A time sensitive network (TSN) VLAN configuration method supporting deterministic latency

Abstract

This invention relates to the field of network communication technology, specifically to a VLAN configuration method for time-sensitive networks (TSNs) supporting deterministic latency. The method includes the following steps: extracting key data flow paths and link load distribution based on the TSN, identifying latency spikes and bandwidth anomalies, analyzing load and bandwidth matching to filter bottleneck links, extracting priority and time slot characteristics to evaluate VLAN performance, calibrating latency deviations, and outputting configuration adjustment control command sets for abnormal links. In this invention, by constructing a link status anomaly identification dataset and analyzing load and bandwidth trends, rapid identification of link bottlenecks and latency anomaly location are achieved. Traffic priority and time slot characteristics are extracted, and anomaly levels are calibrated in latency trend analysis. This enables balanced distribution and optimized control of key traffic, improves the adaptability and accuracy of VLAN configuration, strengthens traffic scheduling coordination, optimizes bandwidth utilization, reduces latency fluctuations, and enhances the determinism of key data flow transmission and network stability.

Description

Technical Field

[0001] This invention relates to the field of network communication technology, and in particular to a method for configuring time-sensitive network VLANs that supports deterministic delay. Background Technology

[0002] The field of network communication technology involves research on computer networks, information exchange, and data transmission, primarily including core components such as network architecture, data link layer protocols, transmission control mechanisms, network topology construction, and packet forwarding and routing. The focus of this field is to achieve high-speed, reliable, and low-latency data transmission between multiple nodes, and to ensure communication stability and determinism through switching technologies, virtual LAN partitioning, and bandwidth scheduling. With the increasing demand for real-time communication in scenarios such as industrial automation, connected vehicles, and intelligent manufacturing, Time-Sensitive Networking (TSN) has become a key direction in network communication technology. TSN introduces mechanisms such as time synchronization, traffic shaping, and bandwidth reservation into Ethernet to achieve strict control over communication latency and jitter, thereby supporting deterministic communication transmission.

[0003] Traditional time-sensitive network (TLS) VLAN configuration methods that support deterministic latency refer to the approach of manually setting VLAN identifiers, priorities, and traffic forwarding paths in Ethernet communication to differentiate and manage different communication flows. Traditional configuration relies on static VLAN segmentation, manually setting port identifiers, frame priorities, and time slot allocation to ensure timely transmission of critical data flows in the network. It also requires allocating packet scheduling windows according to predefined time synchronization protocols and using fixed forwarding tables to complete path determination and forwarding control. These methods often employ centralized configuration strategies, uniformly allocating VLAN IDs and TSN flow identifiers through the network manager and manually updating configuration table entries in each switching node to ensure controllable end-to-end latency.

[0004] Traditional VLAN configuration methods for time-sensitive networks rely on static manual settings. They achieve deterministic latency control through centralized configuration and fixed path management, but lack the ability to dynamically perceive and adaptively adjust the real-time network status. The configuration process requires manual updates of table entries, making it susceptible to human error and topology changes. In multi-node communication environments, it is difficult to reflect link load and latency changes in a timely manner, resulting in some links being overloaded or idle for extended periods. This leads to low bandwidth resource utilization, decreased latency control accuracy, unbalanced data flow priority management, and an inability to effectively identify and locate communication bottlenecks, affecting the real-time transmission stability of critical service flows and the determinism of overall network communication. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a time-sensitive network VLAN configuration method that supports deterministic delay.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for configuring a time-sensitive network VLAN that supports deterministic delay, comprising the following steps: S1: Based on the traffic classification rules of time-sensitive networks, extract key data flow paths and link load distribution in the network, identify latency surge intervals and bandwidth utilization anomalies on the time axis, and generate a link status anomaly identification dataset by combining link number and topology boundary information. S2: Based on the link status anomaly identification dataset, extract the link load distribution curve and bandwidth utilization trend map, analyze the matching consistency between the two at the link junction, filter links with unbalanced load and abnormal delay, and form the link bottleneck area identification result. S3: Based on the link bottleneck area identification results, extract the traffic priority distribution matrix and time slot allocation characteristics within the link, analyze priority conflicts and time slot allocation deviations, filter and match abnormal links, and obtain the VLAN configuration performance evaluation data table. S4: Based on the VLAN configuration performance evaluation data table, analyze the delay distribution trend in the network topology corresponding to the link, evaluate the degree of deviation from the ideal delay model, mark the abnormal level of the link according to the deviation magnitude, and output the VLAN configuration decision table for balanced allocation of key traffic time slots.

[0007] As a further embodiment of the present invention, the link status anomaly identification dataset includes link numbers with sudden delay increases, abnormal bandwidth utilization points, link coordinate markers, and time series anomaly identifiers; the link bottleneck area identification results include link identifiers with abnormal delays, link boundary connectivity units, and link boundary consistency blocks; the VLAN configuration performance evaluation data table includes priority conflicting continuous links, time slot allocation deviation areas, load mutation overlap areas, and configuration anomaly link numbers; and the critical traffic time slot balanced allocation VLAN configuration decision table includes risk level labels, link response deviation values, local delay anomaly indicators, and bandwidth offset levels.

[0008] As a further aspect of the present invention, the steps for obtaining the link state anomaly identification dataset are as follows: S111: Based on the traffic classification rules of time-sensitive networks, extract the key data flow paths and link load distribution curves within the link, compare and analyze the two types of data within the same link, and obtain the trend value of the difference between link load and delay distribution. S112: Based on the trend value of the difference between the link load and the delay distribution, identify the sudden increase in the link load and the fluctuation value of the delay distribution curve, superimpose the two types of values ​​on time periods, extract the time intervals in which the sudden increase exceeds the baseline value and the fluctuation exceeds the set threshold, and generate a set of high-frequency abnormal interval time periods. S113: For the set of high-frequency abnormal interval time periods, match the corresponding link number and topology boundary information, extract the link location where the signal occurred, and generate a link status abnormality identification dataset.

[0009] As a further aspect of the present invention, the steps for obtaining the link bottleneck region identification result are specifically as follows: S211: Based on the link status anomaly identification dataset, extract the load distribution curve and bandwidth utilization trend map of the link, extract the projection trajectory of the two at the link boundary, identify the distribution number and aggregation degree of the boundary point in the link, and obtain the link boundary consistency map. S212: Based on the link boundary consistency map, filter the boundary areas with a aggregation degree higher than the average level, compare the spatial boundaries of the network topology map, identify continuous boundary clusters belonging to the same link, and obtain the bottleneck area division within the link. S213: Invoke the bottleneck region division within the aforementioned link, and perform integrated analysis on link boundary consistency, load distribution dispersion, bandwidth utilization balance, and link latency, using the following formula: ; Calculate the link performance anomaly coefficient, perform link matching based on the response blocks in the layer, and form the link bottleneck area identification result; in, Represents the link performance anomaly coefficient. Represents the number of link segments. This represents the response block value of the i-th link segment. This represents the average response block size of a link segment. This represents the load value of the i-th link segment. This represents the bandwidth value of the i-th link segment.

[0010] As a further aspect of the present invention, the steps for obtaining the VLAN configuration performance evaluation data table are as follows: S311: Based on the link bottleneck area identification results, extract the traffic priority distribution matrix and time slot allocation features of the numbered links in the layer, align the data in the link with timestamps, identify the priority conflict intensity and time slot allocation deviation, and obtain the network local abnormal response feature set. S312: Based on the network local anomaly response feature set, perform joint analysis on priority conflicts and time slot allocation deviations within the link, calculate priority time slot coupling feature values, filter links with priority time slot coupling in the layer, and establish a priority time slot collaborative response spatial distribution map. S313: Call the priority time slot collaborative response spatial distribution map, cluster the links in the coupling feature value layer that exceed the collaborative identification benchmark, mark the link codes and coordinates corresponding to the continuous abnormal areas, and obtain the VLAN configuration performance evaluation data table.

[0011] As a further aspect of the present invention, the steps for obtaining the critical traffic time slot balanced allocation VLAN configuration decision table are as follows: S411: Based on the VLAN configuration performance evaluation data table, extract the link delay distribution curve under the specified number, perform time uniform processing, identify the unit time delay change, and obtain the link delay abnormal change rate set. S412: Based on the set of abnormal link delay change rates, identify the delay distribution curve of the ideal delay stage, compare the current delay change sequence with the reference curve, identify the link delay deviation level, extract and mark the links whose deviation level exceeds the warning upper limit, and obtain the set of links with sudden increase in deviation. S413: Based on the set of links with sudden deviations, bind the deviation level value of each link to the position number in the network topology diagram, sort them according to risk level, and output a critical traffic time slot balanced allocation VLAN configuration decision table.

[0012] As a further aspect of the present invention, the location number in the network topology diagram refers to the unique identifier number assigned to each link in the network topology based on the physical or logical location of the link in the network, thus obtaining a set of location numbers. The risk level sorting refers to sorting each link in the set of links with sudden increases in deviation according to the link delay deviation level and the corresponding network location number, and outputting the sorting results in descending order of risk level.

[0013] As a further aspect of the present invention, the method further includes step S5: S5: Call the critical traffic time slot balanced allocation VLAN configuration decision table, identify the corresponding number of the link in the network topology function diagram, extract the optimized response link list, compare the response level with the network protection priority sequence, filter the link numbers that need to adjust the response coverage, and output the configuration adjustment control command group for abnormal links. The configuration adjustment control command group for abnormal links includes adjusting the target link number, response level adjustment parameters, protection priority comparison items, and linkage response trigger types.

[0014] As a further aspect of the present invention, the step of obtaining the configuration adjustment control command group for abnormal links specifically includes: S511: Call the critical traffic time slot balanced allocation VLAN configuration decision table, extract the link number in the network topology function diagram, map the link risk level value with the area coordinate boundary, identify the link information corresponding to the network protection level, and generate a network task risk distribution map. S512: Based on the network task risk distribution map, extract the optimized response link number and response level, match the link risk level with the optimized response level, identify the link number with insufficient response coverage, and obtain the network task response risk disconnect list. S513: Based on the network task response risk disconnect list, extract the key link numbers that need to improve response coverage according to the level number in the network protection priority sequence, output the adjustment control parameters linked with the optimized response links in sequence, and output the configuration adjustment control command group for abnormal links.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, by constructing a link status anomaly identification dataset and analyzing load distribution and bandwidth trends, rapid identification of link bottleneck areas and accurate location of latency anomalies are achieved. Furthermore, traffic priority and time slot allocation characteristics are extracted, a configuration performance evaluation system is established, and link anomaly levels are calibrated based on latency trend analysis to form a decision table. This enables balanced allocation and optimized configuration control of critical traffic, effectively improving the adaptability and accuracy of VLAN configuration, strengthening the coordination of traffic scheduling in the network topology, optimizing bandwidth resource utilization, reducing communication latency fluctuations, and enhancing the determinism of critical data flow transmission and network operation stability. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the main steps of the present invention; Figure 2 This is a flowchart illustrating the process of obtaining the link status anomaly identification dataset in this invention. Figure 3 This is a flowchart illustrating the process of obtaining the link bottleneck region identification results in this invention. Figure 4 This is a flowchart illustrating the process of obtaining the VLAN configuration performance evaluation data table in this invention. Figure 5 This is a flowchart illustrating the process of obtaining the VLAN configuration decision table for balanced allocation of critical traffic time slots in this invention. Figure 6 This is a flowchart illustrating the process of obtaining the configuration adjustment control command group for abnormal links in this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0019] Example 1 Please see Figure 1 This invention provides a technical solution, a method for configuring a time-sensitive network (VLAN) that supports deterministic latency, comprising the following steps: S1: Based on the traffic classification rules of time-sensitive networks, extract key data flow paths and link load distribution in the network, identify latency surge intervals and bandwidth utilization anomalies on the time axis, and generate a link status anomaly identification dataset by combining link number and topology boundary information. S2: Based on the link status anomaly identification dataset, extract the link load distribution curve and bandwidth utilization trend map, analyze the matching consistency between the two at the link junction, filter links with unbalanced load and abnormal latency, and form the link bottleneck area identification results. S3: Based on the link bottleneck area identification results, extract the traffic priority distribution matrix and time slot allocation characteristics within the link, analyze priority conflicts and time slot allocation deviations, screen and match abnormal links, and obtain the VLAN configuration performance evaluation data table. S4: Based on the VLAN configuration performance evaluation data table, analyze the delay distribution trend in the network topology corresponding to the link, evaluate the degree of deviation from the ideal delay model, mark the abnormal level of the link according to the deviation magnitude, and output the VLAN configuration decision table for balanced allocation of key traffic time slots. S5: Call the critical traffic time slot balanced allocation VLAN configuration decision table, identify the corresponding number of the link in the network topology function diagram, extract the optimized response link list, compare the response level with the network protection priority sequence, filter the link numbers that need to adjust the response coverage, and output the configuration adjustment control command group for abnormal links.

[0020] The link status anomaly identification dataset includes link numbers with sudden delay increases, abnormal bandwidth utilization locations, link coordinate markers, and time series anomaly identifiers. The link bottleneck area identification results include link identifiers with abnormal delays, link boundary connectivity units, and link boundary consistency blocks. The VLAN configuration performance evaluation data table includes priority conflicting continuous links, time slot allocation deviation areas, load change overlap areas, and configuration anomaly link numbers. The critical traffic time slot balanced allocation VLAN configuration decision table includes risk level labels, link response deviation values, local delay anomaly indicators, and bandwidth offset levels. The configuration adjustment control command group for abnormal links includes the target link number to be adjusted, response level adjustment parameters, protection priority comparison items, and linkage response trigger types.

[0021] Please see Figure 2 The specific steps for obtaining the link status anomaly identification dataset are as follows: S111: Based on the traffic classification rules of time-sensitive networks, extract the key data flow paths and link load distribution curves within the link, compare and analyze the two types of data within the same link, and obtain the trend value of the difference between link load and delay distribution. Based on a traffic classification rule for time-sensitive networks, data flows transmitted within a link are classified. This classification rule divides data flows into critical and non-critical data flows based on the VLAN Priority Code Point (PCP) value and the target application protocol type. Specifically, data flows with a PCP value of 4 to 7, whose payload is control signaling or a synchronization clock signal conforming to the IEEE 1588PTP protocol, are identified as critical data flows. Data flows with a PCP value of 0 to 3, whose payload is background file transmission or network status monitoring video, are identified as non-critical data flows. For a specific link numbered LN-001, within a continuous monitoring period of 3600 seconds, with a sampling interval of 60 seconds, the traffic rates of the critical data flow path and the non-critical data flow path are statistically analyzed and plotted, forming two time series data, namely the link load distribution curve. At the same time, the average end-to-end delay of the two types of data flows from entering to leaving the link within each sampling interval is recorded and calculated, and plotted separately. To obtain the delay distribution curves for critical and non-critical data flows, a comparative analysis is performed on the two types of data at the same sampling time point, such as the 600th second. At the 600th second, the load record for the critical data flow is 150 Mbps, and the load record for the non-critical data flow is 350 Mbps. The load difference is calculated as |150-350|=200 Mbps. At the same time, the average delay record for the critical data flow is 25 microseconds, and the average delay record for the non-critical data flow is 85 microseconds. The delay distribution difference is calculated as |25-85|=60 microseconds. The load difference value and the delay distribution difference value at each sampling time point are used to form two independent time series. The product of the two time series at each time point is calculated, and this product is used as the difference trend value at that time point. For example, at the 600th second, the difference trend value is 200 Mbps multiplied by 60 µs, which equals 12000 Mbps·µs. The calculation results of all sampling points are concatenated to obtain the link load and delay distribution difference trend value.

[0022] S112: Based on the trend value of the difference between link load and delay distribution, identify the sudden increase in link load and the fluctuation value of delay distribution curve, superimpose the two types of values ​​in time periods, extract the time intervals in which the sudden increase exceeds the baseline value and the fluctuation exceeds the set threshold, and generate a set of high-frequency abnormal interval time periods. Based on the trend of the difference between link load and latency distribution, a dynamic load baseline is established. This baseline is set as the arithmetic mean of the non-critical data flow load over the past 10 sampling periods. For example, if the average non-critical data flow load over the past 600 seconds (from 1200 to 1799) is 320 Mbps at 1800 seconds, then the current baseline is 320 Mbps. When the measured non-critical data flow load at 1800 seconds is 550 Mbps, the excess over the baseline is 550 Mbps minus 320 Mbps, which equals 230 Mbps. This is the sudden increase in load. Simultaneously, the fluctuation of the latency distribution curve is identified by establishing a latency fluctuation threshold. This threshold is set according to the upper limit requirement for latency jitter of critical data flows in the Network Service Level Agreement (SLA). The agreement requires that the latency jitter of critical data flows not exceed 15 microseconds. To ensure identification sensitivity, the threshold is set to this upper limit. 80%, which is 15 microseconds multiplied by 80%, equals 12 microseconds. When the critical data stream delay jumps from 28 microseconds in the previous minute to 42 microseconds in the 1800th second, its fluctuation value is the absolute value of the difference between 42 microseconds and 28 microseconds, which is 14 microseconds. The two types of values ​​are superimposed on the time period. The identified load surge period is matched with the delay fluctuation period. In the 1800th second, the load surge increase of 230Mbps exceeds the baseline value, and the delay fluctuation value of 14µs exceeds the set threshold of 12µs. Therefore, the time point of the 1800th second is marked. All marked time points in the entire monitoring cycle are checked, and the time points that appear consecutively are merged into a time interval. For example, if the above conditions are met from the 1800th second to the 1920th second, an abnormal interval of [1800, 1920] seconds is formed. All such continuous or discontinuous abnormal intervals are summarized to generate a high-frequency abnormal interval time set.

[0023] S113: For high-frequency abnormal interval time sets, match the corresponding link number and topology boundary information, extract the link location where the signal occurred, and generate a link status anomaly identification dataset. For high-frequency abnormal time intervals, such as sets containing time intervals of [1800, 1920] seconds, [2700, 2760] seconds, etc., a query is performed from the network management database to confirm that all abnormal events occur on link numbered LN-001. The topology boundary information of this link is defined as its starting node, i.e., port P1 of switch SW-A, and its ending node, i.e., port P3 of switch SW-B. The location of the link where the signal occurred is extracted. This process involves associating and binding the logical number LN-001 of the link with its physical topology information SW-A: P1 to SW-B: P3 to form a complete link. The system generates a complete abnormal link location record, which includes fields such as timestamp, link number, starting device identifier, starting port number, ending device identifier, and ending port number. Each field is filled with precise measurement or configuration data. For each time period in the high-frequency abnormal interval time period set, the above matching and extraction operations are performed. All generated abnormal link location records are integrated into a structured dataset. Each row of this dataset represents an independent and confirmed link status abnormal event, which includes the time, location, and related topology attribution information of the event. Finally, a link status abnormality identification dataset is generated.

[0024] Please see Figure 3 The specific steps for obtaining the link bottleneck region identification results are as follows: S211: Based on the link status anomaly identification dataset, extract the load distribution curve and bandwidth utilization trend map of the link, extract the projection trajectory of the two at the link boundary, identify the distribution number and aggregation degree of the boundary point in the link, and obtain the link boundary consistency map. Based on the link status anomaly identification dataset, extract the curve of the total load (i.e., the sum of critical and non-critical data flows) over time for link LN-001 during the abnormal period of [1800, 1920] seconds, and its bandwidth utilization (i.e., the total load divided by the total link bandwidth) over time. This link has a total bandwidth of 1Gbps, and at second 1860, the total load reaches 700Mbps, with a bandwidth utilization of 70%. Extract the projection trajectory of these two values ​​at the link boundary, which represents the physical or logical interface where data flows leave the current link and enter the next link, i.e., port P3 of switch SW-B. At this boundary, record the load value and bandwidth utilization to form a data pair. (700Mbps, 70%), the data pairs of all sampling points during the abnormal period are aggregated to form a projection trajectory, and the distribution and aggregation degree of the boundary points in the links are identified. This operation analyzes how many abnormal links are in the network topology, such as LN-005 and LN-008, whose exits also converge to switch SW-B. If it is found that the abnormal traffic of multiple links all points to different ports of the same switch, the boundary points are spatially aggregated. The degree of aggregation is quantified by calculating the number of abnormal link exit nodes in the network neighborhood with a radius of 2 hops centered on switch SW-B. For example, if it is found that the boundary points of 3 abnormal links converge at SW-B, the link boundary consistency map is obtained.

[0025] S212: Based on the link boundary consistency map, filter the boundary areas with a aggregation degree higher than the average level, compare the spatial boundaries of the network topology map, identify continuous boundary clusters belonging to the same link, and obtain the bottleneck area division within the link. Based on the link boundary consistency map, which graphically displays the spatial distribution and connectivity of all abnormal link boundaries in the network, boundary areas with a higher-than-average aggregation degree are first selected. The aggregation degree of all boundary areas in the network, i.e., the number of abnormal links converging into that area, is calculated, and their arithmetic mean is taken. Assuming there are 10 boundary areas in the network and a total of 15 abnormal links, the average aggregation degree is 1.5. For the boundary area where switch SW-B is located, its aggregation degree is 3. Since 3 is greater than 1.5, this area is selected. By comparing the spatial boundaries of the network topology map, continuous boundary clusters belonging to the same link are identified. Specifically, the convergence of links to the same link is checked. Whether the three abnormal links LN-001, LN-005, and LN-008 of SW-B form a continuous data forwarding path in the topology. For example, if the data flow passes through LN-008, switch SW-C, LN-005, switch SW-A, LN-001, and switch SW-B in sequence, since the three links are continuous in the data flow path and all point to a congestion point SW-B, the path segment formed by the three links, that is, from the starting point of LN-008 to the ending point of LN-001, is identified as a bottleneck area. All links LN-008, LN-005, and LN-001 in this area are marked to obtain the bottleneck area division within the links.

[0026] S213: Invoke the bottleneck region division within the link, and perform integrated analysis on link boundary consistency, load distribution dispersion, bandwidth utilization balance, and link latency, using the following formula: ; Calculate the link performance anomaly coefficient, perform link matching based on the response blocks in the layer, and form the link bottleneck area identification result; in, Represents the link performance anomaly coefficient. Represents the number of link segments. This represents the response block value of the i-th link segment. This represents the average response block size of a link segment. This represents the load value of the i-th link segment. This represents the bandwidth value of the i-th link segment; The bottleneck region within the call link was divided, and the result clearly identified a continuous bottleneck area consisting of links LN-008, LN-005, and LN-001. Four indicators were collected: link boundary consistency, load distribution dispersion, bandwidth utilization balance, and link latency. The non-numerical indicators were then quantified into response block values. The quantification criteria are as follows: 20 points are allocated to boundary consistency; if the link exit is a boundary point with a aggregation degree higher than the average, 20 points are awarded, otherwise 0 points are awarded. 30 points are allocated to load distribution dispersion, scored based on the standard deviation of the load: 30 points for a standard deviation less than 100Mbps, 15 points for 100-200Mbps, and 0 points for greater than 200Mbps. 20 points are allocated to bandwidth utilization balance, scored based on time utilization differences: 20 points for differences less than 20%, 10 points for 20%-40%, and 0 points for greater than 40%. 30 points are allocated to link latency, scored based on the average latency of critical flows: 30 points for latency less than 30µs, 15 points for 30-50µs, and 0 points for greater than 50µs. The specific scores for each link segment are not specified. The sum of its four scores; Table 1: Link Parameter Table for Bottleneck Areas Table 1 lists the response block values ​​for each link segment within the bottleneck area. and the corresponding load and bandwidth The response block values ​​are calculated based on the aforementioned quantification standards. Next, the formula is used. The parameters and calculation logic in the formula for calculating the link performance anomaly coefficient are explained below: The link performance anomaly coefficient represents the overall fluctuation of the state of each link segment within the bottleneck area and the load pressure. The larger the value, the more sensitive the area is to anomalies and the more unstable its state is. This represents the total number of link segments within the bottleneck area. ; The index representing the link segment, from 1 to... ; The response block value representing the i-th link segment is a comprehensive score that reflects the link's performance in terms of boundary consistency, load stability, bandwidth utilization efficiency, and latency control. The average response block value across all link segments is calculated as follows: It serves as a benchmark for measuring the deviation of each link segment's status from the average level; This represents the average load value of the i-th link during the abnormal period, in Mbps; This represents the rated bandwidth value of the i-th link segment, in Mbps; The bandwidth utilization of the i-th link is calculated and used as a weighting factor to reflect the importance of high-load links in the overall evaluation. Calculate the absolute difference between the response block value of the i-th link segment and the average value, which represents the degree of deviation of the link state; Multiplying the state deviation by the bandwidth utilization makes high-load and state-unstable links have a higher impact value. Taking the square root of the product can smooth out the influence of extreme values, making the result more stable; The calculated impact values ​​of all link segments within the bottleneck area are summed to obtain the final link performance anomaly coefficient. ; The advantage of the formula is that it combines the deviation of the link state. Its actual load pressure By combining these methods and summing the square roots, we can more accurately identify those "true" bottleneck links that are both unstable and carrying high loads, avoiding the one-sidedness of single-indicator assessment and achieving precise quantification of the risk in bottleneck areas.

[0027] The specific calculation process is as follows: Calculate the average response block value Calculate the contribution value of each link segment individually: For LN-008 ( ): ; For LN-005 ( ): ; For LN-001 ( ): ; Calculate the link performance anomaly coefficient : Result 7.17 indicates that the bottleneck region has high anomaly sensitivity, with link LN-005 contributing the most and being the core of the bottleneck. Finally, based on this coefficient value, matching is performed in a preset risk layer to... The region consisting of LN-008, LN-005, and LN-001 is marked to form the link bottleneck region identification result.

[0028] Please see Figure 4 The specific steps for obtaining the VLAN configuration performance evaluation data table are as follows: S311: Based on the link bottleneck area identification results, extract the traffic priority distribution matrix and time slot allocation features of the numbered links in the layer, perform timestamp alignment on the data in the link, identify the priority conflict intensity and time slot allocation deviation, and obtain the network local anomaly response feature set. Based on the link bottleneck area identification results, the results indicate that the bottleneck area consists of LN-008, LN-005, and LN-001, and its link performance anomaly coefficient is 7.17. The traffic priority distribution matrix and time slot allocation features of each numbered link within this area are extracted. Specifically, for link LN-005, within the abnormal period [1800, 1920] seconds, the number and size of data packets with different PCP priorities (0-7) in its transmitted traffic are counted, forming an 8×N matrix (N is the number of samples). Simultaneously, the gate control list of the Time Aware Shaper (TAS) configured on this link is queried to extract the start time and duration of the time slot window allocated to each priority queue. For example, the time slot allocated to the critical traffic with priority 4 is [0.1] within a 1ms period. [0.3]ms, timestamp alignment is performed on the data within the link. The timestamp of the captured data packet is compared with the time slot window time of the TAS to identify the priority conflict intensity and time slot allocation deviation. The priority conflict intensity is quantified by calculating the number of high-priority data packets transmitted in a time slot not designated for it. For example, if 5 data packets with PCP of 4 are detected in a time slot open for non-critical traffic (PCP0-3), the conflict intensity is recorded as 5. The time slot allocation deviation is obtained by calculating the difference between the actual sending timestamp of the data packet and the center point of its time slot window. For example, a data packet with PCP of 4 has a time slot window center of 0.2ms, but the actual sending timestamp is 0.25ms, so the deviation is +0.05ms. All identified conflict intensities and deviations are summarized to obtain the network local anomaly response feature set.

[0029] S312: Based on the network local anomaly response feature set, a joint analysis of priority conflicts and time slot allocation deviations within the link is performed, using the following formula: ; Calculate the priority slot coupling characteristic value, filter the links with priority slot coupling degree in the layer, and establish a spatial distribution map of priority slot cooperative response; in, Represents the priority slot coupling characteristic value. Representing the The deviation in the allocation of time slots, Representing the The weight of each time slot, Represents the total number of time slots; Based on the network local anomaly response feature set, which contains priority conflicts and time slot allocation deviation data for each link within the bottleneck area, the parameters and calculation logic in the formula are explained below: This represents the priority slot coupling characteristic value, which reflects the weighted severity of the impact of slot allocation deviation on high-priority traffic. Positive values ​​indicate general delays, negative values ​​indicate general advances, and the larger the absolute value, the more severe the coupling problem. This represents the total number of time slots analyzed within a complete scheduling cycle. Here, we assume that the analysis is performed on a cycle containing 10 key time slots. ; Index representing time slots, from 1 to ; Representing the The average allocation deviation of data packets within a time slot, in milliseconds, is calculated by averaging the deviations of all data packets within that time slot. Representing the The weight of each time slot is determined by referencing the traffic priority of the service it serves. The weight is positively correlated with the priority to highlight the importance of high-priority traffic. The weight setting rules are as follows: Where PCP is the traffic priority served by that time slot. For example, a time slot allocated to traffic with PCP=4 has a weight of... The time slots allocated for traffic with PCP=5 have the following weights: ; Calculate the first The absolute value of the average deviation of each time slot; Using time slot weights to weight the original deviation values ​​amplifies the impact of deviations on high-priority traffic. The weighted bias is further adjusted using the absolute value of the bias, so that the time slot with the larger bias dominates in the final result; Summing the numerator and denominator separately, the numerator accumulates the weighted and adjusted bias effects of all time slots, while the denominator represents the total weight. The entire formula calculates a double-weighted (weighted) result regarding time slot bias. and deviation itself The average value of ) The advantage of the formula lies in introducing weights related to the traffic priority index. and combined with deviation The amplitude of the value itself is weighted twice to make the calculated coupling eigenvalues It can extremely sensitively reflect high-priority, large-deviation time slot allocation problems, thereby accurately locating the scheduling defects in VLAN configuration that have the most serious impact on critical services; The specific calculation process is as follows, assuming that three key time slots were analyzed ( ): Parameter assignment: Time slot 1 (k=1), service PCP=4, average deviation ms, weight ; Time slot 2 (k=2), service PCP=5, average deviation ms, weight ; Time slot 3 (k=3), service PCP=4, average deviation ms, weight ; Calculate the total weight: ; Calculate the terms in the molecule: k=1: ; k=2: ; k=3: ; Calculate the sum of the numerators: ; Calculate priority time slot coupling eigenvalues : The result 0.002025 is a quantified coupling characteristic value. Its absolute value represents the severity of the coupling problem. Links in the filter layer with priority slot coupling higher than a preset threshold are selected by comparing the calculated Q value with the preset threshold (e.g., 0.001, which is set by statistically analyzing a large amount of normally operating network data and taking the 95th percentile of its Q value distribution). Since 0.002025 > 0.001, link LN-005 is identified as a high-coupling link. Finally, all similar high-coupling links and their Q values ​​are visualized and labeled on the network topology to establish a priority slot collaborative response spatial distribution map.

[0030] S313: Call the priority slot collaborative response spatial distribution map, cluster the links in the coupling feature value layer that exceed the collaborative identification benchmark, mark the link codes and coordinates corresponding to continuous abnormal areas, and obtain the VLAN configuration performance evaluation data table. The priority slot cooperative response spatial distribution map is invoked. This map, based on the network topology, uses different colors or heatmap intensities to mark the priority slot coupling characteristic value Q on the links. For example, link LN-005 is marked as Q=0.002025. Links in the coupling characteristic value layer that exceed the cooperative identification benchmark are clustered. The cooperative identification benchmark is a preset value, set with reference to the statistical distribution of Q values ​​in historical network operation data. Specifically, by analyzing the Q values ​​collected continuously for 30 days under normal operating conditions, the 95th percentile is taken as the benchmark value. Assuming this benchmark value is 0.001, all links with Q values ​​greater than 0.001 are determined to exceed the cooperative identification benchmark. The clustering operation uses a density-based scanning algorithm (DBSCAN), which identifies links that are topologically adjacent (physical distance less than or equal to 2 hops) and whose Q values ​​all exceed the benchmark. Links are grouped into the same cluster. For example, if links LN-005 (Q=0.002025), LN-001 (Q=0.001850), and LN-008 (Q=0.001500) are topologically adjacent and their Q values ​​are all greater than 0.001, they are clustered into a continuous anomalous region. The link codes and coordinates corresponding to the continuous anomalous region are labeled. For the clusters formed above, all the link codes contained therein, i.e., {LN-005, LN-001, LN-008}, are extracted, and the topological coordinates of each link are recorded, i.e., the unique identifiers of the start and end nodes. For example, the coordinates of LN-005 are "SA01-SB03". The information, including the cluster ID, the list of links within the cluster, the Q value of each link, and the topological coordinates, is integrated and formatted to obtain the VLAN configuration performance evaluation data table.

[0031] Please see Figure 5 The specific steps for obtaining the critical traffic time slot balanced allocation VLAN configuration decision table are as follows: S411: Based on the VLAN configuration performance evaluation data table, extract the link delay distribution curve under the specified number, perform time-unified processing, identify the unit time delay change, and obtain the link delay abnormal change rate set. Based on the VLAN configuration performance evaluation data table, which is generated by clustering and labeling the priority time slot collaborative response spatial distribution map, this data table includes links identified as having configuration performance issues, such as LN-005, and their related parameters. For the specified number, link LN-005, the link delay distribution curve is extracted. This curve records the set of end-to-end delay values ​​for all critical data packets on the link within a unit time, e.g., 1 second. Time unification processing is then performed to ensure that all delay data points are based on a common reference clock, eliminating time deviations between different measurement points and identifying the unit time delay variation. This is accomplished by calculating the first-order difference sequence of the delay data set, i.e., subtracting the delay of the previous data packet from the delay of the next data packet. For example, if the delays of two consecutive critical data packets are 25µs and 45µs respectively, the delay variation is +20µs. This process is performed on all consecutive data packet pairs within a unit time to obtain a time series containing all delay variations, i.e., the link delay abnormal change rate set.

[0032] S412: Based on the set of abnormal link delay change rates, identify the delay distribution curve of the ideal delay stage, compare the current delay change sequence with the reference curve, identify the link delay deviation level, extract and mark the links whose deviation level exceeds the warning upper limit, and obtain the set of links with sudden increase in deviation. Based on the abnormal link latency change rate set, a curve is selected from historical data, corresponding to the latency distribution of similar critical traffic when the network is operating under optimal conditions. This curve has an average latency of 20µs and a standard deviation of 2µs. This curve is used as a reference curve. The current latency change sequence is compared with the reference curve. By calculating the statistical characteristics of the current latency distribution, such as an average of 40µs and a standard deviation of 15µs, and comparing them with the characteristics of the reference curve, the deviation level of link latency is identified. The evaluation criteria for the deviation level are: Level 1 (Normal), average latency difference less than 10µs and standard deviation difference less than 5µs; Level 2 (Attention), average latency difference between 10µs and 20µs or standard deviation difference between 5µs and 10µs; etc. Level 3 (Warning): Average latency difference is between 20µs and 30µs or standard deviation difference is between 10µs and 15µs; Level 4 (Severe): Average latency difference is greater than 30µs or standard deviation difference is greater than 15µs. For link LN-005, its average latency difference is 40 minus 20 equals 20µs, and its standard deviation difference is 15 minus 2 equals 13µs. According to the standard, its latency deviation level is rated as Level 3. Links with deviation levels exceeding the upper warning limit are extracted and marked. The upper warning limit is set to Level 2, that is, all links rated as Level 3 or Level 4 are considered to exceed the upper limit. Since LN-005 is Level 3, it is extracted and marked. All marked links are summarized to obtain the set of links with sudden increase in deviation.

[0033] S413: Based on the set of links with sudden deviations, bind the deviation level value of each link to the position number in the network topology diagram, sort them according to risk level, and output the critical traffic time slot balanced allocation VLAN configuration decision table. The location number in a network topology diagram refers to the unique identifier assigned to each link in the network topology based on its physical or logical location in the network, resulting in a set of location numbers. Risk level sorting refers to sorting each link in the set of links with sudden increases in deviation based on the link delay deviation level and the corresponding network location number, and outputting the sorting results in descending order of risk level; Based on the set of links showing a sudden increase in deviation, which currently includes link LN-005, the location number in the network topology diagram is a unique identifier assigned to each link in the network. For example, link LN-005 connects port P1 of switch SW-A and port P3 of switch SW-B, and its location number is defined as SA01-SB03. The deviation level value 3 of LN-005 is bound to the location number SA01-SB03, forming the record {Link: LN-005, Location: SA01-SB03, Deviation Level: 3}. This record is then sorted according to risk level. This sorting directly... The risk level is determined based on the deviation level value. The higher the value, the higher the risk level. If there are multiple links with the same level, they can be further sorted according to their position in the bottleneck area. Assuming that the deviation surge link set also includes links LN-008 (level 3) and LN-021 (level 4), the sorted list is: [{link: LN-021, level: 4}, {link: LN-005, level: 3}, {link: LN-008, level: 3}]. The sorted list is organized and presented in tabular form, and the critical traffic time slot balanced allocation VLAN configuration decision table is output.

[0034] Please see Figure 6 The specific steps for obtaining the configuration adjustment control command group for abnormal links are as follows: S511: Call the critical traffic time slot balanced allocation VLAN configuration decision table, extract the link number in the network topology function diagram, map the link risk level value with the area coordinate boundary, identify the link information corresponding to the network protection level, and generate a network task risk distribution map. The critical traffic time slot balanced allocation VLAN configuration decision table is invoked. This table lists the links to be adjusted in order of risk level. This function map not only includes physical connections but also marks the service functions carried by each link, such as "control loop A". The link risk level value, i.e., deviation from level 4, is mapped to the area coordinate boundary. Here, the area coordinate is the division of the network on a logical grid. Each grid corresponds to a protection level. For example, the area carrying core control services is defined as the highest protection level 5. Link LN-021 is located in this area. The link information corresponding to the network protection level is identified, i.e., it is confirmed that link LN-021 with risk level 4 has a network area protection level of 5. This information is highlighted on the topology map with a specific color or symbol. For example, link LN-021 is covered with a red block. The color depth of the block is associated with the risk level, generating a network task risk distribution map.

[0035] S512: Based on the network task risk distribution map, extract the optimized response link number and response level, match the link risk level with the optimized response level, identify the link number with insufficient response coverage, and obtain the network task response risk disconnect list. Based on the network task risk distribution map, this map visually displays the risk level of each link and the protection requirements of its respective area. The optimized response is a preset automated network adjustment strategy. For example, a "high" response level corresponds to triggering VLAN reconfiguration and traffic rerouting, while a "medium" response level corresponds to adjusting queue scheduling weights. By querying the network policy library, it was found that for the area with protection level 5, the preset optimized response is "high," covering all links in that area, matching the link risk level with the optimized response level. For link LN-021, its risk level is 4, and its area (protection level 5) has already been matched with a "high" level. The optimized response is therefore sufficient. Next, analysis is performed on the next risky link, LN-005, whose risk level is 3 and its area protection level is 4. The optimized response for protection level 4 areas in the policy library is "medium," so this match is also sufficient. Assuming another link, LN-035, has a risk level of 3, but its area protection level is 2, the corresponding optimized response is "low," only issuing an alarm. In this case, the actual risk of the link (level 3) is higher than its available response capability (low), indicating a disconnect. The link number with insufficient response coverage is identified, and LN-035 is recorded, resulting in a network task response risk disconnect list.

[0036] S513: Based on the network task response risk disconnect list and the level number in the network protection priority sequence, extract the key link number that needs to improve the response coverage, output the adjustment control parameters linked with the optimized response links in sequence, and output the configuration adjustment control command group for abnormal links. Based on the network task response risk disconnect list, which includes links with insufficient response such as LN-035, the network protection priority sequence is an ordered list defining protection levels from highest to lowest, for example, [Level 5, Level 4, Level 3]. The link with the highest protection level requirement is selected from the list for priority processing. Assuming LN-035 is the link with the highest protection level requirement in the list, it is selected as the primary adjustment target. Adjustment control parameters linked to the optimized response links are output sequentially. This operation aims to alleviate the pressure on the target link by adjusting the configuration of adjacent or related links. Topology queries reveal... Traffic flowing to LN-035 mainly originates from link LN-034, and the current optimized response level of LN-034 is configured as "low". To improve the response coverage of LN-035, an adjustment command is generated to upgrade the response level of LN-034 to "medium". This operation involves modifying the scheduling policy of traffic sent to LN-035 on the LN-034 egress switch, such as increasing the bandwidth guarantee of relevant queues. Specific parameters, such as queue ID and bandwidth value, constitute the adjustment control parameters. The parameters are formatted into a command-line script that can be executed by the device, and the configuration adjustment control command group for abnormal links is output.

[0037] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for configuring a time-sensitive network (VLAN) that supports deterministic delay, characterized in that, Includes the following steps: S1: Based on the traffic classification rules of time-sensitive networks, extract key data flow paths and link load distribution in the network, identify latency surge intervals and bandwidth utilization anomalies on the time axis, and generate a link status anomaly identification dataset by combining link number and topology boundary information. S2: Based on the link status anomaly identification dataset, extract the link load distribution curve and bandwidth utilization trend map, analyze the matching consistency between the two at the link junction, filter links with unbalanced load and abnormal delay, and form the link bottleneck area identification result. S3: Based on the link bottleneck area identification results, extract the traffic priority distribution matrix and time slot allocation characteristics within the link, analyze priority conflicts and time slot allocation deviations, filter and match abnormal links, and obtain the VLAN configuration performance evaluation data table. S4: Based on the VLAN configuration performance evaluation data table, analyze the delay distribution trend in the network topology corresponding to the link, evaluate the degree of deviation from the ideal delay model, mark the abnormal level of the link according to the deviation magnitude, and output the VLAN configuration decision table for balanced allocation of key traffic time slots.

2. The time-sensitive network VLAN configuration method supporting deterministic delay according to claim 1, characterized in that, The link status anomaly identification dataset includes link numbers with sudden delay increases, abnormal bandwidth utilization points, link coordinate markers, and time series anomaly identifiers. The link bottleneck area identification results include link identifiers with abnormal delays, link boundary connectivity units, and link boundary consistency blocks. The VLAN configuration performance evaluation data table includes priority conflicting continuous links, time slot allocation deviation areas, load mutation overlap areas, and configuration anomaly link numbers. The critical traffic time slot balanced allocation VLAN configuration decision table includes risk level labels, link response deviation values, local delay anomaly indicators, and bandwidth offset levels.

3. The time-sensitive network VLAN configuration method supporting deterministic delay according to claim 1, characterized in that, The specific steps for obtaining the link status anomaly identification dataset are as follows: S111: Based on the traffic classification rules of time-sensitive networks, extract the key data flow paths and link load distribution curves within the link, compare and analyze the two types of data within the same link, and obtain the trend value of the difference between link load and delay distribution. S112: Based on the trend value of the difference between the link load and the delay distribution, identify the sudden increase in the link load and the fluctuation value of the delay distribution curve, superimpose the two types of values ​​on time periods, extract the time intervals in which the sudden increase exceeds the baseline value and the fluctuation exceeds the set threshold, and generate a set of high-frequency abnormal interval time periods. The benchmark value is defined through statistical analysis of raw link load data or by setting a network performance threshold. S113: For the set of high-frequency abnormal interval time periods, match the corresponding link number and topology boundary information, extract the link location where the signal occurred, and generate a link status abnormality identification dataset.

4. The time-sensitive network VLAN configuration method supporting deterministic delay according to claim 3, characterized in that, The specific steps for obtaining the link bottleneck region identification results are as follows: S211: Based on the link status anomaly identification dataset, extract the load distribution curve and bandwidth utilization trend map of the link, extract the projection trajectory of the two at the link boundary, identify the distribution number and aggregation degree of the boundary point in the link, and obtain the link boundary consistency map. S212: Based on the link boundary consistency map, filter the boundary areas with a aggregation degree higher than the average level, compare the spatial boundaries of the network topology map, identify continuous boundary clusters belonging to the same link, and obtain the bottleneck area division within the link. S213: Invoke the bottleneck region division within the aforementioned link, and perform integrated analysis on link boundary consistency, load distribution dispersion, bandwidth utilization balance, and link latency, using the following formula: ； Calculate the link performance anomaly coefficient, perform link matching based on the response blocks in the layer, and form the link bottleneck area identification result; in, Represents the link performance anomaly coefficient. Represents the number of link segments. This represents the response block value of the i-th link segment. This represents the average response block size of a link segment. This represents the load value of the i-th link segment. This represents the bandwidth value of the i-th link segment.

5. The time-sensitive network VLAN configuration method supporting deterministic delay according to claim 4, characterized in that, The specific steps for obtaining the VLAN configuration performance evaluation data table are as follows: S311: Based on the link bottleneck area identification results, extract the traffic priority distribution matrix and time slot allocation features of the numbered links in the layer, align the data in the link with timestamps, identify the priority conflict intensity and time slot allocation deviation, and obtain the network local abnormal response feature set. S312: Based on the network local anomaly response feature set, perform joint analysis on priority conflicts and time slot allocation deviations within the link, calculate priority time slot coupling feature values, filter links with priority time slot coupling in the layer, and establish a priority time slot collaborative response spatial distribution map. S313: Call the priority time slot collaborative response spatial distribution map, cluster the links in the coupling feature value layer that exceed the collaborative identification benchmark, mark the link codes and coordinates corresponding to the continuous abnormal areas, and obtain the VLAN configuration performance evaluation data table.

6. The time-sensitive network VLAN configuration method supporting deterministic delay according to claim 5, characterized in that, The specific steps for obtaining the critical traffic time slot balanced allocation VLAN configuration decision table are as follows: S411: Based on the VLAN configuration performance evaluation data table, extract the link delay distribution curve under the specified number, perform time uniform processing, identify the unit time delay change, and obtain the link delay abnormal change rate set. S412: Based on the set of abnormal link delay change rates, identify the delay distribution curve of the ideal delay stage, compare the current delay change sequence with the reference curve, identify the link delay deviation level, extract and mark the links whose deviation level exceeds the warning upper limit, and obtain the set of links with sudden increase in deviation. S413: Based on the set of links with sudden deviations, bind the deviation level value of each link to the position number in the network topology diagram, sort them according to risk level, and output a critical traffic time slot balanced allocation VLAN configuration decision table.

7. The time-sensitive network VLAN configuration method supporting deterministic delay according to claim 6, characterized in that, The location number in the network topology diagram refers to the unique identifier assigned to each link in the network topology based on the link’s physical or logical location in the network, resulting in a set of location numbers. The risk level sorting refers to sorting each link in the set of links with sudden increases in deviation according to the link delay deviation level and the corresponding network location number, and outputting the sorting results in descending order of risk level.

8. The time-sensitive network VLAN configuration method supporting deterministic delay according to claim 1, characterized in that, The method also includes step S5: S5: Call the critical traffic time slot balanced allocation VLAN configuration decision table, identify the corresponding number of the link in the network topology function diagram, extract the optimized response link list, compare the response level with the network protection priority sequence, filter the link numbers that need to adjust the response coverage, and output the configuration adjustment control command group for abnormal links. The network protection priority sequence refers to the ranking of links in the network based on factors such as stability, load capacity, and fault recovery capability, according to their reliability, priority, and quality of service requirements. The configuration adjustment control command group for abnormal links includes adjusting the target link number, response level adjustment parameters, protection priority comparison items, and linkage response trigger types.

9. The time-sensitive network VLAN configuration method supporting deterministic delay according to claim 8, characterized in that, The specific steps for obtaining the configuration adjustment control command group for abnormal links are as follows: S511: Call the critical traffic time slot balanced allocation VLAN configuration decision table, extract the link number in the network topology function diagram, map the link risk level value with the area coordinate boundary, identify the link information corresponding to the network protection level, and generate a network task risk distribution map. S512: Based on the network task risk distribution map, extract the optimized response link number and response level, match the link risk level with the optimized response level, identify the link number with insufficient response coverage, and obtain the network task response risk disconnect list. S513: Based on the network task response risk disconnect list, extract the key link numbers that need to improve response coverage according to the level number in the network protection priority sequence, output the adjustment control parameters linked with the optimized response links in sequence, and output the configuration adjustment control command group for abnormal links.

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