Intelligent monitoring system and method for 400G coherent optical module

Abstract

The invention discloses an intelligent monitoring system and method for a 400G coherent optical module, and relates to the technical field of optical communication, and the system comprises a historical wave band switching data obtaining module which is used for loading historical wave band data which comprises a received signal, a switching type, environment information and an accuracy rate label; the waveband boundary locking template construction module can construct a waveband boundary locking template based on historical waveband switching data; the real-time wave band switching data monitoring module can obtain real-time wave band switching data according to the wave band switching instruction; and the wave band boundary adjusting module is used for adjusting the wave band boundary of the 400G coherent optical module according to the template analysis data. The technical problems that an existing 400G coherent optical module is prone to losing lock in the wave band switching process, and communication stability is affected are solved, and the technical effects that dynamic changes in wave band switching are accurately coped with through an intelligent monitoring and dynamic adjusting mechanism, wave band losing lock is effectively reduced, and communication stability is improved are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical communication technology, in particular to an intelligent monitoring system and method for a 400G coherent optical module. BACKGROUND

[0002] In modern high-speed communication networks, 400G coherent optical modules, as key transmission equipment, undertake the task of efficient transmission of massive data. However, when switching between different wavelength bands, the wavelength band often loses lock due to fiber characteristics, environmental changes, and signal processing complexity, i.e., the signal cannot be stably locked to the target wavelength band, thereby causing transmission interruption or quality degradation, seriously affecting the stability and reliability of the communication system. Traditional wavelength switching methods mostly rely on fixed preset parameters, lack adaptability to environmental changes and network load fluctuations, and thus are prone to inaccurate adjustment during wavelength switching, resulting in lock loss or signal attenuation. SUMMARY

[0003] The present application provides an intelligent monitoring system and method for a 400G coherent optical module to solve the technical problem of lock loss during wavelength switching of existing 400G coherent optical modules, affecting communication stability.

[0004] In a first aspect, the present application provides an intelligent monitoring system for a 400G coherent optical module, the system comprising: a historical wavelength switching data acquisition module for loading historical wavelength switching data of the 400G coherent optical module within a preset period, the historical wavelength switching data including historical wavelength receiving signals, wavelength switching type labels, wavelength signal transmission environment information, and accuracy labels representing wavelength signal receiving accuracy, wherein the wavelength switching type labels at least include a switching type of C-band switching to L-band and a switching type of L-band switching to C-band; a wavelength boundary lock template construction module for constructing a wavelength boundary lock template based on the historical wavelength switching data; a real-time wavelength switching data monitoring module for determining whether the 400G coherent optical module receives a wavelength switching instruction, and if the 400G coherent optical module receives a wavelength switching instruction, acquiring real-time wavelength switching data obtained by monitoring; a wavelength boundary adjustment module for analyzing the real-time wavelength switching data using the constructed wavelength boundary lock template, obtaining a first wavelength boundary corresponding to the wavelength switching instruction, and adjusting the wavelength boundary of a tunable laser in the 400G coherent optical module based on the first wavelength boundary.

[0005] In a second aspect of the present application, an intelligent monitoring method for a 400G coherent optical module is provided, the method comprising: loading historical band switching data of the 400G coherent optical module within a preset period, the historical band switching data comprising historical band received signals, band switching type labels, band signal transmission environment information, and accuracy labels representing band signal receiving accuracy, wherein the band switching type labels at least include a switching type of C-band switching to L-band and a switching type of L-band switching to C-band; constructing a band boundary locking template based on the historical band switching data; determining whether the 400G coherent optical module receives a band switching instruction, and if the 400G coherent optical module receives a band switching instruction, obtaining real-time band switching data obtained through monitoring; analyzing the real-time band switching data using the constructed band boundary locking template to obtain a first band boundary corresponding to the band switching instruction, and adjusting a band boundary of a tunable laser in the 400G coherent optical module based on the first band boundary.

[0006] The one or more technical solutions provided in the present application have at least the following technical effects or advantages: The intelligent monitoring system and method for a 400G coherent optical module provided in the present application relate to the field of optical communication technology, and the technical effects are achieved through the following steps: constructing a band boundary locking template based on historical band switching data, and analyzing real-time band switching data using the template after band switching to dynamically adjust a band boundary of a tunable laser in the 400G coherent optical module, thereby solving the technical problem that the existing 400G coherent optical module is prone to losing lock during band switching and affecting communication stability, and achieving the technical effects of precisely responding to dynamic changes in band switching through an intelligent monitoring and dynamic adjustment mechanism, effectively reducing band loss, and improving communication stability. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0008] Figure 1 The intelligent monitoring system structure schematic diagram for a 400G coherent optical module provided in the embodiments of the present application is shown in the figure. Figure 2 The intelligent monitoring method flowchart for a 400G coherent optical module provided in the embodiments of the present application is shown in the figure.

[0009] Figure labeling: Historical band switching data acquisition module 10, band boundary locking template construction module 20, real-time band switching data monitoring module 30, band boundary adjustment module 40. Detailed Implementation

[0010] This application provides an intelligent monitoring system and method for 400G coherent optical modules, which solves the technical problem that existing 400G coherent optical modules are prone to loss of lock during band switching, affecting communication stability.

[0011] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0012] It should be noted that the terms "first," "second," etc., in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to such processes, methods, products, or devices.

[0013] Example 1, as Figure 1 As shown, this application provides an intelligent monitoring system for a 400G coherent optical module, the system comprising: The historical band switching data acquisition module 10 is used to load historical band switching data of the 400G coherent optical module within a preset period. The historical band switching data includes historical band received signals, band switching type tags, band signal transmission environment information, and accuracy tags characterizing the band signal reception accuracy. The band switching type tags at least include switching types from C-band to L-band and from L-band to C-band. Furthermore, the 400G coherent optical module also includes an extension module, which extends multiple bands and updates multiple band switching type tags based on these multiple bands.

[0014] Furthermore, when acquiring the accuracy tag that characterizes the accuracy of band signal reception, the historical band switching data acquisition module 10 is also used to perform the following steps: P11: performing band accuracy evaluation on the historical band received signal, including wavelength attenuation slope, filter loss gain, and signal bit error rate; P12: performing weight calculation on the wavelength attenuation slope, filter loss gain, and signal bit error rate to obtain an accuracy label representing the band signal receiving accuracy of the historical band received signal.

[0015] It should be understood that the main task of the historical band switching data acquisition module 10 of the present application is to load and process the historical band switching data of the 400G coherent optical module within a preset period, to provide a decision basis based on historical data for the system.

[0016] Firstly, the historical band switching data contains multiple important data items, including historical band received signal, band switching type label, band signal transmission environment information, and accuracy label. Among them, the historical band received signal is the signal intensity received by the 400G coherent optical module under different bands. The band switching type label is used to clearly identify the switching type between different bands. For example, the switching type of C band to L band, and the switching type of L band to C band, etc. The band switching type label at least includes the switching type of C band to L band and the switching type of L band to C band, and the system can distinguish the type and characteristics of different band switching through these labels. The band signal transmission environment information records the environmental changes in the transmission process, such as temperature, humidity and other external factors, which will affect the transmission quality of the band signal. And the accuracy label is used to represent the accuracy of the band signal receiving, and the accuracy label is generated based on the evaluation of the signal quality.

[0017] Among them, the accuracy label is the core index to measure the quality of band signal receiving, which comprehensively evaluates the accuracy and reliability of the signal through multiple key parameters. Specifically, in obtaining the accuracy label representing the band signal receiving accuracy, firstly, the historical band received signal is evaluated for band accuracy, and the evaluation content includes wavelength attenuation slope, filter loss gain, and signal bit error rate. The wavelength attenuation slope is the degree of attenuation caused by wavelength change in the signal transmission process, which can reflect the transmission loss of the signal under different bands; the filter loss gain is the influence of the signal through the filter, and the gain or loss of the signal may occur in the filtering process; the signal bit error rate (BER) is a key indicator to evaluate the quality of signal transmission, which represents the error proportion between the received signal and the transmitted signal. Each evaluation indicator represents a dimension of signal quality.

[0018] Next, based on the pre-set weight distribution model, the wavelength attenuation slope, filter loss gain and signal bit error rate are comprehensively weighted to generate an accuracy label. Among them, the influence degree of each factor on the final accuracy label is different, and the weight of each evaluation index can be calculated by experience rule or regression analysis based on historical data. For example, the signal bit error rate may have a more significant impact on the accuracy than the wavelength attenuation slope, so its weight will be higher. In this way, the influence of different factors can be considered to allocate weight coefficients, and the final accuracy label is calculated according to these weights to indicate the accuracy of the received signal of a specific wavelength band.

[0019] On this basis, the 400G coherent optical module further includes an expansion module for expanding multiple wavelength bands to cope with more complex wavelength switching requirements. As the number of wavelength bands increases, the wavelength switching type label will also be updated to cover more wavelength switching cases. For example, when the expansion module introduces a new O wavelength band, the wavelength switching type label will be updated in time to add new switching types such as C wavelength switching to O wavelength, O wavelength switching to L wavelength, etc. This enables the system to cope with more complex wavelength switching tasks and improves the flexibility and reliability of wavelength switching.

[0020] The wavelength boundary locking template construction module 20 is configured to construct a wavelength boundary locking template based on the historical wavelength switching data.

[0021] Further, the wavelength boundary locking template construction module 20 is further configured to perform the following steps: P21: collect a first group of historical wavelength switching data of C wavelength switching to L wavelength, and a second group of historical wavelength switching data of L wavelength switching to C wavelength; P22: take the historical wavelength received signal and wavelength signal transmission environment information corresponding to the first group of historical wavelength switching data and the second group of historical wavelength switching data as input data, take the wavelength boundary as a variable space, and take the accuracy label corresponding to the first group of historical wavelength switching data and the second group of historical wavelength switching data as a supervision label to perform regression analysis, to obtain a wavelength boundary regression model; P23: construct a wavelength boundary locking template based on the wavelength boundary regression model.

[0022] Optionally, the main function of the wavelength boundary locking template construction module 20 of the present application is to construct an intelligent locking template for adjusting the wavelength boundary by analyzing the historical wavelength switching data. The template can dynamically adjust the wavelength boundary according to the historical data during the actual wavelength switching process to ensure the stability and signal quality during the switching process.

[0023] In the process of constructing the band boundary locking template, first, the band boundary locking template construction module 20 needs to collect two sets of specific historical band switching data. The first set of data comes from the switching data from C band to L band, and the second set of data comes from the switching data from L band to C band. These two sets of data cover different conditions and characteristics of switching between different bands, including band signal reception, band signal transmission environment information, and accuracy label information during actual switching. Through these historical data, detailed background information about band switching can be obtained, including band signal quality, transmission environment, and corresponding accuracy.

[0024] Next, the historical band reception signals and band signal transmission environment information corresponding to the first set of historical band switching data and the second set of historical band switching data are used as input data. These data include signal strength under different bands, influencing factors of transmission environment, etc., and the accuracy labels in each set of data are used as supervision labels. At the same time, taking the band boundary as the adjustable variable space, the accuracy labels corresponding to the first set and the second set of historical band switching data are used as supervision labels. Through regression analysis method, the input data and supervision labels are processed to obtain a band boundary regression model. This regression model can reveal the internal relationship between the band boundary and the signal reception accuracy, providing a theoretical basis for subsequent band boundary locking.

[0025] Finally, based on the obtained band boundary regression model, a band boundary locking template is constructed, which integrates and encapsulates the key parameters and rules in the band boundary regression model to form a template that can be called in real time. In the subsequent band switching process, the system can quickly and accurately adjust the band boundary according to the parameters and rules in the band boundary locking template, thereby effectively avoiding the occurrence of band loss of lock phenomenon and ensuring the stable transmission of communication signals.

[0026] Further, in the band boundary locking template construction module 20: The band boundary regression model includes a first interval regression model for switching from C band to L band, and a second interval regression model for switching from L band to C band; the band switching type of the band switching instruction is disassembled, and the real-time band switching data is analyzed according to the first interval regression model or the second interval regression model according to the band switching type.

[0027] In a possible embodiment of the present application, in the construction of the band boundary regression model, the band boundary locking template construction module 20 can also accurately apply the corresponding regression model for analysis according to different band switching types, thereby further improving the accuracy and reliability of band switching.

[0028] Specifically, the band boundary regression model includes a first interval regression model for C-band switching to L-band and a second interval regression model for L-band switching to C-band. These two regression models are used to process data of different band switching types, and can provide more accurate band boundary adjustment suggestions for different switching directions and band characteristics.

[0029] When the band boundary locking template construction module 20 receives a band switching instruction, it first disassembles the instruction to determine the specific band switching type. This process can be achieved by analyzing the key information in the band switching instruction, such as disassembling using the specified starting band and target band in the instruction, and according to the disassembly result, determining whether the current band switching is C-band switching to L-band or L-band switching to C-band. According to the different types of switching, the corresponding interval regression model can be selected to analyze the real-time band switching data. For example, for the case of C-band switching to L-band, the first interval regression model can be called for analysis, while for the case of L-band switching to C-band, the second interval regression model can be used. This targeted analysis method can fully utilize the characteristics and advantages of each interval regression model, ensuring that the analysis results of real-time band switching data are more accurate and reliable.

[0030] Through this design, the band boundary locking template construction module 20 can more accurately dynamically adjust the band boundary according to different band switching types, optimize the performance of the 400G coherent optical module in various band environments, and ensure that the system can still maintain a high-efficiency and stable working state during complex band switching processes.

[0031] Further, the band boundary locking template construction module 20, after obtaining the band boundary regression model, is further configured to perform the following steps: P22-1a: training a generator according to the band boundary regression model, the generator being configured to obtain incremental band switching data of the historical band switching data; P22-2a: incrementally optimizing the band boundary regression model according to the incremental band switching data, and outputting an optimized band boundary regression model.

[0032] Specifically, after the band boundary regression model is initially constructed, the band boundary locking template construction module 20 can further improve the accuracy and adaptability of the model through an incremental optimization mechanism.

[0033] After obtaining the band boundary regression model, a generator is first trained through the band boundary regression model. The main function of the generator is to generate incremental band switching data related to historical band switching data based on the existing band boundary regression model. These incremental data are not simply copied from the historical data, but are generated through the prediction and simulation functions of the band boundary regression model to generate new band switching data with certain changes and diversity. These data can reflect new situations that may occur under different environmental conditions, different signal quality conditions, and different band switching types. Specifically, the generator will use the parameters and rules in the band boundary regression model, combined with certain randomness and change rules, to generate new band receiving signals, band signal transmission environment information, and corresponding accuracy labels, that is, incremental band switching data. These incremental band switching data can supplement the deficiencies of historical data, increase the richness and coverage of data, and thus provide more comprehensive training materials for subsequent model optimization.

[0034] After obtaining the incremental band switching data, the band boundary locking template construction module 20 will perform incremental optimization on the existing band boundary regression model based on these new data. By using the incremental data as new training samples, the model can learn more new features and rules, thereby improving its adaptability and prediction accuracy for different band switching situations. For example, during the incremental optimization process, the band boundary regression model will adjust its internal parameters and weights according to the input features in the incremental data, such as band receiving signals, band signal transmission environment information, and corresponding supervision labels (accuracy labels). The adjustment process can be based on optimization algorithms of regression analysis, such as least squares method, gradient descent method, etc., to minimize the error between the model prediction value and the actual accuracy label. Unlike traditional batch training methods, incremental optimization can reduce repeated training on all historical data, save computing resources, and can adjust the model in real time as new data is added, ensuring that it adapts to new environments and band switching patterns.

[0035] Finally, after completing the incremental optimization, the optimized band boundary regression model is evaluated to verify its performance on new data. If the performance of the model meets the expected standard, it will be used as the new band boundary regression model for subsequent band boundary locking template construction and real-time band switching adjustment. Through this incremental optimization mechanism, the band boundary regression model can continuously learn and improve in actual application, ensuring that its performance is continuously improved in the long run, and it can better cope with changes in the communication environment and new band switching requirements.

[0036] Further, the band boundary locking template construction module 20, when constructing the band boundary locking template based on the band boundary regression model, is further configured to perform the following steps: P23-1: cluster the band signal transmission environment information of the first set of historical band switching data, obtain a plurality of representative transmission environment information, obtain the preferred band boundary corresponding to the plurality of representative transmission environment information according to the first interval regression model, and construct a first band boundary locking template; P23-2: cluster the band signal transmission environment information of the second set of historical band switching data, obtain a plurality of representative transmission environment information, obtain the preferred band boundary corresponding to the plurality of representative transmission environment information according to the second interval regression model, and construct a second band boundary locking template; P23-3: construct a band boundary locking template according to the first band boundary locking template and the second band boundary locking template.

[0037] Optionally, in the band boundary locking template construction module 20, the process of constructing the band boundary locking template can be further refined. By further analyzing the historical data in detail, the accuracy and adaptability of the template can be improved.

[0038] First, the first set of historical band switching data (C band switching to L band data) is clustered by band signal transmission environment information, similar transmission environment information is classified into a category, and a plurality of representative transmission environment information, that is, representative transmission environment information, is obtained. These representative transmission environment information can cover band switching under different conditions, providing diversified reference points for subsequent band boundary optimization.

[0039] Next, using the first interval regression model, the corresponding preferred band boundary is calculated for these representative transmission environment information. The first interval regression model is specially constructed for the case of C band switching to L band, so it can accurately calculate the preferred band boundary most suitable for the environment according to the different transmission environments. Integrating these preferred band boundaries into the first band boundary locking template can be used to guide the band boundary adjustment of C band switching to L band.

[0040] Similarly, the second set of historical band switching data is clustered by band signal transmission environment information to obtain a plurality of representative transmission environment information. Then, using the second interval regression model, the corresponding preferred band boundary is obtained for these representative transmission environment information. Based on these preferred band boundaries, a second band boundary locking template is constructed to guide the band boundary adjustment of L band switching to C band.

[0041] Finally, the first waveband boundary locking template and the second waveband boundary locking template are integrated to construct a complete waveband boundary locking template. This final waveband boundary locking template will combine the switching requirements of C-band to L-band and L-band to C-band, providing a set of waveband boundary settings suitable for different waveband switching conditions. The template not only considers historical data and regression analysis results, but also optimizes waveband boundary values according to different transmission environments, ensuring that the system can maintain high performance in complex practical application environments.

[0042] The real-time waveband switching data monitoring module 30 is used to determine whether the 400G coherent optical module receives a waveband switching instruction. If the 400G coherent optical module receives a waveband switching instruction, real-time waveband switching data obtained by monitoring is acquired.

[0043] It should be understood that the task of the real-time waveband switching data monitoring module 30 of the present application is to monitor in real time whether the 400G coherent optical module receives a waveband switching instruction, and to acquire relevant real-time waveband switching data in time after receiving the instruction. In order to achieve this goal, the real-time waveband switching data monitoring module 30 first needs to effectively determine whether there is a waveband switching instruction input. These instructions are usually issued by the control center of the system or other modules, and are used to inform the 400G coherent optical module to switch to a specified waveband. The waveband switching instruction needs to include explicit waveband switching requirements, such as switching from C-band to L-band, or switching from L-band to C-band, etc.

[0044] When receiving the waveband switching instruction, the real-time waveband switching data monitoring module 30 will enter an active state and begin to monitor the real-time waveband data related to the current waveband switching, that is, to acquire the waveband information involved in the current switching instruction, including the reception quality of the signal, the transmission environment of the waveband signal, and the accuracy of signal reception, etc. Through these data, the performance of the waveband after switching can be fully understood, and key performance indicators such as signal strength, noise interference, and bit error rate can be evaluated to ensure that the signal quality after waveband switching meets the expected requirements.

[0045] In addition, the real-time waveband switching data monitoring module 30 also records the transmission environment information of the waveband signal, including environmental influences that may be encountered during transmission, such as temperature changes, humidity fluctuations, and other factors that may interfere with the signal. Through continuous monitoring of these environmental information, the performance of waveband switching under different environments can be evaluated, thereby providing a basis for subsequent waveband adjustment. At the same time, the accuracy of waveband signal reception needs to be monitored, including wavelength attenuation slope, filtering loss gain, and signal bit error rate parameters. Real-time monitoring of these parameters can help the system evaluate the signal quality during waveband switching and ensure the reliability of waveband switching.

[0046] During the band switching process, the real-time band switching data monitoring module 30 can constantly update and provide the signal state of the current band, ensuring that other modules can adjust the band boundary according to the latest data to avoid the phenomenon of band loss of lock, and ensuring that the 400G coherent optical module always maintains stable signal transmission during band switching.

[0047] The band boundary adjustment module 40 is configured to analyze the real-time band switching data using the constructed band boundary locking template, obtain a first band boundary corresponding to the band switching instruction, and adjust the band boundary of the tunable laser in the 400G coherent optical module based on the first band boundary.

[0048] Further, when the band boundary adjustment module 40 analyzes the real-time band switching data using the constructed band boundary locking template, it is further configured to perform the following steps: P41: Extract the band signal transmission environment information in the real-time band switching data; P42: Extract the band signal transmission environment information template of the band boundary locking template, match the band signal transmission environment information based on the band signal transmission environment information template, and obtain a matching band boundary; P43: Output the matching band boundary as the first band boundary corresponding to the band switching instruction.

[0049] Optionally, the main task of the band boundary adjustment module 40 of the present application is to analyze and adjust the band boundary using real-time band switching data based on the constructed band boundary locking template, so as to ensure that the 400G coherent optical module can accurately adjust the band setting of the laser during band switching, thereby improving the system stability and signal quality.

[0050] When performing band boundary adjustment, the band boundary adjustment module 40 first extracts the band signal transmission environment information from the real-time band switching data, including the transmission conditions of the current band, such as temperature, humidity, distance and other factors that may affect signal quality. These environmental factors have a significant impact on the quality and stability of the signal. By extracting this information, the band boundary adjustment module 40 can understand the specific environmental conditions under which the current band switching is taking place.

[0051] Next, the waveband signal transmission environment information template in the waveband boundary locking template is extracted. This template is constructed based on the historical data and the waveband boundary regression model after incremental optimization, and contains a variety of representative transmission environment information and the corresponding preferred waveband boundary. The waveband boundary adjustment module 40 can match the waveband signal transmission environment information in the real-time waveband switching data with the information in the waveband boundary locking template, and find the closest matching item through algorithm analysis. For example, a plurality of parameters can be comprehensively evaluated to obtain a matching waveband boundary, such as by calculating the similarity of real-time environment information and template environment information, or by predicting the optimal waveband boundary under the current environment through the regression model, to obtain a specific waveband boundary, i.e. the matching waveband boundary.

[0052] Finally, the obtained matching waveband boundary is output as the first waveband boundary corresponding to the waveband switching instruction, and the precise setting of this waveband boundary is realized by adjusting the tunable laser in the 400G coherent optical module. This first waveband boundary is the result of optimization based on the current transmission environment and historical data, and can effectively guide the precise waveband boundary adjustment of the tunable laser in the 400G coherent optical module, so that the 400G coherent optical module can automatically adapt to different environmental conditions and waveband switching requirements, and effectively avoid the loss of lock during the waveband switching process.

[0053] Further, the waveband boundary adjustment module 40 is also used to perform the following steps: P44: matching the waveband signal transmission environment information based on the waveband signal transmission environment information template, if the matching is unsuccessful, re-calling the first interval regression model or the first interval regression model for prediction, and outputting a predicted waveband boundary; P45: outputting the predicted waveband boundary as the first waveband boundary corresponding to the waveband switching instruction.

[0054] Specifically, during the execution of the waveband boundary adjustment process, the waveband boundary adjustment module 40 not only relies on the matching of the waveband signal transmission environment information template, but also adds an emergency mechanism to deal with matching failure, which can ensure that in some cases the environment information template cannot be successfully matched with real-time data, the system can still predict a suitable waveband boundary through the regression model, thereby avoiding problems such as waveband loss or unstable signal.

[0055] In the matching process, first, based on the constructed band signal transmission environment information template, attempt to match the band signal transmission environment information in the real-time band switching data. When the system successfully matches the environment information template, a suitable matching band boundary can be directly generated. However, if the matching based on the band signal transmission environment information template is unsuccessful in the matching process, that is, no template item matching the current transmission environment information can be found, the emergency mechanism is triggered. In this case, the band boundary adjustment module 40 will re-call the first interval regression model or the second interval regression model for prediction. Which interval regression model to call depends on the type of band switching instruction. For example, if the band switching instruction is to switch from C band to L band, the first interval regression model is called; if it is to switch from L band to C band, the second interval regression model is called. Through the prediction of the regression model, a predicted band boundary is output, which is based on the current transmission environment information and the prediction result of the regression model.

[0056] Then, the predicted band boundary is used as the first band boundary output corresponding to the current band switching instruction. This predicted band boundary is obtained through the analysis of the regression model, taking into account the current transmission environment information and the regularity in the historical data, and thus can provide a reasonable band boundary adjustment suggestion for the tunable laser in the 400G coherent optical module. Even in the case of unsuccessful matching, the band boundary adjustment module 40 can ensure the stability and reliability of the band switching process through the prediction ability of the regression model. Finally, this predicted band boundary will ensure that the band boundary setting of the tunable laser in the 400G coherent optical module accurately meets the current band switching requirements, thereby ensuring the stability and quality of signal transmission.

[0057] In summary, the embodiments of the present application have at least the following technical effects: Through intelligent band boundary adjustment, the present application can improve the stability of band switching, reduce the phenomenon of band loss, and ensure the signal quality and system stability during band switching; by using historical data and real-time monitoring to dynamically adjust the band boundary, the adaptive ability of the system is enhanced, so that it can optimize the transmission performance according to different band switching requirements and environmental changes; by accurately adjusting the band boundary, the efficiency and reliability of the 400G coherent optical module in the multi-band switching environment are improved, and the utilization of system resources is optimized, avoiding unnecessary waste of resources.

[0058] The technical effect of accurately responding to dynamic changes in band switching through intelligent monitoring and dynamic adjustment mechanism, effectively reducing band loss, and improving communication stability is achieved.

[0059] Embodiment two, based on the same inventive concept as the intelligent monitoring system for 400G coherent optical modules in the foregoing embodiments, as follows:Figure 2 As shown, the present application provides an intelligent monitoring method for a 400G coherent optical module, and the system and method embodiments in the present application are based on the same inventive concept. The method comprises: loading historical band switching data of the 400G coherent optical module within a preset period, the historical band switching data comprising historical band received signals, band switching type labels, band signal transmission environment information, and accuracy labels representing band signal receiving accuracy, wherein the band switching type labels at least comprise a switching type of switching from a C band to an L band, and a switching type of switching from an L band to a C band; constructing a band boundary locking template based on the historical band switching data; determining whether the 400G coherent optical module receives a band switching instruction, and if the 400G coherent optical module receives a band switching instruction, obtaining real-time band switching data monitored; analyzing the real-time band switching data using the constructed band boundary locking template to obtain a first band boundary corresponding to the band switching instruction, and adjusting a band boundary of a tunable laser in the 400G coherent optical module based on the first band boundary.

[0060] Further, the band boundary locking template is constructed based on the historical band switching data, and the method comprises: collecting a first group of historical band switching data of switching from a C band to an L band, and a second group of historical band switching data of switching from an L band to a C band; taking historical band received signals and band signal transmission environment information corresponding to the first group of historical band switching data and the second group of historical band switching data as input data, taking a band boundary as a variable space, and taking accuracy labels corresponding to the first group of historical band switching data and the second group of historical band switching data as supervision labels to perform regression analysis, thereby obtaining a band boundary regression model; and constructing a band boundary locking template based on the band boundary regression model.

[0061] Further, the band boundary regression model comprises a first interval regression model of switching from a C band to an L band, and a second interval regression model of switching from an L band to a C band; the band switching type of the band switching instruction is disassembled, and the real-time band switching data is analyzed using the first interval regression model or the second interval regression model according to the band switching type.

[0062] Further, the method further comprises: training a generator according to the band boundary regression model, the generator being used to obtain incremental band switching data of the historical band switching data; performing incremental optimization on the band boundary regression model according to the incremental band switching data, and outputting an optimized band boundary regression model.

[0063] Further, the waveband boundary regression model is used to construct a waveband boundary locking template, and the method comprises: The first set of historical waveband switching data is clustered according to waveband signal transmission environment information, and a plurality of representative transmission environment information is obtained. The preferred waveband boundary corresponding to the plurality of representative transmission environment information is obtained according to the first interval regression model, and a first waveband boundary locking template is constructed. The second set of historical waveband switching data is clustered according to waveband signal transmission environment information, and a plurality of representative transmission environment information is obtained. The preferred waveband boundary corresponding to the plurality of representative transmission environment information is obtained according to the second interval regression model, and a second waveband boundary locking template is constructed. The first waveband boundary locking template and the second waveband boundary locking template are used to construct a waveband boundary locking template.

[0064] Further, the constructed waveband boundary locking template is used to analyze the real-time waveband switching data, and the method comprises: Waveband signal transmission environment information in the real-time waveband switching data is extracted. Waveband signal transmission environment information templates of the waveband boundary locking template are extracted. The waveband signal transmission environment information is matched based on the waveband signal transmission environment information templates, and a matched waveband boundary is obtained. The matched waveband boundary is output as the first waveband boundary corresponding to the waveband switching instruction.

[0065] Further, the waveband signal transmission environment information is matched based on the waveband signal transmission environment information templates. If the matching is unsuccessful, the first interval regression model or the first interval regression model is called again to predict a predicted waveband boundary. The predicted waveband boundary is output as the first waveband boundary corresponding to the waveband switching instruction.

[0066] Further, the 400G coherent optical module further comprises an expansion module. A plurality of wavebands are expanded according to the expansion module, and a plurality of waveband switching type labels are updated according to the plurality of wavebands.

[0067] Further, the method for obtaining an accuracy label representing waveband signal reception accuracy comprises: The historical waveband reception signal is evaluated according to waveband accuracy, including wavelength attenuation slope, filter loss gain, and signal bit error rate. The wavelength attenuation slope, filter loss gain, and signal bit error rate are calculated according to weight, and an accuracy label representing waveband signal reception accuracy corresponding to the historical waveband reception signal is obtained.

[0068] It should be noted that the above-mentioned embodiment sequences of the present application are merely for description only, but not for representing the advantages and disadvantages of the embodiments. And the above-mentioned embodiments of the present specification have been described. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or can be advantageous.

[0069] The above only describes the preferred embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0070] The specification and drawings are merely exemplary of the present application, and any and all modifications, variations, combinations or equivalents that are within the scope of the present application should be considered covered by the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalents, the present application is intended to include these modifications and variations.

Claims

1. An intelligent monitoring system for 400G coherent optical modules, characterized in that, The system includes: The historical band switching data acquisition module is used to load the historical band switching data of the 400G coherent optical module within a preset period. The historical band switching data includes historical band received signals, band switching type labels, band signal transmission environment information, and accuracy labels characterizing the accuracy of band signal reception. The band switching type labels include at least the switching type from C-band to L-band and the switching type from L-band to C-band. A band boundary locking template construction module is used to construct a band boundary locking template based on the historical band switching data. The real-time band switching data monitoring module is used to determine whether the 400G coherent optical module receives a band switching command. If the 400G coherent optical module receives a band switching command, it acquires the monitored real-time band switching data. The band boundary adjustment module is used to analyze the real-time band switching data using the constructed band boundary locking template, obtain the first band boundary corresponding to the band switching command, and adjust the band boundary of the tunable laser in the 400G coherent optical module based on the first band boundary.

2. The intelligent monitoring system for a 400G coherent optical module as described in claim 1, characterized in that, The band boundary locking template construction module is also used for: Collect the first set of historical band switching data from C-band to L-band, and the second set of historical band switching data from L-band to C-band; Using the historical band received signal and band signal transmission environment information corresponding to the first set of historical band switching data and the second set of historical band switching data as input data, and the band boundary as the adjustable variable space, the accuracy labels corresponding to the first set of historical band switching data and the second set of historical band switching data are used as supervision labels for regression analysis to obtain the band boundary regression model. A band boundary locking template is constructed based on the band boundary regression model.

3. The intelligent monitoring system for a 400G coherent optical module as described in claim 2, characterized in that, In the band boundary locking template construction module: The band boundary regression model includes a first interval regression model for switching from C-band to L-band, and a second interval regression model for switching from L-band to C-band. The band switching type of the band switching command is decomposed, and the real-time band switching data is analyzed according to the band switching type using the first interval regression model or the second interval regression model.

4. The intelligent monitoring system for a 400G coherent optical module as described in claim 2, characterized in that, After obtaining the band boundary regression model, the band boundary locking template construction module is also used for: A generator is trained based on the band boundary regression model, and the generator is used to obtain incremental band switching data from the historical band switching data. The band boundary regression model is incrementally optimized based on the incremental band switching data, and the optimized band boundary regression model is output.

5. The intelligent monitoring system for a 400G coherent optical module as described in claim 3, characterized in that, When constructing a band boundary locking template based on the band boundary regression model, the band boundary locking template construction module is also used for: Cluster the first set of historical band switching data for band signal transmission environment information to obtain multiple representative transmission environment information. Obtain the preferred band boundaries corresponding to the multiple representative transmission environment information according to the first interval regression model, and construct the first band boundary locking template. Cluster the band signal transmission environment information of the second group of historical band switching data to obtain multiple representative transmission environment information. According to the second interval regression model, obtain the preferred band boundary corresponding to the multiple representative transmission environment information and construct the second band boundary locking template. A band boundary locking template is constructed based on the first band boundary locking template and the second band boundary locking template.

6. The intelligent monitoring system for a 400G coherent optical module as described in claim 3, characterized in that, When the band boundary adjustment module analyzes the real-time band switching data using the constructed band boundary locking template, it is also used for: Extract the band signal transmission environment information from the real-time band switching data; Extract the band signal transmission environment information template of the band boundary locking template, and match the band signal transmission environment information based on the band signal transmission environment information template to obtain the matched band boundary; The matching band boundary is output as the first band boundary corresponding to the band switching command.

7. The intelligent monitoring system for a 400G coherent optical module as described in claim 6, characterized in that, The band boundary adjustment module is also used for: The band signal transmission environment information is matched based on the band signal transmission environment information template. If the matching fails, the first interval regression model is called again to make a prediction and the predicted band boundary is output. The predicted band boundary is output as the first band boundary corresponding to the band switching command.

8. The intelligent monitoring system for a 400G coherent optical module as described in claim 1, characterized in that, The 400G coherent optical module also includes an extension module, which extends multiple bands and updates multiple band switching type labels based on the multiple bands.

9. The intelligent monitoring system for a 400G coherent optical module as described in claim 1, characterized in that, When acquiring the accuracy tag that characterizes the accuracy of band signal reception, the historical band switching data acquisition module is also used for: Systems for obtaining accuracy labels that characterize the reception accuracy of band signals include: The band accuracy of historical band received signals is evaluated, including wavelength attenuation slope, filter loss gain, and signal bit error rate. The wavelength attenuation slope, filter loss gain, and signal error rate are weighted and calculated to obtain the accuracy label representing the signal reception accuracy of the historical band received signal.

10. An intelligent monitoring method for 400G coherent optical modules, characterized in that, The method includes: Load the historical band switching data of the 400G coherent optical module within a preset period. The historical band switching data includes historical band received signals, band switching type labels, band signal transmission environment information, and accuracy labels characterizing the accuracy of band signal reception. The band switching type labels include at least the switching type from C-band to L-band and the switching type from L-band to C-band. A band boundary locking template is constructed based on the historical band switching data; Determine whether the 400G coherent optical module receives a band switching command. If the 400G coherent optical module receives a band switching command, obtain the real-time band switching data obtained from monitoring. The real-time band switching data is analyzed using the constructed band boundary locking template to obtain the first band boundary corresponding to the band switching command, and the band boundary of the tunable laser in the 400G coherent optical module is adjusted based on the first band boundary.

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