellites in the current planning cycle and meets the following conditions. Mapping with the leadership satellite of the previous planning cycle: in, This represents the ranking offset of the leading satellite from the previous planning cycle relative to the current best feasible candidate satellite; This represents the normalized cost difference between the leading satellite of the previous planning cycle and the current best feasible candidate satellite; Indicates the sorting offset threshold; This represents the threshold for the normalized cost difference.

[0087] Step S34: For the unfrozen demand areas, retain the candidate allocation relationship between the demand areas that meet the sorting constraints and the feasible candidate satellites, and perform a one-to-one minimum cost allocation on the candidate allocation relationship to obtain the leader satellite corresponding to each demand area.

[0088] In an optional embodiment of the present invention, step S34 may include: Step S341: Based on the matching relationship between the unfrozen demand areas and feasible candidate satellites, determine the minimum sorting offset threshold that enables the unfrozen demand areas to achieve a complete match, and retain candidate allocation relationships whose sorting offset is not greater than the minimum sorting offset threshold.

[0089] Specifically, the allowable sorting offset range is gradually expanded for unfrozen demand areas, and it is determined whether there is a complete match between the demand areas within the current sorting offset range and feasible candidate satellites. The sorting offset threshold at which a complete match can be achieved for the first time is determined as the minimum sorting offset threshold. And retain candidate assignment relationships that meet the following conditions: in, Indicates candidate satellites Relative to demand area The sorting offset of the optimal feasible candidate satellites; This represents the minimum sort offset threshold that enables a perfect match between unfrozen demand regions.

[0090] Step S342: Determine the matching cost of each candidate allocation relationship based on the sorting offset, normalized cost difference, and consistency between the leader satellite corresponding to each candidate allocation relationship and the leader satellite of the previous planning period.

[0091] Specifically, demand areas With candidate satellites Matching cost between Represented as: Continuous penalty item Represented as: in, Indicates the sorting offset; This represents the difference in normalization costs; Indicates a continuous penalty term; Indicates the demand area in the previous planning cycle. Corresponding leadership satellite; , and Indicates the corresponding weight; Indicates the demand area With candidate satellites The matching cost between them.

[0092] Step S343: Based on the matching cost, perform one-to-one minimum cost matching between the unfrozen demand areas and feasible candidate satellites, and combine the results of the frozen leader satellite allocation to obtain the leader satellite corresponding to each demand area.

[0093] Specifically, the binary search is solved among the candidate assignment relations that satisfy the minimum sorting offset threshold. Figure 1 For a minimum-cost match, the matching result is merged with the frozen leader satellite allocation result to obtain the leader satellite mapping for the current planning cycle. One-to-one minimum cost matching can be achieved using the Hungarian algorithm or an equivalent bipartite graph minimum cost matching algorithm.

[0094] Step S4: Using the leader satellite corresponding to each demand region as the seed, satellite block growth and local repair are carried out under connectivity and diameter constraints based on the topological connection relationship of low-Earth orbit satellites and the demand matching relationship between low-Earth orbit satellites and corresponding demand regions, to obtain the regional-level satellite blocks corresponding to each demand region.

[0095] Specifically, the current low-Earth orbit satellite network is represented by a satellite topology diagram: in, A satellite topology map showing the current moment; Represents a set of satellite nodes; This represents the set of currently available inter-satellite links.

[0096] In an optional embodiment of the present invention, step S4 may include: Step S41: Initialize the corresponding satellite block with the leader satellite corresponding to each demand area, and mark the leader satellite as an allocated satellite.

[0097] Specifically, satellite nodes have unassigned, assigned, and faulty states, while satellite blocks have growable, stable, and invalid states. For demand areas... With the current leadership satellite Initialize satellite block : in, Indicates the demand area The corresponding initial satellite block; Indicates the demand area in the current planning cycle. The corresponding leader satellite. After initialization, the leader satellite is marked as an assigned satellite, and the corresponding satellite block is set to a growable state.

[0098] Step S42: Determine the adjacent satellites of each satellite block based on the low-orbit satellite topology connection relationship, exclude faulty satellites and assigned satellites from the adjacent satellites, and retain the satellites that meet the connectivity and diameter constraints after absorption, to obtain the set of absorbable satellites.

[0099] Specifically, for the current satellite block Adjacent satellite set Represented as: in, Indicates the current satellite block; Indicates the satellites in the current satellite block; This indicates a neighboring satellite that is connected to at least one satellite in the current satellite block via an available inter-satellite link; Represents a set of satellite nodes; Indicates the set of available inter-satellite links; This represents the set of neighboring satellites for the current satellite block. Faulty and already assigned satellites are excluded from this set. It is then determined whether the satellite block formed after adding the remaining neighboring satellites is connected and its diameter is not greater than the maximum diameter of any satellite block. An absorbable satellite set is formed by adjacent satellites that meet certain conditions.

[0100] Step S43: Determine the demand-aware satellite block score after absorbing each absorbable satellite based on the demand affinity between the absorbable satellite and the corresponding demand area, the internal connection scale of the absorbed satellite block, and the external interface direction distribution.

[0101] In an optional embodiment of the present invention, step S43 may include: Step S431: Determine the demand affinity between each absorbable satellite and the corresponding demand area based on the distance attenuation relationship between the sub-satellite point of each absorbable satellite and the representative demand point of the corresponding demand area.

[0102] Specifically, for satellites and demand areas According to satellite Sub-satellite point and demand area The distance attenuation relationship between representative demand points is calculated in advance, and the demand affinity is pre-calculated. Demand affinity varies with the distance between the satellite's nadir point and the representative demand point, and is used to characterize the satellite. and demand areas The degree of matching between the needs.

[0103] Step S432: Aggregate the demand affinity between each satellite in the satellite block and the corresponding demand region, and compress and map the aggregation result to obtain the demand utility of the satellite block.

[0104] Specifically, for the demand area Corresponding satellite blocks Satellite block demand utility Represented as: in, Indicates the demand area The corresponding satellite block; Represents satellite block Satellites in the middle; Indicates satellite and demand areas The degree of affinity between the needs; Represents satellite block The utility of satellite blocks.

[0105] Step S433: Determine the internal connection scale based on the number of internal links of the absorbed satellite block and its complement, and determine the external interface direction concentration based on the cut edge direction between the absorbed satellite block and its complement.

[0106] Specifically, satellite blocks cut edge set Represented as: in, Represents a satellite block; Represents satellite block Relative to the set of satellite nodes The complement; Represents satellite block Satellites in the middle; Indicates complement Satellites in the middle; Indicates the set of available inter-satellite links; Indicates connecting satellite blocks with the supplement The set of cut edges.

[0107] For edge trimming outward unit vector Represented as: in, Indicates external satellite Geocentric coordinates; Represents satellite block The centroid of the geocentric and geofixed coordinates of each satellite node in the system; Indicates cut edge The corresponding outward unit vector.

[0108] satellite blocks External interface direction concentration Represented as: in, This indicates the number of cut edges in the cut edge set; Represents satellite block The concentration of external interface directions. The scale of internal connections within the satellite block depends on the satellite block. and supplements The number of internal links in each induced subgraph is determined.

[0109] Step S434: Determine the demand-aware satellite block score based on the satellite block demand utility, internal connectivity scale, and external interface direction concentration.

[0110] Specifically, demand-aware satellite block scoring Represented as: in, Indicates satellite block The number of internal links in the induced subgraph; Indicates by complement The number of internal links in the induced subgraph; Indicates the demand utility of satellite blocks; Indicates the concentration of external interface directions; This indicates the demand-aware satellite block score.

[0111] Step S44: Add the absorbable satellite with the highest score in the corresponding demand-aware satellite block to the satellite block, and update the satellite allocation status and the set of absorbable satellites until there are no absorbable satellites that meet the connectivity and diameter constraints, thus obtaining the initial regional-level satellite block.

[0112] Specifically, for the demand area The corresponding current satellite block Select the satellite from the set of absorbable satellites that maximizes the demand-aware satellite block score after absorption: in, Indicates the satellite to be absorbed; Represents satellite block The set of adjacent satellites; Indicates absorption satellite The diameter of the satellite-level sub-map of the subsequently formed satellite block; This represents the maximum diameter of the satellite block. After a satellite absorption cycle is completed, the absorbed satellite is marked as an assigned satellite, and the satellite block members, satellite block state, and set of absorbable satellites are updated. When there are no absorbable satellites in the satellite block that satisfy the connectivity and diameter constraints, the satellite block is set to a stable state.

[0113] Step S45: Perform local repairs on the initial regional-level satellite blocks that have faulty satellites, interrupted connections, or do not meet diameter constraints to obtain regional-level satellite blocks corresponding to each required region.

[0114] In an optional embodiment of the present invention, step S45 may include: Step S451: Identify affected satellite blocks from the initial regional satellite blocks that contain faulty satellites, have connection interruptions, or do not meet diameter constraints, and remove faulty satellites from the affected satellite blocks.

[0115] Specifically, after a low-orbit satellite malfunctions or changes in available inter-satellite links, the satellite status, connectivity status, and satellite-level sub-map diameter of each initial regional satellite block are checked, and only the initial regional satellite blocks with corresponding anomalies are identified as affected satellite blocks.

[0116] Step S452: Retain the connected portion that satisfies the connectivity and diameter constraints from the affected satellite block after the faulty satellite is removed, and release the satellites outside the connected portion as unassigned satellites.

[0117] Specifically, when the affected satellite block after removing a faulty satellite contains multiple disconnected connected parts, the largest connected part that satisfies connectivity and diameter constraints can be retained, and satellites in the other connected parts can be marked as unassigned satellites.

[0118] Step S453: Based on the connected components, absorb the unassigned satellites that satisfy connectivity and diameter constraints and demand matching relationships from the boundaries of the connected components to obtain the regional-level satellite blocks corresponding to each demand region.

[0119] Specifically, the retained connected portion is used as the initial satellite block for local repair. According to the satellite block growth method defined in steps S42 to S44, the set of absorbable satellites is redefined from the boundary of the connected portion and constrained satellite absorption is carried out until there are no unassigned satellites that meet the conditions.

[0120] Step S5: Based on the bridging link quality between adjacent regional satellite blocks, the continuity of historical routing domains, and the demand similarity between corresponding demand areas, regional satellite blocks are aggregated under connectivity, size, and diameter constraints to obtain a routing domain set.

[0121] In an optional embodiment of the present invention, step S5 may include: Step S51: Using each regional satellite block as a node, establish block adjacency relationships based on the cross-block satellite links between regional satellite blocks, and determine the set of cross-block satellite links between adjacent regional satellite blocks to obtain a block adjacency graph.

[0122] Specifically, the block adjacency graph is represented as: in, Represents a block adjacency graph; Represents a regional-level set of satellite blocks; This represents the set of block adjacency edges. Block adjacency edges are established between two regional satellite blocks when at least one available inter-satellite link exists between them.

[0123] For adjacent regional satellite blocks and The cross-block satellite link set is represented as Number of cross-block satellite links Represented as: in, and Representing the demand areas respectively and demand areas The corresponding regional-level satellite blocks; This represents the set of cross-block satellite links between two regional-level satellite blocks; This indicates the number of cross-block satellite links.

[0124] Step S52: Determine the bridging link quality between adjacent regional satellite blocks based on the number of links and link distances in each cross-block satellite link set.

[0125] In an optional embodiment of the present invention, step S52 may include: Step S521: Normalize the number of links in each cross-block satellite link set, and determine the corresponding link quality based on the link distance of each cross-block satellite link.

[0126] Specifically, adjacent regional-level satellite blocks and Number of normalized links between Represented as: cross-block satellite links Normalized link quality Represented as: in, Represents regional satellite blocks and The number of cross-block satellite links between them; Indicates the maximum number of cross-block satellite links used for normalization; Indicates cross-block satellite link Link distance; and These represent the minimum and maximum link distances used for link distance normalization, respectively. Indicates the number of normalized links; This indicates the normalized link quality.

[0127] Step S522: Determine the highest link quality for each cross-block satellite link set, and determine the average link quality based on the preset number of cross-block satellite links with the highest quality in the corresponding cross-block satellite link set.

[0128] Specifically, cross-block satellite link sets Highest link quality Represented as: Based on normalized link quality, the cross-block satellite link set The cross-block satellite links in the data are sorted, and the highest quality links are ranked first. The average link quality is obtained by averaging the normalized link quality of each cross-block satellite link. .in, This indicates the number of high-quality cross-block satellite links that participate in the average link quality calculation.

[0129] Step S523: The normalized number of links, the highest link quality, and the average link quality are weighted and combined to obtain the bridging link quality between adjacent regional satellite blocks.

[0130] Specifically, adjacent regional-level satellite blocks and quality of bridging links between Represented as: in, Indicates the number of normalized links; Indicates the highest link quality; The highest quality front Average link quality of cross-block satellite links; , and This represents the corresponding weight coefficient; This indicates the quality of the bridging link between adjacent regional satellite blocks.

[0131] Step S53: Initialize each regional satellite block as a routing domain, and determine candidate routing domain pairs that satisfy the constraints of block adjacency connectivity, number of regional satellite blocks, number of satellites, and diameter of satellite-level subgraph from the block adjacency graph.

[0132] Specifically, initially, one regional satellite block corresponds to one routing domain. For the current set of routing domains... Enumerate adjacent route domain pairs from the block adjacency graph and determine whether the constraints are satisfied after merging adjacent route domains. Constraints This includes connectivity of the merged block adjacency subgraph and the number of regional satellite blocks contained in the merged routing domain not exceeding [a certain value]. The merged routing domain contains no more than [number of satellites]. And the diameter of the merged satellite-level submap is no greater than The constraints are satisfied. The adjacent routing domain pairs are identified as candidate routing domain pairs.

[0133] Step S54: Determine the merging gain of the candidate route domain pair based on the quality of the internal bridging links of the candidate route domain pair, the continuity of historical route domains, and the demand similarity between the corresponding demand areas.

[0134] In an optional embodiment of the present invention, step S54 may include: Step S541: Aggregate the bridging link quality between regional satellite blocks within the merged route domain of the candidate route domain to obtain the internal interface gain.

[0135] Specifically, let the routing domain be set. The routing domain is obtained by aggregating multiple regional-level satellite blocks. Related demand area set Represented as: Routing domain Internal interface gain Represented as: in, Indicates the routing domain; Indicates routing domain The set of related demand areas; Indicates the demand area The corresponding regional-level satellite blocks; Indicates routing domain The set of block adjacency edges between internal regional-level satellite blocks; Represents regional satellite blocks and The quality of the bridging link between them; Indicates routing domain Internal interface gain.

[0136] Step S542: Determine the historical continuity gain based on the route domain affiliation relationship of the candidate route domain to the associated demand area in the previous planning period, and determine the demand consistency gain based on the regional demand intensity difference of the demand area.

[0137] Specifically, when there is a routing domain division from the previous planning period, the routing domain Historical continuity gain Represented as: in, and Indicates routing domain Different related demand areas; Indicates the demand area The routing domain number of the previous planning cycle; Indicates the demand area The routing domain number of the previous planning cycle; This indicates historical continuity gain.

[0138] Demand Area Regional average demand Normalization yields the regional demand intensity. Demand Area and demand areas The similarity of requirements between them is represented as: Routing domain Demand Consistency Gain Represented as: in, and Representing the demand areas respectively and demand areas Regional demand intensity; Indicates a parameter sensitive to differences in demand intensity; This indicates the degree of demand similarity between two demand regions; Indicates routing domain The gain in demand consistency.

[0139] Step S543: Weight the internal interface gain, historical continuity gain, and demand consistency gain to obtain the overall quality of the merged routing domain, and determine the merging gain based on the difference between the overall quality of the merged routing domain and the overall quality of each routing domain before merging.

[0140] Specifically, routing domain Overall mass function Represented as: in, Indicates the internal interface gain; Indicates historical continuity gain; Indicates the demand consistency gain; , and This represents the corresponding weight coefficient; Indicates routing domain The overall quality.

[0141] For adjacent candidate routing domains and Combined gain Represented as: in, Indicates candidate routing domain and candidate routing domains Overall quality of the merged routing domain; and These represent the overall quality of the two candidate routing domains before merging; This represents the merging gain of candidate route domain pairs.

[0142] Step S55: Select the positive gain candidate routing domain pair with the largest merging gain for merging, and update the candidate routing domain pair and its merging gain until there are no more positive gain candidate routing domain pairs, thus obtaining the routing domain set.

[0143] Specifically, in each routing domain merge, from those that satisfy the constraints... The candidate route domain pairs with the highest merge gain and a greater than 0 are selected for merging. After merging, the block adjacency relationships, candidate route domain pairs, and corresponding merge gains are updated. If no candidate route domain pair with a merge gain greater than 0 exists, the route domain aggregation is terminated.

[0144] The optimization objective for route domain aggregation is expressed as: And satisfy: in, Represents a set of routing domains; Represents the set of routing domains The routing domain in; Indicates by routing domain The subgraph induced in the block adjacency graph by the included regional satellite blocks; Indicates routing domain Number of regional satellite blocks included; Indicates the maximum number of regional satellite blocks that a single routing domain is allowed to contain; Indicates routing domain The set of satellites included; Indicates the maximum number of satellites allowed in a single routing domain; Indicates routing domain The corresponding satellite-level sub-map diameter; Represents the diameter of the largest satellite-level subgraph in the routing domain. Satellite set. Represented as: Step S6: Construct a domain-level topology map based on the set of routing domains, and perform domain-level path search, cross-domain boundary link selection, and intra-domain satellite-level path reconstruction under the constraints of link availability and capacity, according to the routing domains to which the source satellite and destination satellite corresponding to the communication request belong, to obtain an end-to-end hierarchical routing path.

[0145] In an optional embodiment of the present invention, step S6 may include: Step S61: Using each routing domain as a domain-level node, establish domain-level edges based on the available cross-domain satellite links between routing domains, and determine the domain-level edge weights based on the link distance, remaining capacity, and link availability of the cross-domain satellite links to obtain a domain-level topology graph.

[0146] Specifically, the domain-level topology graph is represented as follows: in, Represents a domain-level topology graph; Represents a set of routing domains; This represents the set of domain-level edges. Each domain-level node corresponds to a routing domain. When there are available cross-domain satellite links between two routing domains, a domain-level edge is established between the two domain-level nodes, and the set of available cross-domain satellite links corresponding to this domain-level edge is recorded.

[0147] Step S62: Obtain the source satellite, destination satellite, and bandwidth requirement corresponding to the communication request, and determine the source routing domain to which the source satellite belongs and the destination routing domain to which the destination satellite belongs.

[0148] Specifically, for source satellites to the target satellite The communication request identifies the source satellite. The source routing domain to which it belongs and target satellite Destination routing domain It also reads the service bandwidth requirements and service duration of the communication request.

[0149] Step S63: If the source routing domain and the destination routing domain are the same, search for satellite-level paths that meet the link availability and capacity constraints within the corresponding routing domain to obtain candidate end-to-end paths.

[0150] Specifically, when At this time, a satellite-level shortest path search is performed within the source routing domain, and the link availability and remaining capacity of each inter-satellite link in the searched satellite-level path are checked to see if they meet the service bandwidth requirements of the communication request. Satellite-level paths that meet the link availability and capacity constraints are determined as candidate end-to-end paths.

[0151] Step S64: When the source routing domain and the destination routing domain are different, perform a domain-level path search based on the domain-level topology map, and select cross-domain boundary links and reconstruct intra-domain satellite-level paths along the searched domain-level paths to obtain candidate end-to-end paths.

[0152] In an optional embodiment of the present invention, step S64 may include: Step S641: Search for a domain-level path from the source routing domain to the destination routing domain in the domain-level topology graph based on the domain-level edge weights.

[0153] Specifically, when At that time, the source routing domain For origin and destination routing domains As the endpoint, in the domain-level topology graph Search for domain-level paths.

[0154] Step S642: For each pair of adjacent routing domains in the domain-level path, select a cross-domain boundary link from the corresponding cross-domain satellite links that meets the link availability and capacity constraints.

[0155] Specifically, for each domain-level edge in the domain-level path, from the set of available cross-domain satellite links corresponding to that domain-level edge, cross-domain satellite links that are available and whose remaining capacity meets the service bandwidth requirements of the communication request are selected, and the cross-domain satellite link with the smallest distance weight is selected from the selection results as the cross-domain boundary link.

[0156] Step S643: Within each routing domain traversed by the domain-level path, search for intra-domain satellite-level paths that connect inbound and outbound satellites and satisfy link availability and capacity constraints.

[0157] Specifically, for intermediate routing domains in a domain-level path, satellites connected by the preceding cross-domain boundary link within this routing domain are identified as incoming satellites, and satellites connected by the subsequent cross-domain boundary link within this routing domain are identified as outgoing satellites. An intra-domain satellite-level path is then searched between the incoming and outgoing satellites. For the source routing domain, an intra-domain satellite-level path is searched between the source satellite and its corresponding outgoing satellite; for the destination routing domain, an intra-domain satellite-level path is searched between the corresponding incoming and destination satellites.

[0158] Step S644: Connect the cross-domain boundary links and intra-domain satellite-level paths according to the traversal order of the domain-level paths to obtain candidate end-to-end paths.

[0159] Specifically, following the order from the source routing domain to the destination routing domain, satellite-level paths within the source routing domain, cross-domain boundary links, satellite-level paths within intermediate routing domains, and satellite-level paths within the destination routing domain are connected sequentially to form candidate end-to-end paths from the source satellite to the destination satellite.

[0160] Step S65: If the candidate end-to-end path meets the link availability and capacity constraints, the candidate end-to-end path is determined as an end-to-end hierarchical routing path, and the path link capacity is occupied and released according to the service duration of the communication request. If the candidate end-to-end path does not meet the link availability and capacity constraints, the communication request is rejected.

[0161] Specifically, the process examines each cross-domain boundary link and intra-domain inter-satellite link in the candidate end-to-end path. When all links are available and their remaining capacity meets the service bandwidth requirements, the communication request is accepted, the candidate end-to-end path is designated as the end-to-end hierarchical routing path, and the corresponding capacity of each link in the path is occupied during the service duration. The occupied link capacity is released after the service ends. If any cross-domain boundary link or any intra-domain path segment is unavailable, or if the remaining capacity of any link does not meet the service bandwidth requirements, the communication request is rejected.

[0162] Another embodiment of the present invention provides a hierarchical resilient routing planning system for low-Earth orbit communication constellations, used to perform the method of the present invention, the system as follows: Figure 3 As shown, it includes the following steps: The demand heatmap construction module 11 is used to acquire long-term ground business demand proxy data and preprocess the long-term ground business demand proxy data to obtain a ground demand heatmap. The demand gridding and region merging module 12 is used to discretize the ground demand heat map based on the geographic grid, and divide and merge the demand regions according to the grid demand to obtain a set of demand regions and demand characterization information of each demand region. The demand characterization information includes representative demand points, demand weights and regional demand intensity. The leader satellite selection module 13 is used to perform a one-to-one allocation of low-orbit satellites at the regional level based on the demand characterization information of each demand area, the coverage relationship of low-orbit satellites and the time continuity constraints, so as to obtain the leader satellite corresponding to each demand area. Satellite block construction module 14 is used to grow satellite blocks and locally repair them under connectivity and diameter constraints, based on the leader satellites corresponding to each demand area as seeds, according to the topological connection relationship of low-orbit satellites and the demand matching relationship between low-orbit satellites and corresponding demand areas. The routing domain aggregation module 15 is used to aggregate the regional satellite blocks under connectivity, size and diameter constraints based on the bridging link quality between adjacent regional satellite blocks, the continuity of historical routing domains and the demand similarity between corresponding demand areas, to obtain a routing domain set. The hierarchical routing planning module 16 is used to construct a domain-level topology map based on the set of routing domains, and to perform domain-level path search, cross-domain boundary link selection and intra-domain satellite-level path reconstruction under link availability and capacity constraints according to the routing domains to which the source satellite and destination satellite corresponding to the communication request belong, so as to obtain an end-to-end hierarchical routing path.

[0163] The above division of modules is merely illustrative. In practical applications, the functions described above can be assigned to different functional modules as needed to complete all or part of the functions described in the aforementioned method. The specific working process of each module can be found in the corresponding process in the aforementioned method embodiments.

[0164] Another embodiment of the present invention provides a computer device including a memory and a processor, the memory storing a computer program configured to be executed by the processor, the computer program including instructions for performing steps of the method of the present invention.

[0165] Another embodiment of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a computer, implements the steps of the method of the present invention.

[0166] Another embodiment of the present invention provides a computer program product, the computer program product including a computer program, which, when executed by a computer, implements the steps of the method of the present invention.

[0167] Although the present invention has been disclosed above with reference to embodiments, it is not intended to limit the present invention. Appropriate modifications or equivalent substitutions made by those skilled in the art to the technical solutions of the present invention should be covered within the protection scope of the present invention, which is defined by the claims.

Claims

1. A hierarchical resilient routing planning method for low-Earth orbit communication constellations, characterized in that, Includes the following steps: Obtain long-term ground service demand proxy data and preprocess the long-term ground service demand proxy data to obtain a ground demand heat map. The ground demand heatmap is discretized based on a geographic grid, and demand regions are divided and merged according to grid demand to obtain a set of demand regions and demand characterization information for each demand region. The demand characterization information includes representative demand points, demand weights, and regional demand intensity. Based on the demand characterization information of each demand area, the coverage relationship of low-Earth orbit satellites and the time continuity constraints, low-Earth orbit satellites are allocated one-to-one at the regional level to obtain the corresponding leader satellite for each demand area. Using the leader satellite corresponding to each demand region as the seed, satellite block growth and local repair are carried out under connectivity and diameter constraints based on the topological connection relationship of low-Earth orbit satellites and the demand matching relationship between low-Earth orbit satellites and corresponding demand regions to obtain the regional-level satellite blocks corresponding to each demand region. Based on the bridging link quality between adjacent regional satellite blocks, the continuity of historical routing domains, and the demand similarity between corresponding demand areas, the regional satellite blocks are aggregated under connectivity, size, and diameter constraints to obtain a routing domain set. Based on the set of routing domains, a domain-level topology map is constructed. Then, according to the routing domains to which the source and destination satellites corresponding to the communication request belong, domain-level path search, cross-domain boundary link selection, and intra-domain satellite-level path reconstruction are performed under link availability and capacity constraints to obtain end-to-end hierarchical routing paths.

2. The method as described in claim 1, characterized in that, The ground demand heatmap is discretized based on a geographic grid, and demand regions are divided and merged according to the grid demand to obtain a set of demand regions and demand representation information for each region, including: The effective pixels in the ground demand heatmap are mapped to geographic grid cells, and the pixel-level demand proxy values ​​in each geographic grid cell are aggregated to obtain the grid demand value of each geographic grid cell. The high-demand geographic grid units and low-demand geographic grid units are divided according to the grid demand value. The grid granularity of the high-demand geographic grid units is retained, and the low-demand geographic grid units are combined according to the geographic adjacency relationship to obtain the initial demand area. Based on geographical adjacency and regional size constraints, low-demand regions in the initial demand regions are merged to obtain a set of demand regions; Based on the grid demand value and spatial location of the geographic grid units in each demand area, representative demand points, demand weights, and regional demand intensity are determined to obtain demand characterization information for each demand area.

3. The method as described in claim 1, characterized in that, Based on the demand characterization information of each demand region, the coverage relationship of low-Earth orbit (LEO) satellites, and temporal continuity constraints, LEO satellites are allocated one-to-one at the regional level to obtain the corresponding leader satellites for each demand region, including: Based on the representative demand points of each demand area, the coverage relationship of low-orbit satellites to the representative demand points, and the extended candidate window corresponding to each demand area, determine the feasible candidate satellite set for each demand area. The allocation cost of the leader satellite corresponding to each feasible candidate satellite is determined based on the demand weight of each demand region, the relative position of the representative demand point and the feasible candidate satellite, and the coverage relationship. Based on the leader satellite mapping of the previous planning cycle, the cost ranking of each feasible candidate satellite, and the cost difference between each feasible candidate satellite and the optimal feasible candidate satellite, the frozen leader satellite allocation result that satisfies the time continuity constraint is determined. For unfrozen demand regions, the candidate allocation relationship between demand regions that meet the sorting constraints and feasible candidate satellites is retained, and the candidate allocation relationship is allocated one-to-one with minimum cost to obtain the leader satellite corresponding to each demand region.

4. The method as described in claim 3, characterized in that, Based on the leader satellite mapping from the previous planning period, the cost ranking of each feasible candidate satellite, and the cost difference between each feasible candidate satellite and the optimal feasible candidate satellite, the frozen leader satellite allocation result that satisfies the aforementioned time continuity constraint is determined, including: The feasible candidate satellites for each demand area are sorted according to the leader satellite allocation cost, and the sorting offset of the leader satellite in the previous planning period relative to the best feasible candidate satellite is determined. The normalized cost difference is determined based on the allocation cost of the leader satellite corresponding to the leader satellite in the previous planning cycle and the allocation cost of the leader satellite corresponding to the optimal feasible candidate satellite. If the leader satellite of the previous planning period belongs to the current feasible candidate satellite set, and the sorting offset and the normalization cost difference satisfy the corresponding thresholds, the leader satellite of the previous planning period is determined as the frozen leader satellite, and the frozen leader satellite allocation result is obtained.

5. The method as described in claim 3, characterized in that, After step S333, for the unfrozen demand regions, the candidate allocation relationship between the demand regions that satisfy the ranking constraints and the feasible candidate satellites is retained, and the candidate allocation relationship is allocated one-to-one with minimum cost to obtain the leader satellite corresponding to each demand region, including: Based on the matching relationship between the unfrozen demand areas and feasible candidate satellites, determine the minimum sorting offset threshold that enables the unfrozen demand areas to achieve a complete match, and retain candidate allocation relationships whose sorting offset is not greater than the minimum sorting offset threshold. The matching cost of each candidate allocation relationship is determined based on the sorting offset corresponding to each candidate allocation relationship, the normalized cost difference, and the consistency between the leader satellite corresponding to each candidate allocation relationship and the leader satellite of the previous planning cycle. Based on the matching cost, a one-to-one minimum cost matching is performed between the unfrozen demand regions and feasible candidate satellites, and combined with the frozen leader satellite allocation results, the leader satellite corresponding to each demand region is obtained.

6. The method as described in claim 1, characterized in that, Using the leader satellite corresponding to each demand region as the seed, and based on the low-Earth orbit (LEO) satellite topology and the demand matching relationship between LEO satellites and their corresponding demand regions, satellite block growth and local repair are performed under connectivity and diameter constraints to obtain the regional-level satellite blocks corresponding to each demand region, including: Initialize the corresponding satellite block with the leader satellite corresponding to each demand area, and mark the leader satellite as an allocated satellite; Based on the low-Earth orbit satellite topology connection relationship, the adjacent satellites of each satellite block are determined, faulty satellites and assigned satellites are excluded from the adjacent satellites, and satellites that satisfy the connectivity and diameter constraints after absorption are retained to obtain the set of absorbable satellites. The demand-aware satellite block score is determined based on the demand affinity between the absorbable satellite and the corresponding demand area, the internal connection scale of the absorbed satellite block, and the distribution of external interface directions. Add the absorbable satellite with the highest score in the corresponding demand-aware satellite block to the satellite block, and update the satellite allocation status and the set of absorbable satellites until there are no absorbable satellites that satisfy the connectivity and diameter constraints, thus obtaining the initial regional-level satellite block. Local repairs are performed on initial regional-level satellite blocks that have faulty satellites, interrupted connections, or do not meet the diameter constraints to obtain regional-level satellite blocks corresponding to each required region.

7. The method as described in claim 6, characterized in that, Based on the demand affinity between absorbable satellites and corresponding demand areas, the internal connectivity scale of the absorbed satellite block, and the distribution of external interface directions, the demand-aware satellite block score after absorbing each absorbable satellite is determined, including: Based on the distance attenuation relationship between the sub-satellite point of each absorbable satellite and the representative demand point of the corresponding demand area, the demand affinity between each absorbable satellite and the corresponding demand area is determined. The demand affinity between each satellite in the absorbed satellite block and its corresponding demand region is aggregated, and the aggregation result is compressed and mapped to obtain the demand utility of the satellite block. The internal connection scale is determined based on the number of internal links of the absorbed satellite block and its complement, and the external interface direction concentration is determined based on the cut direction between the absorbed satellite block and its complement. The demand-aware satellite block score is determined based on the satellite block demand utility, the internal connectivity scale, and the external interface direction concentration.

8. The method as described in claim 1, characterized in that, Based on the bridging link quality between adjacent regional satellite blocks, the continuity of historical routing domains, and the demand similarity between corresponding demand areas, the regional satellite blocks are aggregated under connectivity, size, and diameter constraints to obtain a routing domain set, including: Using each regional-level satellite block as a node, block adjacency relationships are established based on the cross-block satellite links between the regional-level satellite blocks, and the set of cross-block satellite links between adjacent regional-level satellite blocks is determined to obtain a block adjacency graph. The quality of bridging links between adjacent regional satellite blocks is determined based on the number of links and the link distance in each cross-block satellite link set; Each regional satellite block is initialized as a routing domain, and candidate routing domain pairs that satisfy the constraints of block adjacency connectivity, number of regional satellite blocks, number of satellites, and diameter of satellite-level subgraphs are determined from the block adjacency graph. The merging gain of the candidate route domain pairs is determined based on the internal bridging link quality, historical route domain continuity, and demand similarity between corresponding demand areas. Select the candidate routing domain pair with the largest positive gain for merging, and update the candidate routing domain pair and its merging gain until there are no more positive gain candidate routing domain pairs, thus obtaining the routing domain set.

9. The method as described in claim 8, characterized in that, The merging gain of the candidate route domain pairs is determined based on the internal bridging link quality, historical route domain continuity, and demand similarity between corresponding demand areas, including: The internal interface gain is obtained by aggregating the bridging link quality between each regional-level satellite block within the merged routing domain of the candidate routing domain pairs. The historical continuity gain is determined based on the routing domain affiliation of the candidate routing domain to the associated demand area in the previous planning period, and the demand consistency gain is determined based on the regional demand intensity differences of the demand area. The internal interface gain, the historical continuity gain, and the demand consistency gain are weighted and combined to obtain the overall quality of the merged routing domain, and the merging gain is determined based on the difference between the overall quality of the merged routing domain and the overall quality of each routing domain before merging.

10. A hierarchical resilient routing planning system for low-Earth orbit communication constellations, characterized in that, Includes the following steps: The demand heatmap construction module is used to acquire long-term ground business demand proxy data and preprocess the long-term ground business demand proxy data to obtain a ground demand heatmap. The demand gridding and region merging module is used to discretize the ground demand heat map based on the geographic grid, and divide and merge demand regions according to the grid demand to obtain a set of demand regions and demand representation information of each demand region. The demand representation information includes representative demand points, demand weights and regional demand intensity. The leader satellite selection module is used to perform a one-to-one allocation of low-Earth orbit satellites at the regional level based on the demand characterization information of each demand area, the coverage relationship of low-Earth orbit satellites, and time continuity constraints, so as to obtain the leader satellite corresponding to each demand area. The satellite block construction module is used to grow satellite blocks and perform local repairs under connectivity and diameter constraints, based on the leader satellites corresponding to each demand region as seeds, according to the topological connection relationship of low-Earth orbit satellites and the demand matching relationship between low-Earth orbit satellites and the corresponding demand regions. The routing domain aggregation module is used to aggregate the regional satellite blocks under connectivity, size, and diameter constraints based on the bridging link quality between adjacent regional satellite blocks, the continuity of historical routing domains, and the demand similarity between corresponding demand areas, to obtain a routing domain set. The hierarchical routing planning module is used to construct a domain-level topology map based on the set of routing domains, and to perform domain-level path search, cross-domain boundary link selection, and intra-domain satellite-level path reconstruction under link availability and capacity constraints according to the routing domains to which the source satellite and destination satellite corresponding to the communication request belong, so as to obtain an end-to-end hierarchical routing path.

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