An energy-saving multipath routing method for air-ground integrated networks

Abstract

The present invention discloses an energy-saving multi-path routing method for an integrated space-ground-air network. The method comprises the following steps: S1, constructing an integrated space-ground-air network node set, including a satellite set and a ground station set; S2, constructing a large-scale time slot set, a small-scale time slot set, and a task set based on the network node position information; S3, constructing a dual-scale time-varying graph based on the network node set, the large-scale time slot set, the small-scale time slot set, and the task set; S4, constructing a multi-resource joint optimization problem and a network routing problem based on the constructed dual-scale time-varying graph, and solving decision variables; S5, generating a routing strategy and a power control strategy based on the decision variables. The present invention effectively simplifies the solution complexity of the joint optimization of network routing and power allocation, reduces the total energy consumption of the integrated space-ground-air network, and improves the task transmission efficiency and resource utilization efficiency.

Description

Technical Field

[0001] The present invention belongs to the field of space information technology, and in particular relates to an energy-saving multi-path routing method for an air-space-ground integrated network. Background Art

[0002] As the core infrastructure for achieving global mobile coverage in the sixth-generation communication network, the integrated air-space-ground network integrates non-terrestrial and terrestrial networks to provide end-to-end connectivity and data transmission services for a vast number of devices. It is a key cornerstone for realizing services such as industrial automation, intelligent transportation, and telemedicine. 6G networks, with their higher transmission rates and wider coverage, place high-quality, highly reliable end-to-end transmission service requirements on the integrated air-space-ground network. However, the highly dynamic network topology and limited network node resources of the integrated air-space-ground network make it difficult to ensure the efficiency and stability of end-to-end transmission services, posing a significant challenge to the design of efficient network routing solutions. Therefore, it is crucial to design efficient routing strategies for the integrated air-space-ground network to address the challenge of efficiently utilizing limited network resources to provide end-to-end high-quality, highly reliable data transmission services under highly dynamic network topologies.

[0003] Existing routing strategies for integrated space-ground networks are primarily categorized into single-path and multi-path routing strategies. Multi-path routing strategies allocate multiple paths for data transmission tasks, fully utilizing the integrated space-ground network link resources while also preventing communication interruptions when some network nodes fail. Therefore, compared to single-path routing strategies, multi-path routing strategies offer higher resource utilization and robustness, significantly improving the transmission efficiency and reliability of end-to-end data transmission services in integrated space-ground networks. In integrated space-ground networks, satellite nodes are primarily powered by batteries. Excessive battery discharge not only shortens satellite lifespan but also causes satellite node failures, leading to communication interruptions and severely impacting data transmission efficiency and reliability. However, existing multi-path routing schemes ignore the impact of satellite node energy consumption on data transmission efficiency, making it difficult to improve data transmission efficiency and reliability in integrated space-ground networks. Therefore, it is urgent to propose an energy-efficient multi-path approach for integrated space-ground networks to achieve high-quality, reliable end-to-end data transmission services in highly dynamic networks. Summary of the Invention

[0004] Purpose of the invention: The purpose of the present invention is to provide an energy-saving multi-path routing method for an integrated air-space-ground network to solve the above technical problems.

[0005] Technical solution, in order to achieve the above purpose and solve the above technical problems, the present invention proposes an energy-saving multi-path routing method for an integrated air-space-ground network, the method comprising the following steps:

[0006] S1. Build an integrated space-ground network node set, including a satellite set and a ground station set;

[0007] S2. Construct a large-scale time slot set, a small-scale time slot set, and a task set based on the network node location information;

[0008] S3. Construct a dual-scale time-varying graph based on the network node set, large-scale time slot set, small-scale time slot set and task set. ,in, represents the set of vertices in the graph, represents the set of edges in the graph, Represents the weight set of edges in the graph;

[0009] S4. Construct a multi-resource joint optimization problem and a network routing problem based on the constructed dual-scale time-varying graph, and solve the decision variables, including task transmission decision variables, task storage decision variables, power allocation decision variables, and routing decision variables;

[0010] S5. Generate routing strategy and power control strategy according to the decision variables.

[0011] Furthermore, the specific method of step S1 is as follows:

[0012] A collection of network nodes to build an integrated air-space-ground network ,in, is the total number of network nodes, the network node set includes the relay satellite set , a collection of low-orbit satellites and ground station collection ,Right now ,in, is the total number of relay satellites, is the total number of low-orbit satellites, is the total number of ground stations.

[0013] Furthermore, the specific method of step S2 is as follows:

[0014] S21. Import the satellite's ephemeris and ground station longitude and latitude information into the satellite toolbox software STK to calculate the network node set The time window of line-of-sight communication between two network nodes is , and each time window uses a tuple Indicates that, Indicates the The start time of a time window, Indicates the The end time of the time window, Indicates the number of the time window and constructs a time window set ,in, is the total number of time windows in the time window set;

[0015] S22. Get time window set All the start and end times in the form a time set , gather the moments After removing all duplicates, all elements in the set are sorted in ascending order to obtain the set ,in, For the A moment, and satisfying , Representing a collection The maximum time index of the collection The total time is ;

[0016] S23, according to the collection Constructing a large-scale time slot collection , where the tuple Indicates the Large-scale time slots, For the The start time of a large-scale time slot, For the The end time of a large-scale time slot, a binary Indicates the Large-scale time slots Start from the moment End of time, excluding At the moment, the length of the large-scale time slot is on the order of minutes;

[0017] S24. Traverse the large-scale time slot set , any large-scale time slot , evenly divided into The length is The small-scale time slots are constructed as follows:

[0018]

[0019] Among them, the value is a set of small-scale time slots The total number of small-scale time slots, is the length of the small-scale time slot, which is in the order of seconds;

[0020] S25. Build a task set , Indicates the total number of tasks. , using quintuples Indicates that, Indicates a task The source node, Indicates a task The amount of data, Indicates a task The small-scale time slot index corresponding to the generation time of Indicates a task The small-scale time slot index corresponding to the cutoff time, Indicates a task destination node.

[0021] Furthermore, the specific method of step S3 is as follows:

[0022] S31. Constructing a dual-scale time-varying map The vertex set ,in, Indicates in The vertex set constructed for all network nodes in a large-scale time slot, where Large-scale time slots In the network node Constructing vertices , for large-scale time slots The vertex set constructed is ;

[0023] S32. Constructing a dual-scale time-varying map The edge set ,in, Represents the data transmission edge set, including the inter-satellite data transmission edge set and satellite-to-ground data transmission edge set , Represents a data storage edge set;

[0024] S321. Constructing an edge set for inter-satellite data transmission ;

[0025] S3211, Initialization , ;

[0026] S3212, in each large-scale time slot Inside, from the vertex set Select the nodes corresponding to the satellite network Vertex , construct a set of satellite vertices ;

[0027] S3213, determine the satellite vertex set in sequence Two vertices in and Whether there is an intersatellite transmission link between them is determined by placing the vertex and Corresponding satellite network nodes and The ephemeris table is imported into the STK software to determine the two satellite nodes in each large-scale time slot. Is there an intersatellite transmission link?

[0028] If there is a satellite network node arrive The intersatellite transmission link is at two vertices. and Add directed edges between , and update ;

[0029] If there is a satellite network node arrive The intersatellite transmission link is at two vertices. and Add directed edges between , and update ;

[0030] S3214, if , then update the index And return to step S3212; otherwise, end step S321;

[0031] S322. Constructing a satellite-to-ground data transmission edge set ;

[0032] S3221, Initialization , ;

[0033] S3222, from the vertex set Select the network node corresponding to the ground station Vertex , construct the ground station vertex set , and calculate the set The number of vertices is denoted as , according to the obtained satellite vertex set , calculate the number of its vertices, recorded as ,set up and ;

[0034] S32221, get the collection Middle Vertices Corresponding ground station Latitude and longitude and collection Middle Vertices Corresponding satellite nodes The ephemeris is imported into the STK software to calculate both at each large-scale time slot Is there a satellite-to-ground transmission link?

[0035] If there is a ground station To satellite node The satellite-to-ground transmission link is at two vertices. and Add directed edges between , and update ;

[0036] If there is a satellite node To the ground station The satellite-to-ground transmission link is at two vertices. and Add directed edges between , and update ;

[0037] S32222, if , then update the index , and return to step S32221; otherwise, make the following judgment:

[0038] like , then update the index , and set , and return to step S32221; otherwise, complete the collection Build, and end step S322;

[0039] S323, use the constructed inter-satellite data transmission edge set And the constructed satellite-to-ground data transmission edge set Constructing a data transmission edge set ,Right now ;

[0040] S324. Build a data storage edge set ;

[0041] S3241, Initialization , , calculate the vertex set The number of vertices is denoted as ,set up ;

[0042] S3242, respectively obtain vertex sets and Middle Vertices and , and construct from the vertex To the top Directed edges , update the data storage edge set ;

[0043] S3243, if , then update the index , return to step S3242, otherwise, update the index , and make the following judgment:

[0044] like , then set , and return to step S3242; otherwise, complete the collection Build, and end step S324;

[0045] S325. Use the constructed data transmission edge set and the constructed data storage edge collection Constructing a dual-scale time-varying map The edge set ,Right now ;

[0046] S33. Constructing a dual-scale time-varying map The edge weight set ;

[0047] S331. Traverse the intersatellite data transmission edge set All edges in constitute the set of inter-satellite data transmission edge capacity , where the set and collection , Represents the edge corresponding to the intersatellite link In small time slots The channel capacity is calculated by the formula calculate, For small time slots Internal allocation to edge The power, For Link In small time slots The channel gain, for In small time slots The transmit antenna gain, for In small time slots The receiving antenna gain, For small time slots Free space loss, For small time slots The total line loss, is the Boltzmann constant, is the total system noise temperature, is the ratio of the required received energy per bit to the noise density, is the link margin;

[0048] S332. Traverse the satellite-to-ground data transmission edge set All edges in constitute the set of satellite-to-ground data transmission edge capacity ,in, Indicates the edge corresponding to the satellite-to-ground link In small time slots The channel capacity is calculated by the formula Calculate, where is the channel bandwidth, For the edge In small time slots The signal-to-noise ratio, ,in, , is the noise power;

[0049] S333. Constructing a data transmission edge capacity set , including the set of inter-satellite data transmission edge capacity and satellite-to-ground data transmission edge capacity, namely ;

[0050] S334. Traverse the data storage edge set All edges in , build a data storage edge capacity set ,in, Represents an edge Maximum storage space;

[0051] S335. Construct edge weight set , including the data transmission edge capacity set and the data storage edge capacity set ,Right now .

[0052] Furthermore, the method of step S4 is as follows:

[0053] S41. Get the static energy consumption of all edges, using Indicates either side In small time slots Static energy consumption within

[0054] S42, construct the dynamic energy consumption of all edges, using Indicates either side In small time slots Dynamic energy consumption within, where ;

[0055] S43. Construct the optimization objective function of the multi-resource joint optimization problem MJO ,in, Is 01 decision variable A collection of which Represents an edge In small time slots Used for task transmission, otherwise , Is a storage policy variable A collection of Representation Flow In small time slots Assigned to the vertex The proportion of storage, where It's a task The data flow, is the power decision variable A collection of Indicates the total energy consumption of the system, including the total static energy consumption and dynamic total energy consumption ;

[0056] S44. Initialize constraint set , set the number of iterations and the maximum number of iterations ;

[0057] S45. The constraints for constructing the MJO optimization problem are as follows:

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] in, For the task The set of all data streams, i.e. , Represents the vertex network nodes, Indicates a task The source node, Indicates a task The small-scale time slot index corresponding to the generation time of Indicates a task The destination node, Indicates a task The small-scale time slot index corresponding to the cutoff time, Indicates a task The amount of data, Represents an edge Maximum storage space, and Represent the maximum transmission power and the minimum transmission power respectively, and the set Defined as ;

[0070] S46, the optimization objective function constructed in S43 , constraints and constraint sets constructed in S45 The constraint input solver in solves the MJO problem and the obtained solution is recorded as ,in, is the decision variable solution A collection of Is the storage strategy variable solution A collection of is the power decision variable solution A collection of

[0071] S47, use Constructing network routing problems;

[0072] S471, the optimization goal of constructing the NR optimization problem is ,in, is a set of routing variables, i.e. , Is a routing variable, indicating the flow In small time slots Assign to edge percentage of transfers made;

[0073] S472. The constraints for constructing the NR optimization problem are as follows:

[0074]

[0075]

[0076]

[0077]

[0078] in, ;

[0079] S473. Based on the constructed NR optimization problem, the optimization objective function of its dual optimization problem NR-D is constructed as follows: ,in, The expression is as follows:

[0080] in, is the Lagrange multiplier corresponding to the inequality constraint The collection of , is the Lagrange multiplier corresponding to the equality constraint The collection of , is the Lagrange multiplier corresponding to the inequality constraint The collection of ;

[0081] S474. The constraints for constructing the NR-D optimization problem are as follows:

[0082]

[0083]

[0084]

[0085] in, ;

[0086] S475, optimize the objective function in S473 And all the constraints constructed in S474 are input into the solver to solve the NR-D optimization problem, and the obtained solution is recorded as ,in, corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of , corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of , corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of ;

[0087] S476, calculation The value is determined as follows:

[0088] like , it means that there is a feasible solution to the network routing problem NR. Input the optimization objective in S471 and the constraints in S472 into the solver to solve the optimal solution of the routing problem NR. , and set the number of iterations Otherwise, it means that there is no feasible solution to the network routing problem NR, and the constraint , and add it to the constraint set ,Right now ,in, The expression is as follows:

[0089]

[0090] And set the number of iterations ;

[0091] S48, determine the number of iterations and the maximum number of iterations The size relationship between them is as follows:

[0092] like , the iteration ends; otherwise, jump to step S46 for the next round of iteration.

[0093] Furthermore, the method of step S5 is as follows:

[0094] S51, obtain the optimal solution according to the calculation in S4 and ;

[0095] S52, according to the collection All The value of is scheduled, that is, for all In the case of small-scale time slot sets Select the Small-scale time slots, select the edge in this small-scale time slot Perform data transmission;

[0096] S53, according to the collection All The value of determines the power allocation, that is, from the small-scale time slot set Select the Small-scale time slots, within which the edge The allocated power is ;

[0097] S54, according to the collection All The value of determines the percentage of the node storage task data volume, that is, from the small-scale time slot set Select the small-scale time slots, in which nodes Storage data stream The data volume accounts for ;

[0098] S55, according to the collection All The value of determines the percentage of task data volume allocated to the link, that is, from the small-scale time slot set Select the Small-scale time slots, within which the edge Corresponding link allocation data flow The proportion of data volume is .

[0099] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0100] (1) The present invention uses a dual-scale time-varying graph to characterize the time-varying network topology and network resources in the space-ground integrated network, which greatly reduces the consumption of storage space and simplifies the complexity of network resource management.

[0101] (2) The present invention proposes a multi-path routing method for the air-space-ground integrated network, fully utilizes the link and storage resources in the air-space-ground integrated network, designs a high-performance task transmission method, and effectively improves the data transmission efficiency.

[0102] (3) The present invention takes into account the energy consumption factors generated during data transmission, designs a high-energy-efficiency multi-path task transmission method in an integrated air-space-ground network, comprehensively considers the restrictive relationship between energy consumption and task transmission, and obtains the optimal solution for energy consumption and routing selection of task transmission, effectively reducing energy consumption while ensuring data transmission efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 It is a general flow chart for realizing the present invention;

[0104] Figure 2 This is a schematic diagram of a network scenario of the air-space-ground integrated network used in the present invention;

[0105] Figure 3 is a schematic diagram of a dual-scale time-varying graph of the present invention;

[0106] Figure 4 This is a simulation comparison curve chart of the number of tasks and total energy consumption obtained by the present invention and the solution using the convex optimization toolbox. DETAILED DESCRIPTION

[0107] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0108] In order to make the purpose, technical solutions and advantages of the present invention more clear, the specific implementation of the present invention is further described below in conjunction with the accompanying drawings and embodiments, which are intended to explain rather than limit the present invention.

[0109] like Figure 1 As shown, the present invention proposes an energy-saving multi-path routing method for an air-ground integrated network, the method comprising the following steps:

[0110] S1. Build an integrated space-ground network node set, including a satellite set and a ground station set;

[0111] S2. Construct a large-scale time slot set, a small-scale time slot set, and a task set based on the network node location information;

[0112] S3. Construct a dual-scale time-varying graph based on the network node set, large-scale time slot set, small-scale time slot set and task set. ,in, represents the set of vertices in the graph, represents the set of edges in the graph, Represents the weight set of edges in the graph;

[0113] S4. Construct a multi-resource joint optimization problem and a network routing problem based on the constructed dual-scale time-varying graph, and solve the decision variables, including task transmission decision variables, task storage decision variables, power allocation decision variables, and routing decision variables;

[0114] S5. Generate routing strategy and power control strategy according to the decision variables.

[0115] See also Figure 2 The application scenario of step S1 is to build a network node set of the air-space-ground integrated network. ,in, is the total number of network nodes, the network node set includes the relay satellite set , a collection of low-orbit satellites and ground station collection ,Right now ,in, is the total number of relay satellites, is the total number of low-orbit satellites, is the total number of ground stations.

[0116] See also Figure 3 , the specific method of step S2 is as follows:

[0117] S21. Import the satellite's ephemeris and ground station longitude and latitude information into the satellite toolbox software STK to calculate the network node set The time window of line-of-sight communication between two network nodes is , and each time window uses a tuple Indicates that, Indicates the The start time of a time window, Indicates the The end time of the time window, Indicates the number of the time window and constructs a time window set ,in, is the total number of time windows in the time window set;

[0118] S22. Get time window set All the start and end times in the form a time set , gather the moments After removing all duplicates, all elements in the set are sorted in ascending order to obtain the set ,in, For the A moment, and satisfying , Representing a collection The maximum time index of the collection The total time is ;

[0119] See also Figure 3 , available ,gather The total number of internal moments is 4;

[0120] S23, according to the collection Constructing a large-scale time slot collection , where the tuple Indicates the Large-scale time slots, For the The start time of a large-scale time slot, For the The end time of a large-scale time slot, a binary Indicates the Large-scale time slots Start from the moment End of time, excluding At the moment, the length of the large-scale time slot is on the order of minutes;

[0121] See also Figure 3 , the large-scale time slot set is ;

[0122] S24. Traverse the large-scale time slot set , any large-scale time slot , evenly divided into The length is The small-scale time slots are constructed as follows:

[0123]

[0124] Among them, the value is a set of small-scale time slots The total number of small-scale time slots, is the length of the small-scale time slot, which is in the order of seconds;

[0125] See also Figure 3 , corresponding to Large-scale time slots , can be evenly divided into 7 sections of length The small-scale time slots, and then the small-scale time slot set is:

[0126] ,

[0127] in, , , , , , , , .

[0128] S25. Build a task set , Indicates the total number of tasks. , using quintuples Indicates that, Indicates a task The source node, Indicates a task The amount of data, Indicates a task The small-scale time slot index corresponding to the generation time of Indicates a task The small-scale time slot index corresponding to the cutoff time, Indicates a task destination node.

[0129] The specific method of step S3 is as follows:

[0130] S31. Constructing a dual-scale time-varying map The vertex set ,in, Indicates in The vertex set constructed for all network nodes in a large-scale time slot, where Large-scale time slots In the network node Constructing vertices , for large-scale time slots The vertex set constructed is ;

[0131] See also Figure 3 , for large-scale time slots The vertex set constructed is ;

[0132] S32. Constructing a dual-scale time-varying map The edge set ,in, Represents the data transmission edge set, including the inter-satellite data transmission edge set and satellite-to-ground data transmission edge set , Represents a data storage edge set;

[0133] S321. Constructing an edge set for inter-satellite data transmission ;

[0134] S3211, Initialization , ;

[0135] S3212, in each large-scale time slot Inside, from the vertex set Select the nodes corresponding to the satellite network Vertex , construct a set of satellite vertices ;

[0136] S3213, determine the satellite vertex set in sequence Two vertices in and Whether there is an intersatellite transmission link between them is determined by placing the vertex and Corresponding satellite network nodes and The ephemeris table is imported into the STK software to determine the two satellite nodes in each large-scale time slot. Is there an intersatellite transmission link?

[0137] If there is a satellite network node arrive The intersatellite transmission link is at two vertices. and Add directed edges between , and update ;

[0138] If there is a satellite network node arrive The intersatellite transmission link is at two vertices. and Add directed edges between , and update ;

[0139] S3214, if , then update the index And return to step S3212; otherwise, end step S321;

[0140] S322. Constructing a satellite-to-ground data transmission edge set ;

[0141] S3221, Initialization , ;

[0142] S3222, from the vertex set Select the network node corresponding to the ground station Vertex , construct the ground station vertex set , and calculate the set The number of vertices is denoted as , according to the obtained satellite vertex set , calculate the number of its vertices, recorded as ,set up and ;

[0143] S32221, get the collection Middle Vertices Corresponding ground station Latitude and longitude and collection Middle Vertices Corresponding satellite nodes The ephemeris is imported into the STK software to calculate both at each large-scale time slot Is there a satellite-to-ground transmission link?

[0144] If there is a ground station To satellite node The satellite-to-ground transmission link is at two vertices. and Add directed edges between , and update ;

[0145] If there is a satellite node To the ground station The satellite-to-ground transmission link is at two vertices. and Add directed edges between , and update ;

[0146] S32222, if , then update the index , and return to step S32221; otherwise, make the following judgment:

[0147] like , then update the index , and set , and return to step S32221; otherwise, complete the collection Build, and end step S322;

[0148] S323, use the constructed inter-satellite data transmission edge set And the constructed satellite-to-ground data transmission edge set Constructing a data transmission edge set ,Right now ;

[0149] S324. Build a data storage edge set ;

[0150] S3241, Initialization , , calculate the vertex set The number of vertices is denoted as ,set up ;

[0151] S3242, respectively obtain vertex sets and Middle Vertices and , and construct from the vertex To the top Directed edges , update the data storage edge set ;

[0152] S3243, if , then update the index , return to step S3242, otherwise, update the index , and make the following judgment:

[0153] like , then set , and return to step S3242; otherwise, complete the collection Build, and end step S324;

[0154] S325. Use the constructed data transmission edge set and the constructed data storage edge collection Constructing a dual-scale time-varying map The edge set ,Right now ;

[0155] See also Figure 3 , you can get the data storage edge set and data transmission edge set They are:

[0156]

[0157] ;

[0158] S33. Constructing a dual-scale time-varying map The edge weight set ;

[0159] S331. Traverse the intersatellite data transmission edge set All edges in constitute the set of inter-satellite data transmission edge capacity , where the set and collection , Represents the edge corresponding to the intersatellite link In small time slots The channel capacity is calculated by the formula calculate, For small time slots Internal allocation to edge The power, For Link In small time slots The channel gain, for In small time slots The transmit antenna gain, for In small time slots The receiving antenna gain, For small time slots Free space loss, For small time slots The total line loss, is the Boltzmann constant, is the total system noise temperature, is the ratio of the required received energy per bit to the noise density, is the link margin;

[0160] S332. Traverse the satellite-to-ground data transmission edge set All edges in constitute the set of satellite-to-ground data transmission edge capacity ,in, Indicates the edge corresponding to the satellite-to-ground link In small time slots The channel capacity is calculated by the formula Calculate, where is the channel bandwidth, For the edge In small time slots The signal-to-noise ratio, ,in, , is the noise power;

[0161] S333. Constructing a data transmission edge capacity set , including the set of inter-satellite data transmission edge capacity and satellite-to-ground data transmission edge capacity, namely ;

[0162] S334. Traverse the data storage edge set All edges in , build a data storage edge capacity set ,in, Represents an edge Maximum storage space;

[0163] S335. Construct edge weight set , including the data transmission edge capacity set and data storage edge capacity set ,Right now .

[0164] The method of step S4 is as follows:

[0165] S41. Get the static energy consumption of all edges, using Indicates either side In small time slots Static energy consumption within

[0166] S42, construct the dynamic energy consumption of all edges, using Indicates either side In small time slots Dynamic energy consumption within, where ;

[0167] S43. Construct the optimization objective function of the multi-resource joint optimization problem MJO ,in, Is 01 decision variable A collection of which Represents an edge In small time slots Used for task transmission, otherwise , Is a storage policy variable A collection of Representation Flow In small time slots Assigned to the vertex The proportion of storage, where It's a task The data flow, is the power decision variable A collection of Indicates the total energy consumption of the system, including the total static energy consumption and dynamic total energy consumption ;

[0168] S44. Initialize constraint set , set the number of iterations and the maximum number of iterations ;

[0169] S45. The constraints for constructing the MJO optimization problem are as follows:

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178]

[0179]

[0180]

[0181] in, For the task The set of all data streams, i.e. , Represents the vertex network nodes, Indicates a task The source node, Indicates a task The small-scale time slot index corresponding to the generation time of Indicates a task The destination node, Indicates a task The small-scale time slot index corresponding to the cutoff time, Indicates a task The amount of data, Represents an edge Maximum storage space, and Represent the maximum transmission power and the minimum transmission power respectively, and the set Defined as ;

[0182] S46, the optimization objective function constructed in S43 , constraints and constraint sets constructed in S45 The constraint input solver in solves the MJO problem and the obtained solution is recorded as ,in, is the decision variable solution A collection of Is the storage strategy variable solution A collection of is the power decision variable solution A collection of

[0183] S47, use Constructing network routing problems;

[0184] S471, the optimization goal of constructing the NR optimization problem is ,in, is a set of routing variables, i.e. , Is a routing variable, indicating the flow In small time slots Assign to edge percentage of transfers made;

[0185] S472. The constraints for constructing the NR optimization problem are as follows:

[0186]

[0187]

[0188]

[0189]

[0190] in, ;

[0191] S473. Based on the constructed NR optimization problem, the optimization objective function of its dual optimization problem NR-D is constructed as follows: ,in, The expression is as follows:

[0192] in, is the Lagrange multiplier corresponding to the inequality constraint The collection of , is the Lagrange multiplier corresponding to the equality constraint The collection of , is the Lagrange multiplier corresponding to the inequality constraint The collection of ;

[0193] S474. The constraints for constructing the NR-D optimization problem are as follows:

[0194]

[0195]

[0196]

[0197] in, ;

[0198] S475, optimize the objective function in S473 And all the constraints constructed in S474 are input into the solver to solve the NR-D optimization problem, and the obtained solution is recorded as ,in, corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of , corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of , corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of ;

[0199] S476, calculation The value is determined as follows:

[0200] like , it means that there is a feasible solution to the network routing problem NR. Input the optimization objective in S471 and the constraints in S472 into the solver to solve the optimal solution of the routing problem NR. , and set the number of iterations Otherwise, it means that there is no feasible solution to the network routing problem NR, and the constraint , and add it to the constraint set ,Right now ,in, The expression is as follows:

[0201]

[0202] And set the number of iterations ;

[0203] S48, determine the number of iterations and the maximum number of iterations The size relationship between them is as follows:

[0204] like , the iteration ends; otherwise, jump to step S46 for the next round of iteration.

[0205] The method of step S5 is as follows:

[0206] S51, obtain the optimal solution according to the calculation in S4 and ;

[0207] S52, according to the collection All The value of is scheduled, that is, for all In the case of small-scale time slot sets Select the Small-scale time slots, select the edge in this small-scale time slot Perform data transmission;

[0208] S53, according to the collection All The value of determines the power allocation, that is, from the small-scale time slot set Select the Small-scale time slots, within which the edge The allocated power is ;

[0209] S54, according to the collection All The value of determines the percentage of the node storage task data volume, that is, from the small-scale time slot set Select the small-scale time slots, in which nodes Storage data stream The data volume accounts for ;

[0210] S55, according to the collection All The value of determines the percentage of task data volume allocated to the link, that is, from the small-scale time slot set Select the Small-scale time slots, within which the edge Corresponding link allocation data flow The proportion of data volume is .

[0211] Simulation content and results

[0212] 1. Simulation conditions

[0213] The following is an example of a scenario including three low-orbit satellites, three relay satellites, and one ground station to illustrate the advantages of the present invention.

[0214] Assume that the length of the small-scale time slot is Seconds, there are 40 small-scale time slots in total. Due to the dynamic nature of the satellite-to-ground link, each satellite can choose different satellites and ground stations for data transmission in each time slot. Tasks are randomly generated from the source node of the task in the small-scale time slot, and the data size of the task is randomly generated from the interval Mbits selection. The storage capacity of the node is from the interval Nodes are randomly selected in Mbits. In any small time slot Transmitting antenna gain ,node In any small time slot Receiving antenna gain , in any small-scale time slot Free space loss , total line loss , the Boltzmann constant , total system noise temperature , signal-to-noise ratio , , link margin The minimum transmission power of the satellite , maximum transmit power .

[0215] There are three power allocation and routing strategy schemes used in the simulation: one is the scheme of the present invention; the second is the scheme of the present invention under fixed power; and the third is to directly use the toolbox CVX to directly solve the multipath routing problem under fixed power.

[0216] 2. Simulation content and results

[0217] Simulation 1: The total energy consumption of the present invention and the comparative solution under different task numbers is simulated and compared. The results are as follows: Figure 4 As shown. Figure 4 It can be seen that as the number of tasks increases, at fixed powers of 200W, 150W, and 100W, the total energy consumption of the present invention is equal to that of the CVX solution. This shows that the present invention obtains the optimal routing solution under fixed power. In addition, when the transmission power range is 100-200W, the present invention achieves lower total energy consumption than the case of fixed power allocation. This shows that the present invention can effectively reduce the total energy consumption of the system by taking power control into consideration in the routing solution. In addition, directly using CVX cannot solve the multi-path energy-saving routing problem under study. Therefore, the solution of the present invention has a wider range of application scenarios than the CVX solution.

Claims

1. An energy-saving multi-path routing method for an air-ground integrated network, characterized in that: The method comprises the following steps: S1. Build an integrated space-ground network node set, including a satellite set and a ground station set; S2. Construct a large-scale time slot set, a small-scale time slot set, and a task set based on the network node location information; S3. Construct a dual-scale time-varying graph based on the network node set, large-scale time slot set, small-scale time slot set and task set. ,in, represents the set of vertices in the graph, represents the set of edges in the graph, Represents the weight set of edges in the graph; S4. Construct a multi-resource joint optimization problem and a network routing problem based on the constructed dual-scale time-varying graph, and solve the decision variables, including task transmission decision variables, task storage decision variables, power allocation decision variables, and routing decision variables; S5. Generate a routing strategy and a power control strategy based on the decision variables; The specific method of step S1 is as follows: A collection of network nodes to build an integrated air-space-ground network ,in, is the total number of network nodes, the network node set includes the relay satellite set , a collection of low-orbit satellites and ground station collection ,Right now ,in, is the total number of relay satellites, is the total number of low-orbit satellites, is the total number of ground stations; The specific method of step S2 is as follows: S21. Import the satellite's ephemeris and ground station longitude and latitude information into the satellite toolbox software STK to calculate the network node set The time window of line-of-sight communication between two network nodes is , and each time window uses a tuple Indicates that, Indicates the The start time of a time window, Indicates the The end time of the time window, Indicates the number of the time window and constructs a time window set ,in, is the total number of time windows in the time window set; S22. Get time window set All the start and end times in the form a time set , gather the moments After removing all duplicates, all elements in the set are sorted in ascending order to obtain the set ,in, For the A moment, and satisfying , Representing a collection The maximum time index of the collection The total time is ; S23, according to the collection Constructing a large-scale time slot collection , where the tuple Indicates the Large-scale time slots, For the The start time of a large-scale time slot, For the The end time of a large-scale time slot, a binary Indicates the Large-scale time slots Start from the moment End of time, excluding At the moment, the length of the large-scale time slot is on the order of minutes; S24. Traverse the large-scale time slot set , any large-scale time slot , evenly divided into The length is The small-scale time slots are constructed as follows: ， Among them, the value is a set of small-scale time slots The total number of small-scale time slots, is the length of the small-scale time slot, which is in the order of seconds; S25. Build a task set , Indicates the total number of tasks. , using quintuples Indicates that, Indicates a task The source node, Indicates a task The amount of data, Indicates a task The small-scale time slot index corresponding to the generation time of Indicates a task The small-scale time slot index corresponding to the cutoff time, Indicates a task The destination node; The specific method of step S3 is as follows: S31. Constructing a dual-scale time-varying map The vertex set ,in, Indicates in The vertex set constructed for all network nodes in a large-scale time slot, where Large-scale time slots In the network node Constructing vertices , for large-scale time slots The vertex set constructed is ; S32. Constructing a dual-scale time-varying map The edge set ,in, Represents the data transmission edge set, including the inter-satellite data transmission edge set and satellite-to-ground data transmission edge set , Represents a data storage edge set; S321. Constructing an edge set for inter-satellite data transmission ; S3211, Initialization , ; S3212, in each large-scale time slot Inside, from the vertex set Select the nodes corresponding to the satellite network Vertex , construct a set of satellite vertices ; S3213, determine the satellite vertex set in sequence Two vertices in and Whether there is an intersatellite transmission link between them is determined by placing the vertex and Corresponding satellite network nodes and The ephemeris table is imported into the STK software to determine the two satellite nodes in each large-scale time slot. Whether there is an intersatellite transmission link; If there is a satellite network node arrive The intersatellite transmission link is at two vertices. and Add directed edges between , and update ; If there is a satellite network node arrive The intersatellite transmission link is at two vertices. and Add directed edges between , and update ; S3214, if , then update the index And return to step S3212; otherwise, end step S321; S322. Constructing a satellite-to-ground data transmission edge set ; S3221, Initialization , ; S3222, from the vertex set Select the network node corresponding to the ground station Vertex , construct the ground station vertex set , and calculate the set The number of vertices is denoted as , according to the obtained satellite vertex set , calculate the number of its vertices, recorded as ,set up and ; S32221, get the collection Middle Vertices Corresponding ground station Latitude and longitude and collection Middle Vertices Corresponding satellite nodes The ephemeris is imported into the STK software to calculate both at each large-scale time slot Is there a satellite-to-ground transmission link? If there is a ground station To satellite node The satellite-to-ground transmission link is at two vertices. and Add directed edges between , and update ; If there is a satellite node To the ground station The satellite-to-ground transmission link is at two vertices. and Add directed edges between , and update ; S32222, if , then update the index , and return to step S32221; otherwise, make the following judgment: like , then update the index , and set , and return to step S32221; otherwise, complete the collection Build, and end step S322; S323, use the constructed inter-satellite data transmission edge set And the constructed satellite-to-ground data transmission edge set Constructing a data transmission edge set ,Right now ; S324. Build a data storage edge set ; S3241, Initialization , , calculate the vertex set The number of vertices is denoted as ,set up ; S3242, respectively obtain vertex sets and Middle Vertices and , and construct from the vertex To the top Directed edges , update the data storage edge set ; S3243, if , then update the index , return to step S3242, otherwise, update the index , and make the following judgment: like , then set , and return to step S3242; otherwise, complete the collection Build, and end step S324; S325. Use the constructed data transmission edge set and the constructed data storage edge collection Constructing a dual-scale time-varying map The edge set ,Right now ; S33. Constructing a dual-scale time-varying map The edge weight set ; S331. Traverse the intersatellite data transmission edge set All edges in constitute the set of inter-satellite data transmission edge capacity , where the set and collection , Represents the edge corresponding to the intersatellite link In small time slots The channel capacity is , For small time slots Internal allocation to edge Power, For Link In small time slots The channel gain, for In small time slots The transmit antenna gain, for In small time slots The receiving antenna gain, For small time slots Free space loss, For small time slots The total line loss, is the Boltzmann constant, is the total system noise temperature, is the ratio of the required received energy per bit to the noise density, is the link margin; S332. Traverse the satellite-to-ground data transmission edge set All edges in constitute the set of satellite-to-ground data transmission edge capacity ,in, Indicates the edge corresponding to the satellite-to-ground link In small time slots The channel capacity, , is the channel bandwidth, For the edge In small time slots The signal-to-noise ratio, ,in, , is the noise power; S333. Constructing a data transmission edge capacity set , including the set of inter-satellite data transmission edge capacity and satellite-to-ground data transmission edge capacity, namely ; S334. Traverse the data storage edge set All edges in , build a data storage edge capacity set ,in, Represents an edge Maximum storage space; S335. Construct edge weight set , including the data transmission edge capacity set and data storage edge capacity set ,Right now ; The method of step S4 is as follows: S41. Get the static energy consumption of all edges, using Indicates either side In small time slots Static energy consumption within S42, construct the dynamic energy consumption of all edges, using Indicates either side In small time slots Dynamic energy consumption within, where ; S43. Construct the optimization objective function of the multi-resource joint optimization problem MJO ,in, Is 01 decision variable A collection of which Represents an edge In small time slots is used for task transmission, otherwise , Is a storage policy variable A collection of Representation Flow In small time slots Assigned to the vertex The ratio of storage, where It's a task The data flow, is the power decision variable A collection of Indicates the total energy consumption of the system, including the total static energy consumption and dynamic total energy consumption ; S44. Initialize constraint set , set the number of iterations and the maximum number of iterations ; S45. The constraints for constructing the MJO optimization problem are as follows: ， ， ， ， ， ， ， ， ， ， ， in, For the task The set of all data streams, i.e. , Represents the vertex network nodes, Indicates a task The source node, Indicates a task The small-scale time slot index corresponding to the generation time of Indicates a task The destination node, Indicates a task The small-scale time slot index corresponding to the cutoff time, Indicates a task The amount of data, Represents an edge Maximum storage space, and Represent the maximum transmission power and the minimum transmission power respectively, and the set Defined as ; S46, the optimization objective function constructed in S43 , constraints and constraint sets constructed in S45 The constraint input solver in solves the MJO problem and the obtained solution is recorded as ,in, is the decision variable solution A collection of Is the storage strategy variable solution A collection of is the power decision variable solution A collection of S47, use Constructing network routing problems; S471, the optimization goal of constructing the NR optimization problem is ,in, is a set of routing variables, i.e. , Is a routing variable, indicating the flow In small time slots Assign to edge percentage of transfers made; S472. The constraints for constructing the NR optimization problem are as follows: ， ， ， ， in, ; S473. Based on the constructed NR optimization problem, the optimization objective function of its dual optimization problem NR-D is constructed as follows: ,in, The expression is as follows: in, is the Lagrange multiplier corresponding to the inequality constraint The collection of , is the Lagrange multiplier corresponding to the equality constraint The collection of , is the Lagrange multiplier corresponding to the inequality constraint The collection of ; S474. The constraints for constructing the NR-D optimization problem are as follows: ， ， ， in, ; S475, optimize the objective function in S473 And all the constraints constructed in S474 are input into the solver to solve the NR-D optimization problem, and the obtained solution is recorded as ,in, corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of , corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of , corresponds to the Lagrange multiplier in the NR-D optimization problem The solution set of ; S476, calculation The value is determined as follows: like , it means that there is a feasible solution to the network routing problem NR. Input the optimization objective in S471 and the constraints in S472 into the solver to solve the optimal solution of the routing problem NR. , and set the number of iterations Otherwise, it means that there is no feasible solution to the network routing problem NR, and the constraint , and add it to the constraint set ,Right now ,in, The expression is as follows: ， And set the number of iterations ; S48, determine the number of iterations and the maximum number of iterations The size relationship between them is as follows: like , the iteration ends; otherwise, jump to step S46 for the next round of iteration.

2. The energy-saving multi-path routing method for an air-ground integrated network according to claim 1, characterized in that: The method of step S5 is as follows: S51, obtain the optimal solution based on the calculation in S4 and ; S52, according to the collection All The value of is scheduled, that is, for all In the case of small-scale time slot sets Select the Small-scale time slots, select the edge in this small-scale time slot Perform data transmission; S53, according to the collection All The value of determines the power allocation, that is, from the small-scale time slot set Select the Small-scale time slots, within which the edge The allocated power is ; S54, according to the collection All The value of determines the percentage of the node storage task data volume, that is, from the small-scale time slot set Select the small-scale time slots, in which nodes Storage data stream The data volume accounts for ; S55, according to the collection All The value of determines the percentage of task data volume allocated to the link, that is, from the small-scale time slot set Select the Small-scale time slots, within which the edge Corresponding link allocation data flow The proportion of data volume is .

Images