Service scheduling method and system for photoelectric hybrid low earth orbit satellite network

Abstract

The invention discloses a service scheduling method and system for a photoelectric hybrid low earth orbit satellite network, and relates to the technical field of satellite communication and network resource management.The method comprises the steps that the photoelectric hybrid low earth orbit satellite network is constructed, and topological information, node information and a to-be-scheduled service set of the photoelectric hybrid low earth orbit satellite network are obtained; establishing a mixed integer linear programming model containing node selection constraint, service scheduling sequence constraint and routing constraint by taking the minimum weighted sum of the total service completion energy consumption and the total service completion time as a target; in order to solve the problem of high model complexity, a heuristic algorithm based on simulated annealing is designed, and efficient solution is carried out by iteratively optimizing a scheduling sequence and a routing path of a service; and finally, implementing service scheduling according to the obtained optimal scheduling scheme. According to the method, heterogeneous characteristics of the photoelectric nodes and link resource conflicts are fully considered, dynamic balance of energy consumption and time delay is achieved, network energy efficiency and business service quality are remarkably improved, and the method is suitable for efficient operation of large-scale low-orbit satellite constellations.

Description

Technical Field

[0001] This invention relates to the field of satellite communication and network resource management technology, and in particular to a service scheduling method and system for optoelectronic hybrid low-Earth orbit satellite networks. Background Technology

[0002] With the rapid development of Low Earth Orbit (LEO) satellite constellations, satellite communication networks have become a crucial infrastructure for achieving global broadband interconnection. Compared to Geostationary Earth Orbit (GEO) satellites, LEO satellites offer advantages such as lower transmission latency, larger communication capacity, and wider coverage, effectively supporting real-time communication and high-bandwidth service demands. However, with the dramatic increase in constellation size and service volume, the contradiction between energy consumption and latency in traditional electrically switched satellite networks has become increasingly prominent. To address this, optical switching technology has been introduced into inter-satellite communication. By directly forwarding signals in the optical domain, it avoids the traditional optical-electrical-optical conversion process, significantly reducing system energy consumption and improving transmission efficiency. However, due to cost and upgrade cycle limitations, optically switched and electrically switched satellites will coexist for the foreseeable future, forming a hybrid optoelectronic LEO satellite network. How to achieve synergistic optimization of energy consumption and latency in this heterogeneous network has become a key issue in current research and practice.

[0003] In existing research, various methods have been proposed for routing and service scheduling problems in low-Earth orbit satellite networks. Typical strategies include the shortest latency strategy, which aims to minimize end-to-end transmission delay and often uses the shortest path algorithm, but ignores energy consumption. The minimum energy consumption strategy prioritizes optical switching node paths to reduce energy consumption from photoelectric conversion, but it is prone to optical node congestion, affecting overall performance. In addition, multi-objective optimization methods and heuristic algorithms have been extensively studied. For example, mixed-integer linear programming is used to model energy consumption and latency, and simulated annealing and genetic algorithms are combined for solving the problem to improve the scalability of the algorithm. In recent years, some studies have also explored photoelectric hybrid routing methods based on intelligent strategies such as Q-learning. However, these methods are mostly focused on theoretical modeling or single service scenarios, lacking a systematic solution that can simultaneously consider multi-service scheduling, link conflicts, and energy consumption-latency balance.

[0004] Although existing technologies have improved network performance to some extent, the following shortcomings still exist: (1) There is a lack of effective joint optimization mechanism for energy consumption and time delay. Traditional strategies tend to focus on a single objective, making it difficult to achieve a balance between the two in actual dynamic networks. (2) The heterogeneous characteristics of nodes are not fully considered. Optical switching and electrical switching nodes have significant differences in energy consumption and processing latency, while existing models mostly treat nodes as homogeneous. (3) Insufficient consideration of link queuing and conflict constraints. Some methods ignore the waiting time and scheduling order constraints under business concurrency, making the model difficult to apply in actual deployment. (4) The computational complexity is high and the solution efficiency is low. Mixed integer linear programming is prone to computational infeasibility under large-scale constellations, while heuristic algorithms are difficult to guarantee stable global optimal performance. (5) There is a lack of a unified scheduling framework. Currently, there is no comprehensive optimization mechanism that can simultaneously take into account energy efficiency and latency and support parallel scheduling of multiple services.

[0005] Therefore, there is an urgent need for a service scheduling method that can adapt to the characteristics of optoelectronic hybrid low-Earth orbit satellite networks and achieve an efficient balance between energy consumption and latency, so as to improve the overall energy efficiency and service quality of the network. Summary of the Invention

[0006] To address these issues, this invention provides a service scheduling method and system for optoelectronic hybrid low-Earth orbit satellite networks, which solves the problems of difficulty in coordinating energy consumption and latency optimization, insufficient consideration of node heterogeneity, and low scheduling efficiency caused by link resource conflicts in the prior art.

[0007] To address the aforementioned technical problems, embodiments of the present invention provide a service scheduling method for a hybrid optoelectronic low-Earth orbit satellite network, the method comprising: Construct a hybrid optoelectronic low-Earth orbit satellite network, and obtain the topology information, node information, and service set to be scheduled for the hybrid optoelectronic low-Earth orbit satellite network. The node information includes the node's switching type and remaining energy. With the optimization objective of minimizing the weighted average of total service completion energy consumption and total service completion time, a mixed-integer linear programming model for service scheduling is established. The mixed-integer linear programming model includes node selection constraints, service scheduling order constraints, and routing constraints. The mixed-integer linear programming model is solved using a preset optimization algorithm. By iteratively optimizing the scheduling order and routing path of services, the optimal service scheduling scheme is obtained. Service scheduling is performed in the optoelectronic hybrid low-Earth orbit satellite network according to the optimal service scheduling scheme.

[0008] Preferably, the optimization objective is: ; in, Energy consumption for total business operations. Total business completion time and , which is a weighting coefficient used to balance the importance of energy consumption and completion time in the optimization objective.

[0009] Preferably, the total energy consumption for completing a service is calculated by summing the single-hop energy consumption of all services. The single-hop energy consumption is obtained. The energy consumption includes link transmission energy consumption, node processing energy consumption, and laser terminal alignment energy consumption, and its calculation formula is as follows: ; in, For coefficients, Indicates satellite node and nodes The distance between them For business In the link The duration of the single-hop delay; This is a binary variable representing a satellite node. The exchange type; For electrical exchange energy consumption, Indicates satellite node and The orbital relationship between them and The energy consumption for same-track and different-track laser alignment are respectively.

[0010] Preferably, the total service completion time is the maximum value among the completion times of all services, and the single-hop delay of each service is... It includes transmission delay, propagation delay, and node processing delay, and its calculation formula is: ; in, For business The amount of data, For link bandwidth, Indicates satellite node and nodes The distance between them At the speed of light, The processing delay caused by electrical switching.

[0011] Preferably, the node selection constraints include: The next-hop satellite node selected for the service must have remaining energy at a preset percentage of its maximum energy storage value after the service communication is completed. The distance between two satellite nodes communicating must be less than their maximum line-of-sight distance.

[0012] Preferably, the service scheduling order constraint is achieved by introducing binary order variables. with maximum value To ensure that multiple services on a shared link are executed sequentially in a linear manner to avoid conflicts, specifically including: ; ; ; ; in, For business Reaching the node Time, For business Leave node Time, For business In the link The duration of the single-hop delay; It is a binary variable. Indicates business Select Link , Indicates business Do not select link ; For business Leave node Time, For business In the link End time on It is a binary variable. Indicates business The scheduling order takes precedence over the business. Scheduling order; It is a binary variable. Indicates business The scheduling order takes precedence over the business. Scheduling order; For business In the link The end time.

[0013] Preferably, the routing constraints employ a flow conservation strategy to constrain the inflow and outflow of traffic at each node, specifically including: ; ; ; in, For nodes and nodes The link between them For containing source nodes The set of links, To include the destination node The set of links, For business Select Link binary variables, For business Select Link binary variables, For the set of services to be scheduled, It is a set of satellite nodes in a hybrid optoelectronic low-Earth orbit satellite network. It is a set of links in a hybrid optoelectronic low-Earth orbit satellite network.

[0014] Preferably, the step of solving the mixed-integer linear programming model using a preset optimization algorithm, and obtaining the optimal service scheduling scheme by iteratively optimizing the service scheduling order and routing path, specifically includes: Initialization: Initialize the satellite network topology and business set Set optimization algorithm parameters, including maximum temperature, minimum temperature, cooling coefficient, number of runs at each temperature, and maximum number of runs; determine the initial scheduling order of services and the weights of the evaluation function; Generate initial solution: Based on the initial service scheduling order, use Dijkstra's algorithm to solve for the service deployment path at this point, calculate the service completion energy consumption and service completion time, and calculate the evaluation function according to the set weights. ; Iterative optimization: Before the termination condition is met, repeatedly execute the following steps: Step a: At the current temperature, randomly swap the scheduling order of the two services to generate a new solution; Step b: Check if there is a service deployment path that satisfies the node selection constraint under the new scheduling order. If so, use Dijkstra's algorithm to solve for the service deployment path under the new scheduling order and calculate the service completion energy consumption and service completion time at this time. Then, calculate the evaluation function at this time according to the set weights. If it does not exist, return to step a; Step c: Calculate the increment of the evaluation value ,like If the solution is correct, then accept the new solution; otherwise, calculate the probability of acceptance. ,in Generate a random number between 0 and 1 for the current temperature. :like If so, then accept the new solution and update; if If the new solution is not found, proceed to step d. Step d: Determine whether the number of iterations at the current temperature has reached the set number of running rounds at that temperature. If not, return to step a. If so, determine whether the total number of iterations has reached the maximum number of running rounds. If not, reduce the temperature according to the cooling coefficient and return to step a. If so, output the optimal business scheduling scheme.

[0015] This invention also provides a service scheduling system for a hybrid optoelectronic low-Earth orbit satellite network. This system implements the aforementioned service scheduling method for a hybrid optoelectronic low-Earth orbit satellite network, specifically including: The network and service modeling module is used to construct a hybrid optoelectronic low-Earth orbit satellite network and obtain the topology information, node information, and service set to be scheduled of the hybrid optoelectronic low-Earth orbit satellite network. The node information includes the node's switching type and remaining energy. The optimization model construction module is used to establish a mixed-integer linear programming model for service scheduling with the optimization objective of minimizing the weighted average of total service completion energy consumption and total service completion time. The mixed-integer linear programming model includes node selection constraints, service scheduling order constraints, and routing constraints. The model solving module is used to solve the mixed integer linear programming model using a preset optimization algorithm, and obtain the optimal service scheduling scheme by iteratively optimizing the scheduling order and routing path of the services. The service scheduling execution module is used to perform service scheduling in the optoelectronic hybrid low-Earth orbit satellite network according to the optimal service scheduling scheme.

[0016] This invention also provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the service scheduling method for optoelectronic hybrid low-Earth orbit satellite networks described above.

[0017] As can be seen from the above technical solutions, this invention application has the following beneficial effects: (1) Achieved efficient collaborative optimization of energy consumption and latency: This invention fundamentally solves the limitation of traditional strategies that often favor a single objective (such as the shortest latency or the minimum energy consumption) by establishing a mixed integer linear programming (MILP) model with the objective of minimizing the weighted sum of total service completion energy consumption and total service completion time. The model flexibly adjusts the importance of energy consumption and completion time in the optimization objective through weight coefficients, thereby achieving an effective balance between the two in a dynamic network environment and significantly improving the overall performance and resource utilization efficiency of the network.

[0018] (2) The heterogeneous characteristics of the network and resource conflicts are fully considered, improving the practicality and scheduling efficiency of the solution: The technical solution of this invention meticulously models the heterogeneous characteristics of nodes in the optoelectronic hybrid network (such as switching type and remaining energy) and the actual constraints of link resources (such as maximum line-of-sight distance). In particular, by introducing binary sequence variables and linearization methods, strict service scheduling order constraints are designed, effectively solving the conflict problem of multiple services on shared links. This makes the scheduling scheme closer to the actual network operating conditions and solves the problem that the model results of traditional models are difficult to implement due to ignoring node differences and service concurrency conflicts.

[0019] (3) By employing an efficient solution algorithm, both solution quality and computational efficiency are balanced in large-scale scenarios: Addressing the problem that the constructed MILP model has high computational complexity and is difficult to solve directly in large-scale constellations, this invention innovatively designs a heuristic algorithm based on simulated annealing (SA). This algorithm combines Dijkstra's shortest path search and effectively avoids getting trapped in local optima by introducing a mechanism of probabilistic acceptance of differential solutions, thereby obtaining a high-quality service scheduling scheme within an acceptable computational time. This design successfully overcomes the shortcomings of computational infeasibility of exact algorithms (such as MILP solvers) and the difficulty in guaranteeing stable performance of simple heuristic algorithms, ensuring the feasibility and effectiveness of this method in large-scale low-Earth orbit satellite networks. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Referring to the drawings will make the features and advantages of the present invention clearer. The drawings are illustrative and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a flowchart of a service scheduling method for a hybrid optoelectronic low-Earth orbit satellite network provided by the present invention; Figure 2 This is a flowchart of the simulated annealing algorithm in this invention; Figure 3 This is a flowchart of the service scheduling process in this invention; Figure 4 This is a schematic diagram of the topological connections of the constellation diagram in this invention; Figure 5 This is a block diagram of a service scheduling system for a hybrid optoelectronic low-Earth orbit satellite network provided by the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: To address the problems in existing technologies such as the difficulty in coordinating energy consumption and latency optimization, insufficient consideration of node heterogeneity, and low scheduling efficiency due to link resource conflicts, such as... Figure 1 As shown, this invention proposes a service scheduling method for optoelectronic hybrid low-Earth orbit satellite networks, the method comprising: S1: Construct a hybrid optoelectronic low-Earth orbit satellite network, and obtain the topology information, node information and service set to be scheduled of the hybrid optoelectronic low-Earth orbit satellite network. The node information includes the node's switching type and remaining energy. S2: With the optimization objective of minimizing the weighted average of total business completion energy consumption and total business completion time, a mixed-integer linear programming model for business scheduling is established. The mixed-integer linear programming model includes node selection constraints, business scheduling order constraints, and routing constraints. S3: The mixed-integer linear programming model is solved using a preset optimization algorithm. The optimal service scheduling scheme is obtained by iteratively optimizing the scheduling order and routing path of the services. S4: Perform service scheduling in the optoelectronic hybrid low-Earth orbit satellite network according to the optimal service scheduling scheme.

[0023] As can be seen from the above technical solution, this invention proposes a service scheduling method for a hybrid optoelectronic low-Earth orbit satellite network. By constructing the network and acquiring topology and service information to lay the foundation for optimization, a hybrid integer linear programming model that integrates node selection, scheduling order, and routing constraints is established to achieve joint optimization of energy consumption and latency. Then, a heuristic algorithm based on simulated annealing is used for iterative solution to obtain a high-quality scheduling scheme within an acceptable time. Finally, service scheduling is executed according to the optimal scheme, thereby achieving an efficient balance between energy efficiency and service quality in a complex and heterogeneous low-Earth orbit satellite network.

[0024] In step S1, a hybrid optoelectronic low-Earth orbit satellite network is constructed. The system acquires topology information, node information, and the set of services to be scheduled for a hybrid optoelectronic low-Earth orbit satellite network. Node information includes the node's switching type and remaining energy. This represents the set of satellite nodes in the network, and each node in the set corresponds to a variable. This indicates the switching method of the node, with 1 indicating that the node performs optical switching processing on the optical signal and 0 indicating that the node performs electrical switching processing on the optical signal. This represents the set of links in the network. Assume there is a set of service requirements within the network. Correspondingly, there is a set of business source nodes. and the set of destination nodes and use Indicates business The amount of data.

[0025] In step S2, a mixed integer linear programming model (MILP) for service scheduling is established with the optimization objective of minimizing the weighted average of total service completion energy consumption and total service completion time.

[0026] Specifically, the optimization objective is: ;

[0027] in, Energy consumption for total business operations. Total Task Completion Time (TCT) and , which is a weighting coefficient used to balance the importance of energy consumption and completion time in the optimization objective.

[0028] Furthermore, the total energy consumption for satellite service deployment By summing up the single-hop energy consumption of all services The single-hop energy consumption is obtained. The energy consumption includes link transmission energy consumption, node processing energy consumption, and laser terminal alignment energy consumption, and its calculation formula is as follows: (1) in, To calculate the coefficients of other parameters in the free space path loss equation that are independent of the distance variable. Indicates satellite node and nodes The distance between them For business In the link The duration of the single-hop delay; This is a binary variable representing a satellite node. The exchange type; (In this embodiment, 150 is used, the unit is watts) represents the energy consumption for electrical exchange. Indicates the next hop node Optical signals are electrically switched, while optical switching is a transparent forwarding process with negligible energy consumption; Indicates satellite node and The orbital relationship between satellites includes those in the same orbit and those in different orbits. Alignment between satellites in the same orbit is easier than between satellites in different orbits, and therefore the energy consumption for alignment is relatively lower. (In this embodiment, 10 is used, the unit is watts) and (In this embodiment, 15 is used, and the unit is watts) These are the energy consumption for laser alignment on the same track and different tracks, respectively.

[0029] Furthermore, the total service completion time during satellite service deployment is the maximum of the completion times of all services, and the single-hop latency of each service is... It includes transmission delay, propagation delay, and node processing delay, and its calculation formula is: (2) in, For business The amount of data, (In this embodiment, 100 is used, and the unit is Gbps) represents the link bandwidth. Indicates satellite node and nodes The distance between them (This embodiment takes) (The unit is Gbps) represents the speed of light. (In this embodiment, we take 0.005) to represent the processing delay caused by electrical switching.

[0030] Furthermore, the mixed-integer linear programming model includes node selection constraints, service scheduling order constraints, and routing constraints.

[0031] 1. Node selection constraints The next-hop satellite node selected for the service must have remaining energy at least a preset percentage of its maximum energy storage after executing the service communication. Specifically, for each satellite node, using... This indicates the remaining energy at this point. Therefore, for the selected next-hop node, the remaining energy on the node after use must not be less than 30% of the full-load state (a preset percentage in this embodiment). In the formula For nodes and nodes The energy consumption required for communication, i.e., single-hop energy consumption. This represents the full energy (i.e., maximum energy storage) of the satellite node.

[0032] For two satellite nodes to communicate, the distance between them must be less than their maximum line-of-sight distance. Specifically, the maximum line-of-sight distance between the two satellites is calculated based on the Earth's radius, the satellite's orbital altitude, and the atmospheric altitude. The requirement is that the distance between the two communicating satellites must be less than this maximum line-of-sight distance, i.e. In the formula Indicates satellite node and nodes The distance between them.

[0033] 2. Service scheduling order constraints For services with shared links, this invention introduces binary sequence variables. with maximum value To ensure that multiple services on a shared link are executed sequentially in a linear manner to avoid conflicts, specifically including: (3) (4) (5) (6) in, For business Reaching the node Time, For business Leave node Time, For business In the link The duration of the single-hop delay; It is a binary variable. Indicates business Select Link , Indicates business Do not select link ; For business Leave node Time, For business In the link End time on It is a binary variable. Indicates business The scheduling order takes precedence over the business. Scheduling order; It is a binary variable. Indicates business The scheduling order takes precedence over the business. Scheduling order; For business In the link The end time. For formulas (3) and (4), a maximum value is used. To linearize, that is, when selecting a link At that time, all with The terms being multiplied will all be set to 0; otherwise, subtracting a maximum value from the right side of the equation will always satisfy the condition of being greater than or equal to. Specifically, formula (3) ensures that the time to reach the next node is not less than the departure time of the previous node plus the single-hop delay. Formula (4) then guarantees the business. The start time is no earlier than the business The end time; Formula (5) ensures the global order of the two services; Formula (6) ensures the service Reaching the node No earlier than the business In the link The end time.

[0034] 3. Routing constraints To ensure that each service can find the correct route, this invention uses a traffic conservation strategy to constrain the inflow and outflow of traffic at each node, specifically including: (7) (8) (9) in, For nodes and nodes The link between them For containing source nodes The set of links, To include the destination node The set of links, For business Select Link binary variables, For business Select Link binary variables, For the set of services to be scheduled, It is a set of satellite nodes in a hybrid optoelectronic low-Earth orbit satellite network. is the set of links in a hybrid optoelectronic low-Earth orbit satellite network. Formula (7) indicates that the outflow of the source node is 1, formula (8) indicates that the inflow of the destination node is 1, and formula (9) indicates that for each node, the number of inflow nodes and outflow nodes are equal.

[0035] In step S3, a preset optimization algorithm is used to solve the mixed integer linear programming model. By iteratively optimizing the scheduling order and routing path of services, the optimal service scheduling scheme is obtained.

[0036] To address the problem that the constructed service scheduling model has high complexity and that it is difficult to obtain the optimal solution within a limited time by directly using mathematical programming solvers such as AMPL, this invention designs a heuristic solution algorithm based on Simulated Annealing (SA) to improve routing performance while taking into account energy consumption and overall service completion time.

[0037] Simulated annealing can accept poor new solutions with a certain probability, helping to escape local optima. Its solution process is controlled by parameters such as the highest temperature, lowest temperature, cooling coefficient, and maximum number of rounds, which together control the convergence speed and solution quality. The application background of this algorithm is that when multiple services need to share the same inter-satellite link, the service scheduling order directly affects the link waiting time and thus the overall service completion time due to the differences in the paths of each service and the varying resource states of the links they traverse. Furthermore, in different service deployment scenarios, network resource conditions change dynamically, leading to variations in link weights and applied constraints when using Dijkstra's algorithm for shortest path search.

[0038] The specific implementation process of the algorithm is as follows: Figure 2 As shown. First, initialize: initialize the satellite network topology. and business set Set optimization algorithm parameters, including maximum temperature, minimum temperature, cooling coefficient, number of runs at each temperature, and maximum number of runs; determine the initial scheduling order of services and the weights of the evaluation function; Generate initial solution: Based on the initial service scheduling order, use Dijkstra's algorithm to solve for the service deployment path at this point, calculate the service completion energy consumption and service completion time, and calculate the evaluation function according to the set weights. ; Iterative optimization: Before the termination condition is met, repeatedly execute the following steps: Step a: At the current temperature, randomly swap the scheduling order of the two services to generate a new solution; Step b: Check if there is a service deployment path that satisfies the node selection constraint under the new scheduling order. If so, use Dijkstra's algorithm to solve for the service deployment path under the new scheduling order and calculate the service completion energy consumption and service completion time at this time. Then, calculate the evaluation function at this time according to the set weights. If it does not exist, return to step a; Step c: Calculate the increment of the evaluation value ,like If the solution is correct, then accept the new solution; otherwise, calculate the probability of acceptance. ,in Generate a random number between 0 and 1 for the current temperature. This strategy avoids the search process from prematurely falling into local optima: if If so, then accept the new solution and update; if If the new solution is not found, proceed to step d. Step d: Determine whether the number of iterations at the current temperature has reached the set number of running rounds at that temperature. If not, return to step a. If so, determine whether the total number of iterations has reached the maximum number of running rounds. If not, reduce the temperature according to the cooling coefficient and return to step a. If so, output the optimal business scheduling scheme.

[0039] In step S4, service scheduling is performed in the optoelectronic hybrid low-Earth orbit satellite network according to the optimal service scheduling scheme.

[0040] like Figure 3 As shown, a process for service scheduling according to the method of the present invention is given, which specifically includes the following steps: (1) Network Construction: A hybrid electro-optical low-Earth orbit (LEO) satellite network is constructed. Specifically, a Walker-Delta constellation configuration is adopted, constructing a constellation system containing 12 orbital planes, with 12 satellites distributed in each orbital plane, for a total of 144 satellites. In this network, the configuration ratio of electrical switching nodes to optical switching nodes is 0.7:0.3. The initial remaining energy of each satellite node is set to a random value that follows a uniform distribution within the interval [0.5, 0.8]. Each node supports a maximum of 4 inter-satellite links, and the data transmission rate of each link is uniformly set to 100 Gbps. A partial constellation topology diagram of this network is shown below. Figure 4 As shown.

[0041] (2) Service Generation: Generate a set of service pairs for scheduling. The number of service pairs is set to 20, 40, 60, 80 and 100 respectively to cover performance evaluation under different load conditions. The data volume of each service is randomly generated in a uniform distribution between 500 Gbps and 800 Gbps. The source node and destination node of the service are randomly selected from 144 satellites numbered 0 to 143, and it is ensured that the source node and destination node are not the same node.

[0042] (3) Scheduling Execution and Comparative Analysis: All services are scheduled based on the algorithm strategy proposed in this invention. To verify the overall performance of this method, two comparison schemes are set up: one with minimizing energy consumption as the single optimization objective, and the other with minimizing service completion time as the single optimization objective. The total service completion energy consumption under the three schemes is calculated and compared respectively. ) and Total Time to Complete (TCT).

[0043] (4) Output of results: Output the optimal service scheduling scheme obtained by the method of the present invention, and record its corresponding key performance indicators such as energy consumption and service completion time for subsequent analysis and evaluation.

[0044] Example 2: Figure 5 As shown, this invention provides a service scheduling system for a hybrid optoelectronic low-Earth orbit satellite network. This system implements the service scheduling method for a hybrid optoelectronic low-Earth orbit satellite network described in Embodiment 1 above, specifically including: The network and service modeling module 100 is used to construct a hybrid optoelectronic low-Earth orbit satellite network and obtain the topology information, node information and service set to be scheduled of the hybrid optoelectronic low-Earth orbit satellite network. The node information includes the node's switching type and remaining energy. The optimization model building module 200 is used to establish a mixed-integer linear programming model for business scheduling with the optimization objective of minimizing the weighted average of total business completion energy consumption and total business completion time. The mixed-integer linear programming model includes node selection constraints, business scheduling order constraints, and routing constraints. The model solving module 300 is used to solve the mixed integer linear programming model using a preset optimization algorithm. By iteratively optimizing the scheduling order and routing path of services, the optimal service scheduling scheme is obtained. The service scheduling execution module 400 is used to perform service scheduling in the optoelectronic hybrid low-Earth orbit satellite network according to the optimal service scheduling scheme.

[0045] This embodiment provides a service scheduling system for a hybrid optoelectronic low-Earth orbit (LEO) satellite network, which implements the aforementioned service scheduling method for a hybrid optoelectronic LEO satellite network. Therefore, the specific implementation of the service scheduling system for a hybrid optoelectronic LEO satellite network can be found in the preceding embodiment section of the service scheduling method for a hybrid optoelectronic LEO satellite network. For example, the network and service modeling module 100, the optimization model construction module 200, the model solving module 300, and the service scheduling execution module 400 are respectively used to implement steps S1, S2, S3, and S4 in the aforementioned service scheduling method for a hybrid optoelectronic LEO satellite network. Therefore, its specific implementation can be referred to the descriptions of the corresponding embodiments. To avoid redundancy, further details are omitted here.

[0046] Example 3: This embodiment of the invention provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the above-mentioned service scheduling method for optoelectronic hybrid low-Earth orbit satellite networks.

[0047] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0049] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0050] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A traffic scheduling method for opto-electric hybrid low earth orbit satellite networks, characterized in that, The application relates to a business scheduling method for an optoelectronic hybrid low-orbit satellite network. The method comprises the following steps: constructing an optoelectronic hybrid low-orbit satellite network, obtaining topology information, node information and a set of to-be-scheduled businesses of the optoelectronic hybrid low-orbit satellite network, wherein the node information comprises a switching type and residual energy of a node; A mixed integer linear programming model of business scheduling is established by taking the weighted minimization of total business completion energy consumption and total business completion time as an optimization target, wherein the mixed integer linear programming model comprises node selection constraints, business scheduling sequence constraints and routing constraints; A preset optimization algorithm is used to solve the mixed integer linear programming model, the scheduling sequence and routing path of the business are iteratively optimized, and an optimal business scheduling scheme is obtained; Business scheduling is performed in the optoelectronic hybrid low-orbit satellite network according to the optimal business scheduling scheme.

2. The traffic scheduling method for optoelectronic hybrid low earth orbit satellite network according to claim 1, characterized in that, The optimization target is: ； wherein, is the total energy consumption, is the total job completion time, and is a weight coefficient for balancing the importance of energy consumption and completion time in the optimization objective.

3. The traffic scheduling method for opto-electric hybrid low earth orbit satellite network according to claim 1 or 2, characterized in that, The total service completion energy consumption is obtained by accumulating the single-hop energy consumption of all services The single-hop energy consumption is obtained The single-hop energy consumption includes link transmission energy consumption, node processing energy consumption and laser terminal alignment energy consumption, and the calculation formula is: ； wherein, is the coefficient, denotes the distance between satellite nodes and nodes , is the duration of traffic on a link , i.e. single-hop latency; is a binary variable denoting the type of switching of satellite nodes ; is the electrical switching energy consumption, denotes the orbital relationship between satellite nodes and , and are the in-orbit and out-of-orbit laser pointing energy consumption, respectively.

4. The traffic scheduling method for optoelectronic hybrid low earth orbit satellite network according to claim 1 or 2, characterized in that, The total service completion time is the maximum value among the completion times of all services, and the single-hop delay of each service The transmission delay, the propagation delay and the node processing delay are included, and the calculation formula is: ； in, For business The amount of data, For link bandwidth, Indicates satellite node and nodes The distance between them At the speed of light, The processing delay caused by electrical switching.

5. The traffic scheduling method for optoelectronic hybrid low earth orbit satellite network of claim 1, wherein, The node selection constraints comprise: The residual energy of a next-hop satellite node selected for a business after performing communication of the business needs to be not lower than a preset proportion of a maximum energy storage value of the next-hop satellite node; The distance between two satellite nodes performing communication needs to be smaller than a maximum visible distance.

6. The traffic scheduling method for optoelectronic hybrid low earth orbit satellite network of claim 1, wherein, The service scheduling order constraint is ensured in a linearized manner by introducing binary order variables with a maximum value to avoid conflicts, specifically including: ； ； ； ； in, For business Reaching the node Time, For business Leave node Time, For business In the link The duration of the single-hop delay; It is a binary variable. Indicates business Select Link , Indicates business Do not select link ; For business Leave node Time, For business In the link End time on It is a binary variable. Indicates business The scheduling order takes precedence over the business. Scheduling order; It is a binary variable. Indicates business The scheduling order takes precedence over the business. Scheduling order; For business In the link The end time.

7. The traffic scheduling method for optoelectronic hybrid low earth orbit satellite network of claim 1, wherein, The routing constraints constrain the inflow and outflow of traffic at each node by a flow conservation strategy, and specifically comprise: ； ； ； wherein, is a link between a node and a node , is a set of links containing a source node , is a set of links containing a destination node , is a binary variable indicating that a traffic chose link , is a binary variable indicating that a traffic chose link , is a set of traffics to be scheduled, is a set of satellite nodes in an optoelectronic hybrid low earth orbit satellite network, is a set of links in an optoelectronic hybrid low earth orbit satellite network.

8. The traffic scheduling method for optoelectronic hybrid low earth orbit satellite network of claim 1, wherein, The preset optimization algorithm is used to solve the mixed integer linear programming model, the scheduling sequence and routing path of the business are iteratively optimized, and the optimal business scheduling scheme is obtained, and specifically comprises: Initialization: initialize satellite network topology and service set ; set optimization algorithm parameters, including highest temperature, lowest temperature, temperature reduction coefficient, running round number at each temperature, maximum running round number; determine service initial scheduling order and weight of evaluation function; Generating initial solution: based on the initial scheduling order of the service, using Dijkstra algorithm to solve the service deployment path at this time and calculate the service completion energy consumption and service completion time at this time, and calculating the evaluation function at this time according to the set weight ; Iterative optimization: the following steps are cyclically executed before a termination condition is reached: Step a: randomly exchange the scheduling sequence of two businesses at a current temperature to generate a new solution; Step b: check whether there is a service deployment path satisfying the node selection constraint under the new scheduling order, if there is, use Dijkstra algorithm to solve the service deployment path under the new scheduling order and calculate the service completion energy consumption and service completion time at this time, and calculate the evaluation function at this time according to the set weight ; if not, return to step a; Step c: Compute evaluation increment If then accept new solution; otherwise, compute acceptance probability where is current temperature, generate 0-1 random number If then accept new solution and update; if then reject new solution, perform step d; Step d: it is judged whether the iteration number at the current temperature reaches a running round number set at the temperature: if not, the step a is returned; if yes, it is judged whether the total iteration number reaches a maximum running round number: if not, the temperature is lowered according to a temperature lowering coefficient, and the step a is returned; if yes, the optimal business scheduling scheme is output.

9. A traffic scheduling system for an opto-electric hybrid low earth orbit satellite network, characterized in that, The system is used for implementing the business scheduling method for the optoelectronic hybrid low-orbit satellite network in any one of claims 1 to 8, and specifically comprises: A network and business modeling module is used for constructing an optoelectronic hybrid low-orbit satellite network, obtaining topology information, node information and a set of to-be-scheduled businesses of the optoelectronic hybrid low-orbit satellite network, wherein the node information comprises a switching type and residual energy of a node; An optimization model construction module is used for establishing a mixed integer linear programming model of business scheduling by taking the weighted minimization of total business completion energy consumption and total business completion time as an optimization target, wherein the mixed integer linear programming model comprises node selection constraints, business scheduling sequence constraints and routing constraints; A model solving module is used for solving the mixed integer linear programming model by using a preset optimization algorithm, iteratively optimizing the scheduling sequence and routing path of the business, and obtaining an optimal business scheduling scheme; A business scheduling execution module is used for performing business scheduling in the optoelectronic hybrid low-orbit satellite network according to the optimal business scheduling scheme.

10. An electronic device, comprising: The electronic device includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the service scheduling method for a hybrid optoelectronic low-Earth orbit satellite network as described in any one of claims 1 to 8.

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