Method and system for realizing intelligent management of all-optical network based on big data analysis

Abstract

The invention relates to the technical field of data management, and discloses a method for realizing intelligent management of an all-optical network based on big data analysis, which comprises the following steps of: acquiring operation data of the all-optical network, positioning an abnormal point location of link optical power of the all-optical network, and performing data linear repair on power data corresponding to the abnormal point location to obtain target data; analyzing wavelength characteristics of the all-optical network, analyzing signal transmission stability of optical signals of the all-optical network, and analyzing optical signal characteristics of the all-optical network based on the wavelength characteristics and the signal transmission stability; analyzing equipment operation state characteristics of the all-optical network; identifying data service types of the all-optical network, analyzing flow change rules of different service types in the data service types, and analyzing service flow periodicity characteristics of the all-optical network; and performing operation anomaly analysis on the all-optical network to obtain an operation anomaly analysis result, and constructing an intelligent management strategy of the all-optical network based on the operation anomaly analysis result. According to the invention, the operation efficiency and stability of the all-optical network can be improved.

Description

Technical Field

[0001] The present invention relates to a method and system for realizing intelligent management of an all-optical network based on big data analysis, and belongs to the technical field of data management. Background Art

[0002] As a key infrastructure in the field of modern communications, all-optical networks play an extremely important role in data transmission and communication services. With the rapid development of information technology and the increasing demand for network bandwidth, reliability and intelligent management of various services, the scale and complexity of all-optical networks are also increasing. Data management occupies a core position in the intelligent management of all-optical networks. Therefore, an excellent all-optical network intelligent management method can help network operation and maintenance personnel efficiently collect, store, analyze and process the massive data generated during the operation of the all-optical network. At the same time, it can also optimize the configuration of network resources based on the data analysis results, improve the transmission efficiency and service quality of the network, and improve the collaboration efficiency of the entire network operation team.

[0003] At present, most of the intelligent management methods for all-optical networks are based on traditional monitoring methods and simple data statistical analysis methods. Usually, some basic network management tools are used to obtain limited data, and relatively rudimentary methods such as manually setting thresholds are used to determine whether the network is abnormal. However, with the continuous expansion of the scale of all-optical networks, the amount of data generated has exploded, and the data types have become more complex and diverse, including structured equipment performance data and a large amount of unstructured log information, alarm records, etc. This traditional management method is difficult to analyze and process data comprehensively and accurately when faced with massive and complex big data, which makes the intelligent management of all-optical networks have many limitations, resulting in low operating efficiency and stability of all-optical networks. Summary of the invention

[0004] In order to solve the above problems, the present invention provides a method for realizing intelligent management of an all-optical network based on big data analysis, which can improve the operation efficiency and stability of the all-optical network.

[0005] To achieve the above object, the present invention provides a method for realizing intelligent management of all-optical networks based on big data analysis, comprising:

[0006] Collecting operation data of the all-optical network, querying operation configuration parameters of the operation equipment of the all-optical network, removing abnormal data from the operation data based on the operation configuration parameters to obtain regular data, querying the operation abnormality period of the operation equipment to locate the abnormal point of the link optical power of the all-optical network, performing data linear repair on the power data corresponding to the abnormal point to obtain target data;

[0007] Using the target data, constructing the optical wave spectrum of the all-optical network, using the optical wave spectrum, analyzing the energy proportion information of the optical signal energy of the all-optical network, based on the energy proportion information, analyzing the wavelength characteristics of the all-optical network, using the target data, analyzing the signal transmission stability of the optical signal of the all-optical network, based on the wavelength characteristics and the signal transmission stability, analyzing the optical signal characteristics of the all-optical network;

[0008] querying the target data for status monitoring data about the all-optical network, analyzing temperature characteristics and port connection change characteristics of the all-optical network based on the status monitoring data, and analyzing device operation status characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics;

[0009] querying the protocol data of the all-optical network, identifying the data service type of the all-optical network based on the protocol data, analyzing the traffic change rules of different service types in the data service types, and analyzing the service traffic periodicity characteristics of the all-optical network based on the traffic change rules;

[0010] According to the optical signal characteristics, the equipment operation status characteristics and the service traffic periodicity characteristics, a preconfigured all-optical network analysis model is optimized to obtain an optimized analysis model; using the optimized analysis model, a time series analysis is performed on the all-optical network to obtain an analysis result; based on the analysis result, an operation abnormality analysis is performed on the all-optical network to obtain an operation abnormality analysis result; based on the operation abnormality analysis result, an intelligent management strategy for the all-optical network is constructed.

[0011] Optionally, performing linear repair on the power data corresponding to the abnormal point to obtain target data includes:

[0012] Performing time series decomposition on the power data to obtain trend item power data, seasonal item power data and random item power data;

[0013] Analyzing the linear trend characteristics of the trend item power data;

[0014] Based on the linear trend feature, performing trend item data repair on the trend item power number to obtain trend item repair data;

[0015] Constructing a time series graph of the seasonal power data;

[0016] Analyzing the periodic characteristics of the seasonal power data using the time series graph;

[0017] Determining the cycle length of the seasonal power data according to the cycle characteristics;

[0018] Based on the cycle length, performing seasonal item data repair on the seasonal item power data to obtain seasonal item repair data;

[0019] Analyzing random data distribution characteristics and variance statistical properties of the random item power data;

[0020] Based on the statistical property of the variance of the random data distribution characteristics, performing random data repair on the random item power data to obtain random item repair data;

[0021] The trend item repair data, the season item repair data and the random item repair data are combined to obtain target data.

[0022] Optionally,

[0023] The analyzing the wavelength characteristics of the all-optical network based on the energy proportion information includes:

[0024] Based on the energy proportion information, calculating the wavelength energy proportion of the all-optical network;

[0025] Querying optical fiber transmission characteristic parameters of the all-optical network;

[0026] Performing data integration on the wavelength energy proportion and the optical fiber transmission characteristic parameter to obtain integrated data;

[0027] Analyzing the optical loss and optical dispersion of the wavelength light of the all-optical network using the integrated data;

[0028] The wavelength characteristics of the all-optical network are determined using the optical loss and the optical dispersion.

[0029] Optionally, the analyzing the signal transmission stability of the optical signal of the all-optical network by using the target data includes:

[0030] extracting optical power and signal-to-noise ratio from the target data;

[0031] Constructing a visual graph of the optical power and the signal-to-noise ratio;

[0032] Using the intuitive graph, a multi-dimensional time series analysis is performed on the optical power and the signal-to-noise ratio to obtain a multi-dimensional analysis result;

[0033] The signal transmission stability of the optical signal of the all-optical network is determined by utilizing the multi-dimensional analysis result in combination with a preset stability judgment standard.

[0034] Optionally, analyzing the optical signal characteristics of the all-optical network based on the wavelength characteristics and the signal transmission stability includes:

[0035] Constructing a comprehensive evaluation index of the wavelength characteristics and the signal transmission stability;

[0036] Constructing a comprehensive relationship curve between the comprehensive evaluation index and the wavelength of the all-optical network;

[0037] Analyzing the dominant wavelengths of the optical signals of the all-optical network and the dominant transmission time periods corresponding to the dominant wavelengths by using the comprehensive relationship curve, and analyzing the non-dominant wavelengths of the optical signals of the all-optical network and the non-dominant transmission time periods corresponding to the non-dominant wavelengths;

[0038] The optical signal characteristics of the all-optical network are analyzed according to the dominant wavelength, the dominant transmission period, the non-dominant wavelength and the non-dominant transmission period.

[0039] Optionally, constructing a comprehensive evaluation index of the wavelength characteristics and the signal transmission stability includes:

[0040] Querying the wavelength energy ratio, wavelength loss coefficient and wavelength dispersion coefficient in the wavelength characteristics;

[0041] Construct optical power stability index and signal-to-noise ratio stability index for signal transmission stability;

[0042] Using the wavelength energy proportion, the wavelength loss coefficient, the wavelength dispersion coefficient, the optical power stability index and the signal-to-noise ratio stability index, the following formula is used to create a comprehensive evaluation index of the wavelength characteristics and the signal transmission stability:

[0043]

[0044] Among them, β represents the comprehensive evaluation index, δ represents the wavelength in the all-optical network, P(δ) represents the wavelength energy proportion, α(δ) represents the wavelength loss coefficient, β(δ) represents the wavelength dispersion coefficient, L represents the transmission distance of δ, S p Represents the optical power stability index, S snr Represents the signal-to-noise ratio stability index.

[0045] Optionally, the analyzing the device operation status characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics includes:

[0046] Based on the temperature characteristics, query the temperature extreme value data, temperature fluctuation data and temperature change rate data of the all-optical network device corresponding to the all-optical network during operation;

[0047] Analyze the operating state changes of the all-optical network device under the temperature extreme value data, the temperature fluctuation data and the temperature change rate data to obtain temperature-device change information;

[0048] Based on the port connection change characteristics, query the port connection stability data, port connection state transfer data and port connection abnormal mode data when the all-optical network device is running;

[0049] Analyze the operation state change of the all-optical network device under the port connection stability data, the port connection state transfer data and the port connection abnormal mode data to obtain port-device change information;

[0050] Based on the temperature-device change information and the port-device change information, the device operation status characteristics of the all-optical network are analyzed.

[0051] Optionally, the analyzing, based on the traffic variation rule, the periodic characteristics of the service traffic of the all-optical network includes:

[0052] Based on the traffic variation law, collecting service traffic data of different service types of the all-optical network;

[0053] The service flow data is converted into frequency domain using the following formula to obtain frequency domain flow data:

[0054]

[0055] Wherein, X(f) represents the frequency domain traffic data, x(t) represents the service traffic data in the all-optical network, f represents the frequency domain, t represents time, and j represents the imaginary unit;

[0056] Based on the frequency domain traffic data, construct a service traffic spectrum diagram of the all-optical network;

[0057] The service traffic spectrum diagram is used to analyze the periodic characteristics of the service traffic of the all-optical network.

[0058] Optionally, constructing an intelligent management strategy for the all-optical network based on the abnormal operation analysis result includes:

[0059] Based on the operation abnormality analysis result, classifying the abnormal causes of the abnormal operation of the all-optical network to obtain classified abnormal causes;

[0060] Evaluate the impact range of different causes among the classified abnormal causes on the all-optical network to obtain multiple abnormal impacts;

[0061] Based on the multiple abnormal impacts, construct equipment maintenance and management strategies, network resource adjustment strategies and optical signal optimization strategies for the all-optical network;

[0062] The equipment maintenance and management strategy, the network resource adjustment strategy and the optical signal optimization strategy are integrated to obtain an intelligent management strategy.

[0063] In a second aspect, the present invention provides an all-optical network intelligent management system based on big data analysis, the system comprising:

[0064] A data repair module is used to collect the operation data of the all-optical network, query the operation configuration parameters of the operation equipment of the all-optical network, remove abnormal data from the operation data based on the operation configuration parameters to obtain regular data, query the operation abnormality period of the operation equipment to locate the abnormal point of the link optical power of the all-optical network, perform data linear repair on the power data corresponding to the abnormal point to obtain target data;

[0065] an optical signal characteristic analysis module, configured to construct an optical wave spectrum of the all-optical network using the target data, analyze energy proportion information of optical signal energy of the all-optical network using the optical wave spectrum, analyze wavelength characteristics of the all-optical network based on the energy proportion information, analyze signal transmission stability of optical signals of the all-optical network using the target data, and analyze optical signal characteristics of the all-optical network based on the wavelength characteristics and the signal transmission stability;

[0066] an equipment operation characteristic analysis module, used for querying the state monitoring data about the all-optical network in the target data, analyzing the temperature characteristics and port connection change characteristics of the all-optical network based on the state monitoring data, and analyzing the equipment operation state characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics;

[0067] A service characteristic analysis module, used to query the protocol data of the all-optical network, identify the data service type of the all-optical network based on the protocol data, analyze the traffic change rules of different service types in the data service type, and analyze the service traffic periodicity characteristics of the all-optical network based on the traffic change rules;

[0068] An intelligent management strategy construction module is used to optimize the pre-configured all-optical network analysis model according to the optical signal characteristics, the equipment operation status characteristics and the service traffic periodicity characteristics to obtain an optimized analysis model, use the optimized analysis model to perform time series analysis on the all-optical network to obtain analysis results, based on the analysis results, perform operation anomaly analysis on the all-optical network to obtain operation anomaly analysis results, and based on the operation anomaly analysis results, construct an intelligent management strategy for the all-optical network.

[0069] Compared with the problems described in the background technology, the present invention first collects the operating data of the all-optical network to comprehensively and systematically obtain various types of relevant data information generated by the all-optical network during the actual operation process, thereby providing the most original material basis for subsequent in-depth analysis, problem diagnosis, and optimization management operations, and based on the operating configuration parameters, the operating data is subjected to abnormal data elimination to obtain regular data, which can filter out data that does not meet the normal operating range, that is, abnormal data and eliminate it, thereby obtaining regular data that can better reflect the normal operating status of the network; further, the embodiment of the present invention locates the abnormal point of the link optical power of the all-optical network by querying the abnormal operating time period of the operating equipment, so as to clarify which specific location in the network link has an optical power problem, thereby accurately finding the source that may cause network performance degradation, signal transmission abnormality, etc., and using the target data to construct the optical wave spectrum of the all-optical network. Through specific mathematical transformation or analysis methods, the optical wave data is converted from the time domain to the frequency domain to intuitively display the distribution of optical signals at different frequencies; further, the present invention can understand the transmission performance of light of different wavelengths in the all-optical network by analyzing the wavelength characteristics of the all-optical network based on the energy proportion information, such as which wavelengths of light have relatively small energy loss when transmitted in the network, and which wavelengths of light are more susceptible to dispersion, etc., and then the wavelength of light can be reasonably selected and configured according to actual needs to improve the transmission efficiency and quality of the network, and based on the wavelength characteristics and the signal transmission stability, the optical signal characteristics of the all-optical network can be analyzed to select suitable optical signal transmission schemes for different application scenarios. For example, for video stream transmission with high stability requirements, optical signals with good wavelength characteristics and high transmission stability can be selected, and for applications such as file downloads with high speed requirements, optical signals with higher optical power at specific wavelengths can be selected;The present invention can have a comprehensive understanding of the operation status of the all-optical network by querying the state monitoring data about the all-optical network in the target data, avoiding erroneous conclusions caused by missing or inaccurate data during the analysis process, and analyzing the equipment operation status characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics can more accurately evaluate the health status of the all-optical network equipment, timely discover potential problems and risks, and provide strong support for network maintenance, optimization and upgrading. At the same time, it is also helpful to improve the reliability and stability of the all-optical network, ensure the normal operation of the network and the communication needs of users, and based on the protocol data, identifying the data service type of the all-optical network can help users reasonably allocate bandwidth and adjust network configuration according to the needs of different service types to ensure the quality of key services, and at the same time, better understand the user's usage behavior and business needs, and provide a basis for further business development and optimization. Based on the abnormal operation analysis results, constructing the intelligent management strategy of the all-optical network can ensure the stable operation and performance optimization of the network, and can also enhance the user's experience, thereby improving the operation efficiency and stability of the all-optical network. ; BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 A schematic diagram of a process for implementing an all-optical network intelligent management method based on big data analysis provided by an embodiment of the present invention;

[0071] Figure 2 A schematic diagram of a module for implementing an all-optical network intelligent management system based on big data analysis provided by an embodiment of the present invention.

[0072] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0073] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0074] The embodiment of the present application provides a method for realizing intelligent management of an all-optical network based on big data analysis. The execution subject of the method for realizing intelligent management of an all-optical network based on big data analysis includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for realizing intelligent management of an all-optical network based on big data analysis can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0075] Embodiment 1:

[0076] Reference Figure 1FIG. 1 is a flow chart of a method for implementing intelligent management of an all-optical network based on big data analysis according to an embodiment of the present invention. In this embodiment, the method for implementing intelligent management of an all-optical network based on big data analysis includes:

[0077] S1. Collecting operation data of an all-optical network, querying operation configuration parameters of an operation device of the all-optical network, removing abnormal data from the operation data based on the operation configuration parameters to obtain regular data, querying an abnormal operation period of the operation device to locate abnormal points of link optical power of the all-optical network, performing data linear repair on power data corresponding to the abnormal points to obtain target data.

[0078] The embodiment of the present invention can comprehensively and systematically obtain various types of relevant data information generated by the all-optical network during the actual operation process by collecting the operation data of the all-optical network, thereby providing the most original material basis for subsequent in-depth analysis, problem diagnosis, and optimization management. The all-optical network refers to a network in which the signal always exists in the form of light during the network transmission and exchange process, and the information transmission, exchange and other processing processes from the source node to the destination node are all completed in the optical domain without the need for optical / electrical or electrical / optical conversion. The operation data refers to the operation status of the network equipment of the all-optical network, various indicators of optical signal transmission, business flow conditions and other aspects.

[0079] Optionally, the operating data can be collected by deploying deep packet inspection (DPI) equipment at key network nodes (such as core optical switches, edge routers, etc.) of the all-optical network and combining it with traffic mirroring technology.

[0080] In the embodiment of the present invention, by querying the operation configuration parameters of the operation equipment of the all-optical network, various indicator settings of the equipment during normal operation can be understood.

[0081] Optionally, the operation configuration parameters can be obtained by sending specific query instructions to the device corresponding to the operation configuration parameters, according to the format and operation process specified by the protocol, to obtain the current operation configuration parameters of the device, such as various setting values, thresholds, working modes and other information of the device.

[0082] Furthermore, the embodiment of the present invention can filter out data that does not conform to the normal operating range, that is, the abnormal data, by eliminating the abnormal data based on the operation configuration parameters, thereby obtaining regular data that can better reflect the normal operating status of the network. The operation configuration parameters refer to a series of setting values ​​used to define and control the specific working modes and behaviors of various operating devices (such as optical line terminals, optical network units, optical amplifiers, optical add / drop multiplexers, optical cross-connection devices, etc.) in the all-optical network, such as optical channel related parameters, working mode and function parameters, and threshold and limit parameters.

[0083] Optionally, based on the operation configuration parameters, the operation data is subjected to abnormal data elimination, and the normal data can be obtained by setting thresholds for various types of data according to the normal working range of the all-optical network equipment and the normal indicators of the service flow. For example, it is known that the optical power range of a certain type of optical amplifier is between -10dBm and -20dBm during normal operation. If the collected optical power data exceeds this range, it is determined to be an abnormal value and removed. For service flow data, if the normal transmission delay threshold is set to 50 milliseconds, the transmission delay data points exceeding this threshold may be marked as abnormal due to network congestion and other reasons, and special processing or removal is performed.

[0084] Furthermore, the embodiments of the present invention can locate the abnormal point of the link optical power of the all-optical network by querying the abnormal operation time period of the operating equipment, so as to clarify the specific location in the network link where the optical power problem occurs, thereby accurately finding the source that may cause network performance degradation, signal transmission abnormality, etc.

[0085] Optionally, the abnormal operation period can be determined by setting corresponding abnormal thresholds for different operation parameters according to the normal operation range of the equipment and previous operation and maintenance experience. For example, for a certain type of optical amplifier, its normal output optical power range may be between [P1, P2]. Then, when the monitored optical power value is less than P1 or greater than P2, it can be considered that an optical power abnormality has occurred. After determining the abnormal operation period, focus on the data record of the link optical power during the period. Since the all-optical network is composed of multiple links and nodes, the optical power of each link at different nodes may be different. By comparing and analyzing the optical power data of each link during the abnormal period with the data during the normal period, the links with obvious optical power changes and exceeding the normal fluctuation range are found. For the links with obvious optical power changes found, a segmented investigation method can be further adopted. Divide the link into several small segments according to certain rules (such as according to nodes, distances, etc.), and then monitor the optical power of each small segment during the abnormal period. By gradually narrowing the investigation range, the specific point of optical power abnormality is finally determined, such as determining that there is a problem with the optical fiber between two nodes, or that the optical device on a node causes the optical power abnormality.

[0086] The embodiment of the present invention performs data linear repair on the power data corresponding to the abnormal point to obtain the target data, which can adjust the power data that deviates from the normal situation at the abnormal point to make it conform to the normal power change trend and law as much as possible. In this way, the data that originally deviated due to the abnormal situation can be corrected to a relatively reasonable state, so that the data can better reflect the actual situation of the link optical power of the all-optical network under normal operating conditions.

[0087] As an embodiment of the present invention, the power data corresponding to the abnormal point is linearly repaired to obtain target data, including: performing time series decomposition on the power data to obtain trend item power data, seasonal item power data and random item power data, analyzing the linear trend characteristics of the trend item power data, performing trend item data repair on the trend item power data based on the linear trend characteristics to obtain trend item repair data, constructing a time series graph of the seasonal item power data, analyzing the periodic characteristics of the seasonal item power data using the time series graph, determining the periodic length of the seasonal item power data according to the periodic characteristics, performing seasonal item data repair on the seasonal item power data based on the periodic length to obtain seasonal item repair data, analyzing the random data distribution characteristics and variance statistical properties of the random item power data, performing random data repair on the random item power data based on the random data distribution characteristics and variance statistical properties to obtain random item repair data, and combining the trend item repair data, the seasonal item repair data and the random item repair data to obtain target data.

[0088] Among them, the trend item power data refers to a kind of data reflecting the overall change trend or trend of the optical power of the all-optical network link over a long period of time; the seasonal item power data refers to the repetitive and regular fluctuation of the optical power of the all-optical network link in a specific period; the random item power data refers to the remaining parts of the optical power data of the all-optical network link that cannot be well explained by the trend item and the seasonal item.

[0089] Optionally, the time series decomposition of the power data can be implemented by operating a seasonal additive model or a seasonal multiplicative model, the linear trend characteristics of the trend item power data can be obtained by drawing a trend graph of the trend item power data and observing and analyzing it, the trend item data repair of the trend item power data based on the linear trend characteristics to obtain the trend item repaired data can be implemented by operating a moving average method, the time series graph can be drawn by a Matplotlib tool, the use of the time series graph to analyze the periodic characteristics of the seasonal item power data can be analyzed by an observation method, the determination of the period length of the seasonal item power data based on the periodic characteristics can be performed by combining the time series graph with an autocorrelation function analysis, and the seasonal item power data based on the period length can be analyzed. The seasonal item power data is repaired by seasonal item data, and the seasonal item repaired data can be achieved through the seasonal addition model. The random data distribution characteristics of the random item power data can be obtained by drawing the time series diagram corresponding to the random item power data for observation and analysis. The variance statistical properties can be calculated and analyzed by variance function. The random data repair of the random item power data can be carried out by taking appropriate processing methods according to the characteristics of the random items. If the variance of the random item is small and the data distribution is relatively uniform, it can be directly retained in the repaired power data as a supplement to small fluctuations. If there are obvious outliers in the random item, some data processing methods, such as the median replacement method (replacing the outliers with the median of the data series), can be used to ensure that the repaired power data is more stable.

[0090] S2. Using the target data, construct an optical wave spectrum of the all-optical network; using the optical wave spectrum, analyze energy share information of optical signal energy of the all-optical network; based on the energy share information, analyze wavelength characteristics of the all-optical network; using the target data, analyze signal transmission stability of optical signals of the all-optical network; based on the wavelength characteristics and the signal transmission stability, analyze optical signal characteristics of the all-optical network.

[0091] The embodiment of the present invention uses the target data to construct the optical wave spectrum of the all-optical network, and can convert the optical wave data from the time domain to the frequency domain through a specific mathematical transformation or analysis method to intuitively display the distribution of optical signals at different frequencies. The optical wave spectrum refers to the characteristic distribution of the optical wave described from the frequency perspective when analyzing the optical wave signal, which displays information such as the energy distribution or intensity distribution of the optical signal at different frequencies.

[0092] Optionally, the optical wave spectrum can be constructed by extracting optical wave data from the target data and then transforming it using fast Fourier transform.

[0093] The embodiment of the present invention utilizes the optical wave spectrum to analyze the energy proportion information of the optical signal energy of the all-optical network, so that users can understand the relative amount of optical signal energy carried by each frequency band within the entire optical wave spectrum, thereby facilitating users to optimize and troubleshoot the all-optical network.

[0094] Optionally, the energy proportion information of the optical signal energy of the all-optical network analyzed by using the optical wave spectrum can be obtained by calculating the ratio of the optical signal energy to the full-segment energy of the all-optical network.

[0095] The embodiments of the present invention can understand the transmission performance of light of different wavelengths in the all-optical network by analyzing the wavelength characteristics of the all-optical network based on the energy proportion information, such as which wavelengths of light have relatively small energy loss when transmitted in the network, and which wavelengths of light are more susceptible to dispersion, etc., and then reasonably select and configure the wavelength of light according to actual needs to improve the transmission efficiency and quality of the network.

[0096] As an embodiment of the present invention, the analyzing the wavelength characteristics of the all-optical network based on the energy share information includes: calculating the wavelength energy share of the all-optical network based on the energy share information, querying the optical fiber transmission characteristic parameters of the all-optical network, integrating the wavelength energy share and the optical fiber transmission characteristic parameters to obtain integrated data, analyzing the optical loss and optical dispersion of the wavelength light of the all-optical network using the integrated data, and determining the wavelength characteristics of the all-optical network using the optical loss and the optical dispersion.

[0097] Among them, the wavelength energy ratio refers to the ratio of the energy carried by light of different wavelengths in the total energy in an all-optical network, the optical fiber transmission characteristic parameter refers to the index used to describe the various performances and characteristics of the optical fiber when transmitting optical signals, the optical loss refers to the reduction of optical power caused by absorption, scattering and other reasons when the optical signal is transmitted in the optical fiber, and the optical dispersion refers to the phenomenon of optical pulse broadening due to different propagation speeds when light of different wavelengths is transmitted in the optical fiber.

[0098] Optionally, the process of calculating the wavelength energy share of the all-optical network based on the energy share information is: using the energy share information to determine the frequency energy share of the all-optical network, and based on the frequency energy share, using the following formula to calculate the wavelength energy share of the all-optical network:

[0099]

[0100] Among them, λ represents the wavelength energy ratio, f represents the frequency energy ratio, and c represents the speed of light.

[0101] The optical fiber transmission characteristic parameters of the all-optical network can be obtained by querying the database corresponding to the all-optical network, the data integration of the wavelength energy proportion and the optical fiber transmission characteristic parameters, and the obtained integrated data can be integrated by creating a new multidimensional array or data structure in Python, and the process of using the integrated data to analyze the optical loss and optical dispersion of the wavelength light of the all-optical network is called: obtaining the energy proportion corresponding to each wavelength light and the loss coefficient of the wavelength light in the optical fiber from the integrated data to evaluate the optical loss of different wavelengths of light during transmission; using the energy proportion of each wavelength in the integrated data and the corresponding dispersion coefficient information to evaluate the optical dispersion of different wavelengths of light, and using the optical loss and optical dispersion to determine the wavelength characteristics of the all-optical network can be determined by drawing a chart to intuitively display the changes in the comprehensive indicators of optical loss and optical dispersion at different wavelengths.

[0102] Furthermore, the embodiments of the present invention utilize the target data to analyze the signal transmission stability of the optical signal of the all-optical network, so that the user can promptly discover potential transmission problems of the optical signal of the all-optical network, and then take measures to make adjustments in advance to ensure the reliable operation of the network.

[0103] As an embodiment of the present invention, the use of the target data to analyze the signal transmission stability of the optical signal of the all-optical network includes: extracting the optical power and the signal-to-noise ratio in the target data, constructing an intuitive graph of the optical power and the signal-to-noise ratio, using the intuitive graph to perform a multidimensional time series analysis on the optical power and the signal-to-noise ratio to obtain a multidimensional analysis result, and using the multidimensional analysis result in combination with a preset stability judgment standard to determine the signal transmission stability of the optical signal of the all-optical network.

[0104] Among them, the intuitive graph refers to a time series graph, which can be drawn by the SigmaPlot tool. The preset stability judgment standard refers to the judgment standard for the stability of optical signal transmission determined according to the specific application scenarios of the all-optical network (such as telecommunication networks, data center networks, etc.), network design requirements and past operation and maintenance experience, such as the optical power fluctuation range standard: considering that the fluctuation of optical power within a certain range is normal, but excessive fluctuation will affect signal transmission, it is generally set that the fluctuation range of optical power allowed under normal transmission conditions does not exceed a certain proportion of its mean value (such as ±10%, ±15%, etc., the specific proportion is determined according to the actual situation of the network). If the fluctuation of optical power exceeds this range within a certain time period, it is considered that the optical power is unstable during this period, which may have an adverse effect on optical signal transmission; signal-to-noise ratio threshold standard: set a minimum acceptable signal-to-noise ratio threshold. Different application scenarios have different requirements for signal-to-noise ratio. For example, for a network with high-quality video transmission, the signal-to-noise ratio may be required to be no less than 30dB. For an ordinary data transmission network, 20dB may meet basic needs. If the signal-to-noise ratio is continuously lower than the set threshold value within a certain period of time, it means that the noise has a great influence on the optical signal, and the optical signal transmission will be affected, and it is judged that the signal transmission is unstable.

[0105] The multidimensional analysis result can be obtained by performing mean analysis, variance analysis and slope analysis on the intuitive graph. The signal transmission stability of the optical signal of the all-optical network can be comprehensively judged by combining the multidimensional analysis result and the preset stability judgment standard. If the optical power remains relatively stable for a long period of time, its fluctuation amplitude is always within the set range, and the signal-to-noise ratio is also maintained at a high level with small fluctuations, and the set signal-to-noise ratio threshold requirement is met, then it can be considered that the transmission of the optical signal is stable. For example, if the optical power mean is 100mW, the fluctuation range is set to ±10%, and the optical power fluctuation is always between 90mW-110mW during the observed time period, and the signal-to-noise ratio is always maintained above 30dB (assuming that the network requires the signal-to-noise ratio to be not less than 30dB), it can be determined that the optical signal is stable during the transmission period. On the contrary, if the optical power fluctuation exceeds the range, or the signal-to-noise ratio is lower than the threshold, or both are abnormal, then it is determined that the optical signal transmission is unstable, and it is necessary to further check the possible problems of the equipment, links, etc. in the network.

[0106] Furthermore, the embodiments of the present invention can select suitable optical signal transmission schemes for different application scenarios by analyzing the optical signal characteristics of the all-optical network based on the wavelength characteristics and the signal transmission stability. For example, for video stream transmission with high stability requirements, an optical signal with good wavelength characteristics and high transmission stability can be selected; for applications such as file downloads with high speed requirements, an optical signal with higher optical power at a specific wavelength can be selected.

[0107] As an embodiment of the present invention, the analysis of the optical signal characteristics of the all-optical network based on the wavelength characteristics and the signal transmission stability includes: constructing a comprehensive evaluation index of the wavelength characteristics and the signal transmission stability, constructing a comprehensive relationship curve between the comprehensive evaluation index and the wavelength of the all-optical network, using the comprehensive relationship curve to analyze the dominant wavelength of the optical signal of the all-optical network and the dominant transmission time period corresponding to the dominant wavelength, and analyzing the non-dominant wavelength of the optical signal of the all-optical network and the non-dominant transmission time period corresponding to the non-dominant wavelength, and analyzing the optical signal characteristics of the all-optical network according to the dominant wavelength, the dominant transmission time period, the non-dominant wavelength and the non-dominant transmission time period.

[0108] The comprehensive evaluation index refers to a quantified value that combines multiple characteristic parameters related to wavelength and indicators reflecting signal transmission stability through specific mathematical operations, and can perform a comprehensive and integrated evaluation of the performance of optical signals of different wavelengths in an all-optical network under a unified quantization framework. The comprehensive relationship curve refers to a curve that shows the trend of the comprehensive evaluation index changing with wavelength in a very intuitive way.

[0109] Optionally, the comprehensive relationship curve can be constructed through Matplotlib in Python, and the dominant wavelength, the dominant transmission period, the non-dominant wavelength and the non-dominant transmission period can be determined through the comprehensive relationship curve and the calculation result of the comprehensive evaluation index. By setting a threshold interval, when the calculation result of the comprehensive evaluation index is greater than the threshold interval, it is expressed as the dominant wavelength, and the time period corresponding to the dominant wavelength in the comprehensive relationship curve is observed to obtain the dominant transmission period. The non-dominant wavelength and the non-dominant transmission period are obtained when the comprehensive evaluation index is less than the threshold interval.

[0110] Further, as an optional embodiment of the present invention, the construction of the comprehensive evaluation index of the wavelength characteristics and the signal transmission stability includes: querying the wavelength energy ratio, wavelength loss coefficient and wavelength dispersion coefficient in the wavelength characteristics, constructing an optical power stability index and a signal-to-noise ratio stability index of the signal transmission stability, and using the wavelength energy ratio, the wavelength loss coefficient, the wavelength dispersion coefficient, the optical power stability index and the signal-to-noise ratio stability index to create a comprehensive evaluation index of the wavelength characteristics and the signal transmission stability using the following formula:

[0111]

[0112] Among them, β represents the comprehensive evaluation index, δ represents the wavelength in the all-optical network, P(δ) represents the wavelength energy proportion, α(δ) represents the wavelength loss coefficient, β(δ) represents the wavelength dispersion coefficient, L represents the transmission distance of δ, S p Represents the optical power stability index, S snr Represents the signal-to-noise ratio stability index.

[0113] S3. Query the target data for status monitoring data about the all-optical network, analyze temperature characteristics and port connection change characteristics of the all-optical network based on the status monitoring data, and analyze device operation status characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics.

[0114] In the embodiment of the present invention, by querying the state monitoring data about the all-optical network in the target data, a comprehensive understanding of the operation status of the all-optical network can be obtained, thereby avoiding erroneous conclusions caused by missing or inaccurate data during the analysis process. The state monitoring data refers to a set of relevant information that can reflect the actual status of various aspects of the all-optical network during operation, such as device-related data, link-related data, port connection-related data, and environment-related data.

[0115] Optionally, the status monitoring data may be obtained through a built-in monitoring module of the all-optical network device.

[0116] Furthermore, the embodiments of the present invention can help users to promptly detect temperature anomalies by analyzing the temperature characteristics and port connection change characteristics of the all-optical network based on the status monitoring data, and take corresponding heat dissipation or insulation measures to ensure the normal operation of the all-optical network. At the same time, it can promptly detect connection anomalies, help network administrators quickly locate problems, and take corresponding measures to repair them.

[0117] Optionally, the analyzing the temperature characteristics and port connection change characteristics of the all-optical network based on the status monitoring data can be obtained by constructing a time series graph corresponding to the temperature characteristics and port connection change characteristics of the all-optical network using the status monitoring data.

[0118] Furthermore, the embodiments of the present invention can more accurately evaluate the health status of all-optical network equipment by analyzing the equipment operation status characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics, timely discover potential problems and risks, and provide strong support for network maintenance, optimization and upgrading. At the same time, it also helps to improve the reliability and stability of the all-optical network and ensure the normal operation of the network and the communication needs of users.

[0119] As an embodiment of the present invention, the analyzing the equipment operation status characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics includes: based on the temperature characteristics, querying the temperature extreme value data, temperature fluctuation data and temperature change rate data of the all-optical network equipment corresponding to the all-optical network during operation, analyzing the operation status changes of the all-optical network equipment under the temperature extreme value data, the temperature fluctuation data and the temperature change rate data to obtain temperature-equipment change information; based on the port connection change characteristics, querying the port connection stability data, port connection state transfer data and port connection abnormal mode data when the all-optical network equipment is running, analyzing the operation status changes of the all-optical network equipment under the port connection stability data, the port connection state transfer data and the port connection abnormal mode data to obtain port-equipment change information; based on the temperature-equipment change information and the port-equipment change information, analyzing the equipment operation status characteristics of the all-optical network.

[0120] Among them, the temperature extreme value data refers to the highest and lowest temperature values ​​recorded during the temperature monitoring process of each device and environment (such as a computer room, equipment cabinet, etc.) in the all-optical network. The temperature fluctuation data is data used to measure the magnitude of the change in the temperature of the device or environment in the all-optical network within a certain period of time. The temperature change rate data is data indicating how fast the temperature of the device or environment in the all-optical network changes over time. The port connection stability data refers to data used to evaluate the stability of the port connection of the device in the all-optical network. The port connection state transition data regards the port connection state as a discrete random process, and calculates the transition probability between different states by counting the number of transitions between different states. The port connection abnormal pattern data refers to some pattern data that do not conform to the normal connection law identified through long-term observation and analysis of the port connection state data of the device in the all-optical network.

[0121] Optionally, the process of analyzing the operating state changes of the all-optical network equipment under the temperature extreme value data, the temperature fluctuation data and the temperature change rate data to obtain the temperature-equipment change information is called: continuously paying attention to the temperature monitoring data of each device and environment (such as a computer room, equipment cabinet, etc.) in the all-optical network, paying special attention to whether there are extreme values ​​of too high or too low temperature, and observing the performance of the equipment when the temperature is too high (temperature extreme value), and calculating the temperature fluctuation within a certain period of time by analyzing the temperature monitoring data. A common calculation method is to use the standard deviation to measure the degree of temperature fluctuation. For example, for The temperature data of a device in the past hour is recorded every minute. By calculating the standard deviation of these 60 temperature values, the temperature fluctuation of the device in this hour can be obtained, and the temperature change rate over time, that is, the increase or decrease of the temperature per unit time, can be calculated. For example, the difference between two adjacent temperature monitoring data is divided by the time interval. If the temperature is monitored every 5 minutes, the current monitoring temperature is 30°C, and the last monitoring temperature is 28°C, then the temperature change rate is (30-28) / 5=0.4°C / minute. Observe the operating status of the all-optical network device under these conditions;

[0122] The process of analyzing the operation status changes of the all-optical network equipment under the port connection stability data, the port connection status transfer data and the port connection abnormal mode data to obtain the port-equipment change information is as follows: first, the stability of the port connection is analyzed by using the port connection status monitoring data. Common evaluation indicators include the number of port connection interruptions, the port connection duration, etc. For example, the number of connection interruptions of a device port in one day is counted. If the number of interruptions is too many, such as more than 10 times, it means that the port connection stability is poor. At the same time, the distribution of the port connection duration is observed. If most of the connection durations are short, such as the average connection time is less than one hour, it also indicates that the port connection is unstable. The device operation status of the port in stable, unstable and abnormal modes is observed.

[0123] The analysis of the device operation status characteristics of the all-optical network based on the temperature-device change information and the port-device change information can be done by taking the temperature characteristics and the port connection change characteristics as two main analysis dimensions, while considering the position and function of different devices in the all-optical network, and then constructing an association matrix to represent the relationship between the temperature characteristics and the port connection change characteristics and the device operation status.

[0124] S4. Query the protocol data of the all-optical network, identify the data service type of the all-optical network based on the protocol data, analyze the traffic variation pattern of different service types in the data service type, and analyze the service traffic periodicity characteristics of the all-optical network based on the traffic variation pattern.

[0125] The embodiment of the present invention can ensure the accuracy and reliability of subsequent analysis by querying the protocol data of the all-optical network, and avoid wrong judgments caused by lack of understanding of communication rules. Optionally, the protocol data can be queried through the management interface of the network device.

[0126] Furthermore, the embodiments of the present invention can help users to reasonably allocate bandwidth and adjust network configuration according to the needs of different service types by identifying the data service type of the all-optical network based on the protocol data to ensure the quality of key services. At the same time, it can also better understand the user's usage behavior and service needs, providing a basis for further business expansion and optimization.

[0127] Optionally, the identifying of the data service type of the all-optical network based on the protocol data may be performed by performing feature analysis on the protocol data to obtain feature analysis results, and performing classification based on the feature analysis results using a support vector machine.

[0128] The embodiment of the present invention can continuously monitor the flow size, flow peak time, flow fluctuation, etc. in different time periods by analyzing the flow change rules of different service types in the data service types.

[0129] Optionally, the analysis of traffic change patterns of different service types in the data service types may be performed through time series function analysis.

[0130] The embodiment of the present invention further summarizes the periodic characteristics of the overall business traffic of the all-optical network by analyzing the periodic characteristics of the business traffic of the all-optical network based on the traffic change law, which can provide an important reference basis for network planning and design.

[0131] As an embodiment of the present invention, analyzing the service traffic periodicity characteristics of the all-optical network based on the traffic change law includes: based on the traffic change law, collecting service traffic data of different service types of the all-optical network, and performing frequency domain conversion on the service traffic data using the following formula to obtain frequency domain traffic data:

[0132]

[0133] Wherein, X(f) represents the frequency domain traffic data, x(t) represents the service traffic data in the all-optical network, f represents the frequency domain, t represents time, and j represents the imaginary unit;

[0134] Based on the frequency domain traffic data, a service traffic spectrum diagram of the all-optical network is constructed, and the service traffic spectrum diagram is used to analyze the periodic characteristics of the service traffic of the all-optical network.

[0135] Optionally, the service traffic spectrum of the all-optical network constructed based on the frequency domain traffic data can be constructed by Java, and the service traffic spectrum used to analyze the periodic characteristics of the service traffic of the all-optical network can be determined by analyzing the frequencies corresponding to the peaks in the spectrum to determine the periodic changes in the service traffic. For example, if a peak is found in the spectrum to have a frequency of f=0.1 (assuming the sampling frequency f s =1), then this means that the business traffic has a period of The specific time unit depends on the sampling time interval of the original time series.

[0136] S5. According to the optical signal characteristics, the equipment operation status characteristics and the service traffic periodicity characteristics, the pre-configured all-optical network analysis model is optimized to obtain an optimized analysis model; using the optimized analysis model, a time series analysis is performed on the all-optical network to obtain an analysis result; based on the analysis result, an operation abnormality analysis is performed on the all-optical network to obtain an operation abnormality analysis result; based on the operation abnormality analysis result, an intelligent management strategy for the all-optical network is constructed.

[0137] The embodiment of the present invention optimizes the preconfigured all-optical network analysis model according to the optical signal characteristics, the equipment operation status characteristics and the service traffic periodicity characteristics, and obtains the optimized analysis model, which can make the model understand the real behavior of the network more comprehensively and accurately, and improve the accuracy of the model analysis. Wherein, the preconfigured all-optical network analysis model refers to a mathematical model or calculation framework for analyzing various characteristics and operation conditions of the all-optical network, which is pre-built according to relevant theoretical knowledge, industry experience and some general data or typical cases before analyzing the all-optical network, and includes multiple modules, which are used to handle different aspects of network analysis tasks, and there is a special optical signal analysis module for analyzing the characteristics of the wavelength, power, transmission stability and other characteristics of the optical signal; the equipment operation status analysis module is responsible for analyzing the operating status of various devices in the network (such as optical line terminals, optical network units, optical amplifiers, etc.), such as temperature, port connection, performance indicators, etc.; and there is also a service traffic analysis module for studying the traffic distribution, change rules and other conditions of different service types (such as video, voice, file transfer, etc.) in the network. It should be further explained that the preconfigured all-optical network analysis model can be constructed based on the machine learning model.

[0138] Optionally, the optimization analysis model can optimize the model by quantifying the optical signal characteristics, the equipment operation status characteristics and the service flow periodic characteristics, and then combining the back propagation algorithm with stochastic gradient descent.

[0139] The embodiment of the present invention uses the optimization analysis model to perform time series analysis on the all-optical network, and the analysis results obtained can obtain comprehensive analysis results on the all-optical network in the time dimension, and then discover some potential problems that are difficult to detect in static analysis, such as the trend of gradual decline in equipment performance, the source of abnormal fluctuations in business traffic, etc., to provide clues for subsequent abnormal analysis.

[0140] Optionally, the optimization analysis model performs time series analysis on the all-optical network, and the process of obtaining the analysis results is: continue to collect real-time time series data on optical signals, equipment status, business traffic, etc. from various monitoring points in the all-optical network, perform data cleaning on the real-time time series data to obtain cleaned data, input the cleaned data into the optimization analysis model, and perform data analysis.

[0141] The embodiment of the present invention performs operation abnormality analysis on the all-optical network based on the analysis result, and obtains the operation abnormality analysis result, which can timely discover abnormal phenomena in the operation process of the all-optical network, whether it is optical signal transmission abnormality, equipment failure or business flow abnormality. By comparing with the set standard, the link where the problem may exist can be quickly located.

[0142] Optionally, the process of performing an operation abnormality analysis on the all-optical network based on the analysis result to obtain the operation abnormality analysis result is as follows: according to the historical operation data of the all-optical network, the experience data of other similar networks in the same industry and the relevant theoretical research results, the abnormal thresholds and judgment criteria in terms of optical signal, equipment status and service flow are set. For example, for the optical signal power, a lower limit and an upper limit are set according to the power range in normal operation in the past. When the optical signal power exceeds this range, it is determined that the optical signal power is abnormal; for the equipment temperature, a high temperature threshold and a low temperature threshold are set according to the normal operating temperature range provided by the equipment manufacturer and the temperature conditions when the equipment fails in the past. When the equipment temperature exceeds this range, it is determined that the equipment temperature is abnormal; for the service flow, according to the average level of service flow in history, the fluctuation range and the peak and valley conditions of service flow, the threshold of flow abnormality is set. For example, when the service flow exceeds a certain multiple (such as 2 times) of the average flow or is lower than a certain proportion (such as 50%) of the average flow, it is determined that the service flow is abnormal.

[0143] Furthermore, the embodiment of the present invention can ensure the stable operation and performance optimization of the network by constructing the intelligent management strategy of the all-optical network based on the operation abnormality analysis result, and can also improve the user experience.

[0144] As an embodiment of the present invention, the intelligent management strategy of the all-optical network is constructed based on the operation abnormality analysis result, including: based on the operation abnormality analysis result, classifying the abnormal causes of abnormal operation of the all-optical network to obtain classified abnormal causes, evaluating the impact range of different causes in the classified abnormal causes on the all-optical network to obtain multiple abnormal impacts, based on the multiple abnormal impacts, constructing the equipment maintenance and management strategy, network resource adjustment strategy and optical signal optimization strategy of the all-optical network, and integrating the equipment maintenance and management strategy, the network resource adjustment strategy and the optical signal optimization strategy to obtain an intelligent management strategy.

[0145] Optionally, based on the operation abnormality analysis result, the abnormal causes of the abnormal operation of the all-optical network are classified, and the classified abnormal causes can be obtained by querying various abnormal situations detected at different time points in the operation abnormality analysis result, such as optical signal transmission abnormality, equipment failure, business flow abnormality and other specific manifestations and related data, and then classifying the abnormalities. For example, the causes related to the hardware failure of the equipment itself, such as optical line terminal (OLT), optical network unit (ONU), etc., are classified into one category, such as the situation that the equipment cannot operate normally due to damage to the equipment chip, power module failure, etc., and the abnormalities caused by optical fiber link problems are classified into another category, such as optical signal transmission interruption or excessive loss caused by optical fiber breakage and excessive bending of optical fiber. For abnormalities caused by improper network configuration, such as business flow abnormality caused by IP address conflict and unreasonable bandwidth allocation, they are also separately classified into one category. The evaluation of the impact range of different causes in the classified abnormal causes on the all-optical network to obtain multiple abnormal impacts can be achieved by collecting relevant data through the network monitoring system for each type of abnormal cause to evaluate its impact range.For example: for equipment hardware failure, if the OLT device fails, since the OLT is the core device of the all-optical network, it is responsible for connecting multiple ONUs and managing data transmission, then its scope of impact may be all ONU devices connected to it and all user terminals served by these ONUs, which may cause the network connection of a large number of users to be interrupted or the business cannot be carried out normally. This is a case with a large impact range. If only the port of a certain ONU device fails, the impact range is relatively small, and may only involve individual user terminals connected through this port, causing these users to be unable to use network services normally. For optical fiber link anomalies, such as a section of trunk optical fiber is broken, it will affect all business traffic transmitted through the optical fiber, involving users in multiple regions and multiple business types, and the impact range is wide. If it is a small local problem with the branch optical fiber, it may only affect the specific area or user group served by the branch line. Similarly, for abnormalities such as improper network configuration, such as unreasonable bandwidth allocation of the entire network, it can be It can cause all users to face network congestion during peak business hours, affecting the business experience of all users. However, if the IP address of an individual subnet conflicts, it may only affect the communication between some devices in the subnet, and the scope of influence is relatively limited. The process of constructing the equipment maintenance and management strategy, network resource adjustment strategy and optical signal optimization strategy of the all-optical network based on the multiple abnormal impacts is: constructing equipment maintenance and management strategy through regular equipment inspection and maintenance plan and fault emergency response mechanism, constructing network resource adjustment strategy through bandwidth allocation adjustment and network topology optimization, and constructing optical signal optimization strategy through formulating optical fiber link maintenance and repair plan and optical amplifier adjustment and optimization plan. The process of integrating the equipment maintenance and management strategy, the network resource adjustment strategy and the optical signal optimization strategy to obtain the intelligent management strategy is: implementing the integrated management strategy in the all-optical network, and monitoring and managing the all-optical network in real time through network management system, automated operation and maintenance tools and other means.

[0146] Embodiment 2:

[0147] like Figure 2 , is a system functional module diagram of a method for realizing intelligent management of an all-optical network based on big data analysis provided by an embodiment of the present invention.

[0148] The all-optical network intelligent management system 200 based on big data analysis described in the present invention can be installed in an electronic device. According to the functions implemented, the all-optical network intelligent management system based on big data analysis can include a data repair module 201, an optical signal feature analysis module 202, an equipment operation feature analysis module 203, a service feature analysis module 204, and an intelligent management strategy construction module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0149] In the embodiment of the present invention, the functions of each module / unit are as follows:

[0150] The data repair module 201 is used to collect the operation data of the all-optical network, query the operation configuration parameters of the operation equipment of the all-optical network, remove abnormal data from the operation data based on the operation configuration parameters to obtain regular data, query the operation abnormality period of the operation equipment to locate the abnormal point of the link optical power of the all-optical network, perform data linear repair on the power data corresponding to the abnormal point to obtain target data;

[0151] The optical signal characteristic analysis module 202 is used to construct the optical wave spectrum of the all-optical network by using the target data, analyze the energy proportion information of the optical signal energy of the all-optical network by using the optical wave spectrum, analyze the wavelength characteristics of the all-optical network based on the energy proportion information, analyze the signal transmission stability of the optical signal of the all-optical network by using the target data, and analyze the optical signal characteristics of the all-optical network based on the wavelength characteristics and the signal transmission stability;

[0152] The device operation characteristic analysis module 203 is used to query the state monitoring data about the all-optical network in the target data, analyze the temperature characteristics and port connection change characteristics of the all-optical network based on the state monitoring data, and analyze the device operation state characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics;

[0153] The service characteristic analysis module 204 is used to query the protocol data of the all-optical network, identify the data service type of the all-optical network based on the protocol data, analyze the traffic change rules of different service types in the data service type, and analyze the service traffic periodicity characteristics of the all-optical network based on the traffic change rules;

[0154] The intelligent management strategy construction module 205 is used to optimize the pre-configured all-optical network analysis model according to the optical signal characteristics, the equipment operation status characteristics and the service traffic periodicity characteristics to obtain an optimized analysis model, use the optimized analysis model to perform time series analysis on the all-optical network to obtain analysis results, based on the analysis results, perform operation anomaly analysis on the all-optical network to obtain operation anomaly analysis results, and based on the operation anomaly analysis results, construct an intelligent management strategy for the all-optical network.

[0155] In detail, the modules in the all-optical network intelligent management system 200 based on big data analysis described in the embodiment of the present invention are used in the same manner as described above. Figure 1 The technical means described in the specification is the same as the method for realizing intelligent management of all-optical networks based on big data analysis, and can produce the same technical effects, which will not be repeated here.

[0156] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0157] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0158] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0159] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0160] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0161] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for realizing intelligent management of all-optical networks based on big data analysis, characterized in that: The method comprises: Collecting operation data of the all-optical network, querying operation configuration parameters of the operation equipment of the all-optical network, removing abnormal data from the operation data based on the operation configuration parameters to obtain regular data, querying the operation abnormality period of the operation equipment to locate the abnormal point of the link optical power of the all-optical network, performing data linear repair on the power data corresponding to the abnormal point to obtain target data; Using the target data, constructing the optical wave spectrum of the all-optical network, using the optical wave spectrum, analyzing the energy proportion information of the optical signal energy of the all-optical network, based on the energy proportion information, analyzing the wavelength characteristics of the all-optical network, using the target data, analyzing the signal transmission stability of the optical signal of the all-optical network, based on the wavelength characteristics and the signal transmission stability, analyzing the optical signal characteristics of the all-optical network; querying the target data for status monitoring data about the all-optical network, analyzing temperature characteristics and port connection change characteristics of the all-optical network based on the status monitoring data, and analyzing device operation status characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics; querying the protocol data of the all-optical network, identifying the data service type of the all-optical network based on the protocol data, analyzing the traffic change rules of different service types in the data service types, and analyzing the service traffic periodicity characteristics of the all-optical network based on the traffic change rules; According to the optical signal characteristics, the equipment operation status characteristics and the service traffic periodicity characteristics, a preconfigured all-optical network analysis model is optimized to obtain an optimized analysis model; using the optimized analysis model, a time series analysis is performed on the all-optical network to obtain an analysis result; based on the analysis result, an operation abnormality analysis is performed on the all-optical network to obtain an operation abnormality analysis result; based on the operation abnormality analysis result, an intelligent management strategy for the all-optical network is constructed.

2. The method for realizing intelligent management of all-optical network based on big data analysis according to claim 1, characterized in that: The performing linear repair on the power data corresponding to the abnormal point to obtain target data includes: Performing time series decomposition on the power data to obtain trend item power data, seasonal item power data and random item power data; Analyzing the linear trend characteristics of the trend item power data; Based on the linear trend feature, performing trend item data repair on the trend item power number to obtain trend item repair data; Constructing a time series graph of the seasonal power data; Analyzing the periodic characteristics of the seasonal power data using the time series graph; Determining the cycle length of the seasonal power data according to the cycle characteristics; Based on the cycle length, performing seasonal item data repair on the seasonal item power data to obtain seasonal item repair data; Analyzing random data distribution characteristics and variance statistical properties of the random item power data; Based on the statistical property of the variance of the random data distribution characteristics, performing random data repair on the random item power data to obtain random item repair data; The trend item repair data, the season item repair data and the random item repair data are combined to obtain target data.

3. The method for realizing intelligent management of all-optical network based on big data analysis according to claim 1, characterized in that: The analyzing the wavelength characteristics of the all-optical network based on the energy proportion information includes: Based on the energy proportion information, calculating the wavelength energy proportion of the all-optical network; Querying optical fiber transmission characteristic parameters of the all-optical network; Performing data integration on the wavelength energy proportion and the optical fiber transmission characteristic parameter to obtain integrated data; Analyzing the optical loss and optical dispersion of the wavelength light of the all-optical network using the integrated data; The wavelength characteristics of the all-optical network are determined using the optical loss and the optical dispersion.

4. The method for realizing intelligent management of all-optical network based on big data analysis according to claim 1, characterized in that: The using the target data to analyze the signal transmission stability of the optical signal of the all-optical network includes: extracting optical power and signal-to-noise ratio from the target data; Constructing a visual graph of the optical power and the signal-to-noise ratio; Using the intuitive graph, a multi-dimensional time series analysis is performed on the optical power and the signal-to-noise ratio to obtain a multi-dimensional analysis result; The signal transmission stability of the optical signal of the all-optical network is determined by utilizing the multi-dimensional analysis result in combination with a preset stability judgment standard.

5. The method for realizing intelligent management of all-optical network based on big data analysis according to claim 1, characterized in that: The analyzing the optical signal characteristics of the all-optical network based on the wavelength characteristics and the signal transmission stability includes: Constructing a comprehensive evaluation index of the wavelength characteristics and the signal transmission stability; Constructing a comprehensive relationship curve between the comprehensive evaluation index and the wavelength of the all-optical network; Analyzing the dominant wavelengths of the optical signals of the all-optical network and the dominant transmission time periods corresponding to the dominant wavelengths by using the comprehensive relationship curve, and analyzing the non-dominant wavelengths of the optical signals of the all-optical network and the non-dominant transmission time periods corresponding to the non-dominant wavelengths; The optical signal characteristics of the all-optical network are analyzed according to the dominant wavelength, the dominant transmission period, the non-dominant wavelength and the non-dominant transmission period.

6. The method for realizing intelligent management of all-optical network based on big data analysis according to claim 5, characterized in that: The said constructing the comprehensive evaluation index of the wavelength characteristics and the signal transmission stability comprises: Querying the wavelength energy ratio, wavelength loss coefficient and wavelength dispersion coefficient in the wavelength characteristics; Construct optical power stability index and signal-to-noise ratio stability index for signal transmission stability; Using the wavelength energy proportion, the wavelength loss coefficient, the wavelength dispersion coefficient, the optical power stability index and the signal-to-noise ratio stability index, the following formula is used to create a comprehensive evaluation index of the wavelength characteristics and the signal transmission stability: Among them, β represents the comprehensive evaluation index, δ represents the wavelength in the all-optical network, P(δ) represents the wavelength energy proportion, α(δ) represents the wavelength loss coefficient, β(δ) represents the wavelength dispersion coefficient, L represents the transmission distance of δ, S p Represents the optical power stability index, S snr Represents the signal-to-noise ratio stability index.

7. The method for realizing intelligent management of all-optical network based on big data analysis according to claim 1, characterized in that: The analyzing the device operation status characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics includes: Based on the temperature characteristics, query the temperature extreme value data, temperature fluctuation data and temperature change rate data of the all-optical network device corresponding to the all-optical network during operation; Analyze the operating state changes of the all-optical network device under the temperature extreme value data, the temperature fluctuation data and the temperature change rate data to obtain temperature-device change information; Based on the port connection change characteristics, query the port connection stability data, port connection state transfer data and port connection abnormal mode data when the all-optical network device is running; Analyze the operation state change of the all-optical network device under the port connection stability data, the port connection state transfer data and the port connection abnormal mode data to obtain port-device change information; Based on the temperature-device change information and the port-device change information, the device operation status characteristics of the all-optical network are analyzed.

8. The method for realizing intelligent management of all-optical network based on big data analysis according to claim 1, characterized in that: The analyzing the service traffic periodicity characteristics of the all-optical network based on the traffic change rule includes: Based on the traffic variation law, collecting service traffic data of different service types of the all-optical network; The service flow data is converted into frequency domain using the following formula to obtain frequency domain flow data: Wherein, X(f) represents the frequency domain traffic data, x(t) represents the service traffic data in the all-optical network, f represents the frequency domain, t represents time, and j represents the imaginary unit; Based on the frequency domain traffic data, construct a service traffic spectrum diagram of the all-optical network; The service traffic spectrum diagram is used to analyze the periodic characteristics of the service traffic of the all-optical network.

9. The method for realizing intelligent management of all-optical network based on big data analysis according to claim 1, characterized in that: The constructing of the intelligent management strategy of the all-optical network based on the abnormal operation analysis result includes: Based on the operation abnormality analysis result, classifying the abnormal causes of the abnormal operation of the all-optical network to obtain classified abnormal causes; Evaluate the impact range of different causes among the classified abnormal causes on the all-optical network to obtain multiple abnormal impacts; Based on the multiple abnormal impacts, construct equipment maintenance and management strategies, network resource adjustment strategies and optical signal optimization strategies for the all-optical network; The equipment maintenance and management strategy, the network resource adjustment strategy and the optical signal optimization strategy are integrated to obtain an intelligent management strategy.

10. An all-optical network intelligent management system based on big data analysis, characterized in that: The system comprises: A data repair module is used to collect the operation data of the all-optical network, query the operation configuration parameters of the operation equipment of the all-optical network, remove abnormal data from the operation data based on the operation configuration parameters to obtain regular data, query the operation abnormality period of the operation equipment to locate the abnormal point of the link optical power of the all-optical network, perform data linear repair on the power data corresponding to the abnormal point to obtain target data; an optical signal characteristic analysis module, configured to construct an optical wave spectrum of the all-optical network using the target data, analyze energy proportion information of optical signal energy of the all-optical network using the optical wave spectrum, analyze wavelength characteristics of the all-optical network based on the energy proportion information, analyze signal transmission stability of optical signals of the all-optical network using the target data, and analyze optical signal characteristics of the all-optical network based on the wavelength characteristics and the signal transmission stability; an equipment operation characteristic analysis module, used for querying the state monitoring data about the all-optical network in the target data, analyzing the temperature characteristics and port connection change characteristics of the all-optical network based on the state monitoring data, and analyzing the equipment operation state characteristics of the all-optical network based on the temperature characteristics and the port connection change characteristics; A service characteristic analysis module, used to query the protocol data of the all-optical network, identify the data service type of the all-optical network based on the protocol data, analyze the traffic change rules of different service types in the data service type, and analyze the service traffic periodicity characteristics of the all-optical network based on the traffic change rules; An intelligent management strategy construction module is used to optimize the pre-configured all-optical network analysis model according to the optical signal characteristics, the equipment operation status characteristics and the service traffic periodicity characteristics to obtain an optimized analysis model, use the optimized analysis model to perform time series analysis on the all-optical network to obtain analysis results, based on the analysis results, perform operation anomaly analysis on the all-optical network to obtain operation anomaly analysis results, and based on the operation anomaly analysis results, construct an intelligent management strategy for the all-optical network.

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