Algorithm service system and method for optical transport network

Abstract

The invention relates to the technical field of optical communication, in particular to an algorithm service system and method for an optical transport network, and the system comprises an optical network sensing module which is used for collecting optical layer performance parameters, service flow data and node equipment operation states of the optical transport network; the deep algorithm decision center predicts service traffic based on a space-time diagram convolutional network, and optimizes routing wavelength allocation through reinforcement learning; the strategy adaptation module is used for dynamically generating an operation and maintenance strategy and an energy-saving scheduling instruction according to the prediction result and the optimization scheme; the risk assessment unit is used for assessing a link fault risk, a service interruption risk and a network congestion risk in real time, quantifying a risk weight through an analytic hierarchy process, and optimizing an operation and maintenance strategy in combination with historical fault data; and the intelligent resource reconstruction unit is used for performing optical channel reconstruction according to the optimized operation and maintenance strategy, dynamically allocating optical layer resources or switching redundant transmission paths. Therefore, the problems of poor strategy adaptability, low resource scheduling efficiency, insufficient fault-tolerant capability and the like in the prior art are solved.

Description

Technical Field

[0001] The present invention relates to the field of optical communication technology, and in particular to an algorithm service system and method for an optical transport network. Background Art

[0002] As the core infrastructure of modern communications networks, the optical transport network (OTN) undertakes the critical task of transmitting massive amounts of data across regions. Its operational efficiency and reliability are directly related to the continuity and quality of communication services. During its long-term service life, the impact of factors such as the surge in network traffic, the diversification of service types (such as high-definition video and the Industrial Internet), dynamic topology adjustments, and complex electromagnetic environments can easily lead to problems such as lagging routing optimization, unbalanced resource allocation, and slow fault location. These problems increase transmission latency, reduce bandwidth utilization, and even cause regional communication interruptions. The rapid evolution of emerging technologies such as 5G-A and computing networks has raised the bar for OTNs in terms of dynamic bandwidth adjustment, millisecond-level fault recovery, cross-domain resource collaboration, and energy efficiency optimization. There is an urgent need for an algorithmic service system that can perceive the multi-dimensional network status in real time, intelligently adapt to service needs, and efficiently schedule network-wide resources to support the high-performance operation of OTNs in complex scenarios.

[0003] However, traditional optical transport network algorithm services have inherent limitations: routing and resource scheduling strategies are rigid and rely heavily on static topologies and preset rules. They lack deep integration of multi-source information such as real-time traffic characteristics, service priorities, and physical layer parameters (such as optical signal-to-noise ratio and dispersion), making it difficult to adapt to dynamically changing network conditions, resulting in low resource utilization and high service blocking rates. Algorithms and hardware lack coordination, and hardware constraints such as the nonlinear characteristics of optical layer equipment and electrical layer processing delays are not fully considered. Signal transmission errors and equipment performance drift further reduce algorithm execution accuracy. Energy management mechanisms are extensive and lack energy efficiency optimization strategies based on dynamic adjustment of service loads. Redundant energy consumption accounts for a high proportion and easily accelerates equipment aging. In addition, the fault handling mode is simple, mostly using local path recalculation, and there is a lack of cross-domain collaborative recovery mechanisms. Single node or link failures can easily trigger chain reactions, and the average annual failure impact duration far exceeds the stringent requirements of communication networks for high availability. The overall technology faces multiple challenges: poor policy adaptability, low resource scheduling efficiency, and insufficient fault tolerance. Summary of the Invention

[0004] The present application provides an algorithm service system and method for an optical transport network to solve the problems of poor policy adaptability, low resource scheduling efficiency and insufficient fault tolerance in the prior art.

[0005] The first aspect of the present application provides an algorithm service system for an optical transport network, including: an optical network perception module, a deep algorithm decision center, a policy adaptation module, a risk prediction unit, and an intelligent resource reconstruction unit; wherein, the optical network perception module is used to collect optical layer performance parameters, service flow data and node equipment operating status of the optical transport network; the deep algorithm decision center predicts service flow based on a spatiotemporal graph convolutional network, and optimizes routing wavelength allocation through reinforcement learning; the policy adaptation module is used to dynamically generate operation and maintenance strategies and energy-saving scheduling instructions based on prediction results and optimization plans; the risk assessment unit is used to evaluate link failure risks, service interruption risks and network congestion risks in real time, quantify risk weights through a hierarchical analysis method, and optimize the operation and maintenance strategies in combination with historical failure data; the intelligent resource reconstruction unit is used to reconstruct optical channels according to the optimized operation and maintenance strategies, dynamically allocate optical layer resources or switch redundant transmission paths.

[0006] Preferably, the optical network perception module includes an optical layer parameter collection unit, a service flow perception unit, and a status monitoring module, wherein the optical layer parameter collection unit is used to collect in real time the optical power fluctuation, signal-to-noise ratio degradation, polarization mode dispersion value and dispersion compensation margin optical layer performance parameters of the core link of the optical transport network; the service flow perception unit is used to collect the time slot occupancy, real-time bandwidth requirements and service priority labels of each wavelength channel; the status monitoring module is used to collect in real time the equipment operating temperature, port bit error rate, power supply voltage and fan speed status parameters.

[0007] Preferably, the deep algorithm decision center includes a traffic prediction unit and a routing wavelength optimization unit, wherein the traffic prediction unit adopts the attention mechanism in the spatiotemporal graph convolutional network to weight the importance of different links and predict the traffic peak, duration and fluctuation range of each wavelength channel; the routing wavelength optimization unit is based on the PPO algorithm, with minimizing end-to-end delay and maximizing spectrum efficiency as the objective function, and dynamically generates the target routing path and wavelength allocation plan.

[0008] Preferably, the policy adaptation module includes an operation and maintenance strategy generation unit and an energy-saving scheduling instruction generation unit. The wavelength adjustment strategy generation unit is used to generate a wavelength resource allocation operation and maintenance strategy based on traffic prediction results and routing optimization plans; the energy-saving scheduling instruction generation unit is used to generate targeted energy-saving instructions based on link load prediction and equipment operating status.

[0009] Preferably, the risk prediction unit includes a risk identification unit, a risk weight quantification unit, and a strategy optimization unit, wherein the risk identification unit is used to accurately capture early signals of three types of risks from different levels of the optical transport network, identify and evaluate risks; the risk weight quantification unit is used to convert the risk impact degree into quantifiable weights through the hierarchical analysis method, and perform strategy priority sorting; the strategy optimization unit is used to dynamically optimize the operation and maintenance strategy based on the risk weights and risk assessment results.

[0010] Preferably, the intelligent resource reconstruction module includes an optical channel reconstruction execution unit, an optical layer resource dynamic allocation unit and a transmission path switching unit, wherein the optical channel reconstruction execution unit is used to convert the optimization strategy into a physical reconstruction operation of the optical channel, and dynamically adjust the routing and spectrum resources; the optical layer resource dynamic allocation unit accurately schedules the optical layer resources based on the optimization strategy; the transmission path switching unit is used to quickly switch to a redundant path when a link fails or is congested.

[0011] A second aspect of the present application provides an algorithm service method for an optical transport network, including: obtaining optical power, optical signal-to-noise ratio, service flow and node operating status data of the optical transport network; performing correlation analysis on the optical power, optical signal-to-noise ratio, service flow and node operating status data of the optical transport network to generate a network status panoramic data set; based on the network status panoramic data set, predicting bandwidth service flow requirements according to a spatiotemporal graph convolutional network model, optimizing routing wavelength allocation through a PPO algorithm, and obtaining prediction results and an optimization scheme; based on the prediction results, combining real-time monitoring data with the optimization scheme, dynamically adjusting the modulation format and FEC level, generating an operation and maintenance strategy and an optical layer energy-saving instruction, quantifying the weights of link failures, service interruptions and congestion risks through a hierarchical analysis method, and optimizing the operation and maintenance strategy and the optical layer energy-saving instruction in combination with a historical fault library; when OSNR degradation or link interruption is detected, isolating the faulty channel through a ROADM optical cross-connector and switching to a low-latency redundant path according to the optimized operation and maintenance strategy and optical layer energy-saving instruction.

[0012] The third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and runnable on the processor. The processor executes the program to implement an algorithm service method for an optical transport network as described in the above embodiment.

[0013] The fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement an algorithm service method for an optical transport network as described in the above embodiment.

[0014] The fifth aspect of the present application provides a computer program product, including a computer program or instructions, for implementing an algorithm service method for an optical transport network as described in the above embodiment.

[0015] Therefore, this application has the following beneficial effects: The embodiment of the present application uses the optical network perception module to collect optical layer performance, service traffic and equipment status data in full dimensions and in real time, providing accurate data for decision-making; the deep algorithm decision-making center relies on the traffic prediction ability of the spatiotemporal graph convolutional network and the dynamic optimization characteristics of reinforcement learning to improve the timeliness and adaptability of routing wavelength allocation and improve resource utilization; the policy adaptation module generates dynamic operation and maintenance strategies and energy-saving scheduling instructions based on the prediction results, which can intelligently regulate energy consumption according to business load fluctuations and reduce redundant energy consumption; the risk assessment unit quantifies multi-dimensional risks through the hierarchical analysis method and integrates historical data optimization strategies to improve fault warning accuracy and shorten risk response time; the intelligent resource reconstruction unit is used for rapid reconstruction of optical channels and switching of redundant paths, and cooperates with the dynamic resource allocation mechanism to shorten service interruption recovery time, reduce congestion rate and fault impact range, and comprehensively improve the dynamic adaptability, resource scheduling efficiency and risk resistance level of the optical transmission network under complex working conditions. In this way, the problems of poor policy adaptability, low resource scheduling efficiency and insufficient fault tolerance in the existing technology are solved.

[0016] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which: Figure 1 A schematic diagram of the structure of an algorithm service system for an optical transport network provided according to an embodiment of the present application; Figure 2 A schematic diagram of an optical network sensing module provided according to one embodiment of the present application; Figure 3 A schematic diagram of a deep algorithm decision center provided according to one embodiment of the present application; Figure 4 A schematic diagram of a policy adaptation module provided according to an embodiment of the present application; Figure 5 A schematic diagram of a risk prediction unit provided according to one embodiment of the present application; Figure 6 A schematic diagram of an intelligent resource reconstruction unit provided according to an embodiment of the present application; Figure 7A schematic diagram of an algorithm service system for an optical transport network provided according to an embodiment of the present application; Figure 8 A flowchart of an algorithm service method for an optical transport network provided according to one embodiment of the present application; Figure 9 A schematic diagram of an algorithm service method for an optical transport network provided according to one embodiment of the present application; Figure 10 A schematic diagram of the structure of an electronic device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0019] The following describes the algorithm service system and method for optical transport network of the embodiment of the present application with reference to the accompanying drawings. In response to the problem of low response speed mentioned in the above background technology, the present application provides an algorithm service system for optical transport network, in which the optical layer performance, service flow and equipment status data are collected in real time in all dimensions through the optical network perception module to provide accurate data for decision-making; the deep algorithm decision-making center relies on the traffic prediction ability of the spatiotemporal graph convolutional network and the dynamic optimization characteristics of reinforcement learning to improve the timeliness and adaptability of routing wavelength allocation and improve resource utilization; the strategy adaptation module generates dynamic operation and maintenance strategies and energy-saving scheduling instructions based on the prediction results, which can intelligently regulate energy consumption according to business load fluctuations and reduce redundant energy consumption; the risk assessment unit quantifies multi-dimensional risks through hierarchical analysis and integrates historical data optimization strategies to improve the accuracy of fault warning and shorten risk response time; the intelligent resource reconstruction unit is used for rapid reconstruction of optical channels and switching of redundant paths, and cooperates with the dynamic resource allocation mechanism to shorten the service interruption recovery time, reduce the congestion rate and the scope of fault impact, and comprehensively improve the dynamic adaptability, resource scheduling efficiency and risk resistance level of the optical transport network under complex working conditions. This solves the problems of poor policy adaptability, low resource scheduling efficiency and insufficient fault tolerance in the existing technology.

[0020] Figure 1 A schematic diagram of the structure of an algorithm service system for an optical transport network provided in an embodiment of the present application.

[0021] The embodiment of the present application provides an algorithm service system for an optical transport network. The system 10 includes: Optical network perception module 100, deep algorithm decision center 200, strategy adaptation module 300, risk prediction unit 400, intelligent resource reconstruction unit 500.

[0022] Among them, the optical network perception module 100 is used to collect the optical layer performance parameters, service flow data and node equipment operating status of the optical transmission network; the deep algorithm decision center 200 predicts service flow based on the spatiotemporal graph convolutional network, and optimizes routing wavelength allocation through reinforcement learning; the policy adaptation module 300 is used to dynamically generate operation and maintenance strategies and energy-saving scheduling instructions based on the prediction results and optimization plans; the risk assessment unit 400 is used to evaluate the link failure risk, service interruption risk and network congestion risk in real time, quantify the risk weight through the hierarchical analysis method, and optimize the operation and maintenance strategy in combination with historical failure data; the intelligent resource reconstruction unit 500 is used to reconstruct the optical channel according to the optimized operation and maintenance strategy, dynamically allocate optical layer resources or switch redundant transmission paths.

[0023] It can be understood that in the embodiment of the present application, the optical network perception module collects optical layer performance, service traffic and equipment status data in real time in all dimensions to provide accurate data for decision-making; the deep algorithm decision-making center relies on the traffic prediction ability of the spatiotemporal graph convolutional network and the dynamic optimization characteristics of reinforcement learning to improve the timeliness and adaptability of routing wavelength allocation and improve resource utilization; the policy adaptation module generates dynamic operation and maintenance strategies and energy-saving scheduling instructions based on the prediction results, which can intelligently regulate energy consumption according to business load fluctuations and reduce redundant energy consumption; the risk assessment unit quantifies multi-dimensional risks through the hierarchical analysis method and integrates historical data optimization strategies to improve fault warning accuracy and shorten risk response time; the intelligent resource reconstruction unit is used for rapid reconstruction of optical channels and switching of redundant paths, and cooperates with the dynamic resource allocation mechanism to shorten service interruption recovery time, reduce congestion rate and fault impact range, and comprehensively improve the dynamic adaptability, resource scheduling efficiency and risk resistance level of the optical transmission network under complex working conditions. In this way, the problems of poor policy adaptability, low resource scheduling efficiency and insufficient fault tolerance in the existing technology are solved.

[0024] In the embodiment of the present application, the optical network sensing module 100 includes: Figure 2 As shown, there are optical layer parameter collection unit, service traffic perception unit, and status monitoring module.

[0025] Among them, the optical layer parameter collection unit is used to collect the optical power fluctuation, signal-to-noise ratio degradation, polarization mode dispersion value and dispersion compensation margin optical layer performance parameters of the core link of the optical transport network in real time; the service traffic perception unit is used to collect the time slot occupancy rate, real-time bandwidth demand and service priority label of each wavelength channel; the status monitoring module is used to collect the equipment operating temperature, port bit error rate, power supply voltage and fan speed status parameters in real time.

[0026] It can be understood that the embodiment of the present application captures the key performance parameters of the core link in real time through the optical layer parameter collection unit, providing a basis for path quality evaluation; the service traffic perception unit accurately grasps the service status of each wavelength channel, so that resources can be allocated on demand; the status monitoring module tracks the equipment operation status in real time and detects anomalies in time, providing comprehensive and accurate multi-dimensional data for deep algorithm decision-making and strategy adaptation, ensuring the scientific nature of decision-making and the effectiveness of strategies, and improving the overall response speed and adaptability of the system.

[0027] For example, in a long-haul trunk optical transport network, the optical layer parameter collection unit continuously collects parameters such as optical power fluctuation, signal-to-noise ratio degradation, polarization mode dispersion (PMD), and dispersion compensation margin across a core link comprising 50 fiber optic cable segments and 30 optical amplifier sites at 10ms intervals. While monitoring a 100G WDM link passing through a mountainous area prone to thunderstorms, the unit observed a signal-to-noise ratio drop from 25dB to 20dB (with a threshold of 18dB) within 10 minutes. Furthermore, the simultaneously collected PMD value increased from 1.2ps to 3ps, and the dispersion compensation margin decreased from 80ps / nm to 50ps / nm. The optical layer parameter collection unit immediately packages this real-time data and synchronizes it to the deep learning algorithm decision-making center via a 5G bearer channel. The decision-making center, combining the service tags carried by the link, such as government cloud interconnection (highest priority) and cross-border e-commerce data (second-highest priority), quickly triggered a routing recalculation mechanism. Within 0.5 seconds, it screened three candidate paths from 12 pre-stored backup paths, meeting OSNR requirements of ≥ 22dB and latency increase of ≤ 5ms. Simultaneously, it pushed a performance degradation trend curve, including the slope of parameter changes over the past hour, to the risk assessment unit. The risk assessment unit, drawing on a database of link failures in the region during thunderstorms over the past three years, used the analytic hierarchy process to calculate a 72% probability of the link's bit error rate jumping to 1e-6 within two hours. The unit then generated an optical amplifier power compensation command, increasing the EDFA output power of the link from 17dBm to 19dBm and simultaneously activating the backup pump modules at two adjacent sites. After 15 minutes of dynamic adjustment, the link signal-to-noise ratio rebounded to 22dB, and the polarization mode dispersion value stabilized at 2.5ps, successfully avoiding service transmission interruption caused by continuous parameter degradation and ensuring the stable transmission of an average daily data traffic of 1.2Tbit / s. The uninterrupted operation of the real-time interactive service of the government cloud provides key support for cross-regional government collaboration.

[0028] In the embodiment of the present application, the deep algorithm decision center 200 includes: Figure 3 As shown, there are traffic prediction unit and routing wavelength optimization unit.

[0029] Among them, the traffic prediction unit uses the attention mechanism in the spatiotemporal graph convolutional network to weight the importance of different links and predict the traffic peak, duration and fluctuation range of each wavelength channel; the routing wavelength optimization unit is based on the PPO algorithm, with minimizing end-to-end delay and maximizing spectrum efficiency as the objective function, and dynamically generates the target routing path and wavelength allocation plan.

[0030] It can be understood that the traffic prediction unit of the embodiment of the present application accurately weights the importance of different links through the attention mechanism in the spatiotemporal graph convolutional network, focuses on core links and key services, accurately predicts the traffic peak, duration and fluctuation range of each wavelength channel, provides targeted data for subsequent resource scheduling, and avoids blind resource allocation; the routing wavelength optimization unit is based on the PPO algorithm, with minimizing end-to-end delay and maximizing spectrum efficiency as the objective function, and can dynamically generate target routing paths and wavelength allocation plans that adapt to real-time traffic changes, and perform fine matching and efficient utilization of resources, so that the optical transport network can respond more sensitively to business traffic fluctuations, reduce business blocking rate, improve spectrum resource utilization and data transmission efficiency, and reduce resource waste.

[0031] It should be noted that the specific weighted formula of the attention mechanism is:

[0032] in, To predict the importance ratio of target i to link j; To predict the similarity between target i and link j; is the matching score between the predicted target i and all candidate links k; is the total number of links; is an exponential function; k is the index identifier of all candidate links; Identify the target to be predicted; Identifies a single link.

[0033] PPO algorithm formula:

[0034]

[0035] in, is the loss function of the policy network; is the expected value at time step t; is the strategy ratio; is the advantage function; clip is the clipping function; is the clipping threshold; are the parameters of the policy network; For immediate rewards; is the delay penalty weight; is the end-to-end delay; is the spectrum efficiency reward weight; is the spectrum efficiency.

[0036] For example, in a 100G OTN provincial and city integrated optical transport network of a certain power grid, the routing wavelength optimization unit is deeply integrated into the network architecture: based on the PPO algorithm, with minimizing the end-to-end delay and maximizing the spectrum efficiency as the objective function, it can real-time sense the traffic fluctuations and priority differences of services such as "GE→10GE channel upgrade" and "digital twin substation" in the integrated data network. When the digital twin service of a substation in a certain city suddenly occurs (the traffic peak increases by 3 times in a short time), the unit quickly traverses the redundant routes of the "mesh" network, combines the spectrum occupancy status of each link, dynamically filters out the physical path with the optimal delay and idle spectrum, and allocates adjustable wavelength resources for this service - by constraining the update amplitude of the algorithm strategy (to avoid link resource congestion), it not only reduces the end-to-end delay of this service by 30% (meeting the real-time interaction requirements of digital twins), but also improves the resource utilization rate by 15% through spectrum reuse technology (supporting multi-service concurrency of 30 sites and 17 access points in the whole province). This refined routing and wavelength collaborative optimization not only ensures the stable bearing of core services such as power dispatching and video monitoring, but also endows the optical transport network with the intelligent response ability of "perception - decision - adaptation", laying a solid optical network foundation for the AI upgrade of the new power system.

[0037] In the embodiment of the present application, the policy adaptation module 300 includes: as Figure 4 shown, an operation and maintenance policy generation unit and an energy-saving scheduling instruction generation unit.

[0038] Among them, the wavelength adjustment policy generation unit is used to generate a wavelength resource allocation and operation and maintenance policy according to the traffic prediction result and the routing optimization plan; the energy-saving scheduling instruction generation unit is used to generate a targeted energy-saving instruction based on the link load prediction and the device operation state.

[0039] It can be understood that the operation and maintenance policy generation unit in the embodiment of the present application accurately generates a wavelength resource allocation and operation and maintenance policy according to the traffic prediction result and the routing optimization plan, and ensures the stable bearing of services in the traffic fluctuation scenario by dynamically adapting the wavelength resources; the energy-saving scheduling instruction generation unit outputs a targeted energy-saving instruction based on the link load prediction and the device operation state, dynamically regulates the device power consumption, effectively reduces the network energy consumption, improves the response resilience of the optical transport network to service changes, and realizes the two-way empowerment of operation and maintenance intelligence and energy efficiency optimization.

[0040] It should be noted that the operation and maintenance strategy generation unit accurately generates wavelength resource allocation operation and maintenance strategies based on traffic prediction results and routing optimization plans. By dynamically adapting wavelength resources, it ensures stable service carrying in traffic fluctuation scenarios. By combining the peak fluctuations of traffic prediction, service priority and timing characteristics, and the path delay and bandwidth bottleneck constraints in the routing optimization plan, a three-level allocation strategy is generated: pre-allocate continuous wavelengths at the optimal route and aggregate scattered spectrum, map high-priority services to dedicated wavelengths, and include low-priority services in a shared wavelength pool. When encountering traffic thresholds, conflicts are resolved through service migration and spectrum fragmentation consolidation, thereby achieving dynamic adaptation of wavelength resources, ensuring stable service carrying and improving spectrum utilization.

[0041] The energy-saving scheduling command generation unit outputs targeted energy-saving commands based on link load predictions and device operating status, dynamically adjusting device power consumption. Using a link load prediction model, it predicts the load range for each link and collects real-time device operating status data to construct a load-device status correlation matrix. Tiered energy-saving commands are generated for different load scenarios and device status combinations. For example, when the predicted link load remains below a threshold (e.g., 40%) and the device temperature is within the safe range (<50°C), the optical amplifier performs a "stepped power reduction" (reducing power by 0.5 dBm every 15 minutes, down to 60% of rated power). This also triggers "intermittent sleep mode" for switch ports on non-core nodes (sleeping for 30 seconds every hour and quickly restoring connectivity upon wakeup). If the device operating status indicates that a backup power module has been unloaded for four consecutive hours, a "module power-off command" is automatically generated and the wake-up trigger condition is recorded (immediate restart if the main power module load exceeds 90%). Furthermore, when the load is predicted to rebound (e.g., reaching 70% within 30 minutes), a "pre-wake-up command" is sent 10 minutes in advance to gradually adjust the device power consumption back to the rated level to avoid current surges caused by sudden startup.

[0042] The core algorithm formula of the link load prediction model is:

[0043] in, is the time series after d-order difference; is a constant term; is the autoregressive coefficient; is the link load value at time t; is the differential load value at time i; is the moving average coefficient; is the white noise error at time j; is the white noise at the current moment; is the autoregressive order; is the moving average order; i is the link load data at time i in the past; j is the prediction error value at time j in the past.

[0044] For example, the energy-saving scheduling command generation unit of a metropolitan area network backbone transmission network used a predictive model to predict that the core link load would drop below 30% between 2:00 AM and 6:00 AM. Simultaneously, real-time monitoring detected that the optical amplifier output power was 8dBm (rated at 10dBm), the switch backup port utilization remained below 5%, and the equipment temperature remained stable at 42°C. Based on this information, the unit generated three levels of energy-saving commands: 1. The optical amplifier power was reduced by 0.3dBm every 30 minutes (to a minimum of 5dBm); 2. The backup ports were triggered to enter a "15-minute sleep + 5-minute wake-up" cycle; 3. The redundant power supply modules were powered off after six consecutive hours of no load. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 29. 29. 29. 29. 30. 31. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. 52. 53. 54. 55. 56. 57. 58. 59. 60. 61. 61.

[0045] In the embodiment of the present application, the risk prediction unit 400 includes: Figure 5 As shown, there are risk identification unit, risk weight quantification unit, and strategy optimization unit.

[0046] Among them, the risk identification unit is used to accurately capture the early signals of three types of risks from different levels of the optical transport network, identify and assess risks; the risk weight quantification unit is used to convert the risk impact degree into quantifiable weights through the hierarchical analysis method, and perform strategy priority sorting; the strategy optimization unit is used to dynamically optimize the operation and maintenance strategy based on the risk weights and risk assessment results.

[0047] It is understandable that the embodiment of the present application deeply penetrates the physical layer, data link layer, and network layer of the optical transport network through the risk identification unit, accurately captures the early signals of three types of risks: equipment failure, bandwidth congestion, and protocol conflict, and evaluates risks in combination with historical failure cases; the risk weight quantification unit uses the hierarchical analysis method to convert the qualitative indicators of service interruption duration, repair cost, and impact on user scale into quantitative weights in the range of 0-1 to complete the priority sorting of operation and maintenance strategies; the strategy optimization unit dynamically adjusts the operation and maintenance strategy based on the risk weight and evaluation results. By capturing early risk signals, the fault response time is shortened, the blindness of strategy execution is avoided, and the stability of network operation and operation and maintenance efficiency are improved.

[0048] It should be noted that the formula of the hierarchical analysis method is:

[0049]

[0050]

[0051]

[0052] in, is the judgment matrix; is the total number of risk factors; is the matrix element, the importance score of the i-th risk factor relative to the j-th factor; is a symmetrical element; To maintain matrix reciprocity; are diagonal elements; For the same risk factor, the comparison is always 1; It is the consistency test benchmark of the judgment matrix; To store the weight vector of each risk factor; is the quantitative weight of the i-th risk factor; The sum of all weights is 1; is the consistency indicator; is the consistency ratio; is a random consistency indicator.

[0053] The risk identification unit builds an early warning system for optical transport network risks through cross-level three-dimensional monitoring and historical case correlation: at the physical layer, it collects parameters such as fiber attenuation, optical amplifier noise figure, and equipment temperature in real time to capture the budding signals of equipment failures such as optical module aging and fiber micro-bending; at the data link layer, it tracks frame loss rate, bandwidth utilization and traffic burst characteristics to identify precursors of bandwidth congestion; at the network layer, it monitors sudden changes in routing hop counts, OSPF protocol neighbor relationship oscillations, abnormal BGP routing prefix aggregation, etc., to identify protocol conflict anomalies and assess risks based on historical failure cases.

[0054] For example, during the operation of a provincial backbone optical transport network, the risk identification unit simultaneously conducted dynamic monitoring across the physical, data link, and network layers. The physical layer detected a sudden increase in the noise figure of a core node optical amplifier from 0.8dB to 1.4dB (exceeding the 0.5dB threshold), immediately matching it with a record in the historical case library for a similar mutation that caused a module failure within four hours and triggering an alert. The data link layer simultaneously detected a jump in the frame loss rate of a service link from 0.05% to 0.8%, and a sustained 92% bandwidth utilization rate exceeding the 85% warning level, identifying bandwidth congestion risks. The network layer detected an abnormal increase in OSPF route hops from 5 to 8, with neighbor relationships fluctuating six times in an hour, identifying protocol conflict risks. The unit further used a topology association algorithm, combined with the three backbone links connected to the optical amplifier and the dependencies between the government cloud and financial services it carried, to predict an 82% probability that the equipment failure would spread to two adjacent multiplexers within three hours, causing regional service disruption. Ultimately, the unit simultaneously output early signals and diffusion assessment results for the three types of risks, providing a precise basis for subsequent weight quantification and policy response.

[0055] In the embodiment of the present application, the intelligent resource reconstruction unit 500 includes: Figure 6 As shown, there are an optical channel reconstruction execution unit, an optical layer resource dynamic allocation unit and a transmission path switching unit.

[0056] Among them, the optical channel reconstruction execution unit is used to convert the optimization strategy into the physical reconstruction operation of the optical channel, and dynamically adjust the routing and spectrum resources; the optical layer resource dynamic allocation unit is used to accurately schedule the optical layer resources based on the optimization strategy; the transmission path switching unit is used to quickly switch to the redundant path when the link fails or is congested.

[0057] It can be understood that the embodiment of the present application converts the optimization strategy into an optical channel physical reconstruction operation through the optical channel reconstruction execution unit to dynamically adjust the routing and spectrum resources. The optical layer resource dynamic allocation unit accurately schedules the optical layer resources based on the strategy. The transmission path switching unit quickly switches to the redundant path when the link fails or is congested, flexibly adapts and efficiently schedules the optical transport network resources, improves spectrum utilization through real-time resource adjustment, quickly switches paths in the event of failure or congestion, ensures zero service interruption, and enhances the network's risk resistance and dynamic resource adaptability.

[0058] It should be noted that the optical channel reconstruction execution unit deeply analyzes the optimization strategy output by the upper layer and then breaks it down into a sequence of physical operations that can be executed by optical network equipment: by calling the control interface of the optical cross-connect device (OXC), it sends routing adjustment instructions - such as disconnecting the connection between the three optical amplifiers in the original path A and establishing an optical layer connection between the five nodes in path B; at the same time, it links the wavelength selective switch (WSS) to perform fragmented integration and bandwidth expansion operations on spectrum resources - for example, aggregating the three 25GHz idle spectrum blocks originally scattered in the 1550nm band into 75GHz continuous bandwidth, and then superimposing the 125GHz spectrum of the adjacent band to form a target bandwidth of 200GHz, and reconfiguring the signal modulation format through the optical modem.

[0059] For example, in a financial data center's optical transport network, cross-border settlement traffic suddenly doubled (from 50 Gbps to 100 Gbps). The original bearer path, due to spectrum fragmentation (only 30 GHz of available bandwidth remained in the C-band 1548-1550 nm), could no longer meet demand. After receiving the optimization strategy, the optical channel reconstruction execution unit (OCE) first disconnected three nodes in the original path through the optical cross-connect (OXC) and switched to a backup path with 80 GHz of continuous unused spectrum (traversing two optical amplifier stations with a link loss of 0.2 dB / km). It then synchronously activated the wavelength selective switch (WSS) to combine the 50 GHz of spectrum between 1550 nm and 1552 nm with the adjacent 30 GHz of spectrum between 1552 nm and 1553 nm, creating 80 GHz of continuous bandwidth. The optical modem then switched from QPSK (50 Gbps per wavelength) to 64QAM (100 Gbps per wavelength). The unit monitored the optical power (which remained stable at -2.5 dBm) and the bit error rate (<1e-12) in real time. The entire reconstruction process took 150ms, there was no packet loss in cross-border settlement services, and the spectrum utilization rate of the new path increased from 50% to 80%, successfully resolving the bandwidth bottleneck.

[0060] The embodiment of the present application proposes an algorithm service system for optical transport networks. The optical network perception module collects optical layer performance, service flow and equipment status data in full dimensions and in real time to provide accurate data for decision-making. The deep algorithm decision-making center relies on the traffic prediction capability of the spatiotemporal graph convolutional network and the dynamic optimization characteristics of reinforcement learning to improve the timeliness and adaptability of routing wavelength allocation and improve resource utilization. The policy adaptation module generates dynamic operation and maintenance strategies and energy-saving scheduling instructions based on the prediction results, which can intelligently regulate energy consumption according to business load fluctuations and reduce redundant energy consumption. The risk assessment unit quantifies multi-dimensional risks through the hierarchical analysis method and integrates historical data optimization strategies to improve the accuracy of fault warning and shorten the risk response time. The intelligent resource reconstruction unit is used for rapid reconstruction of optical channels and switching of redundant paths. In conjunction with the dynamic resource allocation mechanism, it shortens the service interruption recovery time, reduces the congestion rate and the scope of fault impact, and comprehensively improves the dynamic adaptability, resource scheduling efficiency and risk resistance level of the optical transport network under complex working conditions. In this way, the problems of poor policy adaptability, low resource scheduling efficiency and insufficient fault tolerance in the existing technology are solved.

[0061] The following will describe an algorithm service system for optical transport network through a specific embodiment. Figure 7 As shown, including: In a cross-border settlement business scenario at a multinational bank, the optical transport network algorithm service system built a full-process adaptation mechanism around the core requirements of "low latency, zero packet loss, and high reliability." For the backbone link (48-core optical cable) carrying cross-border settlement, a coherent optical time-domain reflectometer (COTDR) was deployed to perform optical power scans every 10 seconds. When the optical power fluctuation exceeded ±0.05dBm (the settlement service sensitivity threshold), the optical amplifier power compensation was immediately triggered. At the same time, the signal-to-noise ratio (OSNR) was tracked in real time to ensure the integrity of the encrypted transmission of settlement data (AES-256 encryption was used, and the OSNR requirement was ≥ 28dB). When the OSNR drops to 26dB, optical repeater gain adjustment is activated to avoid decryption failures due to signal degradation. The SDN controller collects targeted time slot characteristics of cross-border settlement services. The peak period is from 9:00 to 11:00 on trading days, when the bandwidth demand for a single link bursts from the baseline 50Gbps to 180Gbps (including real-time clearing messages and SWIFT messages), and is marked with "P0+" special priority (fault recovery time ≤100ms). The frame interval (≤1ms) and retransmission rate (threshold ≤0.01%) of settlement messages are simultaneously monitored to provide refined data for traffic forecasting. "Financial-grade monitoring mode" is enabled for OTN equipment carrying settlement services. The optical module port bit error rate must be stable below 1e-15 (1e-12 for conventional services). The power supply system adopts a 3+1 redundant design (switching time ≤ 20ms), and the fan speed is maintained at 3200r / min to ensure that the equipment temperature is ≤ 40°C (to avoid message processing delays caused by high temperature). All data is uploaded to the financial-grade data center via an encrypted channel (IPsecVPN).

[0062] Based on the past 180 days of settlement data (including daily peaks for Hong Kong-Shanghai Stock Connect transactions and cross-border RMB settlement cycle characteristics), the Spatio-Temporal Graph Convolutional Network (ST-GCN) enhances traffic prediction for key nodes such as the 9:30 Hong Kong stock market opening and the 15:00 A-share market closing, achieving 95% accuracy. For example, a 190Gbps peak was predicted at 10:00 AM on a given day, sending an early warning to the routing optimization unit 15 minutes in advance. To meet the rigid requirements of "end-to-end latency ≤ 5ms" and "zero jitter" for settlement services, the PPO reinforcement learning algorithm increases the weight of "financial message transmission latency" to 70%, generating a dedicated routing solution: selecting a low-latency path through two optical amplifier stations (reducing latency by 3ms compared to conventional routes), allocating a dedicated C-band wavelength of 1553nm-1554nm (100GHz bandwidth), using the DP-QPSK modulation format (which offers better noise immunity than 16QAM), and reserving 20GHz of guard bandwidth to prevent adjacent channel interference. A dual-plane protection mechanism is implemented for settlement services. 1+1 optical layer multiplex section protection is deployed on the primary path, and the backup path is pre-configured with the same wavelength resources to ensure seamless failover in the event of a failure. A "hot backup verification" is performed daily from 2:00 AM to 4:00 AM (during the low settlement period). The backup path's latency and jitter are tested using simulated traffic (required to be ≤100μs) and a cross-border settlement link health report is generated. On non-trading days (such as weekends), when the settlement service bandwidth drops to 20Gbps, only one core wavelength channel remains operational, while the remaining channels enter a "sleep-wake" cycle (waking up every 30 minutes to check link status). This reduces power consumption by 30% compared to normal mode, while ensuring a wake-up response time of ≤1 second (meeting emergency settlement requirements).

[0063] Three high-impact risks are monitored: a sudden increase of 0.3dB in the optical amplifier noise figure at the physical layer (potentially leading to settlement message errors); a jump in bandwidth utilization from 60% to 95% within 10 minutes at the data link layer (indicating traffic congestion); and an abnormality in the BGP route prefix carrying a settlement service label (potential route hijacking risk) at the network layer. Historical case studies (in 2023, a link outage caused a 3-minute settlement delay, triggering a regulatory alert) are used to assess the probability of spread. Using the Analytic Hierarchy Process (AHP), the risk weight for "settlement service interruption" is assigned to 0.9 (the maximum for routine services is 0.6). When an optical amplifier anomaly is identified, a "superior response" is immediately triggered: switching to a backup optical amplifier module within 10ms and simultaneously sending an alert to the bank's operations and maintenance center (initiating a SWIFT system-wide suspension of non-urgent message transmissions), accelerating the process by 80% compared to the standard process.

[0064] At 2:30 PM one day, when cross-border RMB exchange rate fluctuations triggered a sudden surge in settlement traffic (50 Gbps → 100 Gbps for eight minutes), the system initiated an extremely rapid response: within 10 milliseconds, the OXC equipment switched over, routing traffic to a pre-configured backup wavelength (1554nm-1555nm). Using a wavelength selective switch, two adjacent 50 GHz spectrum blocks were combined to create 100 GHz of contiguous bandwidth. The modulation format was dynamically upgraded from DP-QPSK to 16QAM (increasing single-wavelength capacity from 100 Gbps to 200 Gbps). 20 GHz of redundant bandwidth was temporarily allocated for agricultural IoT services (marked "Financial Emergency Requisition" and automatically compensated for later), ensuring no queuing delays for settlement messages. During the reconstruction process, it was detected that the bit error rate of the original path had risen to 1e-7, and 50ms-level redundant path switching was immediately initiated. The BFD protocol was linked with the bank's core system to achieve full automation of the "link switching-message retransmission-account consistency verification" process. Ultimately, 32,000 cross-border settlement transactions were completed with zero packet loss, and the single transaction latency was stabilized at 3.5ms±0.2ms, meeting the real-time requirements of SWIFTMT700 messages.

[0065] In summary, the embodiments of the present application meet the stringent requirements of settlement services for signal quality and real-time performance through precise control of optical power and signal-to-noise ratio, high-priority resource guarantee and refined monitoring; with the help of spatiotemporal graph convolutional networks to accurately predict traffic and PPO algorithm to optimize routing, dynamic adaptation and efficient utilization of bandwidth are achieved; the "dual-plane protection mechanism" and risk rapid response strategy have greatly improved the reliability and security of the business, successfully ensuring that a large number of cross-border settlement transactions are completed with zero packet loss and low latency, while taking into account energy saving and reasonable allocation of resources, and fully adapting to the business characteristics and needs of cross-border settlement of multinational banks.

[0066] Next, an algorithm service method for an optical transport network proposed according to an embodiment of the present application is described with reference to the accompanying drawings.

[0067] like Figure 8 As shown, the algorithm service method for optical transport network includes the following steps: In step S101, optical power, optical signal-to-noise ratio, service flow and node operation status data of the optical transport network are obtained.

[0068] It can be understood that the embodiments of the present application obtain the optical power, optical signal-to-noise ratio, service traffic and node operating status data of the optical transport network to provide accurate data for subsequent traffic prediction, route optimization, risk prediction and other links, so as to grasp the network status and service needs in real time, improve the stability of network operation and the efficiency of service processing, and improve the reliability of service transmission and the utilization efficiency of network resources.

[0069] In step S102, correlation analysis is performed on the optical power, optical signal-to-noise ratio, service flow, and node operation status data of the optical transport network to generate a network status panoramic data set.

[0070] Among them, the optical signal-to-noise ratio is the ratio of optical signal power to noise power, and is a key indicator for measuring signal quality in optical transmission.

[0071] It can be understood that the embodiments of the present application can accurately reflect the degree and root cause of signal transmission degradation by correlating and analyzing the optical power, optical signal-to-noise ratio, service traffic and node operating status data of the optical transport network, and determine whether the signal quality has degraded due to power abnormality in combination with optical power data. It can identify the risk of signal degradation under high load by correlating service traffic changes, and locate the impact of equipment failures on signal quality in conjunction with node status data, thereby providing key signal-level basis for panoramic evaluation of network status and improving the stability of network transmission and the reliability of service carrying.

[0072] It should be noted that four types of data are collected in real time through distributed monitoring equipment, and the timestamps, link positions, and key identifiers of node numbers are recorded synchronously to ensure temporal and spatial consistency; data preprocessing is then used to remove outliers, unify formats and dimensions, and construct a standardized data set; then, time series correlation analysis is used to compare the coupling relationship between optical power fluctuations and signal-to-noise ratio changes in the same period, and the correlation between high-load periods and optical power and signal-to-noise ratio anomalies is identified in combination with the business traffic timing curve; at the same time, through the cross-analysis of node operating status and the first three, the triggering logic of equipment anomalies on optical power stability and signal-to-noise ratio degradation is located; finally, the machine learning model is used to mine implicit association rules, and the physical layer performance data, business load data, and equipment status data are integrated and labeled according to the association rules to form a panoramic data set, which clearly presents the causal chain and impact weight between each indicator.

[0073] The specific formula for time series association analysis is:

[0074]

[0075] in, is the correlation strength between the service flow x and the optical power fluctuation y at the lag τ; is the bandwidth utilization at time t; is the optical power value at the time of lag τ; is the long-term average level of business traffic; is the long-term stable value of optical power; is the time delay of the service traffic affecting the optical power; t is the time index; is the total number of samples; is the degree of synchronous fluctuation of traffic load x and signal-to-noise ratio y at a specific frequency f; The periodic frequency of traffic load or optical performance fluctuations; is the joint energy distribution of the two sequences at frequency f; is the energy distribution of the service load sequence at frequency f; is the energy distribution of the signal-to-noise ratio sequence at frequency f.

[0076] Specific algorithm formula of machine learning model:

[0077]

[0078]

[0079] in, is the target item set; is the number of records in the dataset that contain all states of X; is the total number of records in the dataset; is the frequency of occurrence of X; is the conditional probability of Y occurring when X occurs; is the union item set; is the association rule; is the frequency of simultaneous occurrence of X and Y; is the independent occurrence frequency of Y; To measure the extent to which X promotes Y.

[0080] In step S103, based on the network status panoramic data set, the bandwidth service traffic demand is predicted according to the spatiotemporal graph convolutional network model, and the routing wavelength allocation is optimized by the PPO algorithm to obtain the prediction results and optimization plan.

[0081] Among them, the spatiotemporal graph convolutional network model is a deep learning architecture that integrates graph convolution to capture spatial topological associations and combines time series modules to characterize dynamic evolution, and is used to analyze the spatiotemporal dependencies of graph structured data.

[0082] It can be understood that the embodiments of the present application accurately capture the spatial correlation between nodes and links in the optical transport network topology through graph convolution, and use time series to analyze the dynamic evolution of service traffic and physical layer indicators; break through the limitations of traditional time series or static topology analysis, and make multi-dimensional, dynamic and accurate predictions of bandwidth service traffic requirements, providing a reliable time and space preview basis for the subsequent PPO algorithm to optimize routing wavelength allocation, improve resource scheduling adaptability, enhance prediction robustness by associating physical layer status, reduce congestion risks, and ensure transmission quality.

[0083] It should be noted that the spatiotemporal graph convolutional network model formula is:

[0084]

[0085]

[0086] in, is the updated node feature; is the activation function; is the spatial topological relationship between nodes; is the feature of the l-th layer node; is a learnable weight matrix; is the input spatiotemporal feature tensor; is the temporal convolution kernel size; is the output timing characteristics; It is a one-dimensional convolution operation; It is the core parameter for integrating spatiotemporal features; It is the feature of spatiotemporal joint coding.

[0087] For example, in the prediction of bandwidth service traffic in the optical transport network, a topological graph is constructed with each node as a vertex and the physical link as an edge. Data such as the node's historical service traffic, optical power, signal-to-noise ratio, and time series identifier are input. The spatiotemporal graph convolutional network first captures the periodic fluctuation of traffic during the morning peak and evening peak periods through the time convolution module. At the same time, the graph convolution module is used to associate the traffic changes of adjacent nodes (such as the impact of the traffic surge at node A on the transmission of downstream node B). Then, through multi-layer spatiotemporal fusion, the implicit association of "traffic congestion caused by optical power attenuation at a certain regional node during a specific period" is extracted. Finally, the bandwidth demand forecast of each node in the next 24 hours is output, providing an accurate spatiotemporal distribution basis for routing wavelength allocation optimization.

[0088] In step S104, based on the prediction results, combined with real-time monitoring data and optimization plans, the modulation format and FEC level are dynamically adjusted to generate operation and maintenance strategies and optical layer energy-saving instructions. The weights of link failures, service interruptions, and congestion risks are quantified through the hierarchical analysis method, and the operation and maintenance strategies and optical layer energy-saving instructions are optimized in combination with the historical fault library.

[0089] Among them, the hierarchical analysis method is a systematic analysis method that decomposes complex decision-making problems into multi-level indicators, determines the weight of each indicator through pairwise comparison, and combines quantitative calculation and qualitative analysis to obtain the optimal solution.

[0090] It can be understood that the embodiment of the present application uses the hierarchical analysis method to decompose the operation and maintenance indicators of link failure, service interruption and congestion risks that are difficult to quantify directly into a comparable hierarchical structure. By comparing and determining the weight of each risk indicator in the overall decision-making, and combining the actual impact data of various risks in the historical fault library, the rationality of the weight distribution can be calibrated to avoid subjective judgment bias; the weight is integrated into the optimization of the operation and maintenance strategy and the optical layer energy-saving instructions, the modulation format and FEC level are dynamically adjusted, the core priority is highlighted, the operation and maintenance resources are accurately allocated, and the practicality and reliability of the strategy are improved.

[0091] For example, in the optimization of optical transport network operation and maintenance strategies, the application of the hierarchical analysis method is reflected in a systematic decision-making process: first, a clear hierarchical structure is constructed - the target layer is set as "the optimal operation and maintenance strategy that balances risk and energy consumption while ensuring transmission quality"; the criterion layer is refined into three core indicators: link failure (such as fiber breakage and interface damage), service interruption (such as interruption of real-time data transmission for high-priority customers), and congestion risk (such as core node traffic exceeding the carrying threshold); the solution layer includes specific operations such as adjusting the modulation format (such as switching from 16QAM to QPSK to improve anti-interference capabilities), optimizing the FEC (forward error correction) level (such as increasing coding redundancy to reduce bit errors), and dynamically allocating wavelength resources (such as expanding the capacity of high-load links). During the weight determination phase, a 1-9 scale was used to compare the criterion layers pairwise. For example, when comparing business interruption and congestion risk, based on historical data showing that "a single financial business interruption resulted in millions of dollars in direct compensation and brand damage, while congestion of the same magnitude only increased latency," the importance of business interruption was determined to be 7 (strongly important) and the congestion risk to be 1. The calculated weight of business interruption (65%) was significantly higher than the congestion risk (15%). When comparing link failure and business interruption, considering that link failures can directly cause business interruption (such as a trunk fiber break causing entire services to be paralyzed), but a single link failure can be mitigated by redundant routing, link failures were given a weight of 20%, resulting in a priority ranking of "business interruption > link failure > congestion risk." Further calibration was then conducted using a historical fault database. Analysis of data from the past three years revealed that 80% of service interruptions were related to modulation format mismatches and high-load scenarios, while 60% of link failures were due to interface aging (which could be avoided through regular testing). Based on this, weighting coefficients were fine-tuned and solution-level measures were scored. For example, "adjusting the modulation format to match the load" received the highest score (90) for service interruption risk mitigation, while "increasing the FEC level" received a high score (80) for link fault protection. Ultimately, based on a comprehensive calculation of "weight x score," the preferred strategy was "switching the core link modulation format from 16QAM to QPSK during high-traffic periods, while moderately increasing the FEC level to 0.1% redundancy." This targeted approach not only ensured critical service stability through targeted resolution of high-priority risks, but also avoided energy waste caused by blind capacity expansion, achieving refined decision-making based on multiple objectives.

[0092] In step S105, when OSNR degradation or link interruption is detected, the faulty path is isolated through the ROADM optical cross-connector and switched to a low-latency redundant path according to the optimized operation and maintenance strategy and optical layer energy-saving instructions.

[0093] Among them, the ROADM optical cross-connector is an optical network device that can directly perform add-drop multiplexing on wavelength signals at the optical layer, dynamically reconfigure wavelength routing, and does not require optoelectronic conversion. It supports flexible scheduling of optical signal paths to adapt to dynamic network needs.

[0094] It is understandable that the embodiments of the present application utilize ROADM optical cross-connectors. By leveraging their direct optical layer operation characteristics, upon detecting OSNR degradation or link interruption, they can quickly isolate the faulty channel without going through the optoelectronic conversion link, thus preventing the fault from spreading in the network. At the same time, relying on the ability to dynamically reconfigure wavelength routing, they can quickly switch to a preset low-latency redundant path, minimizing service interruption time and ensuring the continuous transmission of high-priority services. Direct optical layer scheduling reduces the latency and loss of electrical layer processing, improving the real-time nature of fault response. Flexible routing reconstruction capabilities adapt to dynamic network changes and enhance topology resilience. Furthermore, the absence of frequent optoelectronic conversion reduces equipment energy consumption and operation and maintenance costs, providing efficient hardware support for optimized operation and maintenance strategies and optical layer energy-saving instructions.

[0095] For example, at the core node of a backbone optical transport network, a ROADM optical cross-connector connects five backbone fiber links, carrying high-priority services such as financial data and high-definition video. When the network monitoring system detects that the optical signal-to-noise ratio (OSNR) of one of these links suddenly drops from 25dB to 18dB (below the safety threshold of 20dB), and that continued degradation could cause video service freezes or even interruptions, the ROADM immediately activates its pre-defined fault response mechanism. First, the ROADM uses its built-in wavelength selective switch (WSS) to quickly locate the 10 service-carrying wavelength channels corresponding to the degraded link and isolate them from the optical cross-connect matrix of the faulty link to prevent the degraded signal from interfering with other links. Simultaneously, a pre-configured low-latency redundant path is deployed. This path detours through three adjacent nodes, increasing the physical distance by 20%. However, the ROADM's non-blocking cross-connect capability enables seamless wavelength-level handover. Within 50 milliseconds, these 10 wavelength channels are dispatched to the redundant link without any optical-to-electrical conversion, ensuring zero packet loss and uninterrupted service traffic. After the faulty link is repaired, the ROADM automatically detects that its OSNR has returned to 26dB. The network management system then issues commands to smoothly switch services back to the original link, freeing up redundant resources for other sudden demands. This process demonstrates both the ROADM's precision in fault isolation and its flexibility in dynamically reconfiguring routing, effectively ensuring the continuity of high-priority services.

[0096] According to an algorithm service method for optical transport networks proposed in an embodiment of the present application, the optical network perception module collects optical layer performance, service traffic and equipment status data in full dimensions and in real time to provide accurate data for decision-making; the deep algorithm decision-making center relies on the traffic prediction ability of the spatiotemporal graph convolutional network and the dynamic optimization characteristics of reinforcement learning to improve the timeliness and adaptability of routing wavelength allocation and improve resource utilization; the policy adaptation module generates dynamic operation and maintenance strategies and energy-saving scheduling instructions based on the prediction results, which can intelligently regulate energy consumption according to business load fluctuations and reduce redundant energy consumption; the risk assessment unit quantifies multi-dimensional risks through the hierarchical analysis method and integrates historical data optimization strategies to improve fault warning accuracy and shorten risk response time; the intelligent resource reconstruction unit is used for rapid reconstruction of optical channels and switching of redundant paths, and cooperates with the dynamic resource allocation mechanism to shorten service interruption recovery time, reduce congestion rate and fault impact range, and comprehensively improve the dynamic adaptability, resource scheduling efficiency and risk resistance level of the optical transport network under complex working conditions. In this way, the problems of poor policy adaptability, low resource scheduling efficiency and insufficient fault tolerance in the existing technology are solved.

[0097] The following will describe an algorithm service method for optical transport network through a specific embodiment. Figure 9 As shown, including: Taking a certain province's trunk optical transport network as an example, Huawei's OSN9800M24 devices with built-in OTN performance monitoring units were deployed at 52 core nodes, supporting the acquisition of 1024 optical power sampling points per second. A distributed deployment strategy was adopted for 120 backbone links, with Yokogawa AQ7280B OTDR devices (spatial resolution 1m, dynamic range 60dB) installed every 20km. Agilent N7744A optical power meters (accuracy ±0.05dBm, measurement range -90 to +20dBm) were deployed at the amplifier output (OA) and optical add / drop multiplexer (OADM) ports to collect real-time power values ​​at 80 wavelengths (λ1-λ80) in the C-band (1530-1565nm). λ1-λ40 are dedicated wavelengths for 5G backhaul, while λ41-λ80 are shared wavelengths for the government cloud and the industrial internet. An EXFO FTB-5240S optical spectrum analyzer (resolution 1m) was used. The system generates an OSNR curve for each wavelength every minute, with two thresholds set: 18dB for early warning and 15dB for alarm. Cisco Nexus 9300 series NetFlow probes are deployed on node access ports, with detailed VLAN division (Government Cloud VLANs 100-105, 5G backhaul VLANs 200-208, and Industrial Internet VLANs 300-306). Statistics are accurate to 100Mbps and support TCP / UDP protocol identification. Data from the Huawei eSight management system is collected via SNMPv3. The sampling period is 10 seconds for CPU usage (threshold 80%), board temperature (accuracy ±0.5°C, threshold 65°C), fan speed (supports three-level speed adjustment), and power supply voltage (-48V ±5%). High temperatures trigger 1-second sampling and activate fan overclocking mode. Data transmission uses a 1510nm monitoring channel (STM-1 rate) and is aggregated to the provincial central data center through the IPoverDCN network. It is stored in an InfluxDB cluster (3-node redundancy). A single record contains an 18-bit unique identifier (such as "N12-L35-λ20-20250716083000123") and a millisecond-accurate timestamp, ensuring that the spatiotemporal alignment error is less than 1ms.

[0098] Outlier processing uses a modified 3σ criterion: For optical power sequences, when three consecutive sampling points deviate from the mean by more than 3σ and are accompanied by synchronous changes in OSNR, it is considered a valid event (for example, a 5dB power jump and a 4dB OSNR drop within 1 second on an L10 link). Otherwise, it is marked as a false alarm (for example, a transient jump caused by a brief reflection at the optical fiber connector). False alarm data is corrected using the Local Weighted Evolution (LOESS) algorithm. Service traffic is normalized to duty cycle (actual traffic / link design bandwidth), and node status data is normalized to the range of 0-1 using the min-max method. A CPU utilization of 50% is recorded as 0.5, and a temperature of 60°C is recorded as 0.8 (based on a threshold of 65°C). Using the Python statsmodels library to analyze 100,000 data points of the L28 link (provincial capital-city backbone), we found that when the average traffic duty cycle was 85% from 7:00 to 9:00 on weekdays, the optical power dropped from -20dBm to -22.5dBm (Pearson correlation coefficient -0.82), and for every 10% increase in traffic duty cycle, the OA module output power attenuated nonlinearly by 0.3-0.5dB. Using the Gephi visualization tool to analyze the node topology, we found that when the temperature of the N18 node (city junction) was greater than 60°C, the OSNR of the downstream L45-L48 link was Gradient descent (2.3dB drop for L45 and 1.7dB drop for L48) verified the physical mechanism of optical module transmit power drift caused by heat conduction. WEKA used the Apriori algorithm to analyze 1,000 fault records for the L15 link (a long-distance 80km transmission segment). With a minimum support of 0.6 and a minimum confidence of 0.8, the Apriori algorithm identified a strong association rule: "Optical power < -28dBm ∧ Traffic duty cycle > 90% → Service interruption" (support of 0.75, confidence of 0.82, and lift of 3.2). This rule achieved 91% accuracy in actual faults in 2024. The panoramic dataset ultimately contains 1.2 million association records, each annotated with three-dimensional attributes: trigger condition (e.g., "N8 temperature > 65°C for 5 minutes"), impact range ("L15-L17 link wavelength λ25-λ30"), and association strength (0.91 calculated based on mutual information). SQL queries and visualization are supported.

[0099] The spatiotemporal graph convolutional network model is built based on the PyTorchGeometric2.3 framework. The input layer uses a 72-hour time series window (one sample every 15 minutes) and includes three types of features: traffic duty cycle, optical power, and temperature. The graph structure is constructed based on the actual topology weight: the nodes are 52 core nodes, and the edge weight is 1 / link length (km), highlighting the strong correlation of short-distance links. The temporal convolution layer uses three 1D convolution kernels (size = 3, 5, 7, step size 1) to capture periodic patterns of 3×15 = 45 minutes, 75 minutes, and 105 minutes, respectively, and fuses multi-scale features through residual connections. The graph convolution layer uses Chebyshev polynomials (K = 3) to learn the spatial influence of 0-2 hop nodes. The output layer uses the sigmoid activation function to predict the traffic duty cycle of each link in the next 24 hours (MAE = 3.2%, RMSE = 4.8%), and the prediction accuracy of Industrial Internet VLAN300-306 is improved to within ±4%. The PPO algorithm uses a reinforcement learning environment built on the OpenAI Gym framework. The state space consists of 52×52 node pairs of link states and 80 wavelength occupancy levels, while the action space is a combination of 120 links and 80 wavelengths. The reward function is designed as R = 0.7 × (-latency in milliseconds) + 0.3 × (wavelength utilization in percent), with penalties for service interruption (-1000) and wavelength conflict (-100). Training parameters include a learning rate of 3e-4, a discount factor of 0.99, gae_lambda = 0.95, a batch size of 64, and convergence after 1000 training rounds. The policy network uses a three-layer MLP (512 hidden layers and 256 neurons). The routing of government cloud services (VLAN 100) from N1 to N25 uses a traditional static allocation latency of 28ms. After optimization, the latency is reduced to 12ms using the L8 link with a λ30 wavelength. Wavelength utilization is increased from 65% to 88%, and the latency jitter of 5G backhaul services is controlled within ±1ms.

[0100] When the traffic duty cycle of the N12 node (Industrial Internet Core) is predicted to reach 90% from 9:00 to 12:00 the next day, a 16QAM→QPSK switch is triggered (implemented through the G.709 standard protocol of Huawei OTN equipment, with a switching time of less than 50ms). Although the rate is reduced from 100Gbps to 50Gbps, the OSNR tolerance is reduced from 18dB to 12dB, and the anti-interference capability is improved. At the same time, the FEC level is increased from RS(255,239) (coding gain 6dB) to RS(2 55,223) (coding gain 8.5dB), redundancy increased from 6.2% to 12.5%, and bit error rate dropped from 1e-12 to 1e-15; during off-peak hours (0:00-6:00), for L50-L60 links with a traffic duty cycle of less than 30%, SNMP instructions were issued to reduce the OA module output power from +20dBm to +15dBm, saving 0.52 kWh of electricity per link per hour, 12.48 kWh of electricity per day on average, 4555 kWh per link throughout the year, and 45,000 kWh of electricity per year for 10 links. The hierarchical analysis method invited five operation and maintenance engineers with more than 10 years of experience to conduct pairwise comparison using the 1-9 scaling method: business interruption (government cloud loses 500,000 yuan per hour) vs. link failure (repair cost 20,000 yuan) = 7:1, business interruption vs. congestion risk (a 10ms increase in latency affects industrial control) = 9:1, and link failure vs. congestion risk = 3:1; a judgment matrix was constructed and passed the consistency test (CR=0.07<0.1), and the weights were calculated: business interruption 65%, link failure 20%, and congestion risk 15%; combined with the historical fault database from 2021 to 2023 (12 business interruptions, 87 link failures, and 45 congestions), the entropy weight method was used to correct the weights (business interruption 63%, link failure 22%, and congestion risk 15%). The final strategy prioritizes the protection of government cloud services (weight coefficient 1.2).

[0101] When the OSNR of the L18 link (long distance 100km) dropped suddenly from 22dB to 14dB (triggering a 15dB alarm), the company called the panoramic data set and matched the rule "OSNR < 16dB ∧ traffic duty cycle 88%". Combined with the BOTDR real-time curve (a sudden 5dB increase in loss at 32km), it was determined within 1.2 seconds that "physical damage to the optical fiber caused signal degradation". The ROADM equipment (Huawei TN16ROADM, supporting Colorless, Directionless, and Contentionless features) executed the preset 1+1 protection strategy and completed the task within 50ms. The switchover from the L18 to L19 link for the λ20-λ30 wavelength was successful, achieving an OSNR of 23dB and a latency of 10ms (2ms lower than L18). The BOTDR positioning accuracy was ±5m, indicating the fault point at 32.003km (120m from the nearest optical cross-connect box). A repair path was generated using the GIS system, and drone inspections confirmed that the fiber had been severed during construction. The maintenance team arrived at the site with a fusion splicer in 40 minutes, using an OTDR to monitor splice loss in real time (controlling it within 0.05dB). The repair was completed in 2 hours, and the link was switched back to the original one, with zero service interruption (packet loss rate monitored by Zabbix was 0%).

[0102] In summary, the embodiments of the present application achieve accurate perception and spatiotemporal alignment of network status through the comprehensive deployment of high-precision monitoring equipment and a multi-dimensional data collection system. The improved 3σ criterion and intelligent algorithm data processing improve the accuracy of anomaly identification and data reliability. By using data analysis to uncover the correlation patterns and fault rules between services and network status, and combining high-precision prediction models with reinforcement learning optimization strategies, service latency is effectively reduced, wavelength utilization, and prediction accuracy are improved. Through adaptive modulation switching, FEC level adjustment, and energy-saving control during off-peak hours, the network's anti-interference capability and energy efficiency are enhanced. When a fault occurs, it can be quickly located and the protection link automatically switched to achieve zero service interruption. Efficient repairs ensure the continuous and stable operation of the network, improving overall network reliability, resource utilization, and service continuity, while combining energy-saving and intelligent operation and maintenance advantages.

[0103] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: A memory 1001 , a processor 1002 , and a computer program stored in the memory 1001 and executable on the processor 1002 .

[0104] When the processor 1002 executes the program, the algorithm service method for an optical transport network provided in the above embodiment is implemented.

[0105] Furthermore, the electronic device further includes: The communication interface 1003 is used for communication between the memory 1001 and the processor 1002 .

[0106] The memory 1001 is used to store computer programs that can be run on the processor 1002 .

[0107] The memory 1001 may include a high-speed RAM (Random Access Memory) memory, and may also include a non-volatile memory, such as at least one disk memory.

[0108] If the memory 1001, processor 1002, and communication interface 1003 are implemented independently, the communication interface 1003, memory 1001, and processor 1002 can be connected to each other via a bus and communicate with each other. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0109] Optionally, in a specific implementation, if the memory 1001, the processor 1002 and the communication interface 1003 are integrated on a chip, the memory 1001, the processor 1002 and the communication interface 1003 can communicate with each other through an internal interface.

[0110] The processor 1002 may be a CPU (Central Processing Unit), or an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiments of the present application.

[0111] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned algorithm service method for an optical transport network.

[0112] In addition, an embodiment of the present application further provides a computer program product, including a computer program or instructions, which, when executed, implements the above-mentioned algorithm service method for an optical transport network.

[0113] In the description of this specification, reference to the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification, as well as features of different embodiments or examples, unless they are mutually inconsistent.

[0114] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features being referred to. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0115] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0116] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having logic gate circuits for implementing logical functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0117] Those skilled in the art will understand that all or part of the steps carried out in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0118] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. An algorithm service system for optical transport network, characterized in that: include: Optical network perception module, deep algorithm decision center, strategy adaptation module, risk prediction unit, intelligent resource reconstruction unit; among them, The optical network sensing module is used to collect optical layer performance parameters, service flow data and node equipment operating status of the optical transport network; The deep algorithm decision center predicts service traffic based on spatiotemporal graph convolutional networks and optimizes routing wavelength allocation through reinforcement learning; The strategy adaptation module is used to dynamically generate operation and maintenance strategies and energy-saving scheduling instructions based on prediction results and optimization plans; The risk assessment unit is used to assess link failure risk, service interruption risk and network congestion risk in real time, quantify risk weights through the analytic hierarchy process, and optimize the operation and maintenance strategy in combination with historical failure data; The intelligent resource reconstruction unit is used to reconstruct the optical channel according to the optimized operation and maintenance strategy, dynamically allocate optical layer resources or switch redundant transmission paths.

2. The algorithm service system for optical transport network according to claim 1, characterized in that: The optical network perception module includes an optical layer parameter collection unit, a service flow perception unit, and a status monitoring module, wherein the optical layer parameter collection unit is used to collect in real time the optical power fluctuation, signal-to-noise ratio degradation, polarization mode dispersion value, and dispersion compensation margin optical layer performance parameters of the core link of the optical transport network; the service flow perception unit is used to collect the time slot occupancy, real-time bandwidth requirements, and service priority labels of each wavelength channel; and the status monitoring module is used to collect in real time the equipment operating temperature, port bit error rate, power supply voltage, and fan speed status parameters.

3. The algorithm service system for optical transport network according to claim 1, characterized in that: The deep algorithm decision center includes a traffic prediction unit and a routing wavelength optimization unit. The traffic prediction unit uses the attention mechanism in the spatiotemporal graph convolutional network to weight the importance of different links and predict the traffic peak, duration and fluctuation range of each wavelength channel; the routing wavelength optimization unit is based on the PPO algorithm, with minimizing end-to-end delay and maximizing spectrum efficiency as the objective function, and dynamically generates the target routing path and wavelength allocation plan.

4. The algorithm service system for optical transport network according to claim 1, characterized in that: The policy adaptation module includes an operation and maintenance strategy generation unit and an energy-saving scheduling instruction generation unit. The wavelength adjustment strategy generation unit is used to generate a wavelength resource allocation operation and maintenance strategy based on traffic prediction results and routing optimization solutions; the energy-saving scheduling instruction generation unit is used to generate targeted energy-saving instructions based on link load prediction and equipment operating status.

5. The algorithm service system for optical transport network according to claim 1, characterized in that: The risk prediction unit includes a risk identification unit, a risk weight quantification unit, and a strategy optimization unit. The risk identification unit is used to accurately capture early signals of three types of risks from different levels of the optical transport network, identify and evaluate risks; the risk weight quantification unit is used to convert the degree of risk impact into quantifiable weights through the hierarchical analysis method, and perform strategy priority sorting; the strategy optimization unit is used to dynamically optimize the operation and maintenance strategy based on the risk weights and risk assessment results.

6. The algorithm service system for optical transport network according to claim 1, characterized in that: The intelligent resource reconstruction unit includes an optical channel reconstruction execution unit, an optical layer resource dynamic allocation unit and a transmission path switching unit, wherein the optical channel reconstruction execution unit is used to convert the optimization strategy into a physical reconstruction operation of the optical channel, and dynamically adjust the routing and spectrum resources; the optical layer resource dynamic allocation unit accurately schedules the optical layer resources based on the optimization strategy; the transmission path switching unit is used to quickly switch to a redundant path when a link fails or is congested.

7. A method for an algorithm service system for an optical transport network according to any one of claims 1 to 6, characterized in that: The method comprises: Obtain optical power, optical signal-to-noise ratio, service flow and node operation status data of the optical transport network; performing correlation analysis on the optical power, optical signal-to-noise ratio, service flow, and node operation status data of the optical transport network to generate a panoramic network status data set; Based on the network status panoramic dataset, bandwidth service traffic demand is predicted according to the spatiotemporal graph convolutional network model, and routing wavelength allocation is optimized through the PPO algorithm to obtain prediction results and optimization solutions; Based on the prediction results, the modulation format and FEC level are dynamically adjusted in combination with real-time monitoring data and the optimization scheme to generate an operation and maintenance strategy and optical layer energy-saving instructions. The weights of link failure, service interruption, and congestion risk are quantified using the analytic hierarchy process, and the operation and maintenance strategy and optical layer energy-saving instructions are optimized in combination with a historical fault database. When OSNR degradation or link interruption is detected, the faulty channel is isolated through the ROADM optical cross-connector and switched to a low-latency redundant path according to the optimized operation and maintenance strategy and optical layer energy-saving instructions.

8. An electronic device, characterized in that: The system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor. The processor executes the program to implement an algorithm service method for an optical transport network according to claim 7.

9. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed, the algorithm service method for optical transport network according to claim 7 is implemented.

10. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed, the algorithm service method for optical transport network according to claim 7 is implemented.

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