CN110611531A - Optical module fault diagnosis and early warning method, device and system - Google Patents
Optical module fault diagnosis and early warning method, device and system Download PDFInfo
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- CN110611531A CN110611531A CN201910822924.2A CN201910822924A CN110611531A CN 110611531 A CN110611531 A CN 110611531A CN 201910822924 A CN201910822924 A CN 201910822924A CN 110611531 A CN110611531 A CN 110611531A
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- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
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- H04B10/077—Arrangements for monitoring or testing transmission systems; Arrangements for fault measurement of transmission systems using an in-service signal using a supervisory or additional signal
Abstract
The invention discloses a method, a device and a system for fault diagnosis and early warning of an optical module, which are used for solving the technical problems that the fault probability of the optical module cannot be given and the model adaptability for judging whether the optical module has faults by using a static threshold value method is limited in the prior art. The method comprises the following steps: acquiring digital diagnosis detection DDM data of a first period of the optical module in real time, processing the DDM data of the first period by adopting an anomaly detection model, and determining a first anomaly vector; calling a sample frequent item set and a corresponding sample confidence level set of an abnormal detection model according to the first abnormal vector, and judging whether the first abnormal vector is matched with an abnormal vector in the sample frequent item set; if so, determining that the fault probability of the optical module in the first period is a sample confidence coefficient corresponding to the abnormal vector in the sample frequent item set; and if not, determining that the optical module has no fault in the first period.
Description
Technical Field
The invention relates to the technical field of optical communication, in particular to a method, a device and a system for fault diagnosis and early warning of an optical module.
Background
The optical module is an important component of an optical communication network and is mainly responsible for conversion between optical signals and electrical signals in a communication process. For a long time, after sensing that communication service is affected, operation and maintenance personnel reversely check the fault of the optical module to replace or repair the fault of the optical module, and the passive operation and maintenance mode results in long affected time of the communication service and high operation and maintenance cost. Therefore, it is necessary to realize automation and intelligence of optical module operation and maintenance.
At present, the existing methods for fault diagnosis and early warning of an optical module mainly include the following methods: (1) based on temperature, length of use, and current: estimating the normal current according to the temperature and the service life of the optical module, and judging whether the optical module has a fault or not by comparing the real-time current with the normal current; (2) based on the cumulative statistical parameter: estimating the service life stage of the optical module by comparing the service life of the optical module, the total plugging times, the alarm times and the preset threshold value; (3) based on Digital Diagnostics Monitoring (DDM) parameters: firstly, certain mathematical processing is carried out on the acquired DDM parameters, such as calculating variance and the like, and then whether the optical module fails or not is judged by comparing the parameters with a preset threshold value. However, these methods either cannot provide the failure probability of the optical module, cannot perform the optical module failure early warning, or use the static failure threshold parameter to determine the parameter that can change with the difference of optical module manufacturers, the difference of operating environments, and the difference of use strengths, resulting in limited adaptability. Therefore, the technical problems that the fault probability of the optical module cannot be given and the model adaptability for judging whether the optical module has faults by using a static threshold value method is limited exist in the prior art.
Disclosure of Invention
The embodiment of the invention provides a method, a device and a system for fault diagnosis and early warning of an optical module, which are used for solving the technical problems that the fault probability of the optical module cannot be accurately given and the model adaptability for judging whether the optical module has faults by using a static threshold value method is limited in the prior art.
In a first aspect, to solve the above technical problem, an embodiment of the present application provides a method for diagnosing and warning a fault of an optical module, where a technical scheme of the method is as follows:
acquiring digital diagnosis detection DDM data of a first period of the optical module in real time, processing the DDM data of the first period by adopting an anomaly detection model, and determining a first anomaly vector; the anomaly detection model is a functional relation model of sample DDM data and a sample anomaly vector, and the sample anomaly vector is used for representing whether the sample DDM data is abnormal or not;
calling a sample frequent item set and a corresponding sample confidence level set of an abnormal detection model according to the first abnormal vector, and judging whether the first abnormal vector is matched with an abnormal vector in the sample frequent item set; the sample frequent item set is a set of sample abnormal vectors which appear simultaneously with port fault data, the sample confidence set is a set of sample confidence degrees corresponding to the sample abnormal vectors which appear simultaneously with the port fault data, and the sample confidence degrees are probabilities of the sample abnormal vectors and the port fault data appearing simultaneously;
if so, determining that the fault probability of the optical module in the first period is a sample confidence coefficient corresponding to the abnormal vector in the sample frequent item set;
and if not, determining that the optical module has no fault in the first period.
In the embodiment of the application, the DDM data of the first cycle of the optical module can be obtained in real time, the DDM data of the first cycle is processed by adopting an anomaly detection model, a first anomaly vector is determined, then a sample frequent item set and a corresponding sample confidence level set of the anomaly detection model are called according to the first anomaly vector, whether the anomaly vectors in the sample frequent item set of the first anomaly vector are matched or not is judged, if so, the fault probability of the optical module in the first cycle is determined to be the sample confidence level corresponding to the anomaly vectors in the sample frequent item set, if not, the fault does not exist in the first cycle, because the sample confidence level is an exact probability, and the anomaly detection model is a model for carrying out anomaly diagnosis on the parameters of a plurality of DDM data by adopting fault threshold parameters which can change according to different environments, different manufacturers and different use frequencies, therefore, the technical problems that the fault probability of the optical module cannot be accurately given and the model adaptability for judging whether the optical module has faults by using a static threshold value method is limited in the prior art are solved, the operation and maintenance efficiency of the optical module is improved, and the workload of operation and maintenance personnel is reduced.
With reference to the first aspect, in a first optional implementation manner of the first aspect, before acquiring digital diagnostic test DDM data of a first cycle of the optical module in real time, the method includes:
acquiring a sample DDM data set of an abnormal detection model; wherein the sample DDM data set comprises a plurality of cycles of DDM data;
according to the DDM data of the plurality of periods, determining an abnormal detection parameter set, an abnormal detection method set and an abnormal detection method parameter set corresponding to the DDM data of each period; the abnormal detection method parameter set corresponds to the abnormal detection method set in a one-to-one mode;
determining a sample abnormal vector set of the abnormal detection model according to the abnormal detection parameter set, the abnormal detection method set and the abnormal detection method parameter set; wherein the sample exception vector set comprises exception vectors corresponding to a plurality of periods of DDM data;
and generating the abnormality detection model according to the sample abnormality vector set.
In the embodiment of the application, a sample DDM data set of an anomaly detection model can be obtained, an anomaly detection parameter set, an anomaly detection method set and an anomaly detection method parameter set corresponding to DDM data of each period are determined according to the sample DDM data set, wherein, the abnormal detection method parameter set corresponds to the abnormal detection method set one by one, then the sample abnormal vector set of the abnormal detection model is determined according to the abnormal detection parameter set, the abnormal detection method set and the abnormal detection method parameter set, and then generating an abnormality detection model according to the sample abnormality vector set, thereby solving the technical problem that the model adaptability for judging whether the optical module has a fault is limited because static fault threshold parameters are used to judge parameters which can change along with different optical module manufacturers, different operating environments and different use strengths in the prior art.
With reference to the first optional implementation manner of the first aspect, in a second optional implementation manner of the first aspect, the abnormality detection parameter set is a set including at least one abnormality detection parameter; the abnormal detection parameter is a value determined by performing mathematical calculation on a parameter of DDM data of one period corresponding to the abnormal detection parameter, and the parameter of the DDM data includes at least one of a working temperature parameter, a working voltage parameter, a bias voltage parameter, a received light power parameter and a transmitted light power parameter of the optical module.
With reference to the first optional implementation manner of the first aspect, in a third optional implementation manner of the first aspect, the determining a sample anomaly vector set according to the anomaly detection parameter set, the anomaly detection method set, and the anomaly detection method parameter set includes:
determining an abnormal detection interval corresponding to a first abnormal detection parameter in an abnormal detection parameter set corresponding to the DDM data of a period according to the abnormal detection methods in the abnormal detection method set corresponding to the DDM data of the period and the abnormal detection method parameters corresponding to the abnormal detection methods;
judging whether the first abnormity detection parameter is in the abnormity detection interval or not;
if not, determining that the first anomaly detection parameter is abnormal, wherein the component size corresponding to the first anomaly detection parameter is a first preset threshold value;
if so, determining that the first abnormal detection parameter is not abnormal, wherein the component size corresponding to the first abnormal detection parameter is a second preset threshold;
and determining an abnormal vector corresponding to the DDM data of the period according to the component size.
With reference to the second optional implementation manner of the first aspect, in a fourth optional implementation manner of the first aspect, the sample exception vector is used to indicate whether the sample DDM data is abnormal, and includes:
the sample exception vector comprises a plurality of vectors; wherein different vectors are used to represent whether different anomaly detection parameters of the sample DDM data are anomalous;
if the component size is a first preset threshold, the component represents that the corresponding abnormal detection parameter of the sample DDM data is abnormal;
and if the component size is a second preset threshold, the component indicates that the abnormal detection parameter of the corresponding sample DDM data is not abnormal.
With reference to the first optional implementation manner of the first aspect, in a fifth optional implementation manner of the first aspect, before invoking a sample frequent item set and a corresponding sample confidence level set of an anomaly detection model according to the first anomaly vector, the method further includes:
acquiring a port fault data set corresponding to the sample DDM data set; wherein, the sample DDM data of one period corresponds to one port fault data;
and determining a sample frequent item set and a sample confidence level set corresponding to the sample DDM data set through a frequent item set mining algorithm according to the sample abnormal vector set corresponding to the sample DDM data set and the port fault data set.
With reference to the first aspect, in a sixth optional implementation manner of the first aspect, the method further includes:
determining DDM data of a second period of the optical module through a time sequence analysis algorithm according to the DDM data of the first period; wherein the second period is a period temporally subsequent to the first period;
determining the fault probability of the optical module in the second period according to the DDM data in the second period;
if the fault probability is larger than a third preset threshold value, generating early warning information according to the fault probability; the early warning information is used for representing the fault probability of the optical module in a second period;
and sending the early warning information to a user terminal of a set operation and maintenance worker.
In the embodiment of the application, DDM data of a second period of an optical module can be determined through a time series analysis algorithm according to DDM data of a first period, a fault probability of the optical module in the second period can be determined according to the DDM data of the second period, if the fault probability is greater than a third preset threshold, early warning information for indicating the fault probability of the optical module in the second period is generated according to the fault probability, and the early warning information is sent to a set user terminal of an operation and maintenance worker.
In a second aspect, an apparatus for diagnosing and warning a fault of an optical module is provided, including:
the determining module is used for acquiring digital diagnosis detection DDM data of a first period of the optical module in real time, processing the DDM data of the first period by adopting an anomaly detection model and determining a first anomaly vector; the anomaly detection model is a functional relation model of sample DDM data and a sample anomaly vector, and the sample anomaly vector is used for representing whether the sample DDM data is abnormal or not;
the processing module is used for calling a sample frequent item set and a corresponding sample confidence level set of an abnormal detection model according to the first abnormal vector and judging whether the first abnormal vector is matched with the abnormal vector in the sample frequent item set; the sample frequent item set is a set of sample abnormal vectors which appear simultaneously with port fault data, the sample confidence set is a set of sample confidence degrees corresponding to the sample abnormal vectors which appear simultaneously with the port fault data, and the sample confidence degrees are probabilities of the sample abnormal vectors and the port fault data appearing simultaneously; if so, determining that the fault probability of the optical module in the first period is a sample confidence coefficient corresponding to the abnormal vector in the sample frequent item set; and if not, determining that the optical module has no fault in the first period.
With reference to the second aspect, in a first optional implementation manner of the second aspect, the apparatus further includes a generating module configured to:
acquiring a sample DDM data set of an abnormal detection model; wherein the sample DDM data set comprises a plurality of cycles of DDM data;
acquiring a sample DDM data set of an abnormal detection model; wherein the sample DDM data set comprises a plurality of cycles of DDM data;
according to the DDM data of the plurality of periods, determining an abnormal detection parameter set, an abnormal detection method set and an abnormal detection method parameter set corresponding to the DDM data of each period; the abnormal detection method parameter set corresponds to the abnormal detection method set in a one-to-one mode;
determining a sample abnormal vector set of the abnormal detection model according to the abnormal detection parameter set, the abnormal detection method set and the abnormal detection method parameter set; wherein the sample exception vector set comprises exception vectors corresponding to a plurality of periods of DDM data;
and generating the abnormality detection model according to the sample abnormality vector set.
With reference to the first optional implementation manner of the second aspect, in a second optional implementation manner of the second aspect, the determining module is further configured to:
determining an abnormal detection interval corresponding to a first abnormal detection parameter in an abnormal detection parameter set corresponding to the DDM data of a period according to the abnormal detection methods in the abnormal detection method set corresponding to the DDM data of the period and the abnormal detection method parameters corresponding to the abnormal detection methods;
judging whether the first abnormity detection parameter is in the abnormity detection interval or not;
if not, determining that the first anomaly detection parameter is abnormal, wherein the component size corresponding to the first anomaly detection parameter is a first preset threshold value;
if so, determining that the first abnormal detection parameter is not abnormal, wherein the component size corresponding to the first abnormal detection parameter is a second preset threshold;
and determining an abnormal vector corresponding to the DDM data of the period according to the component size.
With reference to the first optional implementation manner of the second aspect, in a third optional implementation manner of the second aspect, the determining module is further configured to:
acquiring a port fault data set corresponding to the sample DDM data set; wherein, the sample DDM data of one period corresponds to one port fault data;
and determining a sample frequent item set and a sample confidence level set corresponding to the sample DDM data set through a frequent item set mining algorithm according to the sample abnormal vector set corresponding to the sample DDM data set and the port fault data set.
With reference to the second aspect, in a fourth optional implementation manner of the second aspect, the apparatus further includes a sending module, configured to:
determining DDM data of a second period of the optical module through a time sequence analysis algorithm according to the DDM data of the first period; wherein the second period is a period temporally subsequent to the first period;
determining the fault probability of the optical module in the second period according to the DDM data in the second period;
if the fault probability is larger than a third preset threshold value, generating early warning information according to the fault probability; the early warning information is used for representing the fault probability of the optical module in a second period;
and sending the early warning information to a user terminal of a set operation and maintenance worker.
In a third aspect, a system for diagnosing and warning a fault of an optical module is provided, including:
a memory for storing program instructions;
and the processor is used for calling the program instructions stored in the memory and executing the steps included in any one of the implementation modes of the first aspect according to the obtained program instructions.
In a fourth aspect, there is provided a storage medium having stored thereon computer-executable instructions for causing a computer to perform the steps included in any one of the embodiments of the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application.
FIG. 1 is a schematic block diagram of a system according to an embodiment of the present disclosure;
fig. 2 is a flowchart of a method for diagnosing and warning a fault of an optical module in an embodiment of the present application;
fig. 3 is a schematic structural diagram of an optical module fault diagnosis and early warning apparatus in an embodiment of the present application;
fig. 4 is a schematic structural diagram of a fault diagnosis and early warning system of an optical module in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. In the present application, the embodiments and features of the embodiments may be arbitrarily combined with each other without conflict. Also, while a logical order is shown in the flow diagrams, in some cases, the steps shown or described can be performed in an order different than here.
The terms "first" and "second" in the description and claims of the present application and the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the term "comprises" and any variations thereof, which are intended to cover non-exclusive protection. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
In the embodiments of the present application, "at least one" may mean one or at least two, for example, one, two, three, or more, and the embodiments of the present application are not limited.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in this document generally indicates that the preceding and following related objects are in an "or" relationship unless otherwise specified.
At present, the existing methods for fault diagnosis and early warning of an optical module mainly include the following methods: (1) based on temperature, length of use, and current: estimating the normal current according to the temperature and the service life of the optical module, and judging whether the optical module has a fault or not by comparing the real-time current with the normal current; (2) based on the cumulative statistical parameter: estimating the service life stage of the optical module by comparing the service life of the optical module, the total plugging times, the alarm times and the preset threshold value; (3) based on Digital Diagnostics Monitoring (DDM) parameters: firstly, certain mathematical processing is carried out on the acquired DDM parameters, such as calculating variance and the like, and then whether the optical module fails or not is judged by comparing the parameters with a preset threshold value. However, these methods either cannot provide the failure probability of the optical module, cannot perform the optical module failure early warning, or use the static failure threshold parameter to determine the parameter that can change with the difference of optical module manufacturers, the difference of operating environments, and the difference of use strengths, resulting in limited adaptability. Therefore, the technical problems that the fault probability of the optical module cannot be given and the model adaptability for judging whether the optical module has faults by using a static threshold value method is limited exist in the prior art.
In view of this, an embodiment of the present application provides a method for diagnosing and warning a fault of an optical module, where the method may obtain DDM data of a first cycle of the optical module in real time, process the DDM data of the first cycle using an anomaly detection model, determine a first anomaly vector, call a sample frequent item set and a corresponding sample confidence level set of the anomaly detection model according to the first anomaly vector, determine whether an anomaly vector in the sample frequent item set of the first anomaly vector matches, if so, determine that a fault probability of the optical module in the first cycle is a sample confidence level corresponding to the anomaly vector in the sample frequent item set, and if not, determine that the optical module does not have a fault in the first cycle, thereby solving the technical problems in the prior art that a fault probability of the optical module cannot be accurately given and a model adaptability of the optical module to determine whether the optical module is faulty using a static threshold method is limited, the operation and maintenance efficiency of the optical module is improved, and the workload of operation and maintenance personnel is reduced.
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below through the drawings and the specific embodiments of the specification, and it should be understood that the specific features of the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features of the embodiments and examples of the present application may be combined with each other without conflict.
Fig. 1 is a structure of an optical module fault diagnosis and warning system to which the method provided in the embodiment of the present application is applicable, and it should be understood that the optical module fault diagnosis and warning system shown in fig. 1 is a detailed description of an optical module fault diagnosis and warning system to which the method provided in the embodiment of the present application is applicable, and is not a limitation of the optical module fault diagnosis and warning system to which the method provided in the embodiment of the present application is applicable.
The system for diagnosing and warning the fault of the optical module shown in fig. 1 includes a transceiver 101, a memory 102, a processor 103, and a bus interface 104. The transceiver 101, memory 102 and processor 103 are connected by a bus interface 104. The transceiver 101 is used to transmit and receive information. The memory 102 is used to store program instructions. The processor 103 is configured to call the program instructions stored in the memory 102, and execute all steps included in the method for diagnosing and warning a fault of a light module according to the obtained program instructions.
Referring to fig. 2, an embodiment of the present application provides a method for diagnosing and warning a fault of an optical module, which may be performed by the system shown in fig. 1. The specific flow of the method is described below.
Step 201: the method comprises the steps of acquiring digital diagnosis detection DDM data of a first period of an optical module in real time, processing the DDM data of the first period by adopting an anomaly detection model, and determining a first anomaly vector.
In the embodiment of the application, before digital diagnosis detection DDM data of a first cycle of an optical module is acquired in real time, an anomaly detection model is generated, wherein the anomaly detection model is a functional relationship model of sample DDM data and a sample anomaly vector, and the sample anomaly vector is used for indicating whether the sample DDM data is abnormal or not.
Specifically, a sample DDM data set of an anomaly detection model may be obtained by a Network Management System (Network Management System) deployed on a processor of a System for diagnosing and warning a fault of an optical module, where the sample DDM data set includes multiple periods of DDM data, and then an anomaly detection parameter set, an anomaly detection method set, and an anomaly detection method parameter set corresponding to the DDM data of each period are determined according to the multiple periods of DDM data, where the anomaly detection method parameter sets correspond to the anomaly detection method sets one to one, and then a sample anomaly vector set of the anomaly detection model is determined according to the anomaly detection parameter set, the anomaly detection method set, and the anomaly detection method parameter set, that is, according to an anomaly detection method in the anomaly detection method set corresponding to the DDM data of one period and the anomaly detection method parameter corresponding to the anomaly detection method, determining an abnormal detection interval corresponding to a first abnormal detection parameter in an abnormal detection parameter set corresponding to the periodic DDM data, judging whether the first abnormal detection parameter is in the abnormal detection interval, if not, determining that the first abnormal detection parameter is abnormal, wherein the component size corresponding to the first abnormal detection parameter is a first preset threshold value; if the abnormal vector is detected, determining that the first abnormal detection parameter is not abnormal, determining that the component size corresponding to the first abnormal detection parameter is a second preset threshold, determining an abnormal vector corresponding to the DDM data of the period according to the component size, wherein the sample abnormal vector set comprises abnormal vectors corresponding to the DDM data of a plurality of periods, and then generating an abnormal detection model according to the sample abnormal vector set. For ease of understanding, the following description is given by way of example:
for example, a Simple Network Management Protocol (SNMP) in the Network Management system continuously acquires DDM data of an optical module for one month in a period of one hour, and acquires a sample DDM data set of an anomaly detection model, which includes DDM data of a plurality of periods;
according to the sample DDM data set, determining abnormal detection parameter sets corresponding to DDM data of one period in the sample data set as a working voltage maximum value, a working temperature maximum value, a bias current maximum value, a receiving light power maximum value and a transmitting light power maximum value, wherein corresponding abnormal detection method sets comprise Gaussian abnormal detection and boxplot abnormal detection, and corresponding abnormal detection method parameter sets comprise Gaussian abnormal detection parameters and boxplot abnormal detection parameters;
if the gaussian abnormality detection parameter is 3, the box diagram abnormality detection parameter is 1.5, the maximum value of the working voltage is 3.7V, the abnormality detection interval corresponding to the maximum value of the working voltage under the gaussian abnormality detection is (3.2V, 3.6V), the abnormality detection interval corresponding to the maximum value of the working voltage under the box diagram abnormality detection is (3.3V, 3.8V), the maximum value of the working voltage is abnormal under the gaussian abnormality detection, the maximum value of the working voltage is not abnormal under the box diagram abnormality detection, and the component size corresponding to the maximum value of the working voltage is determined to be a first preset threshold;
if the Gaussian abnormality detection parameter is 3, the boxplot abnormality detection parameter is 1.5, the working temperature maximum value is 70 ℃, the abnormality detection interval corresponding to the working temperature maximum value under Gaussian abnormality detection is (10 ℃, 60 ℃), the abnormality detection interval corresponding to the working temperature maximum value under boxplot abnormality detection is (15 ℃, 60 ℃), the working temperature maximum value is abnormal under Gaussian abnormality detection, and the component size corresponding to the working temperature maximum value is determined to be a first preset threshold value under boxplot abnormality detection;
if the Gaussian abnormality detection parameter is 3, the box diagram abnormality detection parameter is 1.5, the maximum value of the bias current is 65mA, the abnormality detection interval corresponding to the maximum value of the bias current under the Gaussian abnormality detection is (5mA, 75mA), the abnormality detection interval corresponding to the maximum value of the bias current under the box diagram abnormality detection is (6mA, 75mA), the maximum value of the bias current is not abnormal under the Gaussian abnormality detection and is not abnormal under the box diagram abnormality detection, and the component size corresponding to the maximum value of the working temperature is determined to be a second preset threshold;
if the Gaussian abnormality detection parameter is 3, the box diagram abnormality detection parameter is 1.5, the maximum value of the received optical power is-13 dBm, the abnormality detection interval corresponding to the maximum value of the received optical power under the Gaussian abnormality detection is (-11dBm, 0dBm), the abnormality detection interval corresponding to the maximum value of the received optical power under the box diagram abnormality detection is (-10dBm, 0dBm), the maximum value of the received optical power is abnormal under the Gaussian abnormality detection, the abnormality is under the box diagram abnormality detection, and the component size corresponding to the maximum value of the working temperature is determined to be a first preset threshold;
if the Gaussian abnormality detection parameter is 3, the box diagram abnormality detection parameter is 1.5, the maximum value of the emitted light power is-19 dBm, the abnormality detection interval corresponding to the maximum value of the emitted light power under the Gaussian abnormality detection is (-13dBm, 0dBm), the abnormality detection interval corresponding to the maximum value of the emitted light power under the box diagram abnormality detection is (-13dBm, 0dBm), the maximum value of the emitted light power is abnormal under the Gaussian abnormality detection, the abnormality is under the box diagram abnormality detection, and the component size corresponding to the maximum value of the working temperature is determined to be a first preset threshold;
acquiring an abnormal vector corresponding to the DDM data of the period according to the component size, wherein the abnormal vector comprises a plurality of components, different components are used for indicating whether abnormal detection parameters of different DDM data are abnormal, and if the component size is a first preset threshold value, the abnormal detection parameters of the DDM data corresponding to the components are abnormal; if the size of the component is a second preset threshold value, it indicates that the abnormal detection parameter of the DDM data corresponding to the component is not abnormal;
based on the same method, obtaining abnormal vectors corresponding to the DDM data of a plurality of periods, determining a sample abnormal vector set, and generating an abnormal detection model.
In the embodiment of the application, after the anomaly detection model is generated, the DDM data of the optical module in the first period can be acquired in real time through a network management system deployed in a system for fault diagnosis and early warning of the optical module, and the DDM data of the first period is processed by using the anomaly detection model to determine a first anomaly vector.
Step 202: and calling the sample frequent item set and the corresponding sample confidence level set of the abnormal detection model according to the first abnormal vector, and judging whether the first abnormal vector is matched with the abnormal vector in the sample frequent item set.
In the embodiment of the application, before a sample frequent item set and a corresponding sample confidence level set of an abnormality detection model are called according to a first abnormality vector, the sample frequent item set and the corresponding sample confidence level set of the abnormality detection model are determined, wherein the sample frequent item set is a set of sample abnormality vectors which appear simultaneously with port fault data, the sample confidence level set is a set of sample confidence levels corresponding to the sample abnormality vectors which appear simultaneously with the port fault data, and the sample confidence level is a probability that the sample abnormality vectors and the port fault data appear simultaneously.
Specifically, a port fault data set corresponding to a sample DDM data set can be obtained through a Network Management System (Network Management System) deployed on a processor of an optical module fault diagnosis and early warning System, wherein the sample DDM data of one period corresponds to one port fault data, and then a sample frequent item set and a sample confidence level set corresponding to the sample DDM data set are obtained through a frequent item set mining algorithm, such as an association analysis Apriori algorithm, a frequent Pattern tree fp-grow (quick Pattern tree) algorithm, according to a sample abnormality vector set and a port fault data set corresponding to the sample DDM data set, so as to determine the sample frequent item set and the corresponding sample confidence level set of the abnormality detection model.
In this embodiment of the application, after determining the sample frequent item set and the sample confidence level set corresponding to the sample DDM data set, the sample frequent item set and the corresponding sample confidence level set of the anomaly detection model may be called according to the first anomaly vector, and whether the first anomaly vector matches with an anomaly vector in the sample frequent item set or not may be determined.
Step 203: if so, determining that the fault probability of the optical module in the first period is a sample confidence coefficient corresponding to the abnormal vector in the sample frequent item set; and if not, determining that the optical module has no fault in the first period.
In the embodiment of the application, after the first abnormal vector is determined to be matched with the abnormal vector in the sample frequent item set, the fault probability of the optical module in the first period can be determined to be the sample confidence corresponding to the abnormal vector in the sample frequent item set; after it is determined that the first abnormal vector is not matched with the abnormal vector in the sample frequent item set, it may be determined that the failure probability of the optical module in the first period is 0, that is, there is no failure.
Based on the problem that the optical module may fail in a second period after determining the failure probability of the optical module in the first period, in this embodiment of the present application, after determining the failure probability of the optical module in the first period, the failure probability of the optical module in the second period is determined, and if the failure probability of the second period is greater than a third preset threshold, warning information indicating the failure probability of the optical module in the second period is generated, and the warning information is sent to a set user terminal of an operation and maintenance worker.
Specifically, according to the DDM data of the first period, determining the DDM data of the optical module of the second period through a time series analysis algorithm, then according to the DDM data of the second period, determining the failure probability of the optical module of the second period, determining whether the failure probability of the second period is greater than a third preset threshold, if so, generating early warning information for representing the failure probability of the optical module of the second period, and sending the early warning information to a set user terminal of an operation and maintenance worker. For ease of understanding, the following description is given by way of example:
if the fault probability of the second period is 0.3 and the third preset threshold corresponding to the fault probability of the second period is 0.5, determining that the optical module has a low possibility of fault in the second period;
and if the fault probability of the second period is 0.7 and the third preset threshold corresponding to the fault probability of the second period is 0.5, determining that the optical module has a high possibility of fault in the second period, generating early warning information for indicating the fault probability of the optical module in the second period, and transmitting the early warning information to a set user terminal of an operation and maintenance worker through a transceiver in the optical module fault diagnosis and early warning system.
In addition, all the periods can be set by the user, and in the embodiment of the application, all the periods are the same in size.
Based on the same inventive concept, the embodiment of the application provides an optical module fault diagnosis and early warning device, and the optical module fault diagnosis and early warning device can realize the corresponding functions of the optical module fault diagnosis and early warning method. The optical module fault diagnosis and early warning device can be a hardware structure, a software module or a hardware structure and a software module. The fault diagnosis and early warning device for the optical module can be realized by a chip system, and the chip system can be formed by a chip and can also comprise the chip and other discrete devices. Referring to fig. 3, the apparatus for diagnosing and warning a fault of an optical module includes a determining module 301 and a processing module 302, wherein:
a determining module 301, configured to obtain digital diagnostic test DDM data of a first period of the optical module in real time, process the DDM data of the first period by using an anomaly detection model, and determine a first anomaly vector; the anomaly detection model is a functional relation model of sample DDM data and a sample anomaly vector, and the sample anomaly vector is used for representing whether the sample DDM data is abnormal or not;
a processing module 302, configured to call a sample frequent item set and a corresponding sample confidence set of an anomaly detection model according to the first anomaly vector, and determine whether the first anomaly vector matches an anomaly vector in the sample frequent item set; the sample frequent item set is a set of sample abnormal vectors which appear simultaneously with port fault data, the sample confidence set is a set of sample confidence degrees corresponding to the sample abnormal vectors which appear simultaneously with the port fault data, and the sample confidence degrees are probabilities of the sample abnormal vectors and the port fault data appearing simultaneously; if so, determining that the fault probability of the optical module in the first period is a sample confidence coefficient corresponding to the abnormal vector in the sample frequent item set; and if not, determining that the optical module has no fault in the first period.
In an optional implementation manner, the apparatus for diagnosing and warning a fault of a light module further includes a generating module, configured to:
acquiring a sample DDM data set of an abnormal detection model; wherein the sample DDM data set comprises a plurality of cycles of DDM data;
according to the DDM data of the plurality of periods, determining an abnormal detection parameter set, an abnormal detection method set and an abnormal detection method parameter set corresponding to the DDM data of each period; the abnormal detection method parameter set corresponds to the abnormal detection method set in a one-to-one mode;
determining a sample abnormal vector set of the abnormal detection model according to the abnormal detection parameter set, the abnormal detection method set and the abnormal detection method parameter set; wherein the sample exception vector set comprises exception vectors corresponding to a plurality of periods of DDM data;
and generating the abnormality detection model according to the sample abnormality vector set.
In an alternative embodiment, the determining module 301 is further configured to:
determining an abnormal detection interval corresponding to a first abnormal detection parameter in an abnormal detection parameter set corresponding to the DDM data of a period according to the abnormal detection methods in the abnormal detection method set corresponding to the DDM data of the period and the abnormal detection method parameters corresponding to the abnormal detection methods;
judging whether the first abnormity detection parameter is in the abnormity detection interval or not;
if not, determining that the first anomaly detection parameter is abnormal, wherein the component size corresponding to the first anomaly detection parameter is a first preset threshold value;
if so, determining that the first abnormal detection parameter is not abnormal, wherein the component size corresponding to the first abnormal detection parameter is a second preset threshold;
and determining an abnormal vector corresponding to the DDM data of the period according to the component size.
In an alternative embodiment, the determining module 301 is further configured to:
acquiring a port fault data set corresponding to the sample DDM data set; wherein, the sample DDM data of one period corresponds to one port fault data;
and determining a sample frequent item set and a sample confidence level set corresponding to the sample DDM data set through a frequent item set mining algorithm according to the sample abnormal vector set corresponding to the sample DDM data set and the port fault data set.
In an optional implementation manner, the apparatus for diagnosing and warning a fault of a light module further includes a sending module, configured to:
determining DDM data of a second period of the optical module through a time sequence analysis algorithm according to the DDM data of the first period; wherein the second period is a period temporally subsequent to the first period;
determining the fault probability of the optical module in the second period according to the DDM data in the second period;
if the fault probability is larger than a third preset threshold value, generating early warning information according to the fault probability; the early warning information is used for representing the fault probability of the optical module in a second period;
and sending the early warning information to a user terminal of a set operation and maintenance worker.
Based on the same inventive concept, an embodiment of the present application provides a system for diagnosing and warning a fault of an optical module, please refer to fig. 4, where the system for diagnosing and warning a fault of an optical module includes at least one processor 402 and a memory 401 connected to the at least one processor, a specific connection medium between the processor 402 and the memory 401 is not limited in the embodiment of the present application, fig. 4 illustrates an example where the processor 402 and the memory 401 are connected by a bus 400, the bus 400 is represented by a thick line in fig. 4, and a connection manner between other components is only schematically illustrated, and is not limited thereto. The bus 400 may be divided into an address bus, a data bus, a control bus, etc., and is shown with only one thick line in fig. 4 for ease of illustration, but does not represent only one bus or type of bus.
In this embodiment of the application, the memory 401 stores instructions executable by the at least one processor 402, and the at least one processor 402 may execute the steps included in the foregoing method for diagnosing and warning a fault of a light module by calling the instructions stored in the memory 401.
The processor 402 is a control center of the optical module fault diagnosis and warning system, and can connect various parts of the whole optical module fault diagnosis and warning system by using various interfaces and lines, and implement various functions of the optical module fault diagnosis and warning system by executing instructions stored in the memory 401. Optionally, the processor 402 may include one or more processing units, and the processor 402 may integrate an application processor and a modem processor, wherein the application processor mainly handles operating systems, user interfaces, application programs, and the like, and the modem processor mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 402. In some embodiments, processor 402 and memory 401 may be implemented on the same chip, or in some embodiments, they may be implemented separately on separate chips.
Memory 401, which is a non-volatile computer-readable storage medium, may be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The Memory 401 may include at least one type of storage medium, and may include, for example, a flash Memory, a hard disk, a multimedia card, a card-type Memory, a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Programmable Read Only Memory (PROM), a Read Only Memory (ROM), a charge Erasable Programmable Read Only Memory (EEPROM), a magnetic Memory, a magnetic disk, an optical disk, and so on. The memory 401 is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 401 in the embodiments of the present application may also be a circuit or any other device capable of implementing a storage function for storing program instructions and/or data.
The processor 402 may be a general-purpose processor, such as a Central Processing Unit (CPU), digital signal processor, application specific integrated circuit, field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like, that may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method for diagnosing and warning the fault of the optical module disclosed by the embodiment of the application can be directly implemented by a hardware processor, or implemented by combining hardware and software modules in the processor.
By programming the processor 402, the code corresponding to the method for diagnosing and warning the fault of the optical module described in the foregoing embodiment may be solidified in the chip, so that the chip can execute the steps of the method for diagnosing and warning the fault of the optical module when running.
Based on the same inventive concept, embodiments of the present application further provide a storage medium, where the storage medium stores computer instructions, and when the computer instructions run on a computer, the computer is caused to perform the steps of the method for diagnosing and warning the fault of the optical module as described above.
In some possible embodiments, the various aspects of the method for diagnosing and warning the fault of the optical module provided in the present application may also be implemented in the form of a program product, which includes program code for causing a system for diagnosing and warning the fault of the optical module to perform the steps of the method for diagnosing and warning the fault of the optical module according to various exemplary embodiments of the present application described above in this specification when the program product is run on the system for diagnosing and warning the fault of the optical module.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A method for diagnosing and early warning faults of an optical module is characterized by comprising the following steps:
acquiring digital diagnosis detection DDM data of a first period of the optical module in real time, processing the DDM data of the first period by adopting an anomaly detection model, and determining a first anomaly vector; the anomaly detection model is a functional relation model of sample DDM data and a sample anomaly vector, and the sample anomaly vector is used for representing whether the sample DDM data is abnormal or not;
calling a sample frequent item set and a corresponding sample confidence level set of an abnormal detection model according to the first abnormal vector, and judging whether the first abnormal vector is matched with an abnormal vector in the sample frequent item set; the sample frequent item set is a set of sample abnormal vectors which appear simultaneously with port fault data, the sample confidence set is a set of sample confidence degrees corresponding to the sample abnormal vectors which appear simultaneously with the port fault data, and the sample confidence degrees are probabilities of the sample abnormal vectors and the port fault data appearing simultaneously;
if so, determining that the fault probability of the optical module in the first period is a sample confidence coefficient corresponding to the abnormal vector in the sample frequent item set;
and if not, determining that the optical module has no fault in the first period.
2. The method of claim 1, wherein prior to acquiring in real time first cycle digital diagnostic test (DDM) data for the light module, comprising:
acquiring a sample DDM data set of an abnormal detection model; wherein the sample DDM data set comprises a plurality of cycles of DDM data;
according to the DDM data of the plurality of periods, determining an abnormal detection parameter set, an abnormal detection method set and an abnormal detection method parameter set corresponding to the DDM data of each period; the abnormal detection method parameter set corresponds to the abnormal detection method set in a one-to-one mode;
determining a sample abnormal vector set of the abnormal detection model according to the abnormal detection parameter set, the abnormal detection method set and the abnormal detection method parameter set; wherein the sample exception vector set comprises exception vectors corresponding to a plurality of periods of DDM data;
and generating the abnormality detection model according to the sample abnormality vector set.
3. The method of claim 2, wherein the set of anomaly detection parameters is a set comprising at least one anomaly detection parameter; the abnormal detection parameter is a value determined by performing mathematical calculation on a parameter of DDM data of one period corresponding to the abnormal detection parameter, and the parameter of the DDM data includes at least one of a working temperature parameter, a working voltage parameter, a bias voltage parameter, a received light power parameter and a transmitted light power parameter of the optical module.
4. The method of claim 2, wherein determining a set of sample anomaly vectors from the set of anomaly detection parameters, the set of anomaly detection methods, and the set of anomaly detection method parameters comprises:
determining an abnormal detection interval corresponding to a first abnormal detection parameter in an abnormal detection parameter set corresponding to the DDM data of a period according to the abnormal detection methods in the abnormal detection method set corresponding to the DDM data of the period and the abnormal detection method parameters corresponding to the abnormal detection methods;
judging whether the first abnormity detection parameter is in the abnormity detection interval or not;
if not, determining that the first anomaly detection parameter is abnormal, wherein the component size corresponding to the first anomaly detection parameter is a first preset threshold value;
if so, determining that the first abnormal detection parameter is not abnormal, wherein the component size corresponding to the first abnormal detection parameter is a second preset threshold;
and determining an abnormal vector corresponding to the DDM data of the period according to the component size.
5. The method of claim 3, wherein the sample exception vector is used to represent whether the sample DDM data is anomalous, comprising:
the sample exception vector comprises a plurality of components; wherein different components are used to represent whether different anomaly detection parameters of the sample DDM data are anomalous;
if the component size is a first preset threshold, the component represents that the corresponding abnormal detection parameter of the sample DDM data is abnormal;
and if the component size is a second preset threshold, the component indicates that the abnormal detection parameter of the corresponding sample DDM data is not abnormal.
6. The method of claim 2, wherein prior to invoking the sample frequent item set and the corresponding sample confidence set of the anomaly detection model based on the first anomaly vector, further comprising:
acquiring a port fault data set corresponding to the sample DDM data set; wherein, the sample DDM data of one period corresponds to one port fault data;
and determining a sample frequent item set and a sample confidence level set corresponding to the sample DDM data set through a frequent item set mining algorithm according to the sample abnormal vector set corresponding to the sample DDM data set and the port fault data set.
7. The method of claim 1, further comprising:
determining DDM data of a second period of the optical module through a time sequence analysis algorithm according to the DDM data of the first period; wherein the second period is a period temporally subsequent to the first period;
determining the fault probability of the optical module in the second period according to the DDM data in the second period;
if the fault probability is larger than a third preset threshold value, generating early warning information according to the fault probability; the early warning information is used for representing the fault probability of the optical module in a second period;
and sending the early warning information to a user terminal of a set operation and maintenance worker.
8. A device for fault diagnosis and early warning of an optical module is characterized by comprising:
the determining module is used for acquiring digital diagnosis detection DDM data of a first period of the optical module in real time, processing the DDM data of the first period by adopting an anomaly detection model and determining a first anomaly vector; the anomaly detection model is a functional relation model of sample DDM data and a sample anomaly vector, and the sample anomaly vector is used for representing whether the sample DDM data is abnormal or not;
the processing module is used for calling a sample frequent item set and a corresponding sample confidence level set of an abnormal detection model according to the first abnormal vector and judging whether the first abnormal vector is matched with the abnormal vector in the sample frequent item set; the sample frequent item set is a set of sample abnormal vectors which appear simultaneously with port fault data, the sample confidence set is a set of sample confidence degrees corresponding to the sample abnormal vectors which appear simultaneously with the port fault data, and the sample confidence degrees are probabilities of the sample abnormal vectors and the port fault data appearing simultaneously; if so, determining that the fault probability of the optical module in the first period is a sample confidence coefficient corresponding to the abnormal vector in the sample frequent item set; and if not, determining that the optical module has no fault in the first period.
9. A system for fault diagnosis and early warning of optical modules, comprising:
a memory for storing program instructions;
a processor for calling program instructions stored in said memory and for executing the steps comprised by the method of any one of claims 1 to 7 in accordance with the obtained program instructions.
10. A storage medium storing computer-executable instructions for causing a computer to perform the steps comprising the method of any one of claims 1-7.
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