Intelligent operation and maintenance terminal based on data analysis

Abstract

The invention, which relates to the technical field of the operation and maintenance intelligent terminal, discloses an operation and maintenance intelligent terminal based on data analysis, comprising an initial operation and maintenance period setting module, a real-time data acquisition and monitoring module, a data preprocessing and feature extraction module, an intelligent evaluation and prediction module and a dynamic maintenance strategy adjustment module. And the initial operation and maintenance period setting module is used for setting an initial operation and maintenance period for the optical fiber switch and carrying out routine maintenance, inspection and optimization according to a set period after the optical fiber switch is operated. The operation and maintenance intelligent terminal intelligently identifies the abnormal hidden danger of the link by collecting the data of the optical fiber switch in real time and combining the feature engineering and the machine learning model, and dynamically adjusts the maintenance period according to the abnormal severity, thereby improving the fault early warning capability and the maintenance efficiency, avoiding the hysteresis of the fixed period, ensuring the stable operation of the optical fiber link, and reducing the maintenance cost. Network interruption or service faults caused by the fact that problems are not found in time are reduced, and therefore the reliability and operation efficiency of the network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance intelligent terminals, and in particular to an operation and maintenance intelligent terminal based on data analysis. Background Art

[0002] The data analysis-based operation and maintenance intelligent terminal is a device that integrates data collection, real-time monitoring, intelligent analysis and remote control functions, and is mainly used to improve the operation and maintenance management efficiency of equipment or systems. The terminal obtains the equipment operating status, environmental parameters and fault information through sensors or other data acquisition modules, and uses big data analysis, machine learning or deep learning algorithms for intelligent diagnosis, predictive maintenance and anomaly detection. Through edge computing or cloud computing, the terminal can evaluate the health status of the equipment in real time, automatically generate operation and maintenance suggestions, and actively warn when potential risks are detected to avoid sudden failures. In addition, the terminal usually has remote operation and maintenance capabilities. Operation and maintenance personnel can monitor equipment, adjust parameters and schedule operation and maintenance through mobile terminals or cloud platforms to achieve refined management, reduce maintenance costs, and improve system reliability and operation and maintenance efficiency.

[0003] Fiber optic switches are one of the core devices in the operation and maintenance intelligent terminal. They are mainly used in high-bandwidth, low-latency, and high-reliability network environments such as data centers, industrial operation and maintenance, smart grids, and smart transportation. Its function is to achieve high-speed data exchange and intelligent distribution between multiple devices through fiber optic communication technology, and to ensure real-time monitoring and remote management of the operation and maintenance system. Compared with traditional Ethernet switches, fiber optic switches have higher transmission rates (such as 10G, 40G, 100G and above), lower signal attenuation, and stronger anti-interference capabilities. They are suitable for operation and maintenance scenarios that require large-scale data transmission and low-latency control. In addition, fiber optic switches support functions such as virtual local area networks (VLANs), optical link aggregation (LACP), and optical ring protection (ERPS). They can optimize the operation and maintenance network topology, improve communication stability, and provide redundant backup in key operation and maintenance systems to ensure data reliability and business continuity.

[0004] The prior art has the following deficiencies: Existing technologies usually use a fixed operation and maintenance cycle to maintain and manage fiber optic switches, that is, routine inspections, troubleshooting and maintenance work are performed at preset time intervals (such as monthly, quarterly or annually) to reduce equipment failure rates and ensure the long-term stable operation of fiber optic communication networks. However, when there are abnormal hidden dangers in the fiber optic link but the fixed cycle maintenance strategy is still used, serious consequences may occur. As a core network device, if the fiber optic switch fails to detect link failures in time, it may cause the paralysis of key communication links such as data centers, enterprise networks, industrial control systems, and smart grids, causing serious impact on business continuity. In the network of telecommunications operators, link anomalies may cause user network interruptions in a large area, causing large-scale service failures and economic losses. Therefore, relying solely on a fixed operation and maintenance cycle is no longer able to meet high reliability requirements. It is urgent to introduce intelligent monitoring and predictive maintenance to achieve real-time fault warnings and dynamic maintenance strategies to improve the stability and operational efficiency of fiber optic switches.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide an operation and maintenance intelligent terminal based on data analysis. By continuously collecting and processing the operating parameter data of the optical fiber switch, combined with the intelligent evaluation of feature engineering and machine learning models, the operation and maintenance intelligent terminal can accurately identify the potential abnormal hidden dangers of the optical fiber link, and dynamically adjust the maintenance cycle according to the severity of the link abnormality, and respond to the failure risk in time, effectively improving the fault warning capability and maintenance efficiency of the optical fiber switch, avoiding the lag problem of fixed operation and maintenance cycle, ensuring that the network equipment operates in an efficient and healthy state, and significantly reducing network interruptions or business failures caused by failure to discover problems in time, thereby improving the reliability, stability and operational efficiency of the network to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an operation and maintenance intelligent terminal based on data analysis, comprising an initial operation and maintenance cycle setting module, a real-time data acquisition and monitoring module, a data preprocessing and feature extraction module, an intelligent evaluation and prediction module, and a dynamic maintenance strategy adjustment module; The initial operation and maintenance cycle setting module sets an initial operation and maintenance cycle for the fiber optic switch. After the fiber optic switch is in operation, routine maintenance, inspection and optimization are performed according to the established cycle to ensure the normal operation and stable performance of the fiber optic switch. Real-time data collection and monitoring module, during the operation of the fiber optic switch, continuously collects the operating parameter data of the fiber optic switch in real time, establishes a real-time data stream of the device status, ensures that all potential abnormal information can be captured in time, and provides raw data support for subsequent data processing and intelligent diagnosis; The data preprocessing and feature extraction module performs preprocessing operations on the collected operating parameter data to ensure the accuracy and consistency of the data. Based on the preprocessed data, the module extracts the key features that reflect the abnormal health status of the optical fiber link. Under the monitoring window, the module further analyzes the extracted key features through feature engineering technology to provide input for the machine learning model, ensuring that the data input to the machine learning model has high discrimination, so as to improve the accuracy of optical fiber link abnormality hidden danger detection and prediction; The intelligent evaluation and prediction module feeds the features processed by feature engineering into a machine learning model that has been trained based on historical data in advance, and uses the machine learning model to comprehensively evaluate and predict the analyzed features, and automatically detect the operating status of the optical fiber link; The dynamic maintenance strategy adjustment module, when sensing that there are abnormal hidden dangers in the operation status of the optical fiber link, will intelligently and dynamically adjust the periodic maintenance strategy of the optical fiber switch according to the severity of the abnormal hidden danger of the optical fiber link, dynamically shorten the actual operation and maintenance cycle, and detect link fault problems in time.

[0008] Preferably, the initial operation and maintenance cycle refers to the first maintenance time interval comprehensively set for the fiber optic switch based on maintenance records, fault statistics and equipment life assessment data, providing a basic maintenance plan for the operation and maintenance intelligent terminal, ensuring that the equipment will not suffer unpredictable failures due to long-term lack of maintenance in the absence of intelligent monitoring or predictive maintenance.

[0009] Preferably, based on the preprocessed data, key features reflecting the abnormal health status of the optical fiber link are extracted, wherein the extracted features include the rate of power attenuation of the optical signal during transmission and the nonlinear growth of the bit error rate with the transmission distance. Under the monitoring window, the rate of power attenuation of the optical signal during transmission and the nonlinear growth of the bit error rate with the transmission distance are further analyzed through feature engineering technology, and the optical power attenuation factor and the bit error rate nonlinear increase factor are generated respectively. The optical power attenuation factor and the bit error rate nonlinear increase factor are used to quantify the abnormal operating state of the optical fiber link, and provide input for the machine learning model to improve the accuracy of optical fiber link anomaly detection and prediction.

[0010] Preferably, the optical power attenuation factor and the bit error rate nonlinear increase factor after feature engineering processing are sent to a machine learning model that has been trained in advance based on historical data, and a link anomaly index is generated through the machine learning model. The machine learning model is used to perform a comprehensive evaluation and prediction on the analyzed link anomaly index to automatically detect the operating status of the optical fiber link.

[0011] Preferably, the link anomaly index generated when the running state of the optical fiber link is evaluated and predicted by the trained machine learning model is compared and analyzed with the pre-set link anomaly index reference threshold, and the specific detection steps for the automatic detection of the running state of the optical fiber link are as follows: If the link abnormality index is greater than the link abnormality index reference threshold, a link abnormality signal is generated, indicating that there are abnormal risks in the health status of the optical fiber link; if the link abnormality index is less than or equal to the link abnormality index reference threshold, a link normal signal is generated, indicating that the optical fiber link is operating efficiently and healthily.

[0012] Preferably, under the monitoring window, the rate of power attenuation of the optical signal during transmission is further analyzed by feature engineering technology, and the specific steps of generating the optical power attenuation factor are as follows: First, any location on the fiber link L The optical power measured at A nonlinear transformation is performed to calculate the instantaneous decay rate. The expression for the calculation is: ,in: Indicates the position on the optical fiber link L The instantaneous optical power attenuation rate at Indicates the location of the optical fiber link L Real-time measurement of optical power at Indicates the length of the optical fiber L The derivative of reflects the instantaneous rate of change of optical power with distance. The negative sign ensures that when the optical power decays, the instantaneous optical power decay rate is a positive value; In order to obtain the instantaneous optical power attenuation rate along the optical fiber link Finally, the comprehensive attenuation index, namely the optical power attenuation factor, is constructed by using integration and nonlinear transformation. The calculation formula is: ,in: is the optical power attenuation factor, and are the starting and ending positions of the optical fiber link, is an exponential parameter used to amplify the contribution of high decay rate values, reflecting the nonlinear effect of abnormal aggravation. is the sensitivity coefficient, through the exponential function The impact of the local sudden increase in instantaneous decay rate is further amplified to highlight the abnormal phenomenon.

[0013] Preferably, under the monitoring window, the specific steps of further analyzing the nonlinear growth of the bit error rate with the transmission distance through feature engineering technology to generate the nonlinear increase factor of the bit error rate are as follows: In the monitoring window, we first analyze the bit error rate as a function of transmission distance. x The local differential analysis is performed on the changes of to capture the instantaneous characteristics of the nonlinear growth of the curve. The specific method is to calculate the first-order derivative and second-order derivative of the bit error rate, and then define the local curvature function. The expression of the local curvature function is: ,in: The bit error rate of the optical fiber link varies with the transmission distance. x The local curvature of the change is used to describe the curvature of the bit error rate curve at a certain point. is the first-order derivative of the bit error rate, which indicates the instantaneous rate of change of the bit error rate relative to the transmission distance. is the second-order derivative of the bit error rate, indicating the acceleration of the instantaneous rate of change, reflecting the "bending" trend of the bit error rate increase. The denominator Used for normalization to eliminate the impact of data scale on curvature calculation; After obtaining the local curvature function, the local abnormal conditions in the entire monitoring window are further aggregated to generate the final bit error rate nonlinear increase factor. The generation expression of the bit error rate nonlinear increase factor is: ,in: It represents the nonlinear increase factor of bit error rate, Indicates the transmission distance interval within the monitoring window. and They represent the starting point and end point of the transmission distance interval within the monitoring window respectively. is the absolute value of the local curvature, ensuring that each point contributes positively to the anomaly, is the power exponent parameter, which is used to enhance the sensitivity to local extreme outliers.

[0014] Preferably, when it is sensed that there is an abnormal hidden danger in the operation state of the optical fiber link, the periodic maintenance strategy of the optical fiber switch is intelligently and dynamically adjusted according to the severity of the abnormal hidden danger of the optical fiber link. The specific steps are as follows: When an abnormality risk is detected in a fiber link, the excess of the link abnormality index relative to the link abnormality index reference threshold is first calculated to quantify the severity of the fiber link abnormality. The calculation formula is as follows: ,in: It is the fiber link anomaly index evaluated by the machine learning model, reflecting the health of the current link operation status. It is the reference threshold of the link abnormality index, that is, the standard value of the fiber link in normal operation. is the link anomaly excess amplitude, which is used to measure the deviation of the link anomaly index from the reference threshold. The index parameter Used to control the sensitivity of the response to the severity of the anomaly; After quantifying the severity of the fiber link abnormality risk, the actual operation and maintenance cycle of the fiber switch is dynamically adjusted according to the link abnormality excess range to ensure that the maintenance frequency can respond to changes in the link health status in a timely manner. The adjustment formula is as follows: ,in: It is the actual adjusted operation and maintenance cycle, which is used to determine the time of the next maintenance. It is the initial operation and maintenance cycle. It is a dynamic adjustment coefficient, which is used to control the adjustment range.

[0015] In the above technical solution, the technical effects and advantages provided by the present invention are: By continuously collecting and processing the operating parameter data of the fiber optic switch and combining it with the intelligent evaluation of feature engineering and machine learning models, the operation and maintenance intelligent terminal can accurately identify the potential abnormal hidden dangers of the fiber optic link, dynamically adjust the maintenance cycle according to the severity of the link abnormality, and respond to the failure risk in time, effectively improving the fault warning capability and maintenance efficiency of the fiber optic switch, avoiding the lag problem of fixed operation and maintenance cycle, ensuring that the network equipment runs in an efficient and healthy state, and significantly reducing network interruptions or business failures caused by failure to discover problems in time, thereby improving the reliability, stability and operational efficiency of the network. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The present invention is a module schematic diagram of an operation and maintenance intelligent terminal based on data analysis. DETAILED DESCRIPTION

[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0019] The present invention provides Figure 1The intelligent operation and maintenance terminal based on data analysis shown includes an initial operation and maintenance cycle setting module, a real-time data acquisition and monitoring module, a data preprocessing and feature extraction module, an intelligent evaluation and prediction module, and a dynamic maintenance strategy adjustment module; The initial operation and maintenance cycle setting module sets an initial operation and maintenance cycle (such as monthly, quarterly or annually) for the fiber optic switch. After the fiber optic switch is put into operation, routine maintenance, inspection and optimization are performed according to the established cycle to ensure the normal operation and stable performance of the fiber optic switch. The initial operation and maintenance cycle refers to the first maintenance time interval set for the fiber optic switch based on maintenance records, failure statistics and equipment life assessment data. Its main function is to provide a basic maintenance plan to ensure that the equipment will not suffer unpredictable failures due to long-term lack of maintenance in the absence of intelligent monitoring or predictive maintenance.

[0020] This initial operation and maintenance cycle serves as the basis for maintenance management, ensuring that routine inspections are carried out regularly even when there are no abnormal warnings, providing a reliable baseline maintenance framework to ensure regular maintenance of equipment and reduce the accumulation of hidden faults that may result from long-term neglect.

[0021] Based on maintenance records, failure statistics and equipment life assessment data, it means that when setting the initial operation and maintenance cycle of the fiber optic switch, it is necessary to comprehensively consider the historical maintenance status of the equipment, the probability of failure and the expected service life of the equipment to ensure the rationality and scientificity of the maintenance cycle.

[0022] Maintenance records: Maintenance records refer to the maintenance time, maintenance methods, replacement parts, optimized configuration and other information recorded in the past operation and maintenance process of fiber optic switches and related network equipment. These data can reflect the maintenance frequency, maintenance effect and whether there are repetitive faults in different periods of equipment. For example, if a certain model of fiber optic switch needs to clean the optical interface of the optical module every 12 months in the past maintenance record, otherwise there will be optical power attenuation problems, then based on this data, the initial operation and maintenance cycle can be set to 12 months to avoid similar problems.

[0023] Failure statistics: Failure statistics are based on large-scale equipment operation data, and quantitatively analyze the failure rate, failure type, and failure severity of fiber switches in different environments, different loads, and different service life. For example, in a data center network, after a certain brand of fiber switches has been used for 12 months, the bit error rate (BER) has increased significantly, and more than 80% of the failures occurred within 12-18 months. Therefore, a reasonable initial maintenance cycle should be set within 12 months to perform maintenance in advance before problems occur and reduce the risk of network interruption.

[0024] Equipment life assessment data: Equipment life assessment is based on the physical life of the equipment, performance degradation rules, material aging characteristics and life cycle data recommended by the manufacturer, and estimates the expected life of the fiber optic switch and its core components (such as optical modules, power modules, fans, etc.) under different usage conditions. For example, the design life of a certain type of optical module is 5 years, but in a high temperature and high load environment, the actual life may be shortened to 2 years. In this case, the inspection and replacement frequency should be increased after about 2 years of use to avoid unforeseen failures of the equipment before the end of its life.

[0025] Through comprehensive analysis of maintenance records, fault statistics, and equipment life assessment data, the initial operation and maintenance cycle can be formulated more accurately to avoid equipment abnormalities due to insufficient maintenance and to prevent unnecessary operation and maintenance costs due to excessive maintenance. In the intelligent operation and maintenance system, these historical data can also be further used for machine learning model training to achieve predictive maintenance, transforming the operation and maintenance cycle from a fixed mode to dynamic adaptive optimization, and improving the stability and service life of fiber optic switches.

[0026] Real-time data collection and monitoring module, during the operation of the fiber optic switch, continuously collects the operating parameter data of the fiber optic switch in real time, establishes a real-time data stream of the device status, ensures that all potential abnormal information can be captured in time, and provides raw data support for subsequent data processing and intelligent diagnosis; Through embedded sensors, digital optical monitoring (DOM) of optical modules, SNMP protocol and other network monitoring tools, key parameter data is continuously collected in real time. High-frequency and continuous data collection can help detect subtle change trends and provide early warning of possible problems.

[0027] The data preprocessing and feature extraction module performs preprocessing operations on the collected operating parameter data to ensure the accuracy and consistency of the data. Based on the preprocessed data, the module extracts the key features that reflect the abnormal health status of the optical fiber link. Under the monitoring window, the module further analyzes the extracted key features through feature engineering technology to provide input for the machine learning model, ensuring that the data input to the machine learning model has high discrimination, so as to improve the accuracy of optical fiber link abnormality hidden danger detection and prediction; Based on the preprocessed data, key features reflecting the abnormal health status of the optical fiber link are extracted, where the extracted features include the rate of power attenuation of the optical signal during transmission and the nonlinear growth of the bit error rate with the transmission distance. Under the monitoring window, the rate of power attenuation of the optical signal during transmission and the nonlinear growth of the bit error rate with the transmission distance are further analyzed through feature engineering technology, and the optical power attenuation factor and the bit error rate nonlinear increase factor are generated respectively. The optical power attenuation factor and the bit error rate nonlinear increase factor are used to quantify the abnormal operating status of the optical fiber link, and provide input for the machine learning model to improve the accuracy of optical fiber link anomaly detection and prediction.

[0028] During the operation of the fiber optic switch, if the rate of power attenuation of the optical signal in the fiber optic link during transmission increases abnormally, this usually indicates that there is an abnormal hidden danger in the health status of the fiber optic link. Under normal circumstances, the optical signal will have a certain attenuation during the transmission process due to its inherent physical characteristics, but this attenuation should be kept within an expected stable range; once an abnormal increase occurs, it may indicate that the fiber optic link is affected by factors such as fiber aging, connector contamination, excessive bending, physical damage or other environmental factors, resulting in increased signal loss. Such abnormal changes may further cause an increase in bit error rate, data transmission errors, and even cause the entire link to fail. In severe cases, it may cause communication interruption and network paralysis. Therefore, monitoring abnormal changes in the power attenuation rate of optical signals is crucial for timely warning and maintenance measures.

[0029] In the monitoring window, the rate of power attenuation of the optical signal during transmission is further analyzed through feature engineering technology. The specific steps for generating the optical power attenuation factor are as follows: First, any location on the fiber link L The optical power measured at A nonlinear transformation is performed to calculate the instantaneous decay rate. The expression for the calculation is: ,in: Indicates the position on the optical fiber link L The instantaneous optical power attenuation rate at the point, specifically, reflects the attenuation rate of the optical signal power per unit length (or distance). Indicates the location of the optical fiber link L Real-time measurement of optical power at is the natural logarithm of the optical power, the purpose of which is to convert the multiplicative attenuation effect into an additive form for easier processing. Indicates the length of the optical fiber L The derivative of reflects the instantaneous rate of change of optical power with distance. The negative sign ensures that when the optical power decays, the instantaneous optical power decay rate is a positive value; Converting the multiplicative attenuation effect to additive form works as follows: By taking the natural logarithm of the optical signal power value, the multiplication effect of the optical power attenuation with distance in the optical fiber link is converted into an additive effect. Physically, the attenuation of optical signals is usually multiplicative in nature, that is, the signal power will decay at a fixed ratio as the optical fiber transmission distance increases. This attenuation appears as an exponential decrease. By taking the natural logarithm, this exponential attenuation is converted into an additive form, making it a linear change. The purpose of this is to make signal attenuation easier to analyze and process, because the additive effect can be studied through simple differential or derivative operations, while the multiplication effect usually increases the computational complexity. When calculating the attenuation rate, the natural logarithm conversion allows a more direct study of the attenuation rate and pattern.

[0030] The purpose of this step is to convert the original optical power data into a local attenuation rate curve, capture the instantaneous attenuation behavior of the optical signal during transmission, lay the foundation for subsequent comprehensive calculations, and sensitively reflect local abnormal attenuation phenomena.

[0031] In order to obtain the instantaneous optical power attenuation rate along the optical fiber link Finally, the comprehensive attenuation index, namely the optical power attenuation factor, is constructed by using integration and nonlinear transformation. The calculation formula is: ,in: is the optical power attenuation factor, and are the starting and ending positions of the optical fiber link, is an exponential parameter used to amplify the contribution of high decay rate values, reflecting the nonlinear effect of abnormal aggravation. is the sensitivity coefficient, through the exponential function Further amplify the impact of the local sudden increase in instantaneous decay rate to highlight the abnormal phenomenon; The purpose of this step is to cumulatively evaluate the local attenuation rate in the entire optical fiber link, use integral calculation to gather abnormal signals from various locations, and then use nonlinear amplification to make the local anomaly appear in the comprehensive index. It is fully reflected in the data, realizing accurate quantification of abnormal health status of optical fiber links.

[0032] It can be seen from the optical power attenuation factor that, under the monitoring window, the larger the performance value of the optical power attenuation factor generated by further analyzing the rate of power attenuation of the optical signal during transmission through feature engineering technology, the higher the risk of abnormal hidden dangers in the health status of the optical fiber link. The optical power attenuation factor can reflect the changes in signal quality during the transmission of the optical fiber link by comprehensively evaluating the rate and change trend of power attenuation in the optical fiber link. When the value of the optical power attenuation factor is large, it usually means that the link has significant power attenuation or accelerated attenuation in certain areas, which may be caused by optical fiber aging, damage, poor connection or other external factors, indicating that the link has a potential failure risk. On the contrary, when the value of the optical power attenuation factor is small, it means that the optical signal transmission of the link is stable and the attenuation rate is relatively slow, indicating that the health status of the optical fiber link is good and the abnormal hidden dangers are small. Therefore, as a key indicator, the optical power attenuation factor can help operation and maintenance personnel identify health problems of optical fiber links in a timely manner.

[0033] During the operation of the fiber optic switch, if the bit error rate of the fiber optic link shows a nonlinear upward trend with the increase of transmission distance, it indicates that there may be abnormal hidden dangers in the link. Normally, the bit error rate will increase to a certain extent with the increase of distance, but this increase should be stable and gradual; once a nonlinear and sharp increase in the bit error rate occurs, it may mean that there are problems with the fiber, such as physical bending, connector contamination, optical module aging, or abnormal fiber attenuation. These problems will cause the signal quality to deteriorate rapidly, thus causing transmission errors. Capturing this abnormal phenomenon in time is crucial to preventing potential failures, conducting targeted maintenance, and ensuring the stability of overall network communications.

[0034] In the monitoring window, the nonlinear growth of the bit error rate with the transmission distance is further analyzed by feature engineering technology to generate the nonlinear increase factor of the bit error rate. The specific steps are as follows: In the monitoring window, we first analyze the bit error rate as a function of transmission distance. x The local differential analysis is performed on the changes of to capture the instantaneous characteristics of the nonlinear growth of the curve. The specific method is to calculate the first-order derivative and second-order derivative of the bit error rate, and then define the local curvature function. The expression of the local curvature function is: ,in: The bit error rate of the optical fiber link varies with the transmission distance. x The local curvature of the change is used to describe the curvature of the bit error rate curve at a certain point. is the first-order derivative of the bit error rate, which indicates the instantaneous rate of change of the bit error rate relative to the transmission distance. is the second-order derivative of the bit error rate, indicating the acceleration of the instantaneous rate of change, reflecting the "bending" trend of the bit error rate increase. Used for normalization to eliminate the impact of data scale on curvature calculation; The purpose of this step is to convert the nonlinear growth characteristics of the bit error rate into a mathematical curvature value. A higher local curvature indicates a sharp increase in the local bit error rate, thereby revealing potential abnormal risks of the optical fiber link.

[0035] After obtaining the local curvature function, the local abnormal conditions in the entire monitoring window are further aggregated to generate the final bit error rate nonlinear increase factor. The generation expression of the bit error rate nonlinear increase factor is: ,in: It represents the nonlinear increase factor of bit error rate, Indicates the transmission distance interval within the monitoring window. and They represent the starting point and end point of the transmission distance interval within the monitoring window respectively. is the absolute value of the local curvature, ensuring that each point contributes positively to the anomaly, is a power exponential parameter used to enhance the sensitivity to local extreme outliers (e.g. The part with higher local curvature contributes more to the integral).

[0036] It can be seen from the nonlinear rise factor of the bit error rate that, under the monitoring window, the larger the performance value of the nonlinear rise factor of the bit error rate generated by further analyzing the nonlinear growth of the bit error rate with the transmission distance through feature engineering technology, the greater the possibility that the health status of the optical fiber link has abnormal hidden dangers. This is because the nonlinear rise index of the bit error rate with the transmission distance captures the trend of the sharp increase in the bit error rate by quantifying the curvature change of the optical fiber link in the monitoring window. A higher nonlinear rise factor of the bit error rate means that the rate of change and acceleration of the bit error rate within a certain distance increase significantly, which is usually due to damage to the optical fiber link, degradation of the optical module or other external factors, resulting in a sharp decline in signal quality, which are all early signs of potential faults. On the contrary, when the nonlinear rise factor of the bit error rate is low, it means that the growth of the bit error rate with distance is relatively gentle, the health status of the link is relatively stable, and there is no obvious abnormal hidden danger. Therefore, the nonlinear rise factor of the bit error rate can effectively reflect the health status of the optical fiber link. As an important early warning signal in the intelligent operation and maintenance system, it helps to timely discover and repair possible link problems.

[0037] The intelligent evaluation and prediction module feeds the features processed by feature engineering into a machine learning model that has been trained based on historical data in advance, and uses the machine learning model to comprehensively evaluate and predict the analyzed features, and automatically detect the operating status of the optical fiber link; The optical power attenuation factor and bit error rate nonlinear increase factor processed by feature engineering are sent to the machine learning model trained in advance based on historical data. The link anomaly index is generated by the machine learning model. The machine learning model is used to comprehensively evaluate and predict the analyzed link anomaly index to automatically detect the operating status of the optical fiber link.

[0038] The machine learning model trained based on historical data in advance refers to a prediction model built using a supervised or semi-supervised learning algorithm before the real-time fiber link monitoring and anomaly detection is officially put into use. It uses a large amount of operation and maintenance records, fault statistics, equipment life data, and key feature data such as optical power attenuation and bit error rate collected in real time, and after data preprocessing and feature engineering, it is built using supervised or semi-supervised learning algorithms. During the construction process, this model learns and captures the characteristic patterns of the fiber link in normal operation and abnormal state, as well as the complex nonlinear relationship between these features and the risk of failure by inputting various parameters in historical data (such as optical power attenuation factor and bit error rate nonlinear increase factor extracted through feature engineering). The model may use algorithms such as deep neural networks, random forests, and gradient boosting decision trees to continuously adjust internal parameters during repeated training and verification, and achieve accurate association between input features and link anomaly index with the optimal mapping relationship. During the model training process, cross-validation, loss function optimization, and regularization techniques are also used to ensure that the model has high generalization ability and robustness when facing new data. After the trained model parameters are fixed, it can be used as a pre-trained "expert system" to quickly map new data processed by feature engineering to a comprehensive evaluation indicator link anomaly index during real-time monitoring, thereby automatically detecting and predicting the health status of the optical fiber link. The advantage of this pre-trained model is that it can identify subtle anomalies and potential fault signs that may be overlooked by traditional statistical methods through the complex laws contained in historical data, providing data support and decision-making basis for subsequent predictive maintenance.

[0039] In practical applications, the significance of this pre-trained machine learning model is particularly important because it enables the fiber link monitoring system to have adaptive and intelligent characteristics. Specifically, after the model is trained on historical data, it will be deployed on the monitoring platform. When the real-time data stream enters, the system will first pre-process the original sensor data, and then extract quantified key features such as optical power attenuation factor and bit error rate nonlinear increase factor through feature engineering, and then input these features into the pre-trained model for comprehensive evaluation. Based on the historical patterns and abnormal features learned internally, the model quickly outputs a link anomaly index, which not only reflects the abnormality of the current data status, but also can predict the risk of possible failures in the future.

[0040] The machine learning model is not specifically limited here, and can achieve the optical power attenuation factor and the nonlinear rise factor of the bit error rate Perform comprehensive analysis to generate link anomaly index In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; link anomaly index The resulting calculation formula is: , where , Optical power attenuation factor and the nonlinear rise factor of the bit error rate The preset scaling factor of , The preset proportionality coefficient refers to the ratio of different input variables (such as optical power expression index) when calculating the link abnormality index. and the nonlinear rise factor of the bit error rate ) to effectively combine and adjust the constant coefficient and The purpose of these preset scaling factors is to weight the importance of different factors according to the actual situation, so that their contribution to the final result matches the actual observed impact. For example, is the index expressed with optical power The associated preset scaling factor, is the nonlinear increase factor of the bit error rate By adjusting these preset proportional coefficients, the relationship between the two in terms of link anomaly index can be changed. The relative impact in the calculation ensures that the model can accurately reflect the true health status of the fiber link. Especially in actual operation and maintenance, these preset proportional coefficients may be dynamically adjusted according to the characteristics of different devices, environmental factors, fault types, etc., to achieve more accurate anomaly detection and prediction.

[0041] It can be seen from the link anomaly index that, under the monitoring window, the larger the performance value of the optical power attenuation factor generated by further analyzing the rate of power attenuation of the optical signal during transmission through feature engineering technology, the larger the performance value of the bit error rate nonlinear increase factor generated by further analyzing the nonlinear growth of the bit error rate with the transmission distance through feature engineering technology, that is, the larger the performance value of the link anomaly index generated when the operating status of the optical fiber link is evaluated and predicted by the trained machine learning model, the higher the risk of abnormal hidden dangers in the health status of the optical fiber link, and vice versa.

[0042] The link anomaly index generated by evaluating and predicting the operating status of the optical fiber link through the trained machine learning model is compared and analyzed with the pre-set link anomaly index reference threshold to automatically detect the operating status of the optical fiber link. The specific detection steps are as follows: If the link abnormality index is greater than the link abnormality index reference threshold, a link abnormality signal is generated, indicating that there are abnormal risks in the health status of the optical fiber link; if the link abnormality index is less than or equal to the link abnormality index reference threshold, a link normal signal is generated, indicating that the optical fiber link is operating efficiently and healthily.

[0043] The dynamic maintenance strategy adjustment module, when sensing the existence of abnormal hidden dangers in the operation status of the optical fiber link, will intelligently and dynamically adjust the periodic maintenance strategy of the optical fiber switch according to the severity of the abnormal hidden dangers of the optical fiber link, dynamically shorten the actual operation and maintenance cycle, and detect link fault problems in time; When an abnormal hidden danger is detected in the operation status of the optical fiber link, the periodic maintenance strategy of the optical fiber switch is intelligently and dynamically adjusted according to the severity of the abnormal hidden danger of the optical fiber link. The specific steps are as follows: When an abnormality risk is detected in a fiber link, the excess of the link abnormality index relative to the link abnormality index reference threshold is first calculated to quantify the severity of the fiber link abnormality. The calculation formula is as follows: ,in: It is the fiber link anomaly index evaluated by the machine learning model, reflecting the health of the current link operation status. It is the reference threshold of the link abnormality index, that is, the standard value of the fiber link in normal operation. is the link anomaly excess amplitude, which is used to measure the deviation of the link anomaly index from the reference threshold. The index parameter It is used to control the sensitivity of the response to the severity of the abnormality. When the abnormal index exceeds the standard slightly, the impact is small, but when the abnormality exceeds the standard by a large margin, the corresponding adjustment range will be amplified; The purpose of this step is to accurately quantify the severity of the abnormal situation, ensure that the system can dynamically adapt to different levels of abnormal situations, avoid overly frequent adjustments to operation and maintenance strategies, and ensure that it can respond quickly when the abnormality reaches a dangerous level.

[0044] After quantifying the severity of the fiber link abnormality risk, the actual operation and maintenance cycle of the fiber switch is dynamically adjusted according to the link abnormality excess range to ensure that the maintenance frequency can respond to changes in the link health status in a timely manner. The adjustment formula is as follows: ,in: It is the actual adjusted operation and maintenance cycle, which is used to determine the time of the next maintenance. It is the initial operation and maintenance cycle. It is a dynamic adjustment coefficient, which is used to control the adjustment range, ensuring that the adjustment range is small when the abnormality is small, and when the abnormality exceeds the standard by a large amount, the operation and maintenance cycle can be significantly shortened; The purpose of this step is to make the operation and maintenance strategy dynamically adaptable, ensuring that when the fiber link is in good health, the operation and maintenance cycle remains long and unnecessary maintenance is reduced. When the link abnormality intensifies, the system can proactively shorten the maintenance cycle and intervene in advance to prevent potential faults from turning into serious problems.

[0045] Through the above-mentioned data analysis-based operation and maintenance intelligent terminal solution, intelligent real-time monitoring and dynamic operation and maintenance control of the operation status of fiber optic switches can be realized. By continuously collecting and processing the operating parameter data of fiber optic switches, combined with feature engineering and intelligent evaluation of machine learning models, the system can accurately identify potential abnormalities in fiber optic links, and dynamically adjust the maintenance cycle according to the severity of link abnormalities, and respond to failure risks in a timely manner. This solution effectively improves the fault warning capability and maintenance efficiency of fiber optic switches, avoids the lag problem of fixed operation and maintenance cycles, ensures that network equipment operates in an efficient and healthy state, and significantly reduces network interruptions or business failures caused by failure to discover problems in a timely manner, thereby improving network reliability, stability and operational efficiency.

[0046] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0047] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0048] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. An operation and maintenance intelligent terminal based on data analysis, characterized in that: It includes the initial operation and maintenance cycle setting module, real-time data collection and monitoring module, data preprocessing and feature extraction module, intelligent evaluation and prediction module and dynamic maintenance strategy adjustment module; The initial operation and maintenance cycle setting module sets an initial operation and maintenance cycle for the fiber optic switch, and performs routine maintenance, inspection and optimization according to the established cycle after the fiber optic switch is put into operation; Real-time data collection and monitoring module, during the operation of the fiber optic switch, continuously collects the operating parameter data of the fiber optic switch in real time and establishes a real-time data stream of the device status; The data preprocessing and feature extraction module performs preprocessing operations on the collected operating parameter data to ensure the accuracy and consistency of the data. Based on the preprocessed data, the module extracts the key features that reflect the abnormal health status of the optical fiber link. Under the monitoring window, the module further analyzes the extracted key features through feature engineering technology to provide input for the machine learning model. The intelligent evaluation and prediction module feeds the features processed by feature engineering into a machine learning model that has been trained based on historical data in advance, and uses the machine learning model to comprehensively evaluate and predict the analyzed features, and automatically detect the operating status of the optical fiber link; The dynamic maintenance strategy adjustment module, when sensing that there are abnormal hidden dangers in the operation status of the optical fiber link, will intelligently and dynamically adjust the periodic maintenance strategy of the optical fiber switch according to the severity of the abnormal hidden dangers of the optical fiber link.

2. According to claim 1, the intelligent operation and maintenance terminal based on data analysis is characterized in that: The initial operation and maintenance cycle refers to the first maintenance time interval comprehensively set for the fiber optic switch based on maintenance records, failure statistics and equipment life assessment data.

3. The intelligent operation and maintenance terminal based on data analysis according to claim 1, characterized in that: Based on the preprocessed data, key features reflecting abnormal health status of optical fiber links are extracted. The extracted key features include the rate of power attenuation of optical signals during transmission and the nonlinear growth of bit error rate with transmission distance. Under the monitoring window, the extracted key features are further analyzed by feature engineering technology, specifically: Through feature engineering technology, the rate of power attenuation of optical signals during transmission and the nonlinear growth of bit error rate with transmission distance are further analyzed, and the optical power attenuation factor and the bit error rate nonlinear increase factor are generated respectively. The optical power attenuation factor and the bit error rate nonlinear increase factor are used to quantify the abnormal operating status of the optical fiber link, and provide input for the machine learning model to improve the accuracy of optical fiber link anomaly detection and prediction.

4. The operation and maintenance intelligent terminal based on data analysis according to claim 3 is characterized in that: Under the monitoring window, after further analysis of the extracted key features through feature engineering technology, the optical power attenuation factor and bit error rate nonlinear increase factor processed by feature engineering are sent to the machine learning model trained in advance based on historical data. The link anomaly index is generated through the machine learning model, and the machine learning model is used to comprehensively evaluate and predict the analyzed link anomaly index to automatically detect the operating status of the optical fiber link.

5. The operation and maintenance intelligent terminal based on data analysis according to claim 4, characterized in that: The link anomaly index generated by evaluating and predicting the operating status of the optical fiber link through the trained machine learning model is compared and analyzed with the pre-set link anomaly index reference threshold to automatically detect the operating status of the optical fiber link. The specific detection steps are as follows: If the link abnormality index is greater than the link abnormality index reference threshold, a link abnormality signal is generated, indicating that there are abnormal risks in the health status of the optical fiber link; if the link abnormality index is less than or equal to the link abnormality index reference threshold, a link normal signal is generated, indicating that the optical fiber link is operating efficiently and healthily.

6. The intelligent operation and maintenance terminal based on data analysis according to claim 3, characterized in that: In the monitoring window, the rate of power attenuation of the optical signal during transmission is further analyzed through feature engineering technology. The specific steps for generating the optical power attenuation factor are as follows: First, any location on the fiber link L The optical power measured at A nonlinear transformation is performed to calculate the instantaneous decay rate. The expression for the calculation is: ,in: Indicates the position on the optical fiber link L The instantaneous optical power attenuation rate at Indicates the location of the optical fiber link L Real-time measurement of optical power at Indicates the length of the optical fiber L The derivative of reflects the instantaneous rate of change of optical power with distance. The negative sign ensures that when the optical power decays, the instantaneous optical power decay rate is a positive value; In order to obtain the instantaneous optical power attenuation rate along the optical fiber link Finally, the comprehensive attenuation index, namely the optical power attenuation factor, is constructed by using integration and nonlinear transformation. The calculation formula is: ,in: is the optical power attenuation factor, and are the starting and ending positions of the optical fiber link, is an exponential parameter used to amplify the contribution of high decay rate values, reflecting the nonlinear effect of abnormal aggravation. is the sensitivity coefficient, through the exponential function The impact of the local sudden increase in instantaneous decay rate is further amplified to highlight the abnormal phenomenon.

7. The intelligent operation and maintenance terminal based on data analysis according to claim 3 is characterized in that: In the monitoring window, the nonlinear growth of the bit error rate with the transmission distance is further analyzed by feature engineering technology to generate the nonlinear increase factor of the bit error rate. The specific steps are as follows: In the monitoring window, we first analyze the bit error rate as a function of transmission distance. x The local differential analysis is performed on the changes of to capture the instantaneous characteristics of the nonlinear growth of the curve. The specific method is to calculate the first-order derivative and second-order derivative of the bit error rate, and then define the local curvature function. The expression of the local curvature function is: ,in: The bit error rate of the optical fiber link varies with the transmission distance. x The local curvature of the change is used to describe the curvature of the bit error rate curve at a certain point. is the first-order derivative of the bit error rate, which indicates the instantaneous rate of change of the bit error rate relative to the transmission distance. is the second-order derivative of the bit error rate, indicating the acceleration of the instantaneous rate of change, reflecting the "bending" trend of the bit error rate increase. The denominator Used for normalization to eliminate the impact of data scale on curvature calculation; After obtaining the local curvature function, the local abnormal conditions in the entire monitoring window are further aggregated to generate the final bit error rate nonlinear increase factor. The generation expression of the bit error rate nonlinear increase factor is: ,in: Represents the nonlinear increase factor of the bit error rate, Indicates the transmission distance interval within the monitoring window. and They represent the starting point and end point of the transmission distance interval within the monitoring window respectively. is the absolute value of the local curvature, ensuring that each point contributes positively to the anomaly, is the power exponent parameter, which is used to enhance the sensitivity to local extreme outliers.

8. The operation and maintenance intelligent terminal based on data analysis according to claim 5, characterized in that: When an abnormal hidden danger is detected in the operation status of the optical fiber link, the periodic maintenance strategy of the optical fiber switch is intelligently and dynamically adjusted according to the severity of the abnormal hidden danger of the optical fiber link. The specific steps are as follows: When an abnormality risk is detected in a fiber link, the excess of the link abnormality index relative to the link abnormality index reference threshold is first calculated to quantify the severity of the fiber link abnormality. The calculation formula is as follows: ,in: It is the fiber link anomaly index evaluated by the machine learning model, reflecting the health of the current link operation status. It is the reference threshold of the link abnormality index, that is, the standard value of the fiber link in normal operation. is the link anomaly excess amplitude, which is used to measure the deviation of the link anomaly index from the reference threshold. The index parameter Used to control the sensitivity of the response to the severity of the anomaly; After quantifying the severity of the fiber link abnormality risk, the actual operation and maintenance cycle of the fiber switch is dynamically adjusted according to the link abnormality excess range to ensure that the maintenance frequency can respond to changes in the link health status in a timely manner. The adjustment formula is as follows: ,in: It is the actual adjusted operation and maintenance cycle, which is used to determine the time of the next maintenance. It is the initial operation and maintenance cycle. It is a dynamic adjustment coefficient, which is used to control the adjustment range.

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