Artificial bee colony low-orbit satellite network routing method, system, device and medium

Abstract

The embodiments of this specification provide an artificial bee colony low-orbit satellite network routing method, system, device and medium, wherein the method includes: scout bee stage: send scout bees through source node s to obtain the probability of the adjacent node j of the current node i becoming the next hop; bee collection stage: record the scout bees that reach the destination node d and record a complete path as bees, select two paths from the first N complete paths and use the crossover operator in the genetic algorithm to obtain a new path, and compare the fitness of the new path with the two paths that perform the crossover operator operation through the fitness function, obtain the path with the largest fitness as the optimal path, and reversely update the optimal path; observation bee stage: the observation bee selects the next hop node according to the optimal path updated in the bee collection stage. The present application ensures that the path update is carried out in the direction of smaller delay through crossover operations. Different routing update rules are set according to different congestion states.

Description

Technical Field

[0001] This document relates to the field of low-orbit satellite network routing technology, and in particular to an artificial bee colony low-orbit satellite network routing method, system, equipment and medium. Background Art

[0002] With the upcoming 6G era, the integrated space-ground network will become the trend of the future. Compared with medium-orbit and geostationary orbit satellites, low-orbit satellite systems have been widely used due to their two major advantages: First, they can achieve seamless global Internet connection services. Traditional high-orbit synchronous orbit satellites have high construction costs, communication blind spots, longer latency and limited bandwidth, and can no longer meet the capacity needs of global massive interconnection. In contrast, low-orbit satellite systems have lower transmission latency, higher reliability, and lower loss than high-orbit, and can achieve seamless global Internet connection services. Second, low-orbit satellite communications are widely used in scenarios including remote area communications, marine operations and scientific research broadband, aviation broadband and disaster emergency communications, and long-distance data transmission. Among them, the application market in remote areas mainly includes satellite phones, Internet TV and satellite broadband; the marine operation and scientific research application market includes satellite positioning and maritime satellite phones; the aviation application market is mainly airborne Wifi; the disaster recovery application market includes emergency calls, data protection and recovery, and off-site disaster recovery systems.

[0003] In the existing research on low-orbit satellite network routing problems, facing the growing business demand, especially the increase in bursty data services, the network will be congested. Regarding controlling network congestion and achieving traffic balance, scholars at home and abroad have proposed a variety of load balancing routing algorithms based on the characteristics of low-orbit satellite networks. Orbit Prediction Shortest Path First (OPSPF), a new memory-efficient routing method, uses the regularity of satellite constellations to handle the regular topological changes of LEO satellite networks and perform periodic routing calculations. At the same time, it handles the irregular topological changes caused by link failures in an on-demand manner. Although the algorithm has fast routing convergence, it has poor performance in terms of packet loss rate and end-to-end delay. Compact Explicit Multipath Routing (CEMR), evaluates the link congestion level through transmission delay and queuing delay. After calculating the transmission cost, CEMR drives part of the traffic to the suboptimal path, but sometimes the alternative path will cause too much delay. Load Balancing Routing Algorithm Based on Congestion Prediction (LBRA-CP) to achieve effective load balancing in the entire low-Earth orbit satellite network. A multi-objective optimization model was established, which uses correction factors to adjust the path cost, and congestion prediction is also used to predict inter-satellite link congestion. The model is then solved using an ant colony algorithm to find the optimal path for each connection request. LCRA (a low-complexity routing algorithm based on load balancing in low-orbit satellite networks) has a more realistic and complete topology structure and more practical value. However, when a node is busy, LCRA reroutes all traffic through an alternative path, which still needs to be optimized. RBCA improves the traffic classification mechanism based on LCRA, divides traffic into three types according to delay sensitivity, and uses ISL to route some insensitive packets through GEO when congestion occurs. However, routing methods for high bandwidth occupancy traffic have not yet been designed.

[0004] In general, most load balancing routing schemes focus on designing centralized routing schemes for LEO networks, which require path information of the entire network before calculating the optimal path, which consumes a lot of network resources. How to adaptively allocate resources with low computational cost and good scalability under high load conditions has become a difficult task. Summary of the invention

[0005] The present invention provides an artificial bee colony low-orbit satellite network routing method, system, device and medium, aiming to solve the above-mentioned problems.

[0006] An embodiment of the present invention provides an artificial bee colony low-orbit satellite network routing method, comprising:

[0007] Scouting bee stage: The source node s sends a scout bee to obtain the probability that the neighboring node j of the current node i will become the next hop;

[0008] Bee collecting stage: The scout bee that reaches the destination node d and records a complete path is recorded as a bee collecting bee, and two paths from the first N complete paths are selected to use the crossover operator in the genetic algorithm to obtain a new path. The fitness of the new path is compared with the two paths that have been operated with the crossover operator through the fitness function, and the path with the largest fitness is obtained as the optimal path. The optimal path is then reversely updated according to the path blocking mechanism;

[0009] Observation bee stage: The observation bee selects the next hop node based on the optimal path updated in the collection bee stage.

[0010] An embodiment of the present invention provides an artificial bee colony low-orbit satellite network routing system, comprising:

[0011] The scout bee module is used to send scout bees through the source node s to obtain the probability of the neighboring node j of the current node i becoming the next hop;

[0012] The bee collecting module is used to record the scout bees that reach the destination node d and record a complete path as bees collecting, and select two paths from the first N complete paths to use the crossover operator in the genetic algorithm to obtain a new path, and compare the fitness of the new path with the two paths that have been operated with the crossover operator through the fitness function, obtain the path with the largest fitness as the optimal path, and reversely update the optimal path according to the path blocking mechanism;

[0013] The observation bee module is used for the observation bee to select the next hop node according to the optimal path updated by the honey bee stage.

[0014] An embodiment of the present invention further provides an electronic device, including:

[0015] processor; and,

[0016] A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the steps of the artificial swarm low-orbit satellite network routing method as described above.

[0017] An embodiment of the present invention further provides a storage medium for storing computer executable instructions, which, when executed, implement the steps of the above-mentioned artificial bee colony low-orbit satellite network routing method.

[0018] By adopting the embodiment of the present invention, the crossover operation in the genetic algorithm is combined with the artificial bee colony algorithm, thereby solving the problem of the artificial bee colony falling into the local optimum and ensuring global optimization. The fitness of the new path generated by the crossover operation is compared with the original path, and the path with the smallest fitness value is eliminated to achieve greedy evolution, thereby ensuring that the path update is carried out in the direction of smaller delay. In the embodiment of the present invention, each satellite in the network independently determines the best next hop and forwards data packets, and the satellite nodes can independently avoid congestion. Compared with the prior art, this routing strategy has a higher data delivery rate and data throughput, and a lower average delay. By using the waiting queue occupancy rate to judge the path congestion status, this routing strategy sets different routing update rules according to the congestion size, controls the routing path congestion status, and ultimately solves the load balancing problem of low-orbit satellite network routing. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 It is a flow chart of an artificial bee colony low-orbit satellite network routing method according to an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of a genetic algorithm path intersection according to an embodiment of the present invention;

[0022] Figure 3 This is a diagram showing the overall concept of the routing strategy of an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this document.

[0024] Method Embodiment

[0025] The embodiment of the present invention provides an artificial bee colony low-orbit satellite network routing method, Figure 1This is a flow chart of the artificial bee colony low-orbit satellite network routing method according to an embodiment of the present invention. Figure 1 As shown, the artificial bee colony low-orbit satellite network routing method of the embodiment of the present invention includes:

[0026] S1, scout bee stage: the source node s sends a scout bee to obtain the probability that the neighboring node j of the current node i will become the next hop; step S1 specifically includes:

[0027] According to formula 1, the probability that the source node v s sends a scout bee to obtain the adjacent node j of the current node i and becomes the next hop is obtained:

[0028]

[0029] Among them, hop jd represents the number of shortest path hops from the next hop node j to the destination node d, and k is the number of candidate nodes for the next hop of the current node.

[0030] S2, bee collecting stage: the scout bee that reaches the destination node d and records a complete path is recorded as a bee collecting bee, and two paths from the first N complete paths are selected to use the crossover operator in the genetic algorithm to obtain a new path, and the fitness of the new path is compared with the two paths that have been operated with the crossover operator through the fitness function, and the path with the largest fitness is obtained as the optimal path, and the optimal path is reversely updated according to the path blocking mechanism; step S2 specifically includes:

[0031] In the bee-collecting stage, two paths are selected from the first N complete paths, and the single-point crossover method is used when the crossover operator in the genetic algorithm is used. The two selected paths have at least one common gene except the source node and the destination node.

[0032] The fitness function of the bee-collecting stage is obtained by formula 2:

[0033]

[0034] Where P(s, d) represents a path from the source node to the destination node, and Delay represents the total delay of the path, that is, the sum of the delays of all links on the path. Delay is obtained by formula 3:

[0035] Delay p(s,d) =∑ i∈p(s,d) delay(i,i+1)=∑ i∈p(s,d) [Pdelay (i,i+1) +Qdelay (i,i+1) ] Formula 3;

[0036] Among them, Pdelay is the propagation delay and Qdelay is the queuing delay.

[0037] In the bee-collecting stage, the optimal path is updated in reverse according to the path blocking mechanism, including:

[0038] Congestion is determined by formula 4:

[0039]

[0040] Among them, θ ij (t) is the waiting queue occupancy rate of link i and link j at time t, Q total Represents the total capacity of the cache queue, Q i,i+1 (t) is the capacity of the waiting queue of link (i, i+1) at time t;

[0041] The occupancy rate θ ij (t) is compared with the preset occupancy threshold 1 and occupancy threshold 2, and the occupancy threshold 1 is greater than the occupancy threshold 2. If the occupancy θ ij (t) is greater than the occupancy threshold 1, then the link is judged to be first-level congested at this moment, and the probability of selecting this link is Set to 0; if the occupancy rate θ ij (t) is between occupancy threshold 1 and occupancy threshold 2, then the link is judged as level 2 congestion at this moment, and the public

[0042] Formula 5 updates the weight of the link, where the congestion level of the first-level congestion is greater than that of the second-level congestion:

[0043]

[0044] If the occupancy rate θ ij (t) is less than the occupancy threshold 2, the link is determined to be non-congested at this moment, and the link weight is updated by formula 6;

[0045] w t =ρw t-1 +1-ρ Formula 6;

[0046] Where ρ is a constant between (0,1), A constant between 0 and 1.

[0047] S3, observation bee stage: The observation bee selects the next hop node based on the optimal path updated in the honey bee stage.

[0048] The artificial bee colony algorithm used in the embodiment of the present invention is an intelligent optimization algorithm proposed by simulating the honey collection process of bees, which consists of a honey source and a bee colony. The bee colony is divided into scout bees, honey collection bees and observation bees. The honey source represents a feasible solution to the optimization problem, and the corresponding relationship of the algorithm idea is shown in Table 1.

[0049] Table 1 Correspondence between artificial bee colony algorithm and low-orbit satellite routing algorithm

[0050] Honey bee colony behavior Optimization Problem Low Earth Orbit Satellite Routing Algorithm Honey Source Feasible solutions to optimization problems Path from source to destination node Nectar quantity of nectar source Fitness value in optimization problem Evaluation value of the path The process of finding and collecting nectar sources Problem Solving Process Routing process Maximum nectar source The optimal solution to the problem Optimal Path

[0051] Genetic algorithm is a type of randomized search algorithm that draws on the natural selection and natural genetic mechanisms of the biological world. A fixed-size population is set, and each individual in the population represents a possible solution to the problem. Through continuous iterations of selection, crossover, and mutation operations, a population representing a new solution set is generated.

[0052] Each chromosome in the genetic algorithm represents a path, and each satellite node on this path represents a gene on the chromosome. Any satellite node (gene) on the chromosome corresponds to an intersatellite link with its previous and next nodes in the network topology. The role of crossover is to exchange partial paths of two selected chromosomes and generate new paths for comparison. By comparing the fitness values ​​of the two new paths with the fitness of the original two paths, the two paths with the smallest fitness values ​​are eliminated to achieve greedy evolution, so as to ensure that the path is updated in the direction of smaller delay.

[0053] In the embodiment of the present invention, the crossover operation in the genetic algorithm is combined with the artificial bee colony algorithm, and the artificial bee colony algorithm is divided into three stages:

[0054] Scout bee stage: At the initial moment, the source node s sends a scout bee. In this stage, the bee's search is not determined by prior knowledge and is completely random. Every time the scout bee arrives at a node i, it writes the adjacent nodes and transfer probability and other related information in the routing table it carries, as shown in Table 2. The probability that a neighboring node j of the current node i becomes its next hop is:

[0055]

[0056] In formula (1), hop jd represents the number of shortest path hops from the next hop node j to the destination node d, and k is the number of candidate nodes for the next hop of the current node.

[0057] Table 2 Routing table related information

[0058]

[0059] Bee collecting stage: The scout bees that reach the destination node d will become bees collecting honey. Each bee collecting honey records a complete path, that is, the honey source. For the first N paths, the crossover operator in the genetic algorithm is used, and the single-point crossover method is selected, requiring that the two selected chromosomes should have at least one common gene (satellite node) in addition to the source node and the destination node, but they do not need to be located at the same site.

[0060] like Figure 2The figure shows a schematic diagram of path crossing of the transfer algorithm of an embodiment of the present invention. In this low-orbit satellite network topology diagram, the two paths from the source satellite node to the target satellite both pass through satellite No. 14, and the common gene is S14. S14 is selected as the crossover node for single-point crossover operation, and new paths are generated after crossover: S(8, 14, 15, 21, 22, 23, 29) and S(8, 7, 13, 14, 20, 19, 25, 26, 27, 28, 29). The role of crossover is to exchange partial paths of two selected chromosomes and generate new paths for comparison (fitness comparison). The fitness function is expressed as:

[0061]

[0062] In formula (2), P(s, d) represents a path from the source node to the destination node, and Delay represents the total delay of this path, that is, the sum of the delays of all links on the path, as shown in formula (3), which includes the propagation delay Pdelay and the queuing delay Qdelay:

[0063] Delay p(s,d) =∑ i∈p(s,d) delay(i,i+1)=∑ i∈p(s,d) [Pdelay (i,i+1) +Qdelay (i,i+1) ](3)

[0064] After the path is updated, the nectar source with the largest amount of nectar is selected, that is, a path with the largest fitness value is obtained. Return from the destination node to the source node along this optimal path. Every time a node is reached, the path information is reversely updated so that the bee can select a path based on the latest routing information. In the non-congested state, the weight of the link is appropriately increased. The link weight update rule between the two nodes is:

[0065] w t =ρw t-1 +1-ρ (4)

[0066] Here, ρ is a constant between (0,1).

[0067] In the case of congestion, specific processing is performed according to the degree of congestion. The specific operations are as follows:

[0068] θ ij (t) is the waiting queue occupancy rate of link (i, i+1) at time t, which is used to evaluate the congestion level of satellite i and is expressed as:

[0069]

[0070] Where Q total Represents the total capacity of the cache queue, Q ij(t) represents the instantaneous occupancy of the data packets in the link (i, j) cache queue at time t.

[0071] Set threshold 1 and threshold 2, where threshold 1 is greater than threshold 2: If the occupancy rate is greater than threshold 1, the link is judged to be level 1 congestion at this moment, that is, extremely congested, and the probability of selecting this link is Set to 0, that is, unavailable; if the occupancy rate is between threshold 1 and threshold 2, the link is judged as secondary congestion at this moment, that is, general congestion, and the weight of the link is appropriately reduced. The specific update rules are:

[0072]

[0073] in, A constant between 0 and 1.

[0074] Observer bee stage: After the bees collect the routing information, the weight of the link will change, which will affect the probability of the observer bee selecting the link. The larger the weight, the greater the probability of being selected. The observer bee selects the next hop node based on the routing information updated by the bees collect the routing information. Unlike the scout bee, since the observer bee has prior knowledge after the path information is updated, it will tend to choose the path with less delay and less congestion. The observer bee that successfully reaches the destination node will also update the routing information in time to facilitate the subsequent observer bees to select the path: the non-congested state is updated according to the update rule of formula (4); the congested state is specifically handled according to the above congestion degree. Figure 3 This is a diagram showing the overall concept of the routing strategy of an embodiment of the present invention.

[0075] In the scout bee stage, the main task is to select the next hop route according to the minimum number of hops without prior knowledge; in the bee collecting stage: the scout bee that successfully reaches the destination node is transformed into a bee collecting bee and the path information is saved. By crossing the paths of the bee collecting bees, new paths are obtained and compared to obtain the path with the highest fitness. Finally, the bee collecting bees will reversely update the routing information along this optimal path. In the observation bee stage, the observation bee will select the path according to the updated routing information. The observation bee that successfully reaches the destination node will also reversely update the route. When the bee collecting bees and the observation bees reversely update the routing information, they will make a congestion judgment and adopt the corresponding update rules according to the congestion situation: in the non-congested state, the update is carried out according to formula (4); the congestion state is divided into extreme congestion and general congestion. In the case of extreme congestion, the link is stopped from being used, and in the case of general congestion, the update is carried out according to formula (6).

[0076] The following beneficial effects are achieved by adopting the embodiments of the present invention:

[0077] 1. The embodiment of the present invention adopts an artificial bee colony algorithm with few control parameters, easy implementation, simple calculation, strong robustness, and is also widely used to solve the path optimization problem. In our strategy, the crossover operation in the genetic algorithm is combined with the artificial bee colony algorithm to solve the problem of the artificial bee colony falling into the local optimum and ensure global optimization. The fitness of the new path generated by the crossover operation is compared with the original path, and the path with the smallest fitness value is eliminated to achieve greedy evolution, thereby ensuring that the path update is carried out in the direction of smaller delay.

[0078] 2. Compared with the centralized algorithm that needs to detect the information of the global network, the routing strategy proposed in the embodiment of the present invention is a distributed strategy, which only needs to traverse the information of the surrounding neighboring nodes, effectively reducing the transmission overhead, and the distributed solution is easier to implement than the centralized algorithm. In this routing strategy, each satellite in the network independently determines the best next hop and forwards data packets, and the satellite nodes can independently avoid congestion. Compared with the prior art, this routing strategy has a higher data delivery rate and data throughput, and a lower average delay.

[0079] 3. The embodiment of the present invention uses the waiting queue occupancy rate to judge the path congestion status. This routing strategy sets different routing update rules according to the congestion size to control the routing path congestion status, and ultimately can solve the load balancing problem of low-orbit satellite network routing.

[0080] System Example

[0081] An embodiment of the present invention provides an artificial bee colony low-orbit satellite network routing system, comprising:

[0082] The scout bee module is used to send scout bees through the source node s to obtain the probability of the neighboring node j of the current node i becoming the next hop;

[0083] The bee collecting module is used to record the scout bees that reach the destination node d and record a complete path as bees collecting, and select two paths from the first N complete paths to use the crossover operator in the genetic algorithm to obtain a new path, and compare the fitness of the new path with the two paths that have been operated with the crossover operator through the fitness function, obtain the path with the largest fitness as the optimal path, and reversely update the optimal path according to the path blocking mechanism;

[0084] The observation bee module is used for the observation bee to select the next hop node according to the optimal path updated by the honey bee stage.

[0085] Among them, the scout bee module of the embodiment of the present invention is specifically used for:

[0086] According to formula 1, the probability of sending a scout bee to obtain the neighboring node j of the current node i to become the next hop is obtained:

[0087]

[0088] Among them, hop jd represents the number of shortest path hops from the next hop node j to the destination node d, and k is the number of candidate nodes for the next hop of the current node.

[0089] The bee collecting module of the embodiment of the present invention is specifically used for:

[0090] When selecting two paths from the first N complete paths and using the crossover operator in the genetic algorithm, the single-point crossover method is used. The two selected paths have at least one common gene in addition to the source node and the destination node.

[0091] The fitness function of the bee collecting stage is obtained by formula 2:

[0092]

[0093] Where P(s, d) represents a path from the source node to the destination node, and Delay represents the total delay of the path, that is, the sum of the delays of all links on the path. Delay is obtained by formula 3:

[0094] Delay p(s,d) =∑ i∈p(s,d) delay(i,i+1)=∑ i∈p(s,d) [Pdelay (i,i+1) +Qdelay (i,i+1) ] Formula 3;

[0095] Among them, Pdelay is the propagation delay, Qdelay is the queuing delay;

[0096] Congestion is determined by formula 4:

[0097]

[0098] Among them, θ ij (t) is the waiting queue occupancy rate of link (i,i+1) at time t, Q total Represents the total capacity of the cache queue;

[0099] The occupancy rate θ ij (t) is compared with the preset occupancy threshold 1 and occupancy threshold 2, and the occupancy threshold 1 is greater than the occupancy threshold 2. If the occupancy θ ij (t) is greater than the occupancy threshold 1, then the link is judged to be first-level congested at this moment, and the probability of selecting this link is Set to 0; if the occupancy rate θ ij(t) is between occupancy threshold 1 and occupancy threshold 2, then the link is judged as level 2 congestion at this moment, and the weight of the link is updated by formula 5, where the congestion degree of level 1 congestion is greater than that of level 2 congestion:

[0100]

[0101] If the occupancy rate θ ij (t) is less than the occupancy threshold 2, the link is determined to be non-congested at this moment, and the link weight is updated by formula 6;

[0102] w t =ρw t-1 +1-ρ Formula 6;

[0103] Where ρ is a constant between (0,1), A constant between 0 and 1.

[0104] Device Example 1

[0105] An embodiment of the present invention further provides an electronic device, including:

[0106] processor; and,

[0107] A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the steps as described in the above method embodiment.

[0108] Device Example 2

[0109] An embodiment of the present invention further provides a storage medium for storing computer executable instructions, wherein the computer executable instructions, when executed, implement the steps described in the above method embodiment.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An artificial bee colony low-orbit satellite network routing method, characterized in that: include: Scout bee stage: send a scout bee through the source node s to obtain the current node i Neighboring nodes j The probability of becoming the next hop; Bee collecting stage: reaching the destination node d The scout bee that records a complete path is recorded as the collecting bee, and the previous N Two paths in the complete path use the crossover operator in the genetic algorithm to obtain a new path, and compare the fitness of the new path with the two paths that have been subjected to the crossover operator operation through a fitness function, obtain the path with the largest fitness as the optimal path, and perform reverse update on the optimal path according to the path blocking mechanism; Observer bee stage: The observer bee selects the next hop node based on the optimal path updated in the bee collecting stage; The bee collecting stage is before the selection N When the crossover operator in the genetic algorithm is used for the two paths in the complete path, the single-point crossover method is selected. The two selected paths have at least one common gene in addition to the source node and the destination node. The fitness function of the bee-collecting stage is obtained by formula 2: Formula 2: in, P(s,d) Represents a path from a source node to a target node. Delay represents the total delay of this path, that is, the sum of the delays of all links on the path. Delay Obtained by formula 3: Formula 3: in, is the propagation delay, for queue delay; The reverse updating of the optimal path according to the path blocking mechanism in the bee collecting stage specifically includes: Congestion is determined by formula 4: Formula 4: in, for t Time Link i and Link j The waiting queue occupancy rate, Represents the total capacity of the cache queue, for t Time Link( , +1 )The capacity of the waiting queue; The occupancy rate The occupancy rate threshold 1 and the occupancy rate threshold 2 are compared with each other, and the occupancy rate threshold 1 is greater than the occupancy rate threshold 2. If the occupancy rate If the occupancy rate is greater than the threshold value 1, the link is judged to be congested at the first level at this moment, and the probability of selecting the link is Set to 0; if the occupancy rate If the link is between the occupancy threshold 1 and the occupancy threshold 2, the link is judged to be in level 2 congestion at this moment, and the weight of the link is updated by formula 5, where the congestion degree of level 1 congestion is greater than that of level 2 congestion: Formula 5: If the occupancy rate If the occupancy rate is less than the occupancy rate threshold 2, the link is determined to be non-congested at this moment, and the link weight is updated by formula 6; Formula 6 ； in, is a constant between (0,1), A constant between 0 and 1.

2. The method according to claim 1, characterized in that The probability of the adjacent node of the current node becoming the next hop by sending a scout bee to the source node s is obtained according to Formula 1: Formula 1; in, Indicates the next hop node j To the destination node d The shortest path hop count, k is the number of candidate nodes for the next hop of the current node.

3. The artificial bee colony low-orbit satellite network routing system is characterized by: include: The scout bee module is used to send scout bees through the source node s to obtain the current node i Neighboring nodes j The probability of becoming the next hop; The bee collecting module is used to reach the destination node d The scout bee that records a complete path is recorded as the collecting bee, and the previous N Two paths in the complete path use the crossover operator in the genetic algorithm to obtain a new path, and compare the fitness of the new path with the two paths that have been subjected to the crossover operator operation through a fitness function, obtain the path with the largest fitness as the optimal path, and perform reverse update on the optimal path according to the path blocking mechanism; The observation bee module is used for the observation bee to select the next hop node according to the optimal path updated in the honey bee stage; The bee collecting module is specifically used for: Before selection N When the crossover operator in the genetic algorithm is used for the two paths in the complete path, the single-point crossover method is selected. The two selected paths have at least one common gene in addition to the source node and the destination node. The fitness function of the bee collecting stage is obtained by formula 2: Formula 2: in, P(s,d) Represents a path from a source node to a target node. Delay represents the total delay of this path, that is, the sum of the delays of all links on the path. Delay Obtained by formula 3: Formula 3: in, is the propagation delay, for queue delay; Congestion is determined by formula 4: Formula 4: in, for t Time Link( , +1 ) Waiting queue occupancy, Represents the total capacity of the cache queue, for t Time Link( , +1 )The capacity of the waiting queue; The occupancy rate The occupancy rate threshold 1 and the occupancy rate threshold 2 are compared with each other, and the occupancy rate threshold 1 is greater than the occupancy rate threshold 2. If the occupancy rate If the occupancy rate is greater than the threshold value 1, the link is judged to be congested at the first level at this moment, and the probability of selecting the link is Set to 0; if the occupancy rate If the link is between the occupancy threshold 1 and the occupancy threshold 2, the link is judged to be in level 2 congestion at this moment, and the weight of the link is updated by formula 5, where the congestion degree of level 1 congestion is greater than that of level 2 congestion: Formula 5: If the occupancy rate If the occupancy rate is less than the occupancy rate threshold 2, the link is determined to be non-congested at this moment, and the link weight is updated by formula 6; Formula 6 ； in, is a constant between (0,1), A constant between 0 and 1.

4. The system according to claim 3, characterized in that The scout bee module is specifically used for: According to formula 1, the current node is obtained by sending a scout bee through the source node s. i Neighboring nodes j Probability of becoming the next hop: Formula 1; in, Indicates the next hop node j To the destination node d The shortest path hop count, k is the number of candidate nodes for the next hop of the current node.

5. An electronic device comprising: processor; as well as, A memory arranged to store computer executable instructions, which, when executed, cause the processor to perform the steps of the artificial swarm low-orbit satellite network routing method as described in any one of claims 1-2.

6. A storage medium for storing computer executable instructions, which, when executed, implement the steps of the artificial swarm low-orbit satellite network routing method as described in any one of claims 1-2.

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