Service data processing method and device for optical network maintenance and computer equipment

Abstract

The invention relates to a service data processing method and device for optical network maintenance and computer equipment. The method comprises the following steps: acquiring optical network service configuration data; the optical network service configuration data at least comprises equipment configuration data of optical layer equipment and equipment configuration data of electric layer equipment; predicting a service state parameter corresponding to the optical network according to the optical network service configuration data; the service state parameters comprise optical layer performance parameters and electric layer performance parameters; under the condition that the service state parameter does not accord with the expected range, adjusting the optical network service configuration data according to a preset maintenance processing strategy to obtain updated optical network service configuration data; wherein the maintenance processing strategy is used for optimizing the optical network service configuration data by taking the condition that the service state parameters are adjusted to meet the expected range as an optimization target. By adopting the method, the reliability of service maintenance of the optical network can be improved.

Description

Technical Field

[0001] The present application relates to the field of network security technology, and in particular to a method, apparatus, computer equipment, computer-readable storage medium, and computer program product for processing service data for optical network maintenance. Background Art

[0002] Optical networks are communications systems that use optical fiber as their primary transmission medium, enabling high-speed data transmission via optical signals. Optical network maintenance is a systematic process that monitors, optimizes, troubleshoots, and ensures the performance of services carried by the network to ensure continuous and stable service operation and maintain service quality. The importance of optical network maintenance lies primarily in ensuring service quality, improving network efficiency, preventing failures, and reducing operational costs. It is not only a technical requirement, but also a necessity for business development and market competition.

[0003] In traditional technologies, service maintenance of optical networks requires manual configuration query, adjustment, and verification. Its adjustment principles rely heavily on expert experience and cannot adapt to all scenarios and types, resulting in low reliability of service maintenance of optical networks. Summary of the Invention

[0004] Based on this, it is necessary to provide a service data processing method, device, computer equipment, computer-readable storage medium and computer program product for optical network maintenance that can improve the reliability of service maintenance of optical networks in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for processing service data for optical network maintenance, comprising:

[0006] Obtaining optical network service configuration data; the optical network service configuration data at least includes device configuration data of optical layer devices and device configuration data of electrical layer devices;

[0007] Predicting service status parameters corresponding to the optical network based on the optical network service configuration data; the service status parameters include optical layer performance parameters and electrical layer performance parameters; the optical layer performance parameters are used to characterize the transmission quality of optical signals in the optical network, and the electrical layer performance parameters are used to characterize the transmission quality of electrical signals in the optical network;

[0008] When the service status parameter does not meet the expected range, the optical network service configuration data is adjusted according to a preset maintenance processing strategy to obtain updated optical network service configuration data;

[0009] The maintenance processing strategy is used to optimize the optical network service configuration data by adjusting the service status parameter to conform to the expected range as an optimization goal.

[0010] In one embodiment, the maintenance processing strategy includes a strategy for optimizing the optical network service configuration data using a pre-trained configuration data adjustment model, and adjusting the optical network service configuration data according to the preset maintenance processing strategy to obtain updated optical network service configuration data includes:

[0011] Inputting the optical network service configuration data into the pre-trained configuration data adjustment model to obtain adjustment instruction information for the optical network service configuration data;

[0012] According to the adjustment instruction information, the optical network service configuration data is adjusted to obtain the updated optical network service configuration data.

[0013] In one embodiment, predicting a service status parameter corresponding to the optical network based on the optical network service configuration data includes:

[0014] Determining a first parameter prediction model that matches the optical layer device based on core component attributes represented by device configuration data of the optical layer device, and inputting the device configuration data of the optical layer device into the first parameter prediction model to obtain the optical layer performance parameters;

[0015] Determining a second parameter prediction model that matches the electrical layer device based on core component attributes represented by device configuration data of the electrical layer device, and inputting the device configuration data of the electrical layer device into the second parameter prediction model to obtain the electrical layer performance parameters;

[0016] Determine the service status parameters corresponding to the optical network according to the optical layer performance parameters and the electrical layer performance parameters.

[0017] In one embodiment, the method further comprises:

[0018] Using the updated optical network service configuration data as new optical network service configuration data, and returning to the step of predicting the service status parameters corresponding to the optical network based on the optical network service configuration data;

[0019] When the service status parameter meets the expected range, service maintenance feedback information is output; the service maintenance feedback information is used to indicate that the optical network has completed service maintenance.

[0020] In one embodiment, the outputting of service maintenance feedback information includes:

[0021] Determining the current service state parameter as the post-maintenance service state parameter corresponding to the optical network;

[0022] Service maintenance feedback information is generated according to the post-maintenance service state parameters corresponding to the optical network.

[0023] In one embodiment, obtaining optical network service configuration data includes:

[0024] Obtaining optical network service identification information, and determining original service data that matches the optical network service identification information;

[0025] dividing the original service data into device configuration data of the optical layer device and device configuration data of the electrical layer device according to device type information associated with the original service data;

[0026] The optical network service configuration data is determined according to the device configuration data of the optical layer device and the device configuration data of the electrical layer device.

[0027] In a second aspect, the present application further provides a service data processing device for optical network maintenance, comprising:

[0028] An acquisition module is used to acquire optical network service configuration data; the optical network service configuration data at least includes device configuration data of optical layer devices and device configuration data of electrical layer devices;

[0029] A prediction module, configured to predict service status parameters corresponding to the optical network based on the optical network service configuration data; the service status parameters include optical layer performance parameters and electrical layer performance parameters; the optical layer performance parameters are used to characterize the transmission quality of optical signals in the optical network, and the electrical layer performance parameters are used to characterize the transmission quality of electrical signals in the optical network;

[0030] An adjustment module is used to adjust the optical network service configuration data according to a preset maintenance processing strategy when the service status parameters do not meet the expected range, so as to obtain updated optical network service configuration data; wherein the maintenance processing strategy is used to optimize the optical network service configuration data with the adjustment of the service status parameters to meet the expected range as the optimization goal.

[0031] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0032] In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0033] In a fifth aspect, the present application also provides a computer program product, comprising a computer program, which implements the steps of the above method when executed by a processor.

[0034] The above-mentioned service data processing method, device, computer equipment, computer-readable storage medium and computer program product for optical network maintenance obtain optical network service configuration data, wherein the optical network service configuration data at least includes equipment configuration data of optical layer equipment and equipment configuration data of electrical layer equipment; based on the optical network service configuration data, predict the service status parameters corresponding to the optical network, wherein the service status parameters include optical layer performance parameters and electrical layer performance parameters, the optical layer performance parameters are used to characterize the transmission quality of optical signals in the optical network, and the electrical layer performance parameters are used to characterize the transmission quality of electrical signals in the optical network; when the service status parameters do not meet the expected range, the optical network service configuration data are adjusted according to the preset maintenance processing strategy to obtain updated optical network service configuration data, wherein the maintenance processing strategy is used to optimize the optical network service configuration data with the optimization goal of adjusting the service status parameters to meet the expected range. In this way, by obtaining the equipment configuration data of the optical layer equipment and the equipment configuration data of the electrical layer equipment, the optical and electrical data can be decoupled, making the separate predictions of the optical layer performance parameters and the electrical layer performance parameters more accurate. Based on these equipment configuration data, the service status parameters used to reflect key performance indicators can be accurately predicted, thereby realizing comprehensive monitoring of the optical signal transmission quality and the electrical signal transmission quality. When the predicted service status parameters do not meet the expected range, the adjustment of the optical network service configuration data can be automatically triggered according to the preset maintenance processing strategy, thereby realizing the automated maintenance of the optical network service and improving the reliability and efficiency of the optical network service maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments of the present application or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 A diagram illustrating an application environment of a service data processing method for optical network maintenance according to an embodiment;

[0037] Figure 2 A logical diagram of an intelligent network management and control system in one embodiment;

[0038] Figure 3 A schematic flow chart of a service data processing method for optical network maintenance according to an embodiment;

[0039] Figure 4 is a logic diagram of a parameter prediction method in one embodiment;

[0040] Figure 5 A schematic flow chart of a service data processing method for optical network maintenance according to another embodiment;

[0041] Figure 6 A structural block diagram of a service data processing device for optical network maintenance in one embodiment;

[0042] Figure 7 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0044] The service data processing method for optical network maintenance provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the intelligent network management and control system 100 includes a service information issuing unit 102 , a service maintenance prediction and processing unit 104 , and a service maintenance feedback unit 106 .

[0045] To facilitate understanding by those skilled in the art, Figure 2 A logical diagram of an intelligent network management and control system is provided as an example. In practical applications, the intelligent network management and control system 100 transmits service information requiring network maintenance prediction via the service information delivery unit 102. This service information may include, for example, an optical network service identifier. This service information is then sent to the service maintenance prediction and processing unit 104. Based on the service information, the unit 104 retrieves optical network service configuration data, including device configuration data for both optical and electrical layer devices. The unit then automatically predicts the parameters to be maintained. In the automatic prediction link of parameters to be maintained, the service maintenance prediction and processing unit 104 can automatically predict the parameter values ​​to be maintained of the optical layer and electrical layer based on the optical network service configuration data using an artificial intelligence algorithm. The parameter values ​​to be maintained are service status parameters including optical layer performance parameters and electrical layer performance parameters, and then verify whether the parameter values ​​to be maintained are within the pre-divided expected range; if they do not meet the expected range, the service maintenance prediction and processing unit 104 enters the configuration automatic processing link, and automatically adjusts the equipment configuration data based on the corresponding maintenance processing strategy, and determines the new optical network service configuration data according to the new equipment configuration data, and then re-executes the automatic prediction link of parameters to be maintained; if they meet the expected range, the service maintenance feedback unit 106 is used to feedback to the intelligent network management and control system 100 that the service maintenance has been completed.

[0046] The intelligent network management and control system 100 can be used for automatic maintenance of services in optoelectronic decoupling scenarios. When there is a maintenance demand for the service, the service maintenance prediction and processing unit 104 automatically obtains the corresponding prediction model based on the artificial intelligence algorithm, automatically obtains the parameter value and performs maintenance processing. The service maintenance prediction and processing unit 104 involves the entire process from receiving service information to completing service maintenance, and can realize an automated service maintenance process. In addition, in the automatic prediction link of parameters to be maintained, processes such as automatic data collection, model matching, and parameter prediction of the optical layer and the electrical layer are involved; in the automatic configuration processing link, automatic data verification and configuration adjustment can be realized, thereby simplifying the operational process of optical network service maintenance, realizing service maintenance automation, and thus improving the accuracy and efficiency of optical network service maintenance.

[0047] In an exemplary embodiment, Figure 3 As shown, a method for processing service data of optical network maintenance is provided, which is applied to Figure 1 The intelligent network management and control system 100 in FIG. 1 is used as an example to illustrate the invention, including:

[0048] Step S302: Acquire optical network service configuration data.

[0049] The optical network service configuration data at least includes device configuration data of optical layer devices and device configuration data of electrical layer devices.

[0050] Among them, optical network service configuration data is a set of structured data directly related to service transmission quality and equipment operating status in the optical network. It covers the observable parameters and configuration parameters generated by optical layer equipment and electrical layer equipment during operation, and is the core basis for the formulation of subsequent maintenance strategies.

[0051] Optical layer equipment (OLE) is a device that directly transmits, amplifies, routes, and processes optical signals in optical communication networks. It operates in the optical domain and processes optical signals (e.g., optical carriers of different wavelengths). The core functions of optical layer equipment are to enable long-distance transmission of optical signals, wavelength routing, power amplification, and the construction of optical layer network topologies. It does not involve the conversion of electrical signals (processing only within the optical domain). Exemplary optical layer equipment may include optical line amplifiers (OLAs), wavelength selective switches (WSSs), optical repeaters, and wavelength division multiplexers.

[0052] Optical layer device configuration data refers to data generated by the optical layer equipment responsible for optical signal transmission, amplification, and routing within the optical domain. For example, service data for optical layer equipment may include parameters such as the gain or attenuation of the booster amplifier (BA) and pre-amplifier (PA) of an optical line amplifier or wavelength selective switch. In practical applications, since optical layer equipment also includes line-side modules connected to optical fiber links, which facilitate the connection between the optical layer equipment and the transmission link, service data for optical layer equipment may also include information such as the manufacturer, type, and temperature of the line-side modules.

[0053] Electrical layer equipment is the equipment responsible for processing, converting, transmitting, and controlling electrical signals in optical communication networks. It operates in the electrical domain and processes electrical signals (such as digital signals in the form of voltage and current). The core functions of electrical layer equipment are to implement information encoding, modulation, multiplexing, demultiplexing, and electrical layer fault management, providing standardized electrical signal input / output interfaces for optical layer transmission. For example, electrical layer equipment may include service cards and modems. Service cards are the core functional cards within the electrical layer equipment used to access and process customer service signals, implementing functions such as electrical signal encoding, modulation, power management, and performance monitoring.

[0054] The electrical layer device configuration data refers to data generated by the electrical layer equipment responsible for electrical signal processing, encoding, and modulation within the electrical domain. For example, the service data for the electrical layer equipment may include the vendor and type of the service board, transmit / receive power, and configuration parameters related to forward error correction (FEC). In practical applications, since the electrical layer equipment also includes client-side modules connected to user services (clients) and used to access various user service signals, the service data for the electrical layer equipment may also include the vendor, type, and temperature of the client-side modules.

[0055] In one embodiment, obtaining optical network service configuration data includes: obtaining optical network service identification information and determining original service data that matches the optical network service identification information; dividing the original service data into device configuration data of optical layer devices and device configuration data of electrical layer devices based on device type information associated with the original service data; and determining the optical network service configuration data based on the device configuration data of the optical layer devices and the device configuration data of the electrical layer devices.

[0056] The optical network service identification information is information used to uniquely identify a specific service in the optical network, such as the service name, service ID, service type, customer ID, etc., and is used to distinguish different optical network services. In a specific implementation, the service identification information can be used to match the corresponding original service data in the database.

[0057] Among them, the original service data may include initial configuration and operation data associated with the optical network service identification information, covering service electrical layer data and service optical layer data, such as board type, modulation format, baud rate, optical fiber type, optical fiber span length, number of optical fiber spans, channel spacing, combiner insertion loss, channel initial transmission power, amplifier noise figure, number of channels and service channels, etc.

[0058] Among them, the device type information is a classification identifier used to distinguish optical layer devices from electrical layer devices. In a specific implementation, the device type information associated with the original service data can be determined from the device model or function description of the original service data. Based on the device type information, the original service data can be divided into device configuration data for optical layer devices and device configuration data for electrical layer devices. The divided device configuration data can then be format converted, statistically summarized, and analyzed, ultimately obtaining the optical network service configuration data. For example, if a piece of original service data contains the device description "WSS-32", the WSS in the device description represents a wavelength selective switch. Therefore, the device type information associated with the original service data is an optical layer device, and therefore the original service data belongs to the device configuration data of the optical layer device.

[0059] Step S304: predicting service status parameters corresponding to the optical network based on the optical network service configuration data.

[0060] Service status parameters are those that require maintenance and management in optical networks. These parameters are critical to the stable operation of services in optical networks, and abnormalities in these parameters can cause service failures in optical networks. For example, service status parameters may include BER (bit error rate), FER (frame error rate), PER (packet error rate), SNR (signal-to-noise ratio), EVM (error vector magnitude), QoS (quality of service), and throughput.

[0061] The service state parameter includes an optical layer performance parameter and an electrical layer performance parameter. The optical layer performance parameter is used to represent the transmission quality of an optical signal in the optical network, and the electrical layer performance parameter is used to represent the transmission quality of an electrical signal in the optical network. In practical applications, the optical layer performance parameter can be used to measure the optical signal transmission state, including optical signal-to-noise ratio (OSNR), chromatic dispersion, nonlinear effect, optical power, optical amplifier gain, polarization state, return loss, etc. The electrical layer performance parameter can be used to measure the electrical signal processing quality, including bit error rate (BER), jitter, drift, signal-to-noise ratio (SNR), eye diagram parameter, delay, etc.

[0062] In specific implementations, the optical layer performance parameter corresponding to the optical network can be predicted according to the device configuration data of the optical layer device, and the electrical layer performance parameter corresponding to the optical network can be predicted according to the device configuration data of the electrical layer device, so as to realize the automatic maintenance of the optical network service in the optical-electric decoupling scene. The optical-electric decoupling can refer to the separation of the processing processes of the optical signal and the electrical signal, so as to be independently optimized and managed. In the optical-electric decoupling scene, the prediction of the optical-electric layer parameters is more accurate.

[0063] In specific implementations, the optical network service configuration data can be input into an artificial intelligence model, and the service state parameter corresponding to the optical network can be predicted by the artificial intelligence model. Optionally, the artificial intelligence model can include a convolutional neural network (CNN), a random forest (RF), a long short-term memory network (LSTM), etc. For example, the artificial intelligence model can be obtained by training sample optical network service configuration data and corresponding real values (sample labels) of the service state parameter. In the training process, the artificial intelligence model to be trained can output the predicted value of the service state parameter according to the sample optical network service configuration data, and determine the loss function according to the difference between the predicted value and the real value. The model parameters are iteratively optimized by gradient descent method according to the gradient of the loss function on the model parameters, until the loss function converges, and the pre-trained artificial intelligence model can be obtained.

[0064] For example, a matching artificial intelligence model can be determined based on the parameter type of the service status parameter to be predicted, and the optical network service configuration data can be input into the matching artificial intelligence model to obtain the service status parameters corresponding to the optical network. For example, if the service status parameter to be predicted is a periodically varying parameter such as optical amplifier gain or a time-dependent parameter, a time series model such as an autoregressive integrated moving average (ARIMA) model or a long short-term memory (LSTM) model can be selected as the matching artificial intelligence model. For example, if the service status parameter to be predicted is a state-discriminating parameter such as bit error rate or optical signal-to-noise ratio, a classification model such as a decision tree or random forest can be selected as the matching artificial intelligence model. State-discriminating parameters can refer to parameters whose values ​​need to be determined based on a threshold. For example, if the bit error rate exceeds the limit, the value can be determined to be abnormal. Through the above steps, the data mining capabilities of the artificial intelligence algorithm can be used to automatically predict the target parameters and automatically process the configuration, conveniently solving the problems of simplifying and automating the operational process of service maintenance in the optoelectronic decoupling scenario.

[0065] Step S306: When the service status parameter does not meet the expected range, the optical network service configuration data is adjusted according to the preset maintenance processing strategy to obtain updated optical network service configuration data.

[0066] The maintenance processing strategy is used to optimize the optical network service configuration data by adjusting the service status parameters to meet the expected range as the optimization goal.

[0067] Among them, the maintenance processing strategy may include adjustment logic for optical network service configuration data, which can be generated through preset rules or artificial intelligence optimization algorithms.

[0068] In a specific implementation, when the service status parameters do not meet the expected range, the maintenance processing strategy associated with the abnormality type can be determined based on the abnormality type of the service status parameters. For example, a matching maintenance processing strategy can be automatically selected from the predefined strategy library based on the abnormality type, or a corresponding maintenance processing strategy can be generated based on the abnormality type through a pre-trained artificial intelligence model. For example, if the expected range of the optical signal-to-noise ratio is greater than 18 decibels (dB), and the actual optical signal-to-noise ratio is 15bB, the abnormality type can be determined to be too low optical signal-to-noise ratio, and then the corresponding maintenance processing strategy can be determined based on the abnormality type to be adjusting the optical amplifier gain (for example, increasing by 3dB); for another example, if the expected range of the bit error rate is less than , and the actual bit error rate is , it can be determined that the abnormality type is too high bit error rate, and then the corresponding maintenance processing strategy is determined according to the abnormality type to improve the FEC coding gain.

[0069] The updated optical network service configuration data is a set of optical network service configuration data adjusted through the maintenance processing strategy, including the adjusted device configuration data of the optical layer device and the adjusted device configuration data of the electrical layer device.

[0070] In the above-mentioned service data processing method for optical network maintenance, by obtaining the equipment configuration data of the optical layer equipment and the equipment configuration data of the electrical layer equipment, the optical and electrical data can be decoupled, so that the prediction of the optical layer performance parameters and the electrical layer performance parameters performed separately is more accurate. Based on these equipment configuration data, the service status parameters used to reflect the key performance indicators can be accurately predicted, and comprehensive monitoring of the optical signal transmission quality and the electrical signal transmission quality can be achieved; when the predicted service status parameters do not meet the expected range, the adjustment of the optical network service configuration data can be automatically triggered according to the preset maintenance processing strategy, thereby realizing the automated maintenance of the optical network service and improving the reliability and efficiency of the optical network service maintenance.

[0071] In another embodiment, the maintenance processing strategy includes a strategy for optimizing the optical network service configuration data using a pre-trained configuration data adjustment model, adjusting the optical network service configuration data according to the preset maintenance processing strategy to obtain updated optical network service configuration data, including: inputting the optical network service configuration data into the pre-trained configuration data adjustment model to obtain adjustment indication information for the optical network service configuration data; adjusting the optical network service configuration data according to the adjustment indication information to obtain updated optical network service configuration data.

[0072] Among them, the pre-trained configuration data adjustment model can be an artificial intelligence model obtained by training historical optical network service configuration data and corresponding maintenance processing strategies.

[0073] Among them, the adjustment indication information can be the optimal adjustment plan output by the configuration data adjustment model. For example, it can include a plan for adjusting the equipment configuration data of optical layer equipment such as the signal power value of the optical module, the gain value of the optical amplifier, the attenuation value of the optical filter and the optical attenuator. It can also include a plan for adjusting the equipment configuration data of the electrical layer equipment such as the signal rate and modulation format. The plan may include a specific adjustment amplitude, etc.

[0074] In a specific implementation, the adjustment indication information may include an adjustment value for the optical network service configuration data. Therefore, the inputs to the configuration data adjustment model are the optical network service configuration data and the service status parameters corresponding to the optical network. The output of the configuration data adjustment model is the adjustment value for the optical network service configuration data. For example, if the original gain of the optical amplifier is 12 dB, the output of the configuration data adjustment model is an adjustment value of 15 dB.

[0075] In a specific implementation, the configuration data adjustment model can generate adjustment indication information through algorithm modeling such as convex optimization algorithm, heuristic algorithm or gradient descent algorithm.

[0076] Convex optimization algorithms can utilize the properties of convex functions (local optimality is also global optimality) to find the optimal parameter combination that achieves the objective function (e.g., maximizing OSNR and minimizing power consumption) while satisfying device constraints (e.g., optical power limit and gain adjustment range). For example, when the optical signal-to-noise ratio (OSNR) falls outside the desired range, a convex optimization model is constructed using the "optical amplifier gain adjustment amount" and "optical module transmit power" as variables. The constraints are the maximum amplifier gain and the optical module's safe power limit, and the objective function is maximizing OSNR. The optimal gain and power adjustment combination is then solved.

[0077] Heuristic algorithms can simulate natural laws (such as genetic algorithms and ant colony algorithms) or empirical rules to search for feasible solutions in complex nonlinear scenarios. They do not pursue global optimization but focus on real-time performance and adaptability. For example, when the bit error rate (BER) does not meet the expected range and multiple parameters need to be adjusted simultaneously (such as the modulation format, signal rate, and optical filter attenuation), a genetic algorithm is used to generate multiple adjustment schemes. Through iterative optimization using "crossover" and "mutation" operations, a solution that meets the BER requirement and maximizes bandwidth utilization is quickly selected, avoiding local optimization.

[0078] The gradient descent algorithm uses the difference between the current service state parameters and those within the desired range as a loss function. It then calculates the gradient direction and iteratively adjusts the optical network service configuration data until the loss function is minimized (e.g., the service state parameters converge to the desired range). For example, when adjusting the gain of an optical amplifier, the difference between the current OSNR and the target OSNR is used as the loss function. The gradient direction of the gain adjustment is calculated (e.g., for every 1dB increase in gain, the OSNR increases by 0.5dB). Iterative fine-tuning (e.g., by 0.5dB increments) is then performed until the OSNR meets the target.

[0079] The technical solution of this embodiment generates adjustment instruction information for adjusting optical network service configuration data through a pre-trained configuration data adjustment model, which can transform manual experience into data-driven automated decision-making, thereby realizing the automation and precision of optical network maintenance.

[0080] In another embodiment, based on the optical network service configuration data, service status parameters corresponding to the optical network are predicted, including: determining a first parameter prediction model that matches the optical layer device based on the core component attributes represented by the device configuration data of the optical layer device, and inputting the device configuration data of the optical layer device into the first parameter prediction model to obtain optical layer performance parameters; determining a second parameter prediction model that matches the electrical layer device based on the core component attributes represented by the device configuration data of the electrical layer device, and inputting the device configuration data of the electrical layer device into the second parameter prediction model to obtain electrical layer performance parameters; and determining the service status parameters corresponding to the optical network based on the optical layer performance parameters and the electrical layer performance parameters.

[0081] Among them, the first parameter prediction model is an artificial intelligence model for predicting optical layer performance parameters, and the second parameter prediction model is an artificial intelligence model for predicting electrical layer performance parameters. Optionally, the artificial intelligence model may include convolutional neural network (CNN), random forest (RF), long short-term memory network (LSTM), etc.

[0082] For the sake of illustrative purposes, to facilitate understanding by those skilled in the art, Figure 4 This paper provides an illustrative logic diagram of a parameter prediction method. Taking the optical signal-to-noise ratio (OSNR) as an example, the first parameter prediction model can be composed of a Transformer network and an LSTM network. The Transformer network's self-attention mechanism acts as a global feature extractor, assigning different weights to different input information to capture global patterns in the sequence. The LSTM network then further processes temporal features, smoothing local fluctuations and preventing the model from over-reliance on isolated time point data. This first parameter prediction model can accurately predict the OSNR.

[0083] Among them, the core component attributes may include the manufacturer, model specifications, hardware version, etc. of the board.

[0084] In practical applications, let's take the core component attribute of the board manufacturer as an example. For optical layer equipment, different manufacturers' boards vary in terms of optical amplifier gain efficiency, wavelength switching accuracy, and other aspects due to differences in hardware design and process. For example, if the board is an optical line amplifier (OLA), two OLAs from different manufacturers with the same decibel gain will ultimately have different optical signal-to-noise ratios. For electrical layer equipment, different manufacturers' boards also vary in terms of modulation format support, coding efficiency, and other aspects due to differences in hardware design and process. Therefore, a separate first parameter prediction model can be trained for each board manufacturer's optical layer equipment, and a separate second parameter prediction model can be trained for each board manufacturer's electrical layer equipment.

[0085] Therefore, based on the core component attributes represented by the device configuration data of the optical layer device, the first parameter prediction model that matches the optical layer device can be called from the pre-trained model library; and based on the core component attributes represented by the device configuration data of the electrical layer device, the second parameter prediction model that matches the electrical layer device can be called from the pre-trained model library.

[0086] The technical solution of this embodiment improves the reliability and efficiency of optical network service maintenance through accurate and automated matching of device attributes with models.

[0087] In another embodiment, it also includes: using the updated optical network service configuration data as new optical network service configuration data, and returning to the step of predicting the service status parameters corresponding to the optical network based on the optical network service configuration data; when the service status parameters are within the expected range, outputting service maintenance feedback information, and the service maintenance feedback information is used to indicate that the optical network has completed service maintenance.

[0088] In a specific implementation, a cyclic iterative process for optical network service maintenance can be executed. In the current iteration, service status parameters corresponding to the optical network can be predicted based on the optical network service configuration data. If the service status parameters do not meet the expected range, the optical network service configuration data can be adjusted to obtain updated optical network service configuration data. The updated optical network service configuration data is then used as new input and the process returns to the step of predicting service status parameters to initiate the next iteration. In each iteration, the optical network service configuration data can be fine-tuned according to a preset maintenance processing strategy to obtain updated optical network service configuration data. This allows the optimization target to be gradually approached through a closed-loop process of "prediction-adjustment-re-prediction", thereby avoiding the increase in error caused by excessive adjustments in a single iteration. For example, in the current iteration, if the optical signal-to-noise ratio does not meet the expected range, the optical amplifier gain can be fine-tuned (for example, by increasing it by 0.5 dB), and then the next iteration can be entered. If the optical signal-to-noise ratio still does not meet the expected range, the optical amplifier gain can be fine-tuned again (for example, by increasing it by 0.5 dB), until the loop termination condition (the optical signal-to-noise ratio meets the expected range) is met.

[0089] Service maintenance feedback can be a structured report generated after service status parameters return to the expected range, and can include maintenance results and adjustment records. For example, maintenance results may include the latest service status parameters such as "Optical Signal-to-Noise Ratio (OSNR) meets the standard" and "Electrical Layer Bit Error Rate meets the standard." Adjustment records may include data adjustment traces such as "Optical amplifier gain adjusted from 16dB to 18dB."

[0090] The technical solution of this embodiment achieves precise service maintenance through a dynamic iterative mechanism. It uses the adjusted configuration data as new input to drive the continuous optimization of the prediction parameters until feedback is output after the standards are met. This not only ensures the automation of optical network service maintenance, but also avoids the limitations of single decisions through cyclic verification, thereby improving the reliability of optical network service maintenance.

[0091] In another embodiment, outputting the service maintenance feedback information includes: determining the current service state parameters as the post-maintenance service state parameters corresponding to the optical network; and generating the service maintenance feedback information according to the post-maintenance service state parameters corresponding to the optical network.

[0092] Among them, the service status parameters after maintenance are the predicted final service status parameters after the optical network has been adjusted and maintained and reaches the expected state. For example, the service status parameters after maintenance may include optical signal-to-noise ratio (OSNR=18.5dB), optical power (-3dBm), bit error rate (BER=1 10 -10 ) and other final optical layer performance parameters, as well as final electrical layer performance parameters such as modulation format (16QAM), forward error correction coding efficiency (FEC=20%), and jitter (Jitter=25ps).

[0093] In a specific implementation, the service maintenance feedback information may include parameter change trajectories of service status parameters after maintenance, parameter compliance status, and the latest parameter values.

[0094] The technical solution of this embodiment automatically generates service maintenance feedback information according to the service status parameters after maintenance corresponding to the optical network, thereby ensuring the comprehensiveness and accuracy of the feedback information during service maintenance.

[0095] In another embodiment, Figure 5 As shown, a method for processing service data of optical network maintenance is provided, which is applied to Figure 1 The intelligent network management and control system 100 in FIG. 1 is used as an example to illustrate the method, which includes the following steps:

[0096] Step S502: Acquire optical network service configuration data.

[0097] The optical network service configuration data at least includes device configuration data of optical layer devices and device configuration data of electrical layer devices.

[0098] In one embodiment, the obtaining of optical network service configuration data includes: obtaining optical network service identification information and determining original service data that matches the optical network service identification information; dividing the original service data into device configuration data of the optical layer device and device configuration data of the electrical layer device according to device type information associated with the original service data; and determining the optical network service configuration data according to the device configuration data of the optical layer device and the device configuration data of the electrical layer device.

[0099] Step S504: predicting service status parameters corresponding to the optical network based on the optical network service configuration data.

[0100] Among them, the service status parameters include optical layer performance parameters and electrical layer performance parameters; the optical layer performance parameters are used to characterize the transmission quality of optical signals in the optical network, and the electrical layer performance parameters are used to characterize the transmission quality of electrical signals in the optical network.

[0101] In one embodiment, predicting the service status parameters corresponding to the optical network based on the optical network service configuration data includes: determining a first parameter prediction model that matches the optical layer device based on the core component attributes represented by the device configuration data of the optical layer device, and inputting the device configuration data of the optical layer device into the first parameter prediction model to obtain the optical layer performance parameters; determining a second parameter prediction model that matches the electrical layer device based on the core component attributes represented by the device configuration data of the electrical layer device, and inputting the device configuration data of the electrical layer device into the second parameter prediction model to obtain the electrical layer performance parameters; and determining the service status parameters corresponding to the optical network based on the optical layer performance parameters and the electrical layer performance parameters.

[0102] Step S506, determine whether the service status parameter meets the expected range; if not, execute step S508; if so, execute step S512.

[0103] Step S508: When the service status parameters do not meet the expected range, the optical network service configuration data is adjusted according to the preset maintenance processing strategy to obtain updated optical network service configuration data.

[0104] The maintenance processing strategy is used to optimize the optical network service configuration data by adjusting the service status parameters to meet the expected range as the optimization goal.

[0105] In one embodiment, the maintenance processing strategy includes a strategy for optimizing the optical network service configuration data using a pre-trained configuration data adjustment model, and adjusting the optical network service configuration data according to the preset maintenance processing strategy to obtain updated optical network service configuration data includes: inputting the optical network service configuration data into the pre-trained configuration data adjustment model to obtain adjustment indication information for the optical network service configuration data; and adjusting the optical network service configuration data according to the adjustment indication information to obtain the updated optical network service configuration data.

[0106] Step S510: Use the updated optical network service configuration data as new optical network service configuration data, and return to step S504.

[0107] Step S512: When the service status parameters are within the expected range, service maintenance feedback information is output.

[0108] The service maintenance feedback information is used to indicate that the service maintenance of the optical network has been completed.

[0109] In one embodiment, the outputting of the service maintenance feedback information includes: determining the current service status parameter as the post-maintenance service status parameter corresponding to the optical network; and generating the service maintenance feedback information according to the post-maintenance service status parameter corresponding to the optical network.

[0110] It should be noted that the specific definition of the above steps can refer to the specific definition of the service data processing method for optical network maintenance mentioned above.

[0111] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0112] Based on the same inventive concept, embodiments of the present application further provide a device for processing service data for optical network maintenance, for implementing the aforementioned method for processing service data for optical network maintenance. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations in one or more embodiments of the device for processing service data for optical network maintenance provided below can be found in the aforementioned method for processing service data for optical network maintenance, and will not be further elaborated upon here.

[0113] In an exemplary embodiment, Figure 6 As shown, a service data processing device for optical network maintenance is provided, comprising:

[0114] An acquisition module 610 is configured to acquire optical network service configuration data; the optical network service configuration data includes at least device configuration data of optical layer devices and device configuration data of electrical layer devices;

[0115] Prediction module 620, configured to predict service status parameters corresponding to the optical network based on the optical network service configuration data; the service status parameters include optical layer performance parameters and electrical layer performance parameters; the optical layer performance parameters are used to characterize the transmission quality of optical signals in the optical network, and the electrical layer performance parameters are used to characterize the transmission quality of electrical signals in the optical network;

[0116] The adjustment module 630 is used to adjust the optical network service configuration data according to a preset maintenance processing strategy when the service status parameters do not meet the expected range, so as to obtain updated optical network service configuration data; wherein the maintenance processing strategy is used to optimize the optical network service configuration data with the adjustment of the service status parameters to meet the expected range as the optimization goal.

[0117] In one embodiment, the maintenance processing strategy includes a strategy for optimizing the optical network service configuration data using a pre-trained configuration data adjustment model, and the adjustment module 630 is specifically used to input the optical network service configuration data into the pre-trained configuration data adjustment model to obtain adjustment indication information for the optical network service configuration data; and adjust the optical network service configuration data according to the adjustment indication information to obtain the updated optical network service configuration data.

[0118] In one embodiment, the prediction module 620 is specifically used to determine a first parameter prediction model that matches the optical layer device based on the core component attributes represented by the device configuration data of the optical layer device, and input the device configuration data of the optical layer device into the first parameter prediction model to obtain the optical layer performance parameters; determine a second parameter prediction model that matches the electrical layer device based on the core component attributes represented by the device configuration data of the electrical layer device, and input the device configuration data of the electrical layer device into the second parameter prediction model to obtain the electrical layer performance parameters; and determine the service status parameters corresponding to the optical network based on the optical layer performance parameters and the electrical layer performance parameters.

[0119] In one of the embodiments, the service data processing device for optical network maintenance further includes an output module;

[0120] The adjustment module 630 is specifically configured to use the updated optical network service configuration data as the new optical network service configuration data and return to the step of predicting the service status parameters corresponding to the optical network according to the optical network service configuration data;

[0121] The output module is specifically configured to output service maintenance feedback information when the service status parameter meets the expected range; the service maintenance feedback information is used to indicate that the service maintenance of the optical network has been completed.

[0122] In one embodiment, the output module is specifically configured to determine the current service status parameter as the post-maintenance service status parameter corresponding to the optical network; and generate service maintenance feedback information according to the post-maintenance service status parameter corresponding to the optical network.

[0123] In one embodiment, the acquisition module 610 is specifically used to obtain optical network service identification information and determine the original service data that matches the optical network service identification information; divide the original service data into the device configuration data of the optical layer device and the device configuration data of the electrical layer device according to the device type information associated with the original service data; and determine the optical network service configuration data according to the device configuration data of the optical layer device and the device configuration data of the electrical layer device.

[0124] Each module in the aforementioned optical network maintenance service data processing device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor within a computer device in the form of hardware, or may be stored in a memory within the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0125] In an exemplary embodiment, a computer device is provided. The computer device may be a server. The server may run the above-mentioned intelligent network management and control system. The internal structure diagram thereof may be as follows: Figure 7 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store business data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a business data processing method for optical network maintenance is implemented.

[0126] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps in the above method embodiments when executing the computer program.

[0127] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0128] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0129] It should be noted that the user information (including but not limited to user device configuration data, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0130] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the various embodiments provided herein may be, but are not limited to, general-purpose processors, central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), programmable logic devices (PLDs), quantum computing-based data processing logic devices, artificial intelligence (AI) processors, and the like.

[0131] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0132] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for processing service data for optical network maintenance, characterized in that: The method comprises: Obtaining optical network service configuration data; the optical network service configuration data at least includes device configuration data of optical layer devices and device configuration data of electrical layer devices; Predicting service status parameters corresponding to the optical network based on the optical network service configuration data; the service status parameters include optical layer performance parameters and electrical layer performance parameters; the optical layer performance parameters are used to characterize the transmission quality of optical signals in the optical network, and the electrical layer performance parameters are used to characterize the transmission quality of electrical signals in the optical network; When the service status parameter does not meet the expected range, the optical network service configuration data is adjusted according to a preset maintenance processing strategy to obtain updated optical network service configuration data; The maintenance processing strategy is used to optimize the optical network service configuration data by adjusting the service status parameter to conform to the expected range as an optimization goal.

2. The method according to claim 1, characterized in that The maintenance processing strategy includes a strategy for optimizing the optical network service configuration data using a pre-trained configuration data adjustment model, wherein the optical network service configuration data is adjusted according to the preset maintenance processing strategy to obtain updated optical network service configuration data, including: Inputting the optical network service configuration data into the pre-trained configuration data adjustment model to obtain adjustment instruction information for the optical network service configuration data; According to the adjustment instruction information, the optical network service configuration data is adjusted to obtain the updated optical network service configuration data.

3. The method according to claim 1, characterized in that The predicting, based on the optical network service configuration data, service status parameters corresponding to the optical network includes: Determining a first parameter prediction model that matches the optical layer device based on core component attributes represented by device configuration data of the optical layer device, and inputting the device configuration data of the optical layer device into the first parameter prediction model to obtain the optical layer performance parameters; Determining a second parameter prediction model that matches the electrical layer device based on core component attributes represented by device configuration data of the electrical layer device, and inputting the device configuration data of the electrical layer device into the second parameter prediction model to obtain the electrical layer performance parameters; Determine the service status parameters corresponding to the optical network according to the optical layer performance parameters and the electrical layer performance parameters.

4. The method according to claim 1, wherein The method further comprises: Using the updated optical network service configuration data as new optical network service configuration data, and returning to the step of predicting the service status parameters corresponding to the optical network based on the optical network service configuration data; When the service status parameter meets the expected range, service maintenance feedback information is output; the service maintenance feedback information is used to indicate that the optical network has completed service maintenance.

5. The method according to claim 4, characterized in that The output service maintenance feedback information includes: Determining the current service state parameter as the post-maintenance service state parameter corresponding to the optical network; Service maintenance feedback information is generated according to the post-maintenance service state parameters corresponding to the optical network.

6. The method according to claim 1, characterized in that The obtaining of optical network service configuration data includes: Obtaining optical network service identification information, and determining original service data that matches the optical network service identification information; dividing the original service data into device configuration data of the optical layer device and device configuration data of the electrical layer device according to device type information associated with the original service data; The optical network service configuration data is determined according to the device configuration data of the optical layer device and the device configuration data of the electrical layer device.

7. A service data processing device for optical network maintenance, characterized in that: The device comprises: An acquisition module is used to acquire optical network service configuration data; the optical network service configuration data at least includes device configuration data of optical layer devices and device configuration data of electrical layer devices; A prediction module, configured to predict service status parameters corresponding to the optical network based on the optical network service configuration data; the service status parameters include optical layer performance parameters and electrical layer performance parameters; the optical layer performance parameters are used to characterize the transmission quality of optical signals in the optical network, and the electrical layer performance parameters are used to characterize the transmission quality of electrical signals in the optical network; An adjustment module is used to adjust the optical network service configuration data according to a preset maintenance processing strategy when the service status parameters do not meet the expected range, so as to obtain updated optical network service configuration data; wherein the maintenance processing strategy is used to optimize the optical network service configuration data with the adjustment of the service status parameters to meet the expected range as the optimization goal.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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