Robust transmission method for time-sensitive network fault uncertainty

Abstract

The invention discloses a robust transmission method for time-sensitive network fault uncertainty, and relates to the field of industrial Internet of Things. According to the method, the fault uncertainty model and the double-layer robust optimization framework are constructed, the method adapts to complex and changeable link fault scenes in the industrial environment, deterministic transmission of key flow can be guaranteed under the worst fault condition, unpredictable interference such as temperature fluctuation and mechanical vibration can be effectively handled, and the anti-risk capacity of the network is remarkably improved. On the basis of an optimization model of mixed integer linear programming, in combination with constraint linearization and hierarchical solution architecture, the exponential complexity of full-space search is reduced to a polynomial level, and routing and scheduling optimization of a large-scale network can be completed within millisecond time. Through a space-time dimension conflict avoidance and resource reservation mechanism, the bandwidth utilization rate is maximized and the occupation of redundant resources is reduced on the premise of ensuring the real-time performance of high-priority services.

Description

Technical Field

[0001] The present invention relates to the field of industrial Internet of Things, and in particular to a robust transmission method for time-sensitive network failure uncertainty. Background Art

[0002] The Industrial Internet of Things (IIoT) is profoundly transforming the production model of modern manufacturing. As a core enabling technology for intelligent manufacturing, IIoT builds intelligent interconnected networks between industrial devices, enabling the digitalization and automation of production processes. Industrial devices are increasingly demanding real-time communication, placing increasingly stringent demands on network transmission reliability and timeliness. This demand is particularly acute in critical application scenarios such as automated production lines and remote equipment control. However, the "best-effort" transmission method used by traditional Industrial Ethernet is no longer able to meet the stringent communication quality standards of modern intelligent manufacturing. Its inherent random contention mechanism leads to significant fluctuations in data transmission latency, making it impossible to guarantee the timely delivery of critical control commands. To address this, the next-generation Time-Sensitive Networking (TSN) technology has emerged, providing deterministic transmission guarantees for industrial communications. With its excellent real-time performance and reliable transmission quality, TSN has become the preferred communication solution for industrial automation, intelligent transportation, and other fields.

[0003] The determinism and reliability of TSN networks stem from its innovative mechanisms in clock synchronization, traffic filtering, and traffic shaping. The IEEE 802.1AS protocol defines a high-precision time synchronization model between devices as the foundation for inter-device network transmission. IEEE 802.1Qci establishes per-stream filtering and policing (PSFP) to achieve deterministic mapping of traffic within a switch to physical cache queues. Building on the clock synchronization model and PSFP queue cache mapping, the IEEE 802.1Qbv protocol introduces the Time-Aware Shaper (TAS), a key mechanism for ensuring deterministic and reliable traffic transmission. TAS configures gates and gate control lists (GCLs) for each switch cache queue, enabling fine-grained and precise control of traffic flow within the network. Specifically, TAS divides the transmission cycle into fixed time slot windows based on predefined GCLs. Each window only allows data frames from a specified priority queue to pass through the physical link. Its core lies in a hard isolation mechanism: high-priority traffic exclusively occupies guaranteed time slots, while non-critical traffic is transmitted in idle windows. Combined with conflict avoidance strategies in both temporal and spatial dimensions (staggering traffic transmission periods on the same link and isolating overlapping services on paths), TAS completely eliminates latency jitter caused by data frame contention. By periodically updating gating parameters and dynamically adjusting time slot ratios, TAS ensures deterministic latency for critical services while adaptively responding to network load fluctuations, ensuring transmission reliability under complex operating conditions.

[0004] Current TSN optimization research focuses primarily on improving transmission efficiency. Efficiency-enhancing methods include constructing a joint optimization framework for path selection and time slot scheduling, improving network throughput through collaborative design of spatial and temporal dimensions. Other methods develop novel scheduling strategies based on specific mathematical tools, significantly reducing the duration of computing resource utilization. However, existing technology systems generally overlook a key issue: how to maintain stable communication reliability under complex operating conditions. When encountering sudden network fluctuations or equipment anomalies, transmission systems lacking effective protection mechanisms can cause control command loss, which directly threatens industrial production safety and causes significant economic losses.

[0005] Existing approaches to TSN's reliable transmission are primarily divided into two categories: The first involves redundant transmission strategies, which implement multiple data copies by planning multiple independent paths, or by repeatedly sending key frames on a single link based on a time-redundant mechanism. The second involves fault-tolerant designs that prioritize fault modes, such as relying on offline reserved resources to build protection channels or optimizing scheduling in the spatiotemporal dimension to reduce bandwidth consumption. However, these approaches have significant limitations: redundant strategies require additional network resources, resulting in reduced bandwidth utilization; and fault-tolerant models based on fixed failure probabilities struggle to adapt to the complex and changing interference environments of industrial sites. In actual industrial scenarios, communication failures caused by factors such as temperature fluctuations and mechanical vibrations are highly random, and their statistical distribution is difficult to model in advance. This results in a lack of theoretical support for the worst-case interference resistance of existing approaches, making it impossible to guarantee the reliability of TSN transmission.

[0006] Therefore, researchers in this field are dedicated to developing a robust transmission method for time-sensitive networks with fault uncertainty. By building a fault uncertainty model and a two-layer robust optimization framework, this method adapts to the complex and ever-changing link failure scenarios in industrial environments. It ensures deterministic transmission of critical traffic in the worst-case scenario, effectively handles unpredictable interference such as temperature fluctuations and mechanical vibration, and significantly improves the network's resilience to risk. Summary of the Invention

[0007] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is to ensure the anti-interference capability of the time-sensitive network in the worst case and to ensure the reliability of the time-sensitive network transmission.

[0008] To achieve the above object, the present invention provides a robust transmission method for time-sensitive network failure uncertainty, comprising the following steps:

[0009] Step 1: Establish a joint model of TSN routing, scheduling and transmission failure uncertainty;

[0010] Step 2: Perform robust solution.

[0011] Furthermore, in step 1, a set of timing constraints that need to be met to ensure the determinism of traffic transmission is first established, and then an uncertainty set is constructed to describe the normal and faulty states of the link, and the impact of the link state on the normal transmission of traffic is deduced, thereby obtaining the constraints that need to be met under unknown transmission failures.

[0012] Furthermore, the step 1 includes the following steps:

[0013] Step 1.1: Establish a mathematical model of traffic and network topology;

[0014] Step 1.2: Construct the cycle time domain constraints for routing and scheduling;

[0015] Step 1.3: Construct a transmission failure uncertainty set.

[0016] Furthermore, in step 1.2, the constraints include topology routing constraints, flow transmission sequence constraints, queue allocation constraints, deadline constraints, offset constraints and conflict-free constraints.

[0017] Furthermore, in step 1.3, the fault includes a device node level fault and a communication link level fault.

[0018] Furthermore, in step 2, by jointly optimizing reliability and real-time performance, the routing path of traffic and time slot resource allocation are optimized in the event of a failure.

[0019] Furthermore, in step 2, the optimization problem is expressed as a mixed integer linear programming problem through a linearization method.

[0020] Furthermore, in step 2, the optimization problem is decomposed into a main problem and sub-problems, and the optimal solution is approached iteratively.

[0021] Furthermore, in step 2, a two-layer robust deterministic transmission optimization problem of joint routing and scheduling is first established, and the optimization objective and constraint of the optimization problem are converted using a linearization method to minimize the transmission delay in the event of a transmission failure; the linearized problem is then decomposed into a two-stage optimization problem, the first stage is the optimization of the main problem, and an optimization decision is made before an uncertain failure occurs; the second stage is the optimization of the sub-problem, and the original optimization decision is adjusted according to the failure situation after the failure occurs; the optimal solution is gradually approached by iterating the main problem and the sub-problem.

[0022] Furthermore, the step 2 includes the following steps:

[0023] Step 2.1, constrained linearization;

[0024] Step 2.2: Divide the main problem into sub-problems;

[0025] Step 2.3: Based on the division of the main problem and subproblems, robust deterministic transmission solution is obtained.

[0026] There is a lack of models for transmission failure uncertainty in time-sensitive networks. The lack of this model makes it impossible for time-sensitive network routing and GCL scheduling designs to cope with the problem of traffic deterministic transmission being destroyed when a link fails, resulting in damage to network transmission determinism and reliability. The present invention establishes a robust deterministic transmission model. By jointly constructing a deterministic transmission constraint set for TSN routing and scheduling and a mathematical description of the impact of transmission failure uncertainty, the conditions that need to be met for determinism assurance in link failure scenarios are characterized. The present invention first establishes a set of timing constraints that need to be met to ensure traffic transmission determinism, including topological routing, stream transmission sequence, queue allocation, deadline, offset and conflict-free constraints. Secondly, by constructing an uncertainty set to describe the normal and faulty states of the link, and deriving the impact of the link state on the normal transmission of traffic, the constraints that need to be met to withstand unknown transmission failures are given. The robust deterministic transmission model of the present invention constructs the necessary constraints for the TSN network to ensure traffic transmission determinism when there is uncertainty in link transmission failures. The model allows deterministic and reliable transmission to be achieved by jointly optimizing routing paths and time window scheduling in time-sensitive networks.

[0027] There is a lack of robust transmission optimization strategies that can withstand the uncertainty of unknown transmission failures. Existing strategies all require a priori and accurate failure probability distribution, and cannot provide consistent transmission performance when the prior distribution is difficult to obtain. At the same time, existing optimization methods are based on traversal search of the entire optimization space, which makes it difficult to cope with dynamically changing network environments with low time complexity. The present invention proposes a two-layer robust deterministic transmission strategy (RDT) that optimizes the routing path and time slot resource allocation of traffic while considering the worst-case failure by jointly optimizing reliability and real-time performance. This strategy does not rely on prior knowledge of communication link failures and can adapt to situations where up to K communication link failures occur. In RDT, the optimization problem is formulated as a mixed integer linear programming problem (MILP) through linearization, significantly reducing the optimization time complexity, and further decomposing the original two-layer optimization problem into a main problem and sub-problems through problem decomposition, iteratively approximating the optimal solution. The present invention first establishes a joint routing and scheduling optimization problem based on the TSN transmission failure uncertainty model, and establishes a two-layer robust deterministic transmission optimization problem. In view of the nonlinear constraints in this problem, the linearization method is used to transform the optimization objectives and constraints of the original optimization problem, minimizing the transmission delay while tolerating transmission failures. Then, based on the intrinsic characteristics of the robust optimization problem, the linearized problem is broken down into a two-stage optimization problem. The first stage is to optimize the main problem and make a decision before the uncertain failure occurs. The second stage is to optimize the sub-problems. After the failure occurs, the original optimization decision is adjusted according to the specific failure situation. The optimal solution of the original problem is gradually approached by iterating the main problem and sub-problems. The RDT of the present invention significantly reduces the time complexity of problem solving by linearizing the original constraint problem into a mixed integer linear programming problem, and can be solved in combination with existing commercial solvers. After the original optimization problem is decomposed, the sub-problems can identify the most serious traffic transmission failure, consider the cost under the worst failure situation, and promote the main problem to achieve balanced performance when allocating routing paths and time slot resources to each traffic, ensuring the real-time and robustness of traffic transmission in the worst case.

[0028] Compared with the prior art, the present invention has the following obvious substantial features and significant advantages:

[0029] This invention combines technological innovation and engineering practicality, and can provide reliable communication guarantees for high-real-time fields such as industrial Internet and intelligent transportation. It has significant commercial value and broad market prospects.

[0030] 1.Technical advantages:

[0031] 1) High robustness and dynamic adaptability:

[0032] By building a fault uncertainty model and a two-layer robust optimization framework, this approach adapts to complex and variable link failure scenarios (such as sudden, concurrent multi-link failures) in industrial environments without relying on a priori fault probability distributions. Its dynamic adjustment mechanism ensures deterministic transmission of critical traffic under worst-case failure scenarios, effectively addressing unpredictable interference such as temperature fluctuations and mechanical vibration, and significantly improving the network's resilience to risk.

[0033] 2) Resource efficiency and real-time guarantee:

[0034] An optimization model based on mixed-integer linear programming (MILP), combined with constrained linearization and a hierarchical solver architecture, reduces the exponential complexity of traditional full-space search to polynomial complexity, enabling routing and scheduling optimization for large-scale networks within milliseconds. Furthermore, through conflict avoidance and resource reservation mechanisms in both temporal and spatial dimensions, bandwidth utilization is maximized and redundant resources are reduced while ensuring the real-time performance of high-priority services.

[0035] 3) Compatibility and ease of deployment:

[0036] This invention is fully compatible with existing TSN standard protocols (such as IEEE 802.1Qbv and 802.1Qci), enabling smooth upgrades without modifying underlying hardware. Its layered, optimized architecture supports seamless integration with Industrial Internet of Things (IIoT) edge computing platforms and enables centralized policy deployment through software-defined networking (SDN), significantly reducing enterprise deployment costs.

[0037] 2. Performance indicators:

[0038] 1) Transmission reliability: In scenarios where the number of concurrent link failures reaches up to 3, the deterministic guarantee rate of end-to-end latency for critical services is ≥ 99.99%, and the packet loss rate is ≤ 0.001%.

[0039] 2) Computational efficiency: For an industrial network with 100 nodes, the optimized solution time is ≤50ms, meeting the needs of real-time dynamic scheduling.

[0040] 3) Bandwidth utilization: Compared with traditional redundant transmission solutions, network resource overhead is reduced by 10% to 40% while ensuring the same level of reliability.

[0041] 3. Production implementation and industrialization prospects:

[0042] 1) Intelligent manufacturing field:

[0043] In automated production lines, this invention ensures the real-time and reliable transmission of critical services such as robotic arm control instructions and sensor data, preventing production line downtime due to communication interruptions. For example, in automotive manufacturing scenarios, dynamic routing switching and time slot reallocation can reduce production losses caused by link failures.

[0044] 2) Intelligent Transportation System:

[0045] Applicable to vehicle-to-everything (V2X) and rail transit signal control systems, ensuring the deterministic transmission of emergency braking commands and train dispatching signals.

[0046] 3) Energy and Power Internet of Things:

[0047] In smart grids, the present invention can achieve highly reliable transmission of relay protection signals and power status monitoring data, avoiding cascading failures caused by communication delays.

[0048] 4. Conversion feasibility:

[0049] The present invention has the ability to collaborate with the industrial chain, and can form ecological cooperation with mainstream TSN chip manufacturers (such as Intel and NXP) and industrial automation companies (such as Siemens and Schneider), and quickly embed into existing solutions.

[0050] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a time-sensitive network system topology structure of a preferred embodiment of the present invention;

[0052] Figure 2 is a modeled network topology directed graph of a preferred embodiment of the present invention;

[0053] Figure 3 This is a flowchart of iterative optimization of the original optimization problem decomposed into a main problem and sub-problems in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0054] The following describes several preferred embodiments of the present invention with reference to the accompanying drawings to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the scope of protection of the present invention is not limited to the embodiments mentioned herein.

[0055] In the drawings, components with identical structures are denoted by the same reference numerals, and components with similar structures or functions are denoted by similar reference numerals. The size and thickness of each component shown in the drawings are arbitrary and are not limited by the present invention. For clarity, the thickness of components in some places in the drawings is appropriately exaggerated.

[0056] This embodiment includes a joint model of TSN routing, scheduling, and transmission failure uncertainty, and a robust solution method:

[0057] Joint model of uncertainty in TSN routing, scheduling and transmission failures:

[0058] Step 1: Establish an abstract mathematical model of traffic and network topology. Each flow can be represented by its corresponding feature tuple <v src,i ,v dst,i ,b i ,T i ,t ddl ,α i > represents the source node, destination node, frame packet length, period, maximum tolerance of end-to-end communication delay and importance of the flow, respectively. The flow path is represented by The total number of hops is given by H i The network topology of TSN is abstracted

[0059] For an undirected graph G:<V,E> , where V represents the set of TSN switches and E represents the set of TSN links. A data flow link [x,y]∈E represents a directed edge from x to y, and x,y∈V. The GCL of the TAS mechanism in this system operates in a cyclic rolling manner around the time slot, and the time span is the least common multiple of all traffic cycles t hp =LCM(T), which is called a super cycle. TSN routing and scheduling restrict the behavior of each frame within a super cycle. The network topology of this embodiment and its corresponding abstract mathematical model are shown in the attached figure. The network physical structure is as follows. Figure 1 As shown in Figure 1, the three terminal devices ES0 to ES2 may generate traffic and inject it into the network composed of TSN switches SW0 to SW2. Traffic f1 is generated by ES0 and the destination node is ES2. Traffic f2 is generated by ES1 and the destination node is ES2. After mathematical abstraction, the system is as follows Figure 2 As shown, links become bidirectional edges, and nodes are the union of switches and end devices. For f1, its possible transmission paths are SW0-SW2 or SW0-SW1-SW2; for f2, its possible transmission paths are SW1-SW2 or SW1-SW0-SW2. The traffic characteristics are shown in Table 1.

[0060] Step 2: Construct the cycle-time constraints for routing and scheduling. Constraints can be categorized into topology routing constraints, flow transmission sequence constraints, queue allocation constraints, deadline constraints, offset constraints, and conflict-free constraints, as described below.

[0061] 2.1 Topology routing constraints: defining traffic flow f i The routing path indicator variable r i,[x,y] Indicates that traffic will pass through the communication link [x, y]. To ensure the determinism of data transmission, each data flow must be configured from the source node v src,iTo the destination node v dst,i This constraint is implemented in three ways: data flows are prohibited from passing through the same switch node multiple times during transmission, that is, the path must not contain repeated intermediate nodes to avoid loop routing; any data flow cannot be forwarded to the same intermediate node through two different switch nodes during transmission to ensure the linear topology of the path; all data frames of the same data flow must follow the same transmission path to maintain timing characteristics and avoid disorder problems. Specifically:

[0062]

[0063] The first equation ensures that each data flow is only allowed to select a single output link for transmission when it departs from the source node; the second equation ensures that when the data flow reaches the destination node, it must complete the transmission through the uniquely designated input link; the third equation ensures that the data flow must meet the flow conservation condition at the intermediate switching nodes in the path, ensuring the uniqueness of the forwarding path.

[0064] 2.2 Streaming sequence constraint: This constraint specifies that the data frame f ij The forwarding time of each node on the transmission path must satisfy a progressive relationship. Specifically, δ is defined as the maximum clock synchronization error between two nodes, and ζ is the link transmission rate. When a data frame is transmitted from the current node to the next node, the following timing relationship must be satisfied:

[0065]

[0066] 2.3 Queue allocation constraints: Define the discriminant function sgn(·), which outputs 1 if the discriminant holds, otherwise 0. Indicates the maximum buffer length of the output queue l. During data transmission, when the data flow f i Via network link (v a ,v a+1 ) must be temporarily buffered in the output port queue of the source node when transmitting. This constraint sets the following queue resource constraints:

[0067]

[0068]

[0069] 2.4 Deadline Constraints: Definition The time offset of the last hop. The maximum end-to-end delay from when the data stream is sent to when it is received cannot exceed the corresponding deadline. The specific mathematical expression is as follows:

[0070]

[0071] 2.5 Offset Constraint: Since data streams are generated and sent periodically, the sending times of different frames of the same data stream may differ by integer multiples of the period. For frames sent successively from the same data stream, the arrival time of the previous frame should be less than the arrival time of the next frame:

[0072]

[0073] 2.6 No-Conflict Constraint: It stipulates that for a network link shared by two flows, the transmission windows of the two flows cannot overlap, otherwise it will cause frame packet loss. Definition t of,i,m,[x,y] is the time offset of the mth frame of flow i passing through link [x, y], which is expressed as:

[0074]

[0075] To ensure reliable and deterministic data transmission, TSN employs a strict timing isolation mechanism to prevent conflicts between multiple data streams on shared links. When different data streams pass through the same communication link, the system enforces sequential transmission of these streams, ensuring that the previous data stream completes before the next one begins using the link. This constraint works closely with the network scheduling algorithm to establish mutually exclusive transmission channels for various service flows in the time dimension, ensuring the real-time performance of high-priority, critical services while maintaining overall network stability and determinism.

[0076] Step 3: Construct a set of transmission failure uncertainties. In the actual operating environment, network failure phenomena can be divided into two basic forms according to their scope of influence: one is the device node-level failure, which manifests as the functional abnormality of the time-sensitive network switch; the other is the communication link-level failure, which refers to the performance degradation or interruption of the transmission channel. In order to systematically describe these uncertainties, this step constructs a complete set of fault states to characterize various abnormal scenarios that industrial networks may face. Define a binary variable k[x,y] to represent the communication link status. When it is 0, it indicates normal operation, otherwise it indicates a failure. The constant K is used to limit the number of concurrent fault links to characterize the system reliability level. The uncertainty set U is as follows:

[0077] U≡{k|[x,y]∈{0,1}:∑ [x,y]∈E k[x,y]≤K}.

[0078] The impact of the faulty link set U on traffic transmission is described by the following formula:

[0079]

[0080] where Φ iAn indicator variable indicating whether traffic is transmitted normally, indicating whether the traffic can reach the destination node before its deadline. A value of 1 indicates that the traffic cannot be transmitted normally. In this embodiment, if K = 1 and the uncertainty set is U = {k[V1, V2] = 1, k[V0, V1] = 0, k[V0, V2] = 0}, that is, there is a fault on the link [V0, V2], then when traffic f1 takes the route SW0-SW2 and traffic f2 takes the route SW1-SW2, traffic f2 will definitely not reach the destination node before its deadline by passing through the faulty link. Therefore, [V1, V2] = 1 and [V1, V2]∈R1:φ1=1.

[0081] Robust solution method:

[0082] Step 1: Constraint linearization. In the process of solving complex network optimization problems, the linearization of constraints is a key technical means to improve computational efficiency. This method uses clever mathematical transformations to transform the originally difficult-to-handle nonlinear relationship into a form acceptable to standard linear programming problems, which greatly reduces the computational complexity and ensures the accuracy of the solution. In particular, when dealing with conflict avoidance conditions and fault scenario constraints in network transmission, by adding binary decision variables and logical state indicators, the discrete nonlinear expression is successfully reconstructed into a continuous set of linear inequalities. This transformation not only maintains the physical meaning and engineering requirements of the original constraints, but also enables commercial optimization solvers to efficiently handle large-scale network configuration problems. More importantly, the mathematical model after linearization fully retains the constraint strength and solution feasibility of the original problem, ensuring that the obtained optimization solution is strictly consistent with the actual engineering requirements. Define a binary variable β and a sufficiently large constant M. The specific form of the constraint after linearization is as follows:

[0083]

[0084] At the same time, define the auxiliary binary variable Φ i and a sufficiently small positive number ∈ fault condition constraint can also be linearized as:

[0085]

[0086] The final optimization goal becomes:

[0087]

[0088] Step 2: Division of main problem and sub-problem. In order to improve the computational feasibility of the optimization model, a hierarchical solution architecture is adopted to decompose the original complex problem into two logically related subsystems. This hierarchical processing method stems from the inherent characteristics of the robust optimization problem: the system decision variables can be divided into two dimensions: pre-deployment parameters and dynamic adjustment parameters. The pre-deployment parameters correspond to the static strategy determined in the initial configuration phase of the network, including basic routing planning and time slot allocation schemes; the dynamic adjustment parameters optimize the transmission strategy in real time to deal with abnormal situations such as link failures in response to various uncertainties that arise during operation. Through this decoupling of the timing dimension, the integrity of the optimization model is maintained, and the computational scale of a single solution is significantly reduced, enabling the algorithm to efficiently handle configuration problems of industrial-grade network scales. The core advantage of the hierarchical architecture is that the static strategy layer provides the system with a stable basic configuration framework, and the dynamic adjustment layer gives the network the flexibility to deal with emergencies. The two work together to ensure the robustness of the system in complex environments. The form of the original main problem (MP) is:

[0089]

[0090] In this main problem, the objective function is to minimize the total transmission delay under a given transmission path and traffic scheduling solution. The corresponding sub-problem (SP) is:

[0091]

[0092] Where e is the sub-problem iteration count. In this embodiment, when the link rate is ζ = 1000Mbps, for the main problem MP that has not yet failed, its optimal solution includes routing and injection time scheduling. For flow f1, its routing should be ES0-SW0-SW2-ES2, that is, there are variables The rest are 0, injection time For traffic f2, the route should be ES1-SW1-SW2-ES2, which means there are variables The rest are 0, injection time The specific routing and scheduling scheme is shown in Table 2.

[0093] Step 3: Robust deterministic transmission strategy algorithm based on MP and SP division. This solution adopts a two-stage optimization framework, which divides the decision-making process into two key stages: pre-configuration and dynamic adjustment. In the pre-configuration stage, the system predetermines the basic network parameters (including and ), and these decisions need to be completed before the specific fault conditions are unknown. The second stage (dynamic adjustment stage) performs adaptive optimization for various uncertain factors (such as ) that appear in actual operation. This time-separated architecture has the following advantages: the main optimization module focuses on building a basic network configuration that is resistant to interference, while reserving margins to deal with the worst case scenario; the sub-optimization module verifies the robustness boundary of the current configuration by simulating extreme fault scenarios. Through the iterative mechanism of the column constraint generation algorithm, the two modules form a closed-loop optimization: the main module continuously improves the configuration scheme to resist the weaknesses discovered by the sub-module, and the sub-module continues to explore new threat scenarios. This adversarial optimization process not only ensures the reliability of the final solution, but also controls the computational complexity within an acceptable range through problem decomposition. Such as Figure 3 As shown, first initialize the lower bound LB = -∞ and the upper bound UB = +∞, and the iteration counter e = 0. When the error between the upper and lower bounds is not less than the threshold ∈, repeat the following steps: First solve the main problem MP to obtain the optimal solution Update the Nether θ (e)* Substitute SP to get the optimal solution SP (e)* and the optimal solution k (e)* [x,y], and update the upper bound If SP (e)* Create a variable without an upper bound And add the following constraints to MP:

[0094]

[0095] Otherwise, in addition to the above two constraints, add a third constraint:

[0096]

[0097] The final optimization result can be obtained by using a commercial solver. In this embodiment, when the link rate is ζ = 1000Mbps and the link SW1-SW2 fails, the final optimization result can be obtained by iterating the MP and SP. Since flow f1 is high-priority traffic, it is not affected and maintains the original routing and scheduling plan. However, the routing of flow f2 is changed to ES1-SW1-SW0-SW2-ES2, overlapping with flow f1 in the SW0-SW2 segment. Its scheduling plan is changed to: The specific routing and scheduling scheme is shown in Table 3.

[0098] Table 1 Traffic characteristic attributes

[0099]

[0100] Table 2 Before the fault: old transmission scheme

[0101]

[0102] Table 3 After the failure: New transmission plan

[0103]

[0104] The above describes in detail the preferred embodiments of the present invention. It should be understood that numerous modifications and variations based on the concepts of the present invention are possible without inventive effort by those skilled in the art. Therefore, any technical solution that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A robust transmission method for time-sensitive network failure uncertainty, characterized by: The following steps are involved: Step 1: Establish a joint model of TSN routing, scheduling and transmission failure uncertainty; Step 2: Perform robust solution.

2. The robust transmission method for time-sensitive network failure uncertainty according to claim 1, characterized in that: In step 1, a set of timing constraints that must be met to ensure the determinism of traffic transmission is first established. Then, an uncertainty set is constructed to describe the normal and faulty states of the link, and the impact of the link state on the normal transmission of traffic is deduced to obtain the constraints that must be met under unknown transmission failures.

3. The robust transmission method for time-sensitive network failure uncertainty according to claim 1, characterized in that: The step 1 comprises the following steps: Step 1.1: Establish a mathematical model of traffic and network topology; Step 1.2: Construct the cycle time domain constraints for routing and scheduling; Step 1.3: Construct a transmission failure uncertainty set.

4. The robust transmission method for time-sensitive network failure uncertainty according to claim 3, characterized in that: In step 1.2, the constraints include topology routing constraints, flow transmission sequence constraints, queue allocation constraints, deadline constraints, offset constraints, and conflict-free constraints.

5. The robust transmission method for time-sensitive network failure uncertainty according to claim 3, characterized in that: In step 1.3, the failure includes a device node level failure and a communication link level failure.

6. The robust transmission method for time-sensitive network failure uncertainty according to claim 1, characterized in that: The step 2 optimizes the traffic routing path and time slot resource allocation in the event of a failure by jointly optimizing reliability and real-time performance.

7. The robust transmission method for time-sensitive network failure uncertainty according to claim 1, characterized in that: In step 2, the optimization problem is expressed as a mixed integer linear programming problem through a linearization method.

8. The robust transmission method for time-sensitive network failure uncertainty according to claim 1, characterized in that: In step 2, the optimization problem is decomposed into a main problem and sub-problems, and the optimal solution is approached iteratively.

9. The robust transmission method for time-sensitive network failure uncertainty according to claim 1, characterized in that: In step 2, a two-layer robust deterministic transmission optimization problem of joint routing and scheduling is first established, and the optimization objective and constraint of the optimization problem are converted using a linearization method to minimize the transmission delay in the event of a transmission failure. The linearized problem is then decomposed into a two-stage optimization problem. The first stage is the optimization of the main problem, and an optimization decision is made before an uncertain failure occurs. The second stage is the optimization of the sub-problem, and the original optimization decision is adjusted according to the failure situation after the failure occurs. The optimal solution is gradually approached by iterating the main problem and the sub-problem.

10. The robust transmission method for time-sensitive network failure uncertainty according to claim 1, characterized in that: The step 2 comprises the following steps: Step 2.1, constrained linearization; Step 2.2: Divide the main problem into sub-problems; Step 2.3: Based on the division of the main problem and subproblems, robust deterministic transmission solution is obtained.

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