Dynamic Scheduling Method, Device and Medium for Multipath Routing in Satellite Communication System

Abstract

The present invention discloses a multi-path routing dynamic scheduling method, device and medium for a satellite communication system, which relates to the field of satellite data transmission. The method includes: expanding the path between the source node and the target node in the satellite communication system into multiple paths, and obtaining the communication quality index parameters of all satellites in the multiple paths; for each of the multiple paths, constructing a routing cost function through the communication quality index parameters; updating the graph data of the satellite communication system in real time according to the real-time path changes during the operation of the satellite communication system; for each piece of graph data updated in real time, based on the communication quality index parameters, calculating the total routing cost of the multiple paths between the source node and the target node in the graph data, and selecting the first preset number of paths in ascending order of the total routing cost of the multiple paths, and determining the first preset number of paths as the optimal path combination at the current moment. This method can reduce the transmission energy consumption of the satellite network.

Description

Technical Field

[0001] The present invention relates to the field of satellite data transmission, and particularly to a multi-path routing dynamic scheduling method, device, and medium for a satellite communication system. Background Art

[0002] With the development of the Low Earth Orbit (LEO) satellite network, the industrial Internet of Things based on LEO satellites can break through geographical restrictions, provide extensive remote monitoring and intelligent scheduling capabilities, and provide efficient network support for industrial equipment in remote or harsh environments. These characteristics make satellite networks of great strategic significance in the fields of global communication, disaster monitoring, military operations, etc. Satellite networks are included in the category of information infrastructure. With the increase in the number of satellites and the expansion of satellite application scenarios, the reliability, load balancing ability, and energy consumption efficiency of satellite network communication have become important issues that need to be solved urgently.

[0003] In satellite networks, multi-path routing is an effective means to improve the reliability and data transmission efficiency of satellite networks. However, there are still huge challenges in reasonably and efficiently applying this technology while ensuring satellite lifespan and reducing energy consumption. Specifically, without the support of an optimization algorithm, traditional multi-path routing has inaccurate path selection, resulting in high computational complexity and increased energy consumption. Existing heuristic or simple algorithms are difficult to adapt to the dynamic satellite network environment and cannot achieve optimal path selection, thus affecting the transmission energy consumption of satellite networks.

[0004] Therefore, there is an urgent need for a method to reduce the transmission energy consumption of satellite communication systems. Summary of the Invention

[0005] Based on this, it is necessary to provide a multi-path routing dynamic scheduling method, device, and medium for a satellite communication system to address the above technical problems. This method can reduce the transmission energy consumption of satellite communication systems.

[0006] The present invention adopts the following technical solutions:

[0007] The present invention provides a multi-path routing dynamic scheduling method for a satellite communication system, including:

[0008] Expand the path between the source node and the target node in the satellite communication system into multiple paths, and obtain the communication quality index parameters of all satellites in the multiple paths; the source node and the target node are any nodes in the satellite communication system;

[0009] For each of the multiple paths, construct a routing cost function through the communication quality index parameters;

[0010] Use digital twins to simulate the real-time path changes during the operation of the satellite communication system, and update the graph data of the satellite communication system in real time according to the real-time path changes;

[0011] For each piece of map data updated in real time, based on the communication quality metric parameters, calculate the total routing cost of multiple paths between the source node and the target node in the map data through the routing cost function, and select the first preset number of paths in ascending order of the total routing cost of the multiple paths, and determine the first preset number of paths as the optimal path combination at the current moment.

[0012] Preferably, the communication quality metric parameters include transmission delay, remaining battery energy, storage capacity, and packet loss rate. The routing cost function is constructed through the communication quality metric parameters, including:

[0013] Obtain the transmission delay, energy consumption, link capacity, and transmission data packet loss rate between the source node and the target node in the satellite communication system;

[0014] Construct a routing cost function through the transmission delay, energy consumption, link capacity, and transmission data packet loss rate between the source node and the target node.

[0015] Preferably, the routing cost function is:

[0016]

[0017] Where Q x,y is the routing cost from source node x to target node y when maximizing traffic demand, T x,y is the transmission delay between source node x and target node y, T min is the minimum delay in the snapshot, T max the maximum transmission delay in the snapshot, E x,y is the energy consumption for transmitting data from source node x to target node y, C x,y is the link capacity between source node x and target node y, P x,y is the transmission data packet loss rate between source node x and target node y, E min is the minimum energy consumption for transmitting data from source node x to target node y, E max is the maximum energy consumption for transmitting data from source node x to target node y, C min is the minimum link capacity between source node x and target node y, C max is the maximum link capacity between source node x and target node y, P min is the minimum transmission data packet loss rate between source node x and target node y, P max is the maximum transmission data packet loss rate between source node x and target node y, ω1 is the weight of the transmission delay parameter, ω2 is the weight of the energy consumption parameter, ω3 is the weight of the link capacity parameter, and ω4 is the weight of the transmission data packet loss rate parameter.

[0018] Preferably, the process of determining the weights in the routing cost function includes:

[0019] Initializing the particle swarm;

[0020] For any particle, randomly generate the particle position and velocity, and set the local best position of each particle as the initial position; the position of the particle is the weight in the routing cost function;

[0021] Calculate the fitness value according to the routing cost function, and update the local best candidate node, the global best candidate node and the fitness in real time according to the fitness value;

[0022] Continuously update the velocity and position of the particle, adjust the particle velocity of the local best position and the global best position, and update the best candidate node;

[0023] After multiple iterations, return the global best position, and determine the global best position as the weight in the routing cost function.

[0024] Preferably, the method further includes:

[0025] After determining the optimal path combination, designing a multi-path routing evaluation index;

[0026] Evaluate the optimal path combination through the multi-path routing evaluation index to obtain the transmission performance of the satellite communication system.

[0027] Preferably, the multi-path routing evaluation index includes average delay, average throughput, data transmission volume, outage probability, average satellite service life and number of hops.

[0028] The present invention provides a multi-path routing dynamic scheduling device for a satellite communication system, including:

[0029] An acquisition module, configured to expand the path between the source node and the target node in the satellite communication system into multiple paths, and acquire the communication quality index parameters of all satellites in the multiple paths; the source node and the target node are any nodes in the satellite communication system;

[0030] A construction module, configured to construct a routing cost function for each of the multiple paths through the communication quality index parameters;

[0031] An update module, configured to use digital twin to simulate the real-time path change during the operation of the satellite communication system, and update the graph data of the satellite communication system in real time according to the real-time path change;

[0032] A generation module is configured to, for each piece of graph data that is updated in real time, calculate the total routing costs of multiple paths between a source node and a target node in the graph data based on communication quality metric parameters through a routing cost function, select a first preset number of paths in ascending order of the total routing costs of the multiple paths, and determine the first preset number of paths as the optimal path combination at the current moment.

[0033] The present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-mentioned multi-path routing dynamic scheduling method for a satellite communication system.

[0034] The present invention provides a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned multi-path routing dynamic scheduling method for a satellite communication system.

[0035] The above-mentioned at least one technical solution adopted by the present invention can achieve the following beneficial effects:

[0036] The paths between the source node and the target node in the satellite communication system are extended to multiple paths, improving the reliability and robustness of the satellite communication system; for each of the multiple paths, a routing cost function is constructed through communication quality metric parameters to achieve optimal multi-path load distribution without additional resource consumption; digital twins are used to simulate the real-time path changes during the operation of the satellite communication system, and the graph data of the satellite communication system is updated in real time according to the real-time path changes; for each piece of graph data updated in real time, based on the communication quality metric parameters, the total routing costs of multiple paths between the source node and the target node in the graph data are calculated through the routing cost function, a first preset number of paths are selected in ascending order of the total routing costs of the multiple paths, and the first preset number of paths are determined as the optimal path combination at the current moment. The dynamically selected optimal path combination has a low routing cost, and this method can reduce the transmission energy consumption of the satellite communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0038] Figure 1 It is a schematic diagram of a multi-path routing transmission model for a LEO satellite constellation;

[0039] Figure 2 It is a schematic flow diagram of the multi-path routing dynamic scheduling method for a satellite communication system provided by the present invention;

[0040] Figure 3Schematic diagram of the multi-path routing dynamic scheduling device for the satellite communication system provided by the present invention;

[0041] Figure 4 Schematic diagram of a computer device for implementing the multi-path routing dynamic scheduling method for the satellite communication system provided by the present invention. Specific implementation manners

[0042] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0043] As Figure 1 shown, the multi-path routing transmission model of the LEO constellation includes signal towers, mobile devices, LEO satellites, and ground base stations. Based on 66 LEO satellites and their inter-satellite links to form the Iridium constellation, the routing model proposed by the present invention evaluates the path quality based on the expected delay of the path. Figure 1 After node A fails in, the probability of successfully transmitting data decreases, immediately resulting in an increase in the path cost indicated by the dotted line, so that the path can be adjusted in time to find the optimal path. By constructing a Digital Twin Neural Population Dynamics Optimization Algorithm (DTNPDOA), a dynamic scheduling mechanism for multi-path routing is proposed. This mechanism is optimized in terms of multi-index path selection and task allocation to find the global optimal path that meets multiple constraint conditions. While ensuring the reliability of data transmission, it minimizes energy consumption to the greatest extent, provides an energy-saving routing scheduling mechanism for multi-path transmission, and provides a sustainable solution for satellite communication networks with limited resources.

[0044] In practical applications, communication links will be affected by various factors. Among them, transmission delay, remaining battery energy, storage capacity, and packet loss rate are relatively significant factors.

[0045] In satellite Internet, selecting multi-path routing is an effective means to improve network reliability and data transmission efficiency. However, reasonably and efficiently applying this technology while ensuring satellite lifespan and reducing energy consumption still faces huge challenges. Specifically, single-path routing has limitations in high-load or fault situations, unable to effectively share traffic or provide redundant guarantees, resulting in unreliable data transmission. Traditional static weight allocation methods cannot adapt to the dynamic changes of satellite networks, leading to unsatisfactory transmission efficiency and difficulty in coping with sudden changes caused by faults or congestion. Traditional multi-path routing, without the support of optimization algorithms, has inaccurate path selection, resulting in high computational complexity and increased energy consumption. Existing heuristic or simple algorithms are difficult to adapt to dynamic network environments, unable to achieve optimal path selection, and thus affecting transmission efficiency and system stability.

[0046] When modeling the satellite-to-satellite communication model to optimize paths in the prior art, the above factors affecting data transmission on communication links are often ignored, resulting in the system's optimal path being inaccurate and incomplete. Based on the above problems, the present invention proposes a dynamic scheduling method for multi-path routing in a satellite communication system.

[0047] The following will, in conjunction with the accompanying drawings, detail the technical solutions provided by the embodiments of the present invention.

[0048] Figure 2 It is a schematic flowchart of the dynamic scheduling method for multi-path routing in the satellite communication system of the present invention, specifically including the following steps:

[0049] S201: Expand the path between the source node and the target node in the satellite communication system into multiple paths, and obtain the communication quality index parameters of all satellites in the multiple paths; the source node and the target node are any nodes in the satellite communication system.

[0050] In an exemplary embodiment, the communication quality index parameters include transmission delay, energy consumption, link capacity, and transmission data packet loss rate.

[0051] Specifically, propagation delay refers to the time required for a signal to travel from the sending end to the receiving end in the transmission medium. In a LEO satellite network, propagation delay is mainly related to the distance between satellites and the signal propagation speed. In multi-path routing transmission, the signal may be transmitted through different satellite paths, which will increase or decrease the overall propagation delay, depending on the length of the path and the network topology.

[0052] Energy consumption refers to the electrical energy that has not been consumed in the satellite's internal battery, expressed as a percentage of the battery charge. In a LEO satellite network, energy consumption directly affects the satellite's communication ability, orbit control, network reliability, and the efficiency of multi-path routing.

[0053] Link capacity refers to the total amount of data that can be stored in the satellite's internal storage system (such as solid-state storage, flash memory, hard disk, etc.). The link capacity of a satellite determines the amount of data that can be stored and processed. Reasonable planning and management of the satellite's storage capacity can not only improve data transmission efficiency, but also optimize routing strategies, enhance the fault tolerance and scalability of the network, and ensure the long-term stable operation of the satellite network.

[0054] The packet loss rate of transmitted data refers to the ratio of the number of lost data packets to the number of transmitted data packets during satellite network data transmission.

[0055] S202: For each of the multiple paths, construct a routing cost function through communication quality metric parameters.

[0056] Normalize the communication quality metric parameters into the routing cost function.

[0057] In an exemplary embodiment, the communication quality metric parameters of the satellite include transmission delay, remaining battery energy, storage capacity, and packet loss rate. Constructing a routing cost function through the communication quality metric parameters includes: obtaining the transmission delay, remaining battery energy, storage capacity, and packet loss rate of the source node and the destination node in the satellite communication system; constructing a routing cost function through the transmission delay, energy consumption, link capacity, and packet loss rate of transmitted data between the source node and the destination node.

[0058] Specifically, obtain the transmission delay, remaining battery energy, storage capacity, and packet loss rate of all satellites in the satellite communication system; according to the transmission delay, energy consumption, link capacity, and packet loss rate of transmitted data between the source node and the destination node, and construct a routing cost function. The routing cost function is as shown in formula (1):

[0059]

[0060] Among them, Q x,y is the routing cost from the source node x to the destination node y when maximizing traffic demand, T x,y is the transmission delay between the source node x and the destination node y, T min is the minimum delay in the snapshot, T max the maximum transmission delay in the snapshot, E x,y is the energy consumption for transmitting data from the source node x to the destination node y, C x,y is the link capacity between the source node x and the destination node y, P x,y is the packet loss rate of transmitted data between the source node x and the destination node y, E min is the minimum energy consumption for transmitting data from the source node x to the destination node y, E max is the maximum energy consumption for transmitting data from the source node x to the destination node y, C minis the minimum link capacity between source node x and destination node y, C max is the maximum link capacity between source node x and destination node y, P min is the minimum transmission data packet loss rate between source node x and destination node y, P max is the maximum transmission data packet loss rate between source node x and destination node y, ω1 is the weight of the transmission delay parameter, ω2 is the weight of the energy consumption parameter, ω3 is the weight of the link capacity parameter, and ω4 is the weight of the transmission data packet loss rate parameter.

[0061] In an exemplary implementation, the process of determining the weights in the routing cost function includes: initializing the particle swarm; for any particle, randomly generating the particle position and velocity, and setting the local best position of each particle as the initial position; the position of the particle is the weight in the routing cost function; calculating the fitness value according to the routing cost function, and updating the local best candidate node, global best candidate node, and fitness of the particle in real time according to the fitness value; continuously updating the velocity and position of the particle, adjusting the particle velocity of the local best position and global best position, and updating the best candidate node; after multiple iterations, returning the global best position, and determining the global best position as the weight in the routing cost function.

[0062] Specifically, the problem of selecting the optimal satellite path is modeled as a multi-objective optimization problem, and the parameters of transmission delay, remaining battery energy, storage capacity, and packet loss rate are normalized into routing costs to establish a multi-objective optimization model.

[0063] In a multi-path, the calculation method of the propagation delay of the route established between source node s and destination node d is shown in formula (2):

[0064]

[0065] where, T s,d is the propagation delay of the route between source node s and destination node d, k is the kth link, L k is the distance of the kth link, with the unit of m, H is the total number of links in a route, V is the speed of light, defined as 2.998×108 m / s.

[0066] In addition to the nominal operation of the satellite, the energy consumption of each satellite also occurs when there is a sending or receiving service, that is, the calculation method of the energy consumed by the satellite operation is shown in formula (3):

[0067] E C (t) = E R (t) - E N (t) - E T′ (t) - E R′ (t) (3);

[0068] Among them, E C (t) is the remaining energy at the end of the satellite mission, E R (t) is the battery energy at the start of the satellite mission, E N (t) represents the operating energy consumption of the satellite at time t, E T′ (t) is the energy consumed by the satellite to send data at time t, E R′ (t) is the energy consumed by the satellite to receive data at time t.

[0069] The path from the source node s to the destination node d passes through K links, and the calculation of the total capacity consumption is shown in formula (4):

[0070]

[0071] Among them, C is the total capacity consumption, r k is the data transmission data volume of the k-th link, d k is the distance of the k-th link, and K is the total number of links.

[0072] The calculation of the ratio of the number of packets that fail to reach the target node successfully during the transmission of the path from the source node s to the destination node d is shown in formula (5):

[0073]

[0074] Among them, P is the packet loss rate, P lost is the number of lost data packets, that is, the total number of packets that do not reach the destination successfully during the transmission, P sent is the total number of data packets sent, that is, the total number of all data packets sent by the source node.

[0075] Based on the above analysis, in order to select the optimal path that meets the satellite energy-saving metric from the source node s to the destination node d, the present invention models the satellite optimal path problem as a multi-objective optimization problem, expressed as formula (1).

[0076] After constructing the routing cost function, the particle swarm algorithm is used to update the weights in the routing cost function in real time.

[0077] In an exemplary embodiment, the method further includes: initializing a particle swarm; for any particle, randomly generating a particle position and a velocity, and setting the local best position of each particle as the initial position; the position of the particle being the weight in the routing cost function; calculating a fitness value according to the routing cost function, and updating the local best candidate node, the global best candidate node and the fitness in real time according to the fitness value; continuously updating the velocity and position of the particle, adjusting the particle velocities of the local best position and the global best position, and updating the best candidate node; after multiple iterations, returning the global best position and determining the global best position as the weight in the routing cost function.

[0078] Specifically, based on the establishment of a multi-objective model, the present invention addresses the problems of congestion, disconnection and difficulty in ensuring reliable transmission caused by a single path, and proposes a routing algorithm based on multi-path optimization. By expanding the single-path routing into k multi-path routings, path diversification is achieved. The multi-path routing mechanism allows data to be dispersed and transmitted over multiple paths to disperse the traffic load and improve the fault tolerance of the network, enhancing the reliability of the network.

[0079] Static routing has limitations in coping with frequent changes in the network topology, which can easily lead to path interruption and increased latency. Based on the above, the present invention expands the single-path routing into k multi-path routings and uses the Particle Swarm Optimization (PSO) algorithm to dynamically adjust the weights of the multi-paths in real time, ensuring that each path can undertake reasonable transmission tasks according to the current network conditions, so as to achieve optimal multi-path load distribution without additional resource consumption. In the Particle Swarm Optimization Dynamic Weight Multi-path Routing mechanism (PSODW-MPRM) scheme, dynamically adjusting the multi-path weights is the primary task of the energy-saving scheduling mechanism, which requires initializing a particle swarm, including randomly generating particle positions, setting velocities, and setting the local best position of each particle as the initial position. Calculate the fitness by calling a function and update its local and global best candidate nodes and fitness. Then, in the main loop, the algorithm continuously updates the velocity and position of the particle to achieve dynamic adjustment of the multi-path weights. By setting the inertia factor, self-cognition factor and social-cognition factor, adjust the particle velocities of the local best position and the global best position, and update the best candidate node. Finally, after multiple iterations, return the global best position and the final optimal solution to obtain the dynamically adjusted weights. This algorithm utilizes the dynamic adjustment of particles to improve the search efficiency of multi-objective optimization.

[0080] Specifically, in the present invention, by designing a dynamic fitness function, it can dynamically adjust the fitness evaluation criteria of each path in real time according to multiple network performance indicators such as network topology changes, transmission delay, remaining battery energy, storage capacity, and packet loss rate. It not only considers the current network state but also the expected changes within a certain period of time in the future. Thus, the fitness is calculated, and the local best candidate nodes and the global best candidate nodes of the particles are updated along with the fitness.

[0081] In the present invention, the maximum value and the minimum value are used to normalize the parameters of transmission delay, remaining battery energy, storage capacity, and packet loss rate, so as to change the numerical values of the data to the same scale, and they are combined into a link cost that comprehensively considers battery consumption through a weight factor. By defining the minimum energy threshold of the satellite battery for use in the link, a higher weight is given to the link that does not meet the minimum threshold of the remaining battery energy of the satellite battery constituting the link, and the weight factor in formula (1) is dynamically adjusted. This dynamic adjustment of the weight method penalizes the link where the satellite does not reach the minimum battery power level defined in the energy threshold. Even satellites outside the predefined threshold can be used for data traffic, thereby reducing the blocking sources, achieving better traffic balance, and effectively reducing the energy consumption in the network routing process.

[0082] S203: Use digital twins to simulate the real-time path changes during the operation of the satellite communication system, and update the graph data of the satellite communication system in real time according to the real-time path changes.

[0083] Specifically, use digital twins to simulate the real-time state and real-time state changes of the satellite communication system, and update the graph data of the satellite communication system according to the real-time state changes of the satellite communication system.

[0084] S204: For each piece of graph data updated in real time, based on the communication quality index parameters, calculate the total routing cost of multiple paths between the source node and the target node in the graph data through the routing cost function, select the first preset number of paths in ascending order of the total routing cost of the multiple paths, and determine the first preset number of paths as the optimal path combination at the current moment.

[0085] The first preset number is set according to specific engineering practices.

[0086] Specifically, another challenge faced by multi-path routing in practical applications is how to coordinate and schedule multiple paths to fully utilize the resources of each path. Without an optimization algorithm, traditional multi-path routing is prone to inaccurate path selection, low transmission reliability, and easy interruption. The present invention constructs a DTNPDOA algorithm and proposes a dynamic scheduling mechanism for multi-path routing. The PSODW-DTNPDOA-MPRM mechanism is optimized in both path selection and task allocation. Among them, DTNPDOA can perform path selection and task allocation according to various indicators such as the delay, bandwidth, and energy consumption of the path, while ensuring the reliability of data transmission, minimizing energy consumption to the greatest extent. This scheduling mechanism can dynamically identify the states of different paths and allocate traffic to multiple paths with the optimal strategy.

[0087] DTNPDOA is a key part of the energy-saving scheduling mechanism. First, initialize variables to store path information. Then call the constructed function to calculate the initial shortest path. Starting from the second shortest path, iteratively generate the remaining k - 1 paths, and avoid path duplication by assigning infinite weights to the edges with repeated prefixes, achieving efficient multi-path planning. And use the digital twin DT as a virtual simulation system to simulate the changes in the real system and provide real-time feedback for the algorithm. This feedback can help the algorithm find the optimal solution more effectively. By obtaining real-time updated graph data, dynamically calculate the paths from the branch nodes to the destination nodes, and store the merged complete paths in the candidate set. Select the appropriate paths from it to update the result set, and finally efficiently generate k shortest paths. By making full use of multi-path resources, DTNPDOA improves the overall reliability of transmission and reduces the energy consumption of network transmission, providing a sustainable solution for satellite communication networks with limited resources.

[0088] In an exemplary embodiment, the method further includes: after determining the optimal path combination, designing multi-path routing evaluation metrics; evaluating the optimal path combination through the multi-path routing evaluation metrics to obtain the transmission performance of the satellite communication system.

[0089] Specifically, the multi-path routing evaluation metrics are important metrics for measuring the transmission performance of the satellite communication system. The multi-path routing evaluation metrics include average delay, average throughput, data transmission volume, interruption probability, average satellite service life, and number of hops. After designing the multi-path routing evaluation metrics, evaluate the optimal path combination according to the multi-path routing evaluation metrics to obtain the transmission performance of the satellite communication system, and optimize the resource configuration of the satellite communication system according to the obtained transmission performance of the satellite communication system, etc.

[0090] Average latency is a metric that measures the time required from the data source node to the receiving end of the target node, usually including components such as propagation latency, processing latency, and queuing latency. A lower end-to-end latency means higher data transmission efficiency and faster network response speed; while a higher latency may lead to network transmission delays and affect real-time communication and quality of service.

[0091] The satellite service life refers to the length of time that a satellite operates in orbit and maintains normal functions, usually affected by factors such as battery life, hardware wear, and orbital decay. A longer satellite life helps reduce the satellite replacement frequency and lower the network construction and maintenance costs; while a shorter satellite life may lead to frequent satellite replacements and increase the operating costs.

[0092] Average throughput refers to the amount of data transmitted through the network per unit time. Higher throughput means that the network can transmit more data, enhancing the network's capacity and efficiency; lower throughput indicates that the network bandwidth is limited, which may lead to data congestion and slow transmission speed.

[0093] The number of hops refers to the number of intermediate nodes that data passes through from the source node to the target node. Fewer hops mean a shorter data transmission path, lower network transmission latency and load; while more hops may result in higher latency and greater network burden, affecting data transmission efficiency.

[0094] The data transmission volume refers to the total amount of data successfully transmitted within a certain period of time. A larger data transmission volume indicates that the network can support higher communication requirements and enhance the overall effectiveness of the system; while a smaller transmission volume may limit the network's service capabilities.

[0095] Based on the influence of transmission latency, remaining battery energy, storage capacity, and packet loss rate on the multi-path routing evaluation metrics, the present invention constructs a multi-path routing dynamic scheduling mechanism model. This scheduling mechanism can more comprehensively reflect the optimization of various factors for the inter-satellite communication paths and achieve more comprehensive path optimization.

[0096] The calculation method of the average latency of the satellite communication system is shown in formula (6):

[0097]

[0098] Wherein, is the average latency of all established routes in the satellite network for the nth snapshot considering all satellites, with the unit of ms, D n is the total delay between each source and target pair in snapshot n, with the unit of ms, m is the number of source-destination satellite pairs with established flows, EF is the total number of source-destination satellite pairs with established flows, Q Dn is the number of source-target pairs in the nth snapshot, and N is the total number of snapshots.

[0099] The service life of the satellite is calculated by calculating the average life cycle (number of charge / discharge cycles) of the battery in one day (simulation of 15 orbital periods) and multiplying it by the number of days in a year (365 days). The calculation method of the satellite battery life is shown in formula (7):

[0100]

[0101] where, is the number of life cycles occupied by satellite i in snapshot j, L C is the total number of battery life cycles, defined as 36,000 cycles, Considering 200 terminals, the average number of satellites forwarding traffic during 600 snapshots, Considering all satellites and all simulated snapshots, the average number of consumed life cycles, N is the total number of snapshots, which is equal to 600 in the present invention, j is the jth snapshot, M is the total number of satellites, which is equal to 66 in the present invention, and i is the ith satellite.

[0102] Considering the traffic generated by all non-blocking source-destination pairs in each snapshot n, the average throughput of the satellite network is calculated as shown in formula (8):

[0103]

[0104] where, is the average throughput of the satellite network, T n (s,d) is the total traffic volume between each source and destination pair in the nth snapshot, in Mbps, Q Tn is the number of source and destination satellite pairs with network traffic in n snapshots, m is the number of source-destination satellite pairs with established flows, EF is the total number of source-destination satellite pairs with established flows, and N is the total number of snapshots.

[0105] The calculation method of the average number of hops of the satellite communication system is shown in formula (9):

[0106]

[0107] where, is the average number of hops of the satellite network considering all established end-to-end routes and each snapshot, H n (s,d) is the total number of hops between the source and destination pairs in n snapshots, Q Hn is the number of source and destination satellites paired with the established routes in n snapshots, m is the number of source-destination satellite pairs with established flows, and EF is the total number of source-destination satellite pairs with established flows.

[0108] The data transmission volume is the cumulative result of the throughput over a certain period of time. The calculation method of the average data transmission volume of the satellite communication system is shown in Equation (10):

[0109] D = R × T (10);

[0110] where D is the data transmission volume, with the unit of Mb; R is the data transmission rate, representing the amount of data transmitted per second, with the unit of Mb / min; T is the transmission time, representing the duration of data transmission, with the unit of min.

[0111] In satellite communication research, the outage probability refers to the probability that the communication link fails to transmit data normally due to factors such as signal attenuation, interference, or occlusion within a certain period of time. The calculation of the outage probability is shown in Equation (11):

[0112] P out = Pr(γ < γ th ) (11);

[0113] where P out is the outage probability, representing the probability of the communication link being interrupted; Pr is the probability symbol, representing the probability of a specific event occurring; γ is the actual signal reception quality of the link; γ th is the minimum acceptable signal quality threshold of the communication link, that is, communication cannot proceed normally below this threshold. When the signal quality γ of the link is lower than the threshold γth, the communication link will be interrupted. In satellite communication, the lower the outage probability, the higher the reliability of the link.

[0114] Specifically, based on the construction of a multi-objective optimization model, expanding the single-path routing to k multi-path routings, using the PSO algorithm to dynamically adjust the weights of the multi-paths in real time, and constructing a dynamic scheduling mechanism for multi-path routing (PSODW-DTNPDOA-MPRM), based on the index parameters obtained in S201, the optimal path in the inter-satellite communication routing is further obtained, so as to calculate the end-to-end delay, satellite lifetime, throughput, number of hops, data transmission volume, and outage probability in the inter-satellite communication routing, and then comprehensively evaluate the dynamic optimization energy-saving scheduling mechanism for multi-path routing.

[0115] The present invention provides a multi-path routing dynamic scheduling method for a satellite communication system, including designing a routing algorithm based on multi-path optimization (SW-MPRM), expanding single-path routing into k multi-path routings, and accurately simulating routing scheduling in a large-scale constellation, comprehensively considering the constraints of transmission delay, energy consumption, capacity, and packet loss rate; then proposing a particle swarm optimization weight algorithm (PSODW-MPRM) to achieve optimal multi-path load distribution without additional resource consumption; finally, designing a new DTNPDOA multi-path energy-saving routing scheduling mechanism (PSODW-DTNPDOA-MPRM), finding the optimal path through the NPDOA optimization algorithm, and using digital twin technology to simulate path changes to provide real-time feedback for the algorithm. This method can effectively reduce propagation delay, hop count, and interruption probability, significantly improve the remaining energy of the satellite and extend the satellite's lifespan while ensuring data transmission reliability, thus solving the problems of low reliability and high energy consumption faced in large-scale LEO satellite constellations.

[0116] When applying the multi-path routing dynamic scheduling method for a satellite communication system provided by the present invention, it is not necessary to execute according to Figure 1 the order of the steps shown. The specific execution order of each step can be determined as needed, and the present invention does not limit this.

[0117] The above is the multi-path routing dynamic scheduling method for a satellite communication system provided by one or more embodiments of the present invention. Based on the same idea, the present invention also provides a corresponding multi-path routing dynamic scheduling device for a satellite communication system, as Figure 3 shown.

[0118] Figure 3 The figure shows a schematic diagram of the multi-path routing dynamic scheduling device for a satellite communication system provided by the present invention, including:

[0119] An acquisition module 301, configured to expand the path between the source node and the target node in the satellite communication system into multiple paths, and acquire the communication quality index parameters of all satellites in the multiple paths; the source node and the target node are any nodes in the satellite communication system.

[0120] A construction module 302, configured to construct a routing cost function for each of the multiple paths through the communication quality index parameters.

[0121] An update module 303, configured to use digital twin to simulate real-time path changes during the operation of the satellite communication system, and update the graph data of the satellite communication system in real time according to the real-time path changes.

[0122] A generation module 304, for each piece of map data updated in real time, based on communication quality metric parameters, calculates the total routing costs of multiple paths between the source node and the target node in the map data through a routing cost function, selects the first preset number of paths in ascending order of the total routing costs of the multiple paths, and determines the first preset number of paths as the optimal path combination at the current moment.

[0123] For the specific limitations of the satellite communication system multi-path routing dynamic scheduling device, reference can be made to the limitations of the satellite communication system multi-path routing dynamic scheduling method in the above text, which will not be elaborated here. Each module in the above satellite communication system multi-path routing dynamic scheduling device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor in the computer device in hardware form or be independent of it, or can be stored in the memory in the computer device in software form, so as to facilitate the processor to call and execute the operations corresponding to the above modules.

[0124] The present invention also provides a computer-readable storage medium, which stores a computer program, and the computer program can be used to execute the above Figure 2 provided satellite communication system multi-path routing dynamic scheduling method.

[0125] The present invention also provides Figure 4 the structural schematic diagram of the computer device shown in Figure 4 shown. At the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory. Of course, there may also be other hardware required for other services. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the above Figure 2 provided satellite communication system multi-path routing dynamic scheduling method.

[0126] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided by the present invention can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0127] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded by the present invention.

Claims

1. A dynamic scheduling method for multi-path routing in a satellite communication system, characterized in that, Including: Expanding the path between the source node and the target node in the satellite communication system into multiple paths, and obtaining the communication quality index parameters of all satellites in the multiple paths; the source node and the target node are any nodes in the satellite communication system; For each of the multiple paths, constructing a routing cost function through the communication quality index parameters; Using digital twin to simulate the real-time path changes during the operation of the satellite communication system, and updating the graph data of the satellite communication system in real time according to the real-time path changes; For each piece of graph data updated in real time, based on the communication quality index parameters, calculating the total routing cost of the multiple paths between the source node and the target node in the graph data through the routing cost function, selecting the first preset number of paths in ascending order of the total routing cost of the multiple paths, and determining the first preset number of paths as the optimal path combination at the current moment; The communication quality index parameters include transmission delay, energy consumption, link capacity, and transmission data packet loss rate. The constructing of the routing cost function through the communication quality index parameters includes: obtaining the transmission delay, energy consumption, link capacity, and transmission data packet loss rate between the source node and the target node in the satellite communication system; constructing a routing cost function through the transmission delay, energy consumption, link capacity, and transmission data packet loss rate between the source node and the target node; The routing cost function is: Among them, Q x,y is the routing cost from the source node x to the target node y when maximizing the traffic demand, T x,y is the transmission delay between the source node x and the target node y, T min is the minimum delay in the snapshot, T max the maximum transmission delay in the snapshot, E x,y is the energy consumption for transmitting data from the source node x to the target node y, C x,y is the link capacity between the source node x and the target node y, P x,y is the packet loss rate of the transmitted data between the source node x and the target node y, E min is the minimum energy consumption for transmitting data from the source node x to the target node y, E max is the maximum energy consumption for transmitting data from the source node x to the target node y, C min is the minimum link capacity between the source node x and the target node y, C max is the maximum link capacity between the source node x and the target node y, P min is the minimum packet loss rate of the transmitted data between the source node x and the target node y, P max is the maximum packet loss rate of the transmitted data between the source node x and the target node y, ω1 is the weight of the transmission delay parameter, ω2 is the weight of the energy consumption parameter, ω3 is the weight of the link capacity parameter, ω4 is the weight of the packet loss rate parameter of the transmitted data; The determination process of the weights in the routing cost function includes: initializing the particle swarm; for any particle, randomly generating the particle position and velocity, and setting the local best position of each particle as the initial position; the position of the particle is the weight in the routing cost function; calculating the fitness value according to the routing cost function, and updating the local best candidate node, global best candidate node, and fitness of the particle in real time according to the fitness value; continuously updating the velocity and position of the particle, adjusting the particle velocities of the local best position and the global best position, and updating the best candidate node; after multiple iterations, returning the global best position and determining the global best position as the weight in the routing cost function.

2. The method according to claim 1, characterized in that The method further includes: After determining the optimal path combination, designing a multi-path routing evaluation index; Evaluating the optimal path combination through the multi-path routing evaluation index to obtain the transmission performance of the satellite communication system.

3. The method according to claim 2, wherein The multi-path routing evaluation index includes average delay, average throughput, data transmission volume, outage probability, average satellite service life, and number of hops.

4. A multi-path routing dynamic optimization scheduling device for a satellite communication system, characterized in that, Including: An acquisition module, configured to expand the path between the source node and the target node in the satellite communication system into multiple paths, and obtain the communication quality index parameters of all satellites in the multiple paths; the source node and the target node are any nodes in the satellite communication system; A construction module, configured to construct a routing cost function through the communication quality index parameters for each of the multiple paths; The communication quality metric parameters include transmission delay, energy consumption, link capacity, and transmission data packet loss rate. Constructing a routing cost function based on the communication quality metric parameters includes: obtaining the transmission delay, energy consumption, link capacity, and transmission data packet loss rate between a source node and a destination node in the satellite communication system; constructing a routing cost function based on the transmission delay, energy consumption, link capacity, and transmission data packet loss rate between the source node and the destination node; The routing cost function is: Among them, Q x,y is the routing cost from the source node x to the target node y when maximizing the traffic demand, T x,y is the transmission delay between the source node x and the target node y, T min is the minimum delay in the snapshot, T max is the maximum transmission delay in the snapshot, E x,y is the energy consumption for transmitting data from the source node x to the target node y, C x,y is the link capacity between the source node x and the target node y, P x,y is the packet loss rate of the transmitted data between the source node x and the target node y, E min is the minimum energy consumption for transmitting data from the source node x to the target node y, E max is the maximum energy consumption for transmitting data from the source node x to the target node y, C min is the minimum link capacity between the source node x and the target node y, C max is the maximum link capacity between the source node x and the target node y, P min is the minimum packet loss rate of the transmitted data between the source node x and the target node y, P max is the maximum packet loss rate of the transmitted data between the source node x and the target node y, ω1 is the weight of the transmission delay parameter, ω2 is the weight of the energy consumption parameter, ω3 is the weight of the link capacity parameter, and ω4 is the weight of the packet loss rate parameter of the transmitted data; The process of determining the weights in the routing cost function includes: initializing the particle swarm; for any particle, randomly generating the particle position and velocity, and setting the local best position of each particle as the initial position; the position of the particle is the weight in the routing cost function; calculating the fitness value according to the routing cost function, and updating the local best candidate node, global best candidate node, and fitness of the particle in real time according to the fitness value; continuously updating the velocity and position of the particle, adjusting the particle velocities of the local best position and the global best position, and updating the best candidate nodes; after multiple iterations, returning the global best position, and determining the global best position as the weight in the routing cost function; An update module, configured to use digital twin to simulate the real-time path changes during the operation of the satellite communication system, and update the graph data of the satellite communication system in real time according to the real-time path changes; A generation module, configured to, for each piece of graph data updated in real time, calculate the total routing cost of multiple paths from the source node to the destination node in the graph data based on the communication quality metric parameters through the routing cost function, select the first preset number of paths in ascending order of the total routing cost of the multiple paths, and determine the first preset number of paths as the optimal path combination at the current moment.

5. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 3 is implemented.

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