Multi-path routing dynamic scheduling method and device of satellite communication system and medium

Abstract

The invention discloses a multi-path routing dynamic scheduling method and device for a satellite communication system and a medium, and relates to the field of satellite data transmission, and the method comprises the steps: expanding a path between a source node and a target node in the satellite communication system into a plurality of paths, and obtaining communication quality index parameters of all satellites in the plurality of paths; for each of the plurality of paths, constructing a routing cost function through the communication quality index parameter; updating the graph data of the satellite communication system in real time according to the real-time path change 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 a plurality of paths between a source node and a target node in the graph data through a routing cost function, selecting a first preset number of paths from the total routing cost of the plurality of paths according to a sequence from low to high, and determining the first preset number of paths as an optimal path combination at the current moment. The method can reduce the transmission energy consumption of the satellite network.

Description

Technical Field

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

[0002] With the development of low earth orbit (LEO) satellite networks, 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 environmental areas. These characteristics make satellite networks of great strategic significance in the fields of global communications, disaster monitoring, military operations, etc. Satellite networks are included in the scope of information infrastructure. With the increase in the number of satellites and the expansion of satellite application scenarios, the reliability, load balancing capabilities and energy efficiency of satellite network communications have become important issues that need to be solved urgently.

[0003] In satellite networks, multipath routing is an effective means to improve satellite network reliability and data transmission efficiency. However, the rational and efficient application of this technology while ensuring satellite life and reducing energy consumption still faces huge challenges. Specifically, in the absence of optimization algorithm support, traditional multipath 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 satellite dynamic network environment and cannot achieve optimal path selection, which in turn affects 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 method, device and medium for dynamic scheduling of multi-path routing in a satellite communication system in response to the above technical problems. The method can reduce the transmission energy consumption of the satellite communication system.

[0006] The present invention adopts the following technical solutions: The present invention provides a satellite communication system multi-path routing dynamic scheduling method, comprising: The path between the source node and the target node in the satellite communication system is expanded into multiple paths, and the communication quality index parameters of all satellites in the multiple paths are obtained; the source node and the target node are any nodes in the satellite communication system; For each of the plurality of paths, a routing cost function is constructed by using a communication quality indicator parameter; Use digital twins to simulate real-time path changes during the operation of the satellite communication system, and update the satellite communication system graph data in real time according to the real-time path changes; For each graph data updated in real time, based on the communication quality indicator parameters, the total routing cost of multiple paths between the source node and the target node in the graph data is calculated by the routing cost function, and the total routing cost of the multiple paths is selected in order from low to high to select a first preset number of paths, and the first preset number of paths is determined as the optimal path combination at the current moment.

[0007] Preferably, the communication quality indicator parameters include transmission delay, remaining battery energy, storage capacity and packet loss rate, and the routing cost function is constructed by the communication quality indicator parameters, including: 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; The routing cost function is constructed through the transmission delay, energy consumption, link capacity and transmission data packet loss rate between the source node and the target node.

[0008] Preferably, the routing cost function is: ; in, Q x,y From the source node x To the target node y The routing cost when maximizing traffic demand, T x,y For source node x With the target node y The transmission delay between T min is the minimum delay in the snapshot, T max The maximum transmission delay in the snapshot, E x,y From the source node x To the target node y The energy consumption of transmitting data between C x,y For source node x and the target node y The link capacity between P x,y For source node x and the target node y The packet loss rate of the transmitted data, From the source node x To the target node y The minimum energy consumption for transmitting data between From the source node x To the target node y The maximum energy consumption for transmitting data between For source node x and the target nodey The minimum link capacity between For source node x and the target node y The maximum link capacity between For source node x and the target node y The minimum transmission data packet loss rate between For source node x and the target node y The maximum transmission data packet loss rate between is the weight of the transmission delay parameter, is the weight of the energy consumption parameter, is the weight of the link capacity parameter, is the weight of the transmission data packet loss rate parameter.

[0009] Preferably, the process of determining the weight in the routing cost function includes: Initialize particle swarm; For any particle, the particle position and velocity are randomly generated, and the local optimal position of each particle is set as the initial position; the particle position is the weight in the routing cost function; The fitness value is calculated according to the routing cost function, and the local best candidate node, global best candidate node and fitness of the particle are updated in real time according to the fitness value; Continuously update the speed and position of particles, adjust the particle speed at the local optimal position and the global optimal position, and update the best candidate node; After multiple iterations, the global best position is returned and determined as the weight in the routing cost function.

[0010] Preferably, the method further comprises: After determining the optimal path combination, design multi-path routing evaluation indicators; The optimal path combination is evaluated by multi-path routing evaluation indicators to obtain the transmission performance of the satellite communication system.

[0011] Preferably, the multipath routing evaluation indexes include average delay, average throughput, data transmission volume, interruption probability, average satellite service life and number of hops.

[0012] The present invention provides a satellite communication system multipath routing dynamic scheduling device, comprising: An acquisition module is used to expand the path from the source node to 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 is used to construct a routing cost function using a communication quality indicator parameter for each of the plurality of paths; An update module is used to simulate the real-time path changes of the satellite communication system during operation using the digital twin, and to update the graph data of the satellite communication system in real time according to the real-time path changes; A generation module is used to calculate the total routing cost of multiple paths between the source node and the target node in the graph data for each graph data updated in real time, based on the communication quality indicator parameters, through a routing cost function, select a first preset number of paths from the total routing costs of the multiple paths in order from low to high, and determine the first preset number of paths as the optimal path combination at the current moment.

[0013] The present invention provides a computer-readable storage medium, wherein the storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned satellite communication system multi-path routing dynamic scheduling method is implemented.

[0014] The present invention provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the above-mentioned satellite communication system multi-path routing dynamic scheduling method is implemented.

[0015] At least one of the above technical solutions adopted by the present invention can achieve the following beneficial effects: The path between the source node and the target node in the satellite communication system is expanded into multiple paths, thereby 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 indicator parameters to achieve optimal multi-path load distribution without increasing additional resource consumption; a digital twin is 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 graph data updated in real time, based on the communication quality indicator parameters, the total routing cost of multiple paths between the source node and the target node in the graph data is calculated through the routing cost function, and the total routing cost of the multiple paths is selected in order from low to high, and the first preset number of paths is determined as the optimal path combination at the current moment. The routing cost of the dynamically selected optimal path combination is low, and the method can reduce the transmission energy consumption of the satellite communication system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary 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: Figure 1 Schematic diagram of multipath routing transmission model for LEO satellite constellation; Figure 2 A schematic diagram of a flow chart of a method for dynamic scheduling of multi-path routing in a satellite communication system provided by the present invention; Figure 3 A schematic diagram of a satellite communication system multipath routing dynamic scheduling device provided by the present invention; Figure 4 A schematic diagram of a computer device for implementing a method for dynamic scheduling of multi-path routing in a satellite communication system provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution 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 part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown in FIG, the multipath routing transmission model of the LEO constellation includes signal towers, mobile devices, LEO satellites, and ground base stations. Based on the 66 LEO satellites and their inter-satellite links that constitute the Iridium constellation, the routing model proposed in the present invention evaluates the path quality based on the expected delay of the path. Figure 1 After the failure of node A, the probability of successful data transmission decreases, which immediately leads to an increase in the cost of the red path, 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 optimizes multiple indicator path selection and task allocation to find the global optimal path that meets multiple constraints. While ensuring the reliability of data transmission, energy consumption is minimized, providing an energy-saving routing scheduling mechanism for multi-path transmission, and providing a sustainable solution for satellite communication networks with limited resources.

[0019] In practical applications, communication links are affected by many factors, among which transmission delay, remaining battery energy, storage capacity and packet loss rate are more significant factors.

[0020] In satellite Internet, selecting multi-path routing is an effective means to improve network reliability and data transmission efficiency. However, the rational and efficient application of this technology while ensuring the life of the satellite and reducing energy consumption still faces huge challenges. Specifically, single-path routing has limitations under high load or failure conditions, and cannot effectively share traffic or provide redundancy, resulting in unreliable data transmission. Traditional static weight allocation methods cannot adapt to the dynamic changes of satellite networks, resulting in unsatisfactory transmission efficiency and difficulty in coping with sudden changes caused by failures or congestion. In the absence of optimization algorithm support, 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 dynamic network environments and cannot achieve optimal path selection, which in turn affects transmission efficiency and system stability.

[0021] When modeling the inter-satellite communication model to optimize the path, the existing technology often ignores the impact of the above factors on the data transmission of the communication link, resulting in the system's optimal path being inaccurate and incomplete. Based on the above problems, the present invention proposes a multi-path routing dynamic scheduling method for a satellite communication system.

[0022] The technical solutions provided by various embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

[0023] Figure 2 The present invention is a schematic diagram of a method for dynamically scheduling multi-path routing in a satellite communication system, which specifically includes the following steps: S201: Expand the path from the source node to the target node in the satellite communication system into multiple paths, and obtain 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.

[0024] In an exemplary embodiment, the communication quality indicator parameters include transmission delay, energy consumption, link capacity and transmission data packet loss rate.

[0025] Specifically, propagation delay refers to the time required for a signal to travel from the sender to the receiver in the transmission medium. In LEO satellite networks, propagation delay is mainly related to the distance between satellites and the signal propagation speed. In multipath 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.

[0026] Energy consumption refers to the unconsumed electrical energy in the satellite's internal battery, expressed as a percentage of power. In LEO satellite networks, energy consumption directly affects the satellite's communication capabilities, orbit control, network reliability, and multi-path routing efficiency.

[0027] 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 satellite's link capacity 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, improve network fault tolerance and scalability, and ensure the long-term stable operation of the satellite network.

[0028] The transmission data packet loss rate refers to the ratio of the number of lost data packets to the total number of data groups sent when the satellite network transmits data.

[0029] S202: For each of the multiple paths, construct a routing cost function using communication quality indicator parameters.

[0030] The communication quality indicator parameters are normalized into the routing cost function.

[0031] In an exemplary embodiment, the communication quality indicator parameters of the satellite include transmission delay, remaining battery energy, storage capacity and packet loss rate. The routing cost function is constructed using the communication quality indicator parameters, including: obtaining the transmission delay, remaining battery energy, storage capacity and packet loss rate of the source node and the target node in the satellite communication system; and constructing the routing cost function using the transmission delay, energy consumption, link capacity and transmission data packet loss rate between the source node and the target node.

[0032] Specifically, the transmission delay, remaining battery energy, storage capacity and packet loss rate of all satellites in the satellite communication system are obtained; according to the transmission delay, energy consumption, link capacity and transmission data packet loss rate between the source node and the target node, a routing cost function is constructed. The routing cost function is shown in formula (1): (1); in, Q x,y From the source node x To the target node y The routing cost when maximizing traffic demand, T x,y For source node x With the target node y The transmission delay between T min is the minimum delay in the snapshot, T max The maximum transmission delay in the snapshot, E x,y From the source node x To the target node y The energy consumption of transmitting data between C x,y For source node x and the target node yThe link capacity between P x,y For source node x and the target node y The packet loss rate of the transmitted data, From the source node x To the target node y The minimum energy consumption for transmitting data between From the source node x To the target node y The maximum energy consumption for transmitting data between For source node x and the target node y The minimum link capacity between For source node x and the target node y The maximum link capacity between For source node x and the target node y The minimum transmission data packet loss rate between For source node x and the target node y The maximum transmission data packet loss rate between is the weight of the transmission delay parameter, is the weight of the energy consumption parameter, is the weight of the link capacity parameter, is the weight of the transmission data packet loss rate parameter.

[0033] In an exemplary implementation, the process of determining the weight in the routing cost function includes: initializing a particle swarm; for any particle, randomly generating a particle position and a velocity, and setting the local optimal position of each particle as an initial position; the position of the particle is the weight in the routing cost function; The fitness value is calculated according to the routing cost function, and the local best candidate node, the global best candidate node and the fitness of the particle are updated in real time according to the fitness value; the speed and position of the particle are continuously updated, the particle speed of the local best position and the global best position is adjusted, and the best candidate node is updated; after multiple iterations, the global best position is returned, and the global best position is determined as the weight in the routing cost function.

[0034] Specifically, the problem of selecting the optimal satellite path is modeled as a multi-objective optimization problem, and the parameters transmission delay, remaining battery energy, storage capacity, and packet loss rate are normalized into routing costs to establish a multi-objective optimization model.

[0035] In multipath, the propagation delay of the route established between the source node s and the destination node d is calculated as shown in formula (2): (2); in, For source node s and destination node d The propagation delay of the route between k For the k Links, For the k The distance of the link in m , H is the total number of links in a route, V is the speed of light, defined as 2.998×108 m / s .

[0036] 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 energy consumed by the satellite operation is calculated as shown in formula (3): (3); in, is the remaining energy at the end of the satellite mission, is the battery energy at the start of the satellite mission, For Representative t The satellite's operating energy consumption at all times, for t The energy consumed by the satellite to send data at any time, for t The energy consumed by the satellite to receive data at any moment.

[0037] From the source node s To the destination node d The path through K The total capacity consumption is calculated as shown in formula (4): (4); in, C is the total capacity consumption, For the k The amount of data transmitted through the link, For the k The distance of the link, K is the total number of links.

[0038] The calculation of the proportion of data packets that fail to successfully reach the target node during the transmission process from the source node s to the destination node d is shown in formula (5): (5); in, P is the packet loss rate, is the number of lost packets, that is, the total number of packets that failed to reach the destination during transmission. is the total number of packets sent, that is, the total number of all packets sent by the source node.

[0039] Based on the above analysis, in order to select the optimal path from the source node s to the destination node d that satisfies the satellite energy saving metric, the present invention models the problem of selecting the optimal satellite path as a multi-objective optimization problem, which is expressed as formula (1).

[0040] After constructing the routing cost function, the particle swarm algorithm is used to update the weights in the routing cost function in real time.

[0041] In an exemplary embodiment, the method further includes: initializing a particle swarm; for any particle, randomly generating a particle position and speed, and setting the local optimal position of each particle as the initial position; the particle position is the weight in the routing cost function; calculating the fitness value according to the routing cost function, and updating the particle's local optimal candidate node, global optimal candidate node and fitness in real time according to the fitness value; continuously updating the particle's speed and position, adjusting the particle speed at the local optimal position and the global optimal position, and updating the optimal candidate node; after multiple iterations, returning to the global optimal position, and determining the global optimal position as the weight in the routing cost function.

[0042] Specifically, based on the establishment of a multi-objective model, the present invention proposes a routing algorithm based on multi-path optimization to address the problems of congestion, disconnection, and difficulty in ensuring reliable transmission caused by a single path. 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 to multiple paths for transmission, so as to disperse the traffic load and improve the network's ability to resist failures, thereby enhancing the reliability of the network.

[0043] Static routing has limitations when dealing with frequent changes in network topology, which can easily lead to path interruption and increased delay. Based on the above, the present invention expands the single-path routing to k multi-path routing, uses the particle swarm optimization algorithm (PSO) 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, thereby achieving optimal multi-path load distribution without increasing additional resource consumption. Dynamic adjustment of multi-path weights in the PSO-optimized dynamic weight multi-path routing (Particle Swarm Optimization Dynamic Weight Multi-path Routing mechanism, PSODW-MPRM) scheme is the primary task of the energy-saving scheduling mechanism, which requires initializing the particle swarm, including randomly generating particle positions, setting speeds, and setting the local optimal position of each particle to the initial position. The fitness is calculated by calling a function, and its local and global optimal candidate nodes and fitness are updated. Then, in the main loop, the algorithm continuously updates the speed and position of the particles to achieve dynamic adjustment of the multi-path weights. And by setting the inertia factor, self-cognition factor, and social cognition factor, the particle speeds at the local and global optimal positions are adjusted, and the optimal candidate nodes are updated. Finally, after multiple iterations, the global best position and the final optimal solution are returned, and the dynamically adjusted weights are obtained. This algorithm uses the dynamic adjustment of particles to improve the search efficiency of multi-objective optimization.

[0044] Specifically, in the present invention, a dynamic fitness function is designed to dynamically adjust the fitness evaluation criteria of each path according to multiple network performance indicators such as network topology changes, transmission delay, remaining battery energy, storage capacity and packet loss rate in real time. Not only the current network status is considered, but also the expected changes in the future are considered. Thus, the fitness is calculated, and the local best candidate node and the global best candidate node and fitness of the particle are updated.

[0045] In the present invention, the parameters transmission delay, battery remaining energy, storage capacity and packet loss rate are normalized using maximum and minimum values ​​to change the numerical values ​​of the data to the same scale and combined into a link cost of comprehensive battery consumption through weight factors. By defining the minimum energy threshold of the satellite battery, used in the link, a higher weight is given to the link that does not meet the minimum threshold of the satellite battery remaining energy constituting the link, and the weight factor in formula (1) is dynamically adjusted. The dynamic weight adjustment method penalizes the link whose satellite does not reach the minimum battery power level defined in the energy threshold, and even satellites outside the predefined threshold can be used for data traffic, thereby reducing the blocking source, achieving better traffic balance, and effectively reducing the energy consumption in the network routing process.

[0046] S203: Use the digital twin to simulate the real-time path changes of the satellite communication system during operation, and update the map data of the satellite communication system in real time according to the real-time path changes.

[0047] Specifically, the digital twin is used to simulate the real-time status and real-time status changes of the satellite communication system, and the graph data of the satellite communication system is updated according to the real-time status changes of the satellite communication system.

[0048] S204: For each graph data updated in real time, based on the communication quality indicator parameters, the total routing cost of multiple paths between the source node and the target node in the graph data is calculated by the routing cost function, and the total routing cost of the multiple paths is selected in order from low to high to form a first preset number of paths, and the first preset number of paths is determined as the optimal path combination at the current moment.

[0049] The first preset number is set according to specific engineering practice.

[0050] Specifically, another challenge faced by multipath routing in practical applications is how to coordinate and schedule multiple paths to make full use of the resources of each path. Traditional multipath routing, in the absence of an optimization algorithm, is prone to inaccurate path selection, low transmission reliability, and easy interruption. The present invention proposes a dynamic scheduling mechanism for multipath routing by constructing the DTNPDOA algorithm. The PSODW-DTNPDOA-MPRM mechanism is optimized in both path selection and task allocation. Among them, DTNPDOA can perform path selection and task allocation based on multiple indicators such as path delay, bandwidth, and energy consumption, while minimizing energy consumption while ensuring data transmission reliability. The scheduling mechanism can dynamically identify the status of different paths and allocate traffic to multiple paths with the optimal strategy.

[0051] DTNPDOA is a key link in 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 of repeated prefixes, thus achieving efficient multi-path planning. The digital twin DT is used as a virtual simulation system to simulate changes in the real system and provide real-time feedback to the algorithm, which can help the algorithm find the optimal solution more effectively. By obtaining real-time updated graph data, dynamically calculating the path from the branch node to the destination node, and storing the merged complete path in the candidate set, the appropriate path is selected to update the result set, and finally k shortest paths are efficiently generated. 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.

[0052] In an exemplary embodiment, the method further includes: after determining the optimal path combination, designing a multipath routing evaluation index; and evaluating the optimal path combination by the multipath routing evaluation index to obtain the transmission performance of the satellite communication system.

[0053] Specifically, the multipath routing evaluation index is an important index to measure the transmission performance of the satellite communication system. The multipath routing evaluation index includes average delay, average throughput, data transmission volume, interruption probability, average satellite service life and number of hops. After designing the multipath routing evaluation index, the optimal path combination is evaluated according to the multipath routing evaluation index to obtain the transmission performance of the satellite communication system, and the resource configuration of the satellite communication system is optimized according to the obtained transmission performance of the satellite communication system.

[0054] Average latency is an indicator that measures the time required from the data source node to the destination node, and usually includes propagation delay, processing delay, queuing delay, etc. Lower end-to-end latency means higher data transmission efficiency and faster network response; while higher latency may cause network transmission delays, affecting real-time communication and service quality.

[0055] Satellite service life refers to the length of time a satellite can operate in orbit and maintain normal functions, which is usually affected by factors such as battery life, hardware wear and orbital decay. A longer satellite life helps reduce the frequency of satellite replacement and lower network construction and maintenance costs; while a shorter satellite life may lead to frequent satellite replacement and increase operating costs.

[0056] Average throughput refers to the amount of data transmitted through the network per unit time. A higher throughput means that the network can transmit more data, improving the network's capacity and efficiency; a lower throughput means that the network bandwidth is limited, which may lead to data congestion and slow transmission speeds.

[0057] The number of hops refers to the number of transit nodes that data passes through from the source node to the destination node. Fewer hops means a shorter data transmission path, lower network transmission delay and load; while more hops may lead to higher delays and greater network burden, affecting data transmission efficiency.

[0058] Data transmission volume refers to the total amount of data successfully transmitted within a certain period of time. A larger data transmission volume means that the network can support higher communication needs and improve the overall efficiency of the system; while a smaller transmission volume may limit the service capacity of the network.

[0059] The present invention constructs a multi-path routing dynamic scheduling mechanism model based on the influence of transmission delay, battery remaining energy, storage capacity and packet loss rate on multi-path routing evaluation indicators. The scheduling mechanism can more comprehensively reflect the optimization of inter-satellite communication paths by various factors and realize more comprehensive path optimization.

[0060] The calculation method of the average delay of the satellite communication system is shown in formula (6): (6); in, In the case of considering all satellites, n snapshot, the average delay of all established routes in the satellite network, in units of ms , For snapshot n The total delay between each source and destination pair in ms , m is the number of source-destination satellite pairs with a given flow, EF is the total number of source-destination satellite pairs with a given flow, For the n The number of source-destination pairs in the snapshot, N The total number of snapshots.

[0061] 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 (15 orbital cycle simulation) 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): (7); in, For snapshot j Medium Satellite i The number of life cycles occupied, 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, The average number of lifetimes consumed, considering all satellites and all simulation snapshots, N is the total number of snapshots, which is equal to 600 in the present invention, j For the j Snapshots, M is the total number of satellites, which is equal to 66 in the present invention, i For the i satellites.

[0062] 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): (8); in, is the average throughput of the satellite network, For the n The total amount of traffic between each source and destination pair in the snapshot, in units of Mbps, For n The number of source and destination satellite pairs with network traffic in each snapshot, m is the number of source-destination satellite pairs with a given flow, EF is the total number of source-destination satellite pairs with a given flow, N The total number of snapshots.

[0063] The calculation method of the average number of hops in the satellite communication system is shown in formula (9): (9); in, To take into account all established end-to-end routes and the average number of hops in the satellite network for each snapshot, For n The sum of the hop counts between the source and destination pairs in the snapshot, exist n The number of source and destination satellites paired with established routes in the snapshot, m is the number of source-destination satellite pairs with a given flow, EF is the total number of source-destination satellite pairs with a given flow.

[0064] The data transmission volume is the cumulative result of the throughput within a certain period of time. The calculation method of the average data transmission volume of the satellite communication system is shown in formula (10): (10); in, D The data transmission volume is in Mb , R The data transmission rate is the amount of data transmitted per second, in units of Mb / min , T Transmission time, which indicates the duration of data transmission, in units of min .

[0065] In satellite communication research, the interruption probability refers to the probability that a communication link cannot transmit data normally due to factors such as signal attenuation, interference or obstruction within a certain period of time. The calculation of the interruption probability is shown in formula (11): (11); in, is the interruption probability, which indicates the probability of communication link interruption, is a probability symbol, which indicates the probability of a specific event occurring. γ The actual signal reception quality of the link, The minimum acceptable signal quality threshold of the communication link. That is, communication will not be able to proceed normally below this threshold. γ Below threshold γth When the communication link is interrupted, the lower the interruption probability is, the higher the reliability of the link is.

[0066] Specifically, based on the construction of a multi-objective optimization model, the single-path routing is expanded into k multi-path routings, the PSO algorithm is used to dynamically adjust the weights of the multi-paths in real time, and a dynamic scheduling mechanism for multi-path routing (PSODW-DTNPDOA-MPRM) is constructed. Based on the indicator parameters obtained in S201, the optimal path in the inter-satellite communication routing is further obtained, thereby calculating the end-to-end delay, satellite life, throughput, number of hops, data transmission volume and interruption probability in the inter-satellite communication routing, and then comprehensively evaluating the multi-path routing dynamic optimization energy-saving scheduling mechanism.

[0067] The present invention provides a method for dynamic scheduling of multipath routing in a satellite communication system, including designing a routing algorithm based on multipath optimization (SW-MPRM), expanding single-path routing to k multipath routing, and accurately simulating routing scheduling in large-scale constellations, comprehensively considering the constraints of transmission delay, energy consumption, capacity and packet loss rate; then a particle swarm optimization weight algorithm (PSODW-MPRM) is proposed to achieve optimal multipath load distribution without increasing additional resource consumption; finally, a new DTNPDOA multipath energy-saving routing scheduling mechanism (PSODW-DTNPDOA-MPRM) is designed to find the optimal path through the NPDOA optimization algorithm, and use 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 satellite residual energy and extend satellite life under the premise of ensuring data transmission reliability, thereby solving the problems of low reliability and high energy consumption faced in large-scale LEO satellite constellations.

[0068] When applying the satellite communication system multi-path routing dynamic scheduling method provided by the present invention, it is not necessary to Figure 1 The steps are executed in the order shown. The specific execution order of the steps can be determined according to needs, and the present invention does not limit this.

[0069] The above is a method for dynamic scheduling of multi-path routing in 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 device for dynamic scheduling of multi-path routing in a satellite communication system, such as Figure 3shown.

[0070] Figure 3 The schematic diagram of the satellite communication system multipath routing dynamic scheduling device provided by the present invention includes: The acquisition module 301 is used to expand the path from the source node to 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.

[0071] The construction module 302 is used to construct a routing cost function for each of the multiple paths using a communication quality indicator parameter.

[0072] The updating module 303 is used to use the digital twin to simulate the real-time path changes of the satellite communication system during operation, and to update the graph data of the satellite communication system in real time according to the real-time path changes.

[0073] The generation module 304 is used to calculate the total routing cost of multiple paths between the source node and the target node in the graph data for each graph data updated in real time, based on the communication quality indicator parameters, through the routing cost function, select a first preset number of paths from the total routing costs of the multiple paths in order from low to high, and determine the first preset number of paths as the optimal path combination at the current moment.

[0074] The specific definition of the satellite communication system multi-path routing dynamic scheduling device can be found in the above definition of the satellite communication system multi-path routing dynamic scheduling method, which will not be repeated 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 a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0075] The present invention also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 2 A satellite communication system multipath routing dynamic scheduling method is provided.

[0076] The present invention also provides Figure 4 The structural diagram of the computer device shown in FIG. Figure 4 As shown in the figure, at the hardware level, the computer device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 2 A satellite communication system multipath routing dynamic scheduling method is provided.

[0077] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and 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-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. 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. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0078] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, 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, they should be considered to be within the scope of the present invention.

Claims

1. A method for dynamic scheduling of multipath routing in a satellite communication system, characterized in that: include: Expanding a path from a source node to a target node in a satellite communication system into multiple paths, and obtaining 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 plurality of paths, constructing a routing cost function using the communication quality indicator parameters; Using digital twins to simulate real-time path changes of a satellite communication system during operation, and updating graph data of the satellite communication system in real time according to the real-time path changes; For each graph data updated in real time, based on the communication quality indicator parameters, the total routing cost of multiple paths between the source node and the target node in the graph data is calculated by the routing cost function, and a first preset number of paths are selected from the total routing costs of the multiple paths in order from low to high, and the first preset number of paths are determined as the optimal path combination at the current moment.

2. The method according to claim 1, characterized in that The communication quality indicator parameters include transmission delay, energy consumption, link capacity and transmission data packet loss rate, and the routing cost function is constructed by using the communication quality indicator parameters, including: 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; The routing cost function is constructed through the transmission delay, energy consumption, link capacity and transmission data packet loss rate between the source node and the target node.

3. The method according to claim 2, characterized in that The routing cost function is: ； in, Q x,y From the source node x To the target node y The routing cost when maximizing traffic demand, T x,y For source node x With the target node y The transmission delay between T min is the minimum delay in the snapshot, T max The maximum transmission delay in the snapshot, E x,y From the source node x To the target node y The energy consumption of transmitting data between C x,y The source node x and the target node y The link capacity between P x,y The source node x and the target node y The packet loss rate of the transmitted data, From the source node x To the target node y The minimum energy consumption for transmitting data between From the source node x To the target node y The maximum energy consumption for transmitting data between The source node x and the target node y The minimum link capacity between The source node x and the target node y The maximum link capacity between The source node x and the target node y The minimum transmission data packet loss rate between For source node x and the target node y The maximum transmission data packet loss rate between is the weight of the transmission delay parameter, is the weight of the energy consumption parameter, is the weight of the link capacity parameter, is the weight of the transmission data packet loss rate parameter.

4. The method according to claim 3, characterized in that The process of determining the weight in the routing cost function includes: Initialize particle swarm; For any particle, randomly generate the particle position and velocity, and set the local optimal position of each particle as the initial position; the position of the particle is 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 of the particle in real time according to the fitness value; Continuously updating the speed and position of the particle, adjusting the particle speed at the local optimal position and the global optimal position, and updating the optimal candidate node; After multiple iterations, the global optimal position is returned, and the global optimal position is determined as the weight in the routing cost function.

5. The method according to claim 1, characterized in that The method further comprises: After determining the optimal path combination, designing a multi-path routing evaluation index; The optimal path combination is evaluated by the multi-path routing evaluation index to obtain the transmission performance of the satellite communication system.

6. The method according to claim 5, characterized in that The multipath routing evaluation indicators include average delay, average throughput, data transmission volume, interruption probability, average satellite service life and number of hops.

7. A satellite communication system multi-path routing dynamic optimization scheduling device, characterized in that: include: An acquisition module, used for expanding a path from a source node to a target node in a satellite communication system into multiple paths, and acquiring 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, for constructing a routing cost function for each of the plurality of paths using the communication quality indicator parameters; An updating module, for simulating the real-time path changes of the satellite communication system during operation using the digital twin, and updating the graph data of the satellite communication system in real time according to the real-time path changes; A generation module is used to calculate, for each graph data updated in real time, based on the communication quality indicator parameters, 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 a first preset number of paths according to the total routing costs of the multiple paths in order from low to high, and determine the first preset number of paths as the optimal path combination at the current moment.

8. 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 according to any one of claims 1 to 6 is implemented.

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