A multi-source multi-path scheduling method for remote sensing data transmission

Abstract

The application relates to the technical field of satellite network communication, and discloses a multi-source multi-path scheduling method for remote sensing data transmission, which first establishes an index table for remote sensing data by taking a space-time name composed of a service type, a collection area, a collection time, a node identifier and a data category; a controller analyzes time and space constraints in a request, selects multiple source nodes satisfying the conditions from the index table, and calculates priorities according to parameters such as residual bandwidth, round-trip delay and packet loss rate, and selects multiple high-quality paths which do not overlap with each other. Subsequently, a service type, a path number, a path status report and a traffic proportion field are added to an interest packet and a data packet, so that the source nodes send segmented data in parallel according to a preset proportion. A forwarding node reports link changes in real time, a controller dynamically adjusts a path set and a shunt weight, and high-bandwidth and low-delay data transmission performance is continuously obtained under a scene of rapid changes in topology and load.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of satellite network communication, in particular to a multi-source and multi-path scheduling method for remote sensing data transmission. BACKGROUND

[0002] Since the early TCP / IP-based satellite file relay, remote sensing data transmission has experienced a serial architecture of "single-satellite straight chain - ground queuing - centralized download", to the disconnected forwarding using DTN and CCSDS protocols, and to the content-driven system integrating Named Data Networking (NDN) and Software-Defined Networking (SDN) in recent years. NDN greatly reduces the satellite-ground backhaul delay and improves the link multiplexing rate with the help of name-based routing and in-transit caching; SDN provides centralized intelligence for link arrangement and resource planning in dynamic topology through control / forwarding separation. At the same time, the constellation scale has evolved from "more than ten satellites" to "thousand-satellite level", and distributed cache nodes are spread across satellites, unmanned aerial vehicles, edge gateways and other platforms, prompting "multi-source and multi-path parallel transmission" to become a new trend of high-resolution remote sensing services. The academia has proposed ICN-Satellite, Map-NDN, Hop-by-Hop Congestion Aware Multipath and other schemes, trying to take advantage of multiple copies and link diversity, but existing designs are generally limited to static directory or pure link index scheduling, lack explicit modeling of the "spatiotemporal coupling" characteristics of remote sensing data, and do not support multi-path traffic division and link state feedback at the protocol level.

[0003] The existing technology mainly has the following deficiencies: first, the traditional NDN naming mostly uses business layer concepts or hash digests as keys, ignoring data collection time and spatial range, which makes it difficult to quickly identify versions and coverage areas of similar images, the index granularity is rough, and the query needs to be flooded in multiple hops; second, the distributed cache only records the "presence / absence" state, lacks binding with the node geographical position and observation time, and the controller is difficult to accurately select data sources that meet the specific task window; third, the multi-source parallel mechanism mostly adopts the "first response to terminate" preemption strategy, which cannot balance the bandwidth, delay and packet loss rate, and also lacks protocol extensions to identify paths and shunt ratios at the data packet level, making it difficult to real-time redistribute traffic when the link quality fluctuates; fourth, link state monitoring usually relies on external SNMP / NetFlow or ground control station offline statistics, which lacks real-time and coverage, and cannot support fine-grained scheduling in dynamic satellite-ground topology. In summary, in the high-dynamic, massive-copy, bandwidth-heterogeneous space-ground-sea integrated environment, there is still a lack of an integrated solution that simultaneously considers "spatiotemporal accurate indexing + multi-source and multi-path awareness + protocol-level traffic mapping + online adaptive scheduling". SUMMARY

[0004] The present application provides a multi-source multi-path scheduling method for remote sensing data transmission to solve the above problems.

[0005] The present application provides a multi-source multi-path scheduling method for remote sensing data transmission, which comprises the following steps:

[0006] S2: When receiving an interest packet, the controller extracts the time field and space field therefrom, and obtains a multi-source node set according to the time-space copy index table, and sorts the multi-source node set according to a link state matrix to obtain a priority node list; the link state matrix comprises bandwidth, round-trip delay and packet loss rate.

[0007] S3: The controller selects multiple non-overlapping paths according to the priority node list, generates an extended interest packet with a service type, path number, path state and traffic proportion field, and sends the extended interest packet to each source node.

[0008] S4: The forwarding node continuously reports the remaining bandwidth, round-trip delay and packet loss rate through the extended interest packet, and the controller calculates the path adaptability score through the link utilization rate and the comprehensive delay, and distributes the traffic proportion of each path according to the path adaptability score proportion.

[0009] S5: According to the multi-source node list and path traffic mapping issued by the controller, each source node transmits the remote sensing data segment to the user end in parallel through the corresponding path according to the segment number; in the transmission process, the forwarding node continuously reports the link changes, and the controller adjusts the path set and the traffic proportion in real time to ensure the overall transmission bandwidth and delay performance.

[0010] Further, S1 specifically comprises: S11: The controller generates a unique name for each remote sensing data based on the source node identifier , service type , collection coverage area , collection timestamp , node information generated or cached , and data type .

[0011] S12: The unique name and triple structure are written into the time-space copy index table.

[0012] Furthermore, the S2 specifically includes: S21: when receiving the interest packet, the controller extracts the time field in the interest packet and spatial fields .

[0013] S22: Traverse each triple in the spatiotemporal replica index table ,when and Coverage area includes When the node information Join the candidate node set .

[0014] S23: For the candidate node set Each node in , the controller is based on the link state matrix Calculate the overall priority score ;in, are the round-trip delay, bandwidth and packet loss rate of the shortest path from the node to the user, is the preset weight constant.

[0015] S24: Press Sort from small to large, select the first nodes as the priority node list.

[0016] Furthermore, the S4 specifically includes: S41: the forwarding node reports the remaining bandwidth at a preset period , round-trip delay and packet loss rate .

[0017] S42: The controller calculates the link utilization using the following formula: : ;in, Indicates the currently occupied bandwidth. Indicates the upper limit of the physical bandwidth.

[0018] S43: The controller calculates the comprehensive delay using the following formula : ;in, Indicates the transmission delay, represents the propagation delay, represents the queuing delay, Indicates processing delay.

[0019] S44: The controller calculates the path fitness score using the following formula : ;in, is the adjustment weight, and .

[0020] S45: The controller allocates the flow ratio of each path according to the path fitness score ratio using the following formula: ;in, The bandwidth requirements requested for the user task.

[0021] Furthermore, the S5 specifically includes: S51: the controller numbers the remote sensing data Split and generate a path traffic mapping table.

[0022] S52: The source node adds a path number to the local data segment according to the mapping table, and sends the data packet in sequence via the corresponding path. The forwarding node forwards the data packet according to the path number.

[0023] S53: During the forwarding process, the forwarding node periodically encapsulates the link remaining bandwidth, round-trip delay, and packet loss rate into the extended interest packet.

[0024] S54: If the controller detects that the bandwidth of a certain path decreases or the delay exceeds a threshold, it recalculates the path fitness score and traffic ratio and updates the path traffic mapping table.

[0025] (III) Beneficial Effects: Compared with the prior art, the present invention provides a multi-source multi-path scheduling method for remote sensing data transmission, which has the following beneficial effects: 1. The multi-source multi-path scheduling method for remote sensing data transmission introduces the "acquisition area loc + acquisition timestamp time" secondary field in the NDN naming layer and writes the triplet synchronously. The index table enables precise positioning and version identification of image copies within the same mission window within seconds, avoiding the broadcast overhead of traditional "directory-hash" double-hop queries, significantly reducing index traversal latency and improving the retrieval efficiency of the air-ground fusion network.

[0026] 2. The multi-source multi-path scheduling method for remote sensing data transmission adopts bandwidth-delay-packet loss rate weighted scoring on the controller side. and press Traffic weight calculated proportionally The PathId and L_i fields are added to the interest packets and data packets, thereby realizing link quality-aware load balancing among multi-source nodes: high-score paths receive more diversion and low-score paths are automatically loaded down, which not only overcomes the single-source bottleneck caused by "first come, first served" but also ensures throughput and fairness under heterogeneous link conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the overall process of the method of the present invention.

[0028] Figure 2 Schematic diagram of the timing interaction of the method of the present invention.

[0029] Figure 3 Figure 1 is a schematic diagram of the three-layer architecture of the method of the present application.

[0030] Figure 4 Figure 2 is a schematic diagram of the basic components of the architecture of the method of the present application.

[0031] Figure 5 Figure 3 is a schematic diagram of the packet format of the method of the present application.

[0032] Figure 6 Figure 4 is a schematic diagram of the traffic distribution ratio of the method of the present application.

[0033] Figure 7 Figure 5 is a schematic diagram of the first experimental scenario of the method of the present application.

[0034] Figure 8 Figure 6 is a schematic diagram of the second experimental scenario of the method of the present application.

[0035] Figure 9 Figure 7 is a graph of the effect of the interest packet sending frequency on the latency of the method of the present application.

[0036] Figure 10 Figure 8 is a graph of the throughput of the multi-source multi-path scenario of the method of the present application.

[0037] Figure 11 Figure 9 is a graph of the throughput comparison of different scheduling algorithms of the method of the present application.

[0038] Figure 12 Figure 10 is a graph of the download volume of the method of the present application using different scheduling strategies by different ground stations. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0040] In order to enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the drawings and specific embodiments.

[0041] Please refer to Figures 1-6 , Figure 1 Figure 1 is a schematic diagram of the three-layer architecture of the method of the present application. Figure 2 Figure 2 is a schematic diagram of the basic components of the architecture of the method of the present application. Figure 3 Figure 1 is a schematic diagram of the three-layer architecture of the method of the present application. Figure 4 Figure 2 is a schematic diagram of the basic components of the architecture of the method of the present application. Figure 5A schematic diagram of a package format of the method of the present application; Figure 6 A schematic diagram of a flow distribution ratio of the method of the present application; the present application provides a multi-source multi-path scheduling method for remote sensing data transmission, the method comprising: S1: a controller generates a unique name for each piece of remote sensing data using a unified space-time naming structure, and writes the unique name and a triple structure into a space-time replica index table based on node information of generation or caching of the remote sensing data, a collection coverage area, and a collection timestamp.

[0042] S2: when receiving an interest package, the controller extracts the time field and the space field therefrom, and obtains a multi-source node set according to the space-time replica index table, and sorts the multi-source node set according to a link state matrix to obtain a priority node list; the link state matrix comprises bandwidth, round-trip delay, and packet loss rate.

[0043] S3: the controller selects multiple non-overlapping paths according to the priority node list, generates an extended interest package with a service type, a path number, a path state, and a flow ratio field, and sends the extended interest package to each source node.

[0044] S4: a forwarding node continuously reports residual bandwidth, round-trip delay, and packet loss rate through the extended interest package, and a controller calculates a path fitness score through link utilization rate and comprehensive delay, and distributes a flow ratio of each path according to a path fitness score ratio.

[0045] S5: according to the multi-source node list and path flow mapping issued by the controller, each source node transmits remote sensing data segments to a user end in parallel through corresponding paths according to segment numbers; in the transmission process, a forwarding node continuously reports link changes, and a controller adjusts a path set and a flow ratio in real time to ensure overall transmission bandwidth and delay performance.

[0046] Further, in order to quickly and accurately locate a remote sensing data replica in an aerospace-terrestrial-marine integrated environment, a unique name carrying space and time semantics needs to be given to each piece of data, that is, a corresponding naming entry is constructed through space-time attributes such as a collection coverage area and a collection timestamp ; in the naming stage, a controller generates a name for remote sensing data according to a source node identifier a service type , a collection coverage area , a collection timestamp , node information of generation or caching , and a data type , and a triple structure write a spatio-temporal replica index table. This naming-index design can ensure the retrieval of multi-copy data while ensuring the global uniqueness of naming, that is, it can still quickly return the matching source node set when facing large-scale constellations and edge caches; because the index is only maintained and updated by the controller, the rest of the nodes only hold local cache records, so external entities cannot fake or tamper with the spatio-temporal mapping, thereby ensuring the security of the retrieval results. The S1 specifically comprises: S11: the controller generates a unique name for each piece of remote sensing data based on the source node identifier , service type , collection coverage area , collection timestamp , generated or cached node information , and data type .

[0047] S12: write the unique name and triple structure to the spatio-temporal replica index table.

[0048] Further, in order to ensure that user requests can be quickly routed to the most suitable data copy in a high-dynamic remote sensing network, it is necessary to complete the two-level screening of "spatio-temporal matching + link quality assessment" immediately when the interest packet arrives; therefore, when the controller receives the interest packet, it first extracts the time field and space field in the interest packet, and retrieves the multi-source node set that meets and covers in the spatio-temporal replica index table ; then, the controller calculates the comprehensive priority for each node in the set according to the real-time maintained link state matrix, and generates a priority node list from low to high according to the priority. Therefore, the selected node meets the user task window in space and time, and also eliminates the risk of congestion or sudden increase in delay caused by the performance difference of the inter-node link through a unified metric. The S2 specifically comprises: S21: when receiving the interest packet, the controller extracts the time field and space field in the interest packet.

[0049] S22: traverse each triple in the spatio-temporal replica index table, and when and covers , add the node information to the candidate node set .

[0050] S23: for the candidate node set each node in the network , the controller calculates the comprehensive priority score according to the link state matrix ; wherein, respectively are the round-trip delay, bandwidth and packet loss rate of the shortest path from the node to the user, is a preset weight constant.

[0051] S24: according to from small to large, select the first nodes as the priority node list.

[0052] Further, in order to seamlessly transfer the "best source-best path-best split ratio" decision calculated by the controller to the forwarding plane and directly mark "who takes which path and how much" at the protocol layer, the controller first selects multiple end-to-end paths that are mutually non-overlapping and complementary in link indicators according to the topology after obtaining the priority node list, and calculates the target traffic weight for each path; then constructs an extended interest packet, adds a service type field to distinguish the service scenario, uses a path number to identify the selected path sequence number, uses a path state to carry the real-time score of the path for the purpose of writing back the link quality by the nodes along the way, and uses the target traffic weight to clearly indicate the traffic proportion borne by the path. Finally, the interest packet is sent to each source node. The source node can directly label the data block according to the path number at the sending end and fragment according to the traffic weight ratio, avoiding flow splitting in the middle; at the same time, the real-time closed-loop feedback of link quality is formed by means of the path real-time score field, and the selection of multiple mutually non-overlapping paths can eliminate the competition of hot links, realize real multi-source parallel transmission and bandwidth superposition, and thus significantly improve the throughput and reduce the risk of congestion.

[0053] Further, in order to continuously maintain the optimal bandwidth utilization and the lowest end-to-end delay in the long-delay and high-fluctuation star-ground link, the present application requires each forwarding node to embed the remaining bandwidth , round-trip delay and packet loss rate in the path state field of the extended interest packet and report periodically; the controller calculates the link utilization rate and the comprehensive delay including transmission, propagation, queuing and processing components in real time, and then gives a path fitness score; the higher the score, the more idle the path, the more bandwidth it has, and the more friendly the delay. Subsequently, the controller proportionally allocates the to-be-sent traffic to each path, so that the high-score path bears more traffic and the low-score path automatically reduces the load; if the score of a path decreases significantly with the reported data, the new score and traffic ratio can be brought back to the source node by the next round of extended interest packet, so that the throughput remains stable and the delay is controllable when the link quality fluctuates or the node is congested. The S4 specifically comprises: S41: the forwarding node reports the remaining bandwidth , round-trip delay and packet loss rate​ .

[0054] S42: The controller calculates the link utilization rate by the following formula : ; wherein, represents the current occupied bandwidth, represents the upper limit of the physical bandwidth.

[0055] S43: The controller calculates the comprehensive delay by the following formula : ; wherein, represents the transmission delay, represents the propagation delay, represents the queuing delay, represents the processing delay.

[0056] S44: The controller calculates the path fitness score by the following formula : ; wherein, is the adjustment weight, and .

[0057] S45: The controller allocates the traffic proportion of each path according to the path fitness score proportion by the following formula: ; wherein, is the bandwidth requirement of the user task request.

[0058] Further, in order to fully tap the parallel advantages of multiple copies and multiple paths and maintain the quality of service when the link fluctuates, the controller first divides the whole remote sensing data into consecutive numbered segments according to the path weight, and generates a "source node-path-segment" three-dimensional mapping table; after receiving the mapping, each source node directly writes the corresponding path number mark for each data segment locally and synchronously sends packets in the specified path according to the number order, thereby forming a parallel transmission flow of multi-source segmentation, link multiplexing, and bandwidth superposition on the user side. At the same time, the forwarding node encapsulates the real-time measured residual bandwidth, round-trip delay, and packet loss rate into the path state field for continuous reporting, and the controller compares the target traffic proportion with the current path throughput difference. If it is found that the bandwidth of a certain path suddenly decreases or the delay soars, the new path fitness score and the target traffic proportion are recalculated, the mapping table is dynamically rewritten, and the addition or deletion of paths or the adjustment of the segment proportion instruction is issued, so as to realize online load migration and bottleneck avoidance, thereby ensuring that the whole transmission process still maintains the target bandwidth and low delay index in the high-dynamic space-ground link environment.

[0059] The S5 specifically includes: S51: The controller divides the remote sensing data according to the numbered segment, and generates a path traffic mapping table.

[0060] ​S52: The source node adds a path number to the local data segment according to the mapping table, and sends the data packet in sequence via the corresponding path. The forwarding node forwards the data packet according to the path number.

[0061] S53: During the forwarding process, the forwarding node periodically encapsulates the link remaining bandwidth, round-trip delay, and packet loss rate into the extended interest packet.

[0062] S54: If the controller detects that the bandwidth of a certain path decreases or the delay exceeds a threshold, it recalculates the path fitness score and traffic ratio and updates the path traffic mapping table.

[0063] To further verify the effect of the method of the present invention, please refer to Figures 8-12 , Figure 8 Schematic diagram of the second experimental scenario of the method of the present invention; Figure 9 This is a diagram showing the effect of the interest packet sending frequency on the delay of the method of the present invention; Figure 10 This is a throughput effect diagram of the multi-source multi-path scenario of the method of the present invention; Figure 11 This is a throughput comparison diagram of different scheduling algorithms of the method of the present invention; Figure 12 This is a diagram showing the download volume effects of different ground stations using different scheduling strategies according to the method of the present invention.

[0064] Figure 7 A "linear" scenario is shown where a single consumer requests data from two satellites in parallel on a satellite-to-ground link; Figure 8 This is expanded to a "mesh" scenario covering a global six-orbit constellation and multiple ground stations. Nodes numbered Node101-604 form a three-dimensional interconnected backbone, with ground stations in the Arctic, Kashgar, and Sanya participating simultaneously. The two topologies illustrate that this method supports both simple chaining and large-scale, intercontinental multi-hop forwarding and caching coordination, providing sufficient path and replica diversity for subsequent multi-metric scheduling.

[0065] like Figure 9 As shown in the figure, in the first scenario, when the interest packet sending frequency is increased from 1400pkt / s to 2100pkt / s, the average request latency of this method (ST-MMSS) increases linearly from about 32ms to 63ms, but is always 3-6ms lower than the ECMP and RAF synchronization test curves; especially at 1800pkt / s, ST-MMSS is about 6ms and 4ms lower than RAF and ECMP, respectively, indicating that the "spatiotemporal filtering + link scoring + diversion weight" mechanism effectively suppresses the delay expansion caused by queuing and retransmission under high concurrency.

[0066] like Figure 10As shown in the second scenario, when three candidate paths are injected with fixed link delays of 10 ms, 30 ms, and 50 ms respectively, it can be seen that the three curves complete bandwidth climbing within 10 s and stabilize at a platform line of 15-16 Mbps, with a fluctuation amplitude of less than ± 0.5 Mbps; this indicates that even if the heterogeneous RTT difference reaches 5 times, the bandwidth weight and online redistribution can still maintain high link utilization.

[0067] As shown in the second scenario, when three candidate paths are injected with fixed link delays of 10 ms, 30 ms, and 50 ms respectively, it can be seen that the three curves complete bandwidth climbing within 10 s and stabilize at a platform line of 15-16 Mbps, with a fluctuation amplitude of less than ± 0.5 Mbps; this indicates that even if the heterogeneous RTT difference reaches 5 times, the bandwidth weight and online redistribution can still maintain high link utilization. Figure 11 As shown in the same network condition, the steady-state throughput of ST-MMSS is about 17 Mbps, that of ECMP is about 16 Mbps, and that of RAF is only 15 Mbps; the overall improvement is about 6% for ECMP and about 13% for RAF. Figure 10 According to the real-time score, ST-MMSS dynamically migrates traffic, avoids the low-bandwidth path caused by the equal distribution mismatch of ECMP and the path preemption of RAF, and avoids the low-bandwidth path caused by the equal distribution mismatch of ECMP and the path preemption of RAF.

[0068] As shown in the second scenario, when three candidate paths are injected with fixed link delays of 10 ms, 30 ms, and 50 ms respectively, it can be seen that the three curves complete bandwidth climbing within 10 s and stabilize at a platform line of 15-16 Mbps, with a fluctuation amplitude of less than ± 0.5 Mbps; this indicates that even if the heterogeneous RTT difference reaches 5 times, the bandwidth weight and online redistribution can still maintain high link utilization. Figure 12 As shown in the same network condition, the steady-state throughput of ST-MMSS is about 17 Mbps, that of ECMP is about 16 Mbps, and that of RAF is only 15 Mbps; the overall improvement is about 6% for ECMP and about 13% for RAF.

[0069] In summary, multiple groups of simulations show that in the range of link delay 10 ms-50 ms and interest packet rate 1400-2100 pkt / s, the average throughput of the application is improved by about 15%-28%, the end-to-end delay is reduced by 12%-20%, and the download amount can be kept in the lead in a globally distributed scenario, which reflects the efficiency and stability of the application in high-concurrency and high-dynamic remote sensing data distribution.

[0070] It should be noted that in this paper, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or equipment including the element.

[0071] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A multi-source multi-path scheduling method for remote sensing data transmission, characterized in that: The method comprises: S1: The controller uses a unified spatiotemporal naming structure to generate a unique name for each remote sensing data item, and writes the unique name and triple structure into the spatiotemporal replica index table based on the node information, acquisition coverage area, and acquisition timestamp of the remote sensing data. S2: When receiving an interest packet, the controller extracts the time and space fields from it, obtains a multi-source node set based on the time-space replica index table, and sorts the multi-source node set based on the link state matrix to obtain a priority node list; the link state matrix includes bandwidth, round-trip delay, and packet loss rate; S3: The controller selects multiple end-to-end non-overlapping paths based on the priority node list, generates an extended interest packet with service type, path number, path status and target traffic ratio fields, and sends it to each source node; S4: The forwarding node encapsulates the remaining bandwidth, round-trip delay, and packet loss rate in the path status field of the extended interest packet. The controller calculates the path fitness score based on the link utilization and comprehensive delay according to the path status field, writes the updated target traffic ratio into the next round of extended interest packets, and distributes the target traffic ratio of each path according to the path fitness score ratio. S5: According to the multi-source node list and path traffic mapping issued by the controller, each source node sends the remote sensing data segments to the user end in parallel via the corresponding path according to the segment number and the target traffic ratio; during the transmission process, the forwarding node continuously reports link changes, and the controller adjusts the path set and traffic ratio in real time, and dynamically reallocates the data segments according to the updated path traffic mapping to ensure the overall transmission bandwidth and latency performance.

2. The multi-source multi-path scheduling method for remote sensing data transmission according to claim 1, characterized in that: Said S1 specifically includes: S11: Controller identifies source nodes based on remote sensing data , business type , Collection coverage area , collection timestamp , generated or cached node information and data types , generate a unique name for each remote sensing data; S12: The unique name and triplet The structure is written to the time-space copy index table.

3. The multi-source multi-path scheduling method for remote sensing data transmission according to claim 2, characterized in that: The S2 specifically includes: S21: When receiving the interest packet, the controller extracts the time field in the interest packet and spatial fields ; S22: Traverse each triple in the spatiotemporal replica index table ,when and Coverage area includes When the node information Join the candidate node set ; S23: For the candidate node set Each node in , the controller is based on the link state matrix Calculate the overall priority score ;in, are the round-trip delay, bandwidth and packet loss rate of the shortest path from the node to the user, is the preset weight constant; S24: Press Sort from small to large, select the first nodes as the priority node list.

4. The multi-source multi-path scheduling method for remote sensing data transmission according to claim 3, characterized in that: The S4 specifically includes: S41: The forwarding node reports the remaining bandwidth at a preset period , round-trip delay and packet loss rate ; S42: The controller calculates the link utilization using the following formula: : ; in, Indicates the currently occupied bandwidth. Indicates the upper limit of physical bandwidth; S43: The controller calculates the comprehensive delay using the following formula : ;in, Indicates the transmission delay, represents the propagation delay, represents the queuing delay, Indicates processing delay; S44: The controller calculates the path fitness score using the following formula : ; in, is the adjustment weight, and ; S45: The controller allocates the flow ratio of each path according to the path fitness score ratio using the following formula: ; in, The bandwidth requirements requested for the user task.

5. The multi-source multi-path scheduling method for remote sensing data transmission according to claim 4, characterized in that: The S5 specifically includes: S51: The controller sends the remote sensing data by number Split and generate a path flow mapping table; S52: The source node adds a path number to the local data segment according to the mapping table, and sends the data packet in sequence along the corresponding path. The forwarding node forwards the data packet according to the path number. S53: The forwarding node periodically encapsulates the link remaining bandwidth, round-trip delay, and packet loss rate into the extended interest packet during the forwarding process; S54: If the controller detects that the bandwidth of a certain path decreases or the delay exceeds a threshold, it recalculates the path fitness score and traffic ratio and updates the path traffic mapping table.

Images