A routing method, device, apparatus, medium and product of a time sensitive network

Abstract

The application discloses a routing method, device and equipment of a time-sensitive network (TSN), a medium and a product, and the method comprises the following steps: acquiring a network topology structure of a TSN network, determining time-triggered (TT) flows in the network topology structure and basic transmission characteristic parameters of the TT flows, and the network topology structure comprises switches, sending terminals and receiving terminals; determining a link load rate of data links contained in the TSN network according to the basic transmission characteristic parameters; performing TSN load balancing routing on the TT flows based on the link load rate to obtain an optimal path of the TSN network. The data link load rate is determined through the basic transmission characteristic parameters of the TT flows, and then routing planning is performed based on the load rate, so that the TT flows are reasonably distributed in multiple shortest paths, TSN network link load balancing is realized, the situation that multiple TT flows concentrate on occupying the same data link is avoided, and transmission conflicts are reduced through balanced link load.

Description

Technical Field

[0001] This invention relates to the field of network transmission technology, and in particular to a routing method, apparatus, device, medium, and product for time-sensitive networks. Background Technology

[0002] In recent years, the Time-Sensitive Networking (TSN) working group has replaced the event-triggered mechanism with a time-triggered mechanism on the basis of traditional Ethernet, ensuring the real-time and deterministic nature of time-sensitive data stream transmission, and is committed to its widespread application in industries such as industry and aerospace.

[0003] Time-Sensitive Networking (TSN) is an important research direction for the transformation and upgrading of industrial internet infrastructure. TSN typically classifies data flows into three types: Time-Triggered (TT) flows, Rate-Limited flows, and Best-effort flows. TT flows are used to characterize services in industrial control with strict requirements for real-time performance and latency jitter, and have the highest transmission priority. The time-triggered mechanism refers to calculating the transmission time of each TT flow in each device through global scheduling, ensuring that each TT flow exclusively occupies link resources during transmission, thereby guaranteeing the real-time performance of TT flow transmission. TSN uses the shortest path algorithm for TT flow routing planning.

[0004] However, as the number of TT streams increases in TSN, using the shortest path algorithm for path planning can lead to uneven load distribution across links and more collisions during TT stream transmission. Summary of the Invention

[0005] This invention provides a routing method, apparatus, device, medium, and product for time-sensitive networks (TSNs) to achieve load balancing of data links.

[0006] According to a first aspect of the present invention, a routing method for a time-sensitive network is provided, comprising:

[0007] The network topology of a Time-Sensitive Network (TSN) is obtained, and the time-triggered (TT) streams and basic transmission characteristic parameters of the TT streams in the network topology are determined. The network topology includes switches, transmitting terminals, and receiving terminals.

[0008] Based on the basic transmission characteristic parameters, determine the link load rate of the data links included in the TSN network;

[0009] Based on the link load rate, the TT flow is subjected to TSN load balancing routing to obtain the optimal path of the TSN network.

[0010] According to a second aspect of the present invention, a routing apparatus for a time-sensitive network is provided, comprising:

[0011] The data acquisition module is used to acquire the network topology of the Time-Sensitive Network (TSN) and determine the time-triggered (TT) streams and the basic transmission characteristic parameters of the TT streams in the network topology. The network topology includes switches, sending terminals, and receiving terminals.

[0012] The load rate determination module is used to determine the link load rate of the data links included in the TSN network based on the basic transmission characteristic parameters.

[0013] The path determination module is used to perform TSN load balancing routing on the TT flow based on the link load rate to obtain the optimal path of the TSN network.

[0014] According to a third aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the routing method for time-sensitive networks according to any embodiment of the present invention.

[0018] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the routing method for a time-sensitive network according to any embodiment of the present invention.

[0019] According to a fifth aspect of the present invention, embodiments of the present invention also provide a computer program product, the computer program product including a computer program, which, when executed by a processor, implements a time-sensitive network routing method according to any embodiment of the present invention.

[0020] The technical solution of this invention obtains the network topology of a Time-Sensitive Network (TSN) and determines the time-triggered (TT) streams and their basic transmission characteristic parameters. The network topology includes switches, sending terminals, and receiving terminals. Based on the basic transmission characteristic parameters, the link load rate of the data links included in the TSN network is determined. TSN load balancing routing is then performed on the TT streams based on the link load rate to obtain the optimal path for the TSN network. By determining the data link load rate through the basic transmission characteristic parameters of the TT streams, and then performing routing planning based on the load rate, the TT streams are rationally distributed among multiple shortest paths, achieving TSN network link load balancing and avoiding the situation where multiple TT streams occupy the same data link. By balancing the link load, transmission conflicts are reduced.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a routing method for a time-sensitive network according to Embodiment 1 of the present invention;

[0024] Figure 2 This is an example diagram of the network topology in a routing method for a time-sensitive network provided according to Embodiment 2 of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of a routing device for a time-sensitive network according to Embodiment 3 of the present invention;

[0026] Figure 4 This is a schematic diagram of the structure of an electronic device that implements an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a routing method for a time-sensitive network (TSN) according to Embodiment 1 of the present invention. This embodiment is applicable to the determination of the optimal path for a TSN when multiple shortest routing schemes exist during TT (Time-Sensitive Network) stream transmission. This method can be executed by a routing device for a time-sensitive network, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:

[0031] S110. Obtain the network topology of the Time-Sensitive Network (TSN), determine the time-triggered TT stream and its basic transmission characteristic parameters in the network topology, which includes switches, sending terminals, and receiving terminals.

[0032] In this embodiment, the network topology can be understood as the physical / logical connection relationship of nodes such as switches and terminal devices (sending terminals and receiving terminals) in the TSN. Formally, it can be defined as an undirected graph, where vertices represent sets of switches and terminal devices, and the directed link between any two vertices is a data link, which is the basis for TT stream transmission path planning. Time-triggered TT streams refer to the data streams with the highest transmission priority in the TSN, used to characterize services such as industrial control that have strict requirements for real-time performance and latency jitter. They are generated periodically at the sending terminal and transmitted to the receiving terminal. The basic transmission characteristic parameters of TT streams are a set of key parameters used to describe the transmission attributes of TT streams. Core parameters may include period (the generation period of the TT stream at the sending terminal), message length (determining the transmission time of the TT stream in the link), source terminal address, destination terminal address, and transmission path (the link sequence from the sending terminal to the receiving terminal), which are the core basis for calculating link load rate.

[0033] Specifically, the processor can clearly define the TSN network topology, abstract it into an undirected graph containing switches, sending terminals, and receiving terminals, determine the data link connections between each node, identify all TT flows in the topology, identify the sending and receiving terminals of each TT flow, and extract the basic transmission characteristic parameters of each TT flow, including core information such as period, message length, and source and destination addresses, to provide data support for subsequent load calculation and routing planning.

[0034] S120. Determine the link load rate of the data links included in the TSN network based on the basic transmission characteristic parameters.

[0035] In this embodiment, a data link can be understood as a directed transmission link between adjacent nodes (terminal and switch, switch and switch) in a TSN topology. It is the physical carrier for TT stream transmission, and each link has a fixed amount of available bandwidth resources. The link load rate is used to characterize the ratio of the bandwidth occupied by TT streams on a data link per unit time to the total available bandwidth of the link. It is a core indicator for measuring the degree of link congestion.

[0036] Specifically, the processor can determine the load rate of each data link based on the established TSN data link load rate calculation model, combined with the basic transmission characteristic parameters (period, message length) and transmission path of each TT stream.

[0037] S130. Perform TSN load balancing routing on TT flows based on link load rate to obtain the optimal path of the TSN network.

[0038] In this embodiment, TSN load balancing routing can be understood as a routing mechanism that selects the optimal transmission path from multiple shortest paths for TT flows based on the overall network link load rate. The optimal path can be understood as the transmission path that meets the minimum hop count requirement for TT flows, and that maximizes the load distribution across the entire network links while minimizing TT flow transmission conflicts; it is the optimal link combination selected through load balancing routing strategies.

[0039] Specifically, the processor can determine all possible transmission paths based on the sending and receiving terminals of each TT stream, thereby identifying the shortest transmission path among multiple paths. It can then construct an optimization model with the goal of minimizing link load balancing and use optimization algorithms (such as the tundra algorithm) to calculate the optimal path of the TSN network.

[0040] The technical solution of this invention obtains the network topology of a Time-Sensitive Network (TSN) and determines the time-triggered (TT) streams and their basic transmission characteristic parameters. The network topology includes switches, sending terminals, and receiving terminals. Based on the basic transmission characteristic parameters, the link load rate of the data links included in the TSN network is determined. TSN load balancing routing is then performed on the TT streams based on the link load rate to obtain the optimal path for the TSN network. By determining the data link load rate through the basic transmission characteristic parameters of the TT streams, and then performing routing planning based on the load rate, the TT streams are rationally distributed among multiple shortest paths, achieving TSN network link load balancing and avoiding the situation where multiple TT streams occupy the same data link. By balancing the link load, transmission conflicts are reduced.

[0041] Furthermore, based on the above embodiments, the basic transmission characteristic parameters include the period of the TT stream, the message length, the link transmission time, and the transmission path.

[0042] In this embodiment, the period of the TT stream can be understood as the time interval between the periodic generation and transmission of the TT stream at the sending terminal. It is a fixed constant (determined by the requirements of industrial applications) and determines the frequency at which the TT stream occupies link resources. The message length of the TT stream can be understood as the number of bytes of the data packet carried by the TT stream. It is used to calculate the transmission time of the TT stream on any data link and is one of the core parameters for calculating the link load rate.

[0043] Furthermore, the link load rate of the data links included in the TSN can be determined using the following formula based on the basic transmission characteristic parameters:

[0044]

[0045] in, Indicates link load rate. Indicates that it is a switch Pointing to the switch A data link, TT stream set is represented as , This represents the i-th TT stream. express The cycle; express The message length is used to describe The time required for transmission on any link; express The transmission path from the sending terminal to the receiving terminal. This represents the least common multiple of the cycles of all TT flows in the TSN network.

[0046] For example, a specific network topology can be used as an example. Figure 2 This invention provides an example network topology diagram for a routing method in a time-sensitive network, as shown in Embodiment 1. Figure 2 As shown, the TSN topology is formally defined as an undirected graph. , where the vertex This represents the collection of switches and terminal devices in a TSN, as shown in the figure. For switches, This indicates the terminal device; the TT stream is generated at the sending terminal and relayed to the receiving terminal via a switch, such as... Figure 1 middle The sending terminal is The receiving terminal is A directed link between any two vertices is called an edge. ,like Figure 1 middle , indicating by point to A data link, similarly Indicates by point to A data link. The set of TT streams in a TSN can be represented as... For any TT stream In this case, it can be represented as the following quadruple:

[0047]

[0048] in express The cycle, that is In its sending terminal It is generated periodically and transmitted to the receiving terminal; express The message length is used to describe The time required for transmission on any link; express The transmission path from the sending terminal to the receiving terminal, for example Figure 1 middle The initial path is: .

[0049] Furthermore, based on the above embodiments, the steps for performing TSN load balancing routing on TT flows based on link load rate to obtain the optimal path in the TSN network can be refined as follows:

[0050] Based on the load rate of each link, determine the average load rate of all data links in the TSN network; based on the hop count of each transmission path of the TT flow from the sending terminal to the receiving terminal, determine the shortest transmission path set of the TT flow; based on the average load rate and link load rate, establish an optimization model; based on the optimization model and the shortest transmission path set, perform TSN load balancing routing to obtain the optimal path of the TSN network.

[0051] In this embodiment, the average load rate can be understood as the average load rate of all data links in the TSN network, used to reflect the overall load level of the entire network. The hop count of the transmission path can be understood as the number of switch nodes traversed by the TT flow from the sending terminal to the receiving terminal (hop count = total number of switches traversed - 1). The shortest transmission path set can be understood as the set of paths with the fewest hops among all transmission paths for a single TT flow. The optimization model can be understood as a mathematical model established with the core objective of load balancing of TSN network links. The core is to minimize the load balance of the entire network's data links, with the constraint that the TT flow selects transmission paths only from the shortest transmission path set, while also satisfying the requirement of conflict-free transmission of the TT flow.

[0052] Specifically, the processor can determine the sum of the load rates based on the link load rates of all data links, and then divide it by the total number of data links to determine the average load rate of the entire network's data links. It can be calculated using the following formula:

[0053]

[0054] in, This represents the total number of data links.

[0055] Specifically, the processor can traverse each TT flow in the TSN, calculate all transmission paths from the sending terminal to the receiving terminal for each TT flow based on a depth-first search algorithm, sort all transmission paths in ascending order of hop count, identify the minimum hop count threshold, and filter out all transmission paths with a hop count equal to this threshold to form the shortest transmission path set for that TT flow, ensuring that subsequent routing selection does not increase the transmission hop count. The processor can construct a complete mathematical optimization model with minimizing the overall network data link load balancing as the core optimization objective and simultaneously satisfying the constraint of no collisions in multiple TT flow links. The processor can then perform TSN load balancing routing on the optimization model according to the set optimization algorithm to obtain the optimal path for the TSN network.

[0056] For example, for any TT stream In terms of ascending order, all transmission paths from the sending terminal to the receiving terminal are represented as follows:

[0057]

[0058] in, Indicates TT stream From the sending terminal to the receiving terminal, there exists One transmission path, and It has the fewest transmission hops.

[0059] For example, for any TT stream In other words, defining the transmission path The corresponding number of jumps is (i.e., TT flow) (Minimum number of hops from the sending terminal to the receiving terminal), further statistics The number of jumps is All paths are used to obtain the set of shortest transmission paths. It is expressed as follows:

[0060]

[0061] in, Indicates TT stream exist The number of hops corresponding to each transmission path is .

[0062] Furthermore, based on the above embodiments, the steps for establishing an optimization model based on average load rate and link load rate can be refined as follows:

[0063] Constraints are established based on the transmission time of each data link, the least common multiple of the periods of all TT streams in the TSN network, the period of the TT stream, and the message length of the TT stream. An optimization objective function is established based on the difference between the load rate of each link and the average load rate. An optimization model is obtained based on the constraints and the optimization objective function.

[0064] In this embodiment, the transmission time of a data link can be understood as the specific moment when a TT stream begins sending packets on a particular data link. This timeframe is determined by the TSN global scheduling calculation and is a core parameter for avoiding multi-TT stream link conflicts. When a TT stream passes through a data link, this parameter clarifies the time window during which it occupies link resources. The least common multiple (LCM) of TT stream periods can be understood as the least common multiple of the periods of all TT streams in the TSN network. It serves as the time benchmark for determining whether multiple TT streams experience transmission conflicts on a link, ensuring the effectiveness of conflict-free constraints in periodic transmission scenarios. The optimization objective function can be understood as a mathematical expression constructed with network-wide link load balancing as the core objective.

[0065] Specifically, the processor can construct constraints based on the core principle of conflict-free multi-TT stream links in TSN, combined with key parameters. First, extract the period and message length of each TT stream, and calculate the least common multiple (LCM) of all TT stream periods. Second, determine the transmission time point of each TT stream on the data links involved in its transmission path. Finally, based on the above parameters, construct constraints, formally requiring that the transmission time windows of any two TT streams on the same data link do not overlap, i.e., avoiding simultaneous occupation of link resources and ensuring conflict-free transmission. For example, this can be formally expressed as:

[0066]

[0067] in, This represents the least common multiple of the cycles of all TT flows in a TSN network. When any data link... hour, express In the data link The sending time point.

[0068] As shown above, given any TT stream The cycle and message length are usually determined by the specific industrial application (i.e., constants), therefore The transmission path determines the communication conflicts that need to be avoided during transmission, which in turn determines the transmission time of each data link, thus affecting the end-to-end transmission latency. Therefore, the purpose of this patent is to plan the transmission paths of all TT streams in TSN, ensure load balancing of each data link, and reduce link conflicts during TT stream transmission.

[0069] Specifically, the transmission paths of all data streams in a TSN determine the load rate of each data link. The processor can find the optimal transmission path among multiple shortest paths, ultimately minimizing the link load balancing in the TSN as the optimization objective. The established constraints are then integrated with the objective function to form a complete optimization model. This model optimizes by minimizing link load balancing while using conflict-free multi-TT stream links as a boundary constraint.

[0070] For example, the optimization objective function can be expressed as:

[0071]

[0072] The steps involved in performing TSN load balancing routing based on the optimization model and the shortest transmission path set to obtain the optimal path for the TSN network may include:

[0073] Based on the tunic group algorithm and optimization model, TSN load balancing routing is performed on the shortest transmission path set to obtain the optimal transmission path for each TT flow; all optimal transmission paths are then used as the optimal paths for the TSN network.

[0074] In this embodiment, the Salicornia Group Algorithm (SSA) can be understood as a heuristic optimization algorithm that achieves optimization by simulating the cooperative behavior of leaders and followers in a salicornia population. The optimal transmission path of a TT stream can be understood as the path selected from the set of shortest transmission paths for a single TT stream that achieves the best load balancing across the entire network, ensuring the fewest transmission hops while minimizing link conflicts.

[0075] Specifically, the processor can map the optimization model to the tunic group algorithm, and then perform population initialization and fitness calculation in the tunic group algorithm. Through population iterative updates, and when the number of iteration rounds is reached, the optimal transmission path of each TT stream is obtained. The processor can use all the optimal transmission paths as the optimal path of the TSN network.

[0076] The steps involved in performing TSN load balancing routing on the shortest transmission path set based on the tunic swarm algorithm and optimization model to obtain the optimal transmission path for each TT flow may include:

[0077] The overall transmission path scheme of the TSN network is used as the salps population in the salps swarm algorithm; individuals in the salps population are determined based on path selection from the shortest transmission path set of each TT stream; the fitness value of the salps swarm algorithm is determined based on the optimization model; the position of the population is updated to determine the optimal transmission path.

[0078] In this embodiment, the overall transmission path scheme of the TSN network can be understood as a complete set of schemes including all TT flow transmission path selections. Each scheme corresponds to a path allocation combination for all TT flows in the network, and is a mapping prototype of the tunic population. The tunic population can be understood as the set of solutions used for optimization in the tunic swarm optimization algorithm (SSA). In TSN load balancing routing, each individual in the population corresponds to an overall transmission path scheme, and the population size is the number of schemes. Individuals in the tunic population can be understood as the basic unit of optimization in the SSA algorithm. The fitness value can be understood as the core indicator for measuring the quality of an individual in SSA.

[0079] Specifically, the processor can directly map all possible overall transmission path schemes in the TSN network (each scheme includes one path selection for all TT flows) to a population of the tunic swarm algorithm. The population size is the preset number of overall transmission path schemes, ensuring a sufficient optimization range to cover potential optimal solutions. For each individual in the population, its dimension is set to the total number of TT flows in the TSN, and the value range of each dimension is limited to the path index (random integer) in the set of shortest transmission paths for the corresponding TT flow. That is, each dimension value uniquely corresponds to a shortest path for that TT flow, ensuring that the path scheme represented by the individual satisfies the shortest transmission constraint. Based on the established optimization model, each individual in the population (i.e., a set of TT flow path selection schemes) is substituted into the model to calculate the network-wide link load balancing degree corresponding to the scheme. This balancing degree is directly used as the fitness value of the individual; the smaller the fitness value, the better the scheme. The processor can set the number of iteration rounds T. In each iteration, the population position is updated according to the leader-follower mechanism of SSA: the leader individual (the individual with the best current fitness value) moves closer to the "food position" (optimal solution) corresponding to the globally optimal fitness value, adjusting the path index selection of each dimension; the follower individual (the next solution) updates its own dimension values ​​according to the leader's position change, avoiding getting trapped in local optima. After each round of updates, the fitness value of each individual is recalculated, and the globally optimal food position is updated synchronously. After completing T iterations, the population individual corresponding to the globally optimal food position is extracted. The values ​​of each dimension of this individual are the optimal path selection for each TT flow. The path combinations corresponding to all dimensions constitute the optimal transmission path of the TSN network. This path set can achieve network-wide link load balancing and meet the requirements of no collisions and the shortest transmission hops for TT flows.

[0080] The technical solution of this invention constructs a data link load rate calculation model by comprehensively considering the period, length, and transmission path of TT flows, and determines the load rate of the data link. By filtering the shortest transmission path set for TT flows using hop count and combining it with constraints, a load balancing optimization model is established. Routing planning is performed using the tundra algorithm, and relying on the load balancing routing strategy, TT flows are rationally distributed among multiple shortest paths, avoiding the situation where multiple TT flows occupy the same data link. Simultaneously, with the constraint of no conflict in multiple TT flow links, the load rate of each link in the TSN is balanced, reducing link conflicts during TT flow transmission, lowering end-to-end transmission latency, ensuring transmission determinism, and thus reducing the queuing latency of TT flows in each hop switch, thereby effectively shortening the end-to-end transmission latency of TT flows.

[0081] Example 2

[0082] Figure 3 This is a schematic diagram of a routing device for a time-sensitive network provided in Embodiment 2 of the present invention. Figure 3 As shown, the device includes:

[0083] Data acquisition module 31 is used to acquire the network topology of the Time Sensitive Network (TSN) network, determine the time-triggered (TT) streams in the network topology and the basic transmission characteristic parameters of the TT streams, wherein the network topology includes switches, sending terminals and receiving terminals;

[0084] Load rate determination module 32 is used to determine the link load rate of the data links included in the TSN network based on the basic transmission characteristic parameters.

[0085] The path determination module 33 is used to perform TSN load balancing routing on the TT flow based on the link load rate to obtain the optimal path of the TSN network.

[0086] The technical solution of this invention obtains the network topology of a Time-Sensitive Network (TSN) and determines the time-triggered (TT) streams and their basic transmission characteristic parameters. The network topology includes switches, sending terminals, and receiving terminals. Based on the basic transmission characteristic parameters, the link load rate of the data links included in the TSN network is determined. TSN load balancing routing is then performed on the TT streams based on the link load rate to obtain the optimal path for the TSN network. By determining the data link load rate through the basic transmission characteristic parameters of the TT streams, and then performing routing planning based on the load rate, the TT streams are rationally distributed among multiple shortest paths, achieving TSN network link load balancing and avoiding the situation where multiple TT streams occupy the same data link. By balancing the link load, transmission conflicts are reduced.

[0087] Furthermore, the basic transmission characteristic parameters include the period of the TT stream, the message length, the link transmission time, and the transmission path. Correspondingly, the load rate determination module 32 is specifically used for:

[0088] Based on the aforementioned basic transmission characteristic parameters, the link load rate of the data links included in the TSN is determined using the following formula:

[0089]

[0090] in, Indicates link load rate. Indicates that it is a switch Pointing to the switch A data link, TT stream set is represented as , This represents the i-th TT stream. express The cycle; express The message length is used to describe The time required for transmission on any link; express The transmission path from the sending terminal to the receiving terminal. This represents the least common multiple of the cycles of all TT flows in the TSN network.

[0091] Furthermore, the path determination module 33 includes:

[0092] The first determining unit is configured to determine the average load rate of all data links in the TSN network based on the load rate of each link.

[0093] The second determining unit is used to determine the shortest transmission path set of the TT stream based on the number of hops in each transmission path from the sending terminal to the receiving terminal.

[0094] The third determining unit is used to establish an optimization model based on the average load rate and the link load rate;

[0095] The fourth determining unit is used to perform TSN load balancing routing based on the optimization model and the shortest transmission path set to obtain the optimal path of the TSN network.

[0096] Specifically, the third determining unit is used for:

[0097] Constraints are established based on the transmission time of each data link, the least common multiple of the periods of all TT streams in the TSN network, the period of the TT stream, and the message length of the TT stream.

[0098] An optimization objective function is established based on the difference between the load rate of each link and the average load rate.

[0099] Based on the constraints and the objective function, an optimization model is obtained.

[0100] The fourth determining unit includes:

[0101] The first determining subunit is used to perform TSN load balancing routing on the shortest transmission path set based on the tunic group algorithm and the optimization model to obtain the optimal transmission path for each TT stream.

[0102] The second determining subunit is used to determine all the optimal transmission paths as the optimal paths of the TSN network.

[0103] Specifically, the first determining subunit is used for:

[0104] The overall transmission path scheme of the TSN network is used as the salvia population in the salvia swarm algorithm;

[0105] Based on the path selection from the set of shortest transmission paths for each of the TT streams, individuals in the salps population are determined;

[0106] Based on the optimization model, the fitness value of the tunic group algorithm is determined;

[0107] The location of the population is updated to determine the optimal transmission path.

[0108] The routing apparatus for time-sensitive networks provided in this embodiment of the invention can execute the routing method for time-sensitive networks provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0109] Example 3

[0110] Figure 4 A schematic diagram of an electronic device 40 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0111] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 and a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 can also store various programs and data required for the operation of the electronic device 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0112] Multiple components in electronic device 40 are connected to I / O interface 45, including: input unit 46, such as keyboard, mouse, etc.; output unit 47, such as various types of monitors, speakers, etc.; storage unit 48, such as disk, optical disk, etc.; and communication unit 49, such as network card, modem, wireless transceiver, etc. Communication unit 49 allows electronic device 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0113] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as routing methods for time-sensitive networks.

[0114] In some embodiments, the time-sensitive network routing method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the time-sensitive network routing method described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the time-sensitive network routing method by any other suitable means (e.g., by means of firmware).

[0115] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0116] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0117] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0118] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0119] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0120] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0121] In one embodiment, the present invention further includes a computer program product comprising a computer program that, when executed by a processor, implements a time-sensitive network routing method according to any embodiment of the present invention.

[0122] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0124] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A routing method for time-sensitive networks, characterized in that, include: The network topology of a Time-Sensitive Network (TSN) is obtained, and the time-triggered (TT) streams and basic transmission characteristic parameters of the TT streams in the network topology are determined. The network topology includes switches, transmitting terminals, and receiving terminals. Based on the basic transmission characteristic parameters, determine the link load rate of the data links included in the TSN network; Based on the link load rate, the TT flow is subjected to TSN load balancing routing to obtain the optimal path of the TSN network; The basic transmission characteristic parameters include the period of the TT stream, the message length, the link transmission time, and the transmission path. Correspondingly, determining the link load rate of the data links included in the TSN network based on the basic transmission characteristic parameters includes: Based on the aforementioned basic transmission characteristic parameters, the link load rate of the data links included in the TSN is determined using the following formula: in, Indicates link load rate. Indicated by switch Pointing to the switch A data link, TT stream set is represented as , This represents the i-th TT stream. express The cycle; express The message length is used to describe The time required for transmission on any link; express The transmission path from the sending terminal to the receiving terminal. This represents the least common multiple of the cycles of all TT flows in the TSN network.

2. The method according to claim 1, characterized in that, The step of performing TSN load balancing routing on the TT flow based on the link load rate to obtain the optimal path of the TSN network includes: Based on the load rates of each link, determine the average load rate of all data links in the TSN network; The shortest transmission path set of the TT stream is determined based on the hop count of each transmission path from the sending terminal to the receiving terminal. An optimization model is established based on the average load rate and the link load rate; Based on the optimization model and the set of shortest transmission paths, TSN load balancing routing is performed to obtain the optimal path of the TSN network.

3. The method according to claim 2, characterized in that, The step of establishing an optimization model based on the average load rate and the link load rate includes: Constraints are established based on the transmission time of each data link, the least common multiple of the periods of all TT streams in the TSN network, the period of the TT stream, and the message length of the TT stream. An optimization objective function is established based on the difference between the load rate of each link and the average load rate. Based on the constraints and the objective function, an optimization model is obtained.

4. The method according to claim 2, characterized in that, The step of performing TSN load balancing routing based on the optimization model and the shortest transmission path set to obtain the optimal path for the TSN network includes: Based on the tunic group algorithm and the optimization model, TSN load balancing routing is performed on the shortest transmission path set to obtain the optimal transmission path for each TT stream. All the optimal transmission paths are taken as the optimal paths for the TSN network.

5. The method according to claim 4, characterized in that, The method based on the tunic swarm algorithm and the optimization model performs TSN load balancing routing on the shortest transmission path set to obtain the optimal transmission path for each TT stream, including: The overall transmission path scheme of the TSN network is used as the salvia population in the salvia swarm algorithm; Based on the path selection from the set of shortest transmission paths for each of the TT streams, individuals in the salps population are determined; Based on the optimization model, the fitness value of the tunic group algorithm is determined; The location of the salps population is updated to determine the optimal transmission path.

6. A routing device for time-sensitive networks, characterized in that, include: The data acquisition module is used to acquire the network topology of the Time-Sensitive Network (TSN) and determine the time-triggered (TT) streams and the basic transmission characteristic parameters of the TT streams in the network topology. The network topology includes switches, sending terminals, and receiving terminals. The load rate determination module is used to determine the link load rate of the data links included in the TSN network based on the basic transmission characteristic parameters. The path determination module is used to perform TSN load balancing routing on the TT flow based on the link load rate to obtain the optimal path of the TSN network. The basic transmission characteristic parameters include the period of the TT stream, the message length, the link transmission time, and the transmission path. Correspondingly, the load rate determination module is specifically used for: Based on the aforementioned basic transmission characteristic parameters, the link load rate of the data links included in the TSN is determined using the following formula: in, Indicates link load rate. Indicated by switch Pointing to the switch A data link, TT stream set is represented as , This represents the i-th TT stream. express The cycle; express The message length is used to describe The time required for transmission on any link; express The transmission path from the sending terminal to the receiving terminal. This represents the least common multiple of the cycles of all TT flows in the TSN network.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the routing method for a time-sensitive network according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the routing method for a time-sensitive network as described in any one of claims 1-5.

9. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the routing method for time-sensitive networks according to any one of claims 1-5.

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