CN117041029A - Network equipment fault processing method and device, electronic equipment and storage medium - Google Patents
Network equipment fault processing method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a network equipment fault processing method, which comprises the following steps: acquiring an operation and maintenance data set, wherein the operation and maintenance data set comprises operation and maintenance data of at least one network device; performing anomaly detection on the operation and maintenance data set, determining anomaly operation and maintenance data in the operation and maintenance data set, and determining equipment faults based on the anomaly operation and maintenance data; performing root cause positioning on equipment faults, and determining fault root causes; determining at least one candidate treatment strategy for the root cause of the fault based on a predetermined knowledge-graph; testing the at least one candidate treatment strategy by using a digital twin technology, and determining a target treatment strategy from the at least one candidate treatment strategy according to the test result of each candidate treatment strategy; a target treatment policy is executed. The invention increases the detection mechanism by adopting the digital twin technology, thereby solving the problem that the equipment failure can not be solved due to lack of the detection mechanism when the fixed strategy is used for processing the failure, and further improving the accuracy rate of the equipment failure processing.
Description
Technical Field
The application relates to the technical field of intelligent operation and maintenance, in particular to a network equipment fault processing method, a network equipment fault processing device, electronic equipment and a computer readable storage medium.
Background
With various high-availability clusters, various types of safety, networks, servers and other devices are layered endlessly, technical components are more and more, and when components in the devices fail, massive alarm information simply depends on manpower and cannot respond to the obstacle removing requirement quickly.
The existing method for realizing the self-healing of the faults mainly relies on a cured knowledge base which is manually carded in advance, namely a knowledge base containing fault types and corresponding processing schemes, and after the faults occur and the fault types are positioned, the corresponding processing schemes in the cured knowledge base are directly started through preset scripts to realize the automatic processing and repairing of the faults.
However, the manual carding fault handling scheme is not necessarily completely reasonable, that is, the automatic repair of the fault cannot be completed after the scheme is executed, so that the prior art lacks a certain detection mechanism when realizing the automatic handling repair of the equipment fault.
Disclosure of Invention
The embodiment of the application provides a method, a device, electronic equipment, a computer readable storage medium and a computer program product for processing network equipment faults, which can solve the problem that the fault processing lacks a detection mechanism. The technical scheme is as follows:
According to a first aspect of an embodiment of the present application, there is provided a method for processing a network device failure, the method including:
acquiring an operation and maintenance data set, wherein the operation and maintenance data set comprises operation and maintenance data of at least one network device;
performing anomaly detection on the operation and maintenance data set, determining abnormal operation and maintenance data in the operation and maintenance data set, and determining equipment faults based on the abnormal operation and maintenance data set;
performing root cause positioning on the equipment faults, and determining fault root causes;
determining at least one candidate treatment strategy for the root cause of the fault based on a predetermined knowledge graph;
testing the at least one candidate treatment strategy by using a digital twin technology, and determining a target treatment strategy from the at least one candidate treatment strategy according to the test result of each candidate treatment strategy;
executing the target treatment strategy.
Optionally, the operation data includes at least one of log data and performance index data;
the anomaly detection is performed on the operation and maintenance data set, the anomaly operation and maintenance data in the operation and maintenance data set is determined, and equipment faults are determined based on the anomaly operation and maintenance data, and the anomaly detection comprises at least one of the following steps:
for any performance index data, determining a corresponding abnormality detection algorithm according to the type of the performance index data, and performing abnormality detection on the performance index data according to the abnormality detection algorithm, a static threshold strategy and an abnormality aggregation strategy;
For any one of log data, performing anomaly detection on the log data according to at least one of the quantity change degree, keywords and log mode of the log data;
and predicting the variation trend of any performance index data through a machine learning algorithm, and carrying out early warning on the performance index data by combining an early warning strategy.
Optionally, the performing anomaly detection on the operation and maintenance data set, determining the anomaly operation and maintenance data in the operation and maintenance data set, and determining the equipment fault based on the anomaly operation and maintenance data set, further includes:
a severity level of the equipment failure is determined.
Optionally, before determining the at least one candidate treatment strategy for the root cause of the fault based on a predetermined knowledge-graph, the method further comprises:
determining the corresponding relation between the fault root cause and the treatment strategy;
constructing a knowledge graph according to the correspondence between the fault root cause and the treatment strategy, wherein the knowledge graph comprises at least one fault root cause, the treatment strategy with the correspondence between the fault root cause and the treatment strategy and the weight of the corresponding treatment strategy;
optionally, testing the at least one candidate treatment strategy using a digital twin technique, determining a target treatment strategy from the at least one candidate treatment strategy according to a test result of each candidate treatment strategy, including:
Creating a digital twin model of the fault device using digital twin techniques;
according to the sequence from the big to the small of the weights of the candidate strategies of the fault root, carrying out dynamic analog simulation and analysis on the at least one candidate treatment strategy in a digital twin model of the fault equipment;
and taking the dynamic simulation and analysis results of each candidate treatment strategy as test results, and determining a target treatment strategy from the at least one candidate treatment strategy according to the test results of each candidate treatment strategy.
Optionally, the determining a target treatment policy from the at least one candidate treatment policy further includes:
if the weight of the target treatment strategy is not the highest weight, the weight of the target treatment strategy in the knowledge graph is increased.
Optionally, the performing anomaly detection on the operation and maintenance data set further includes:
carrying out data processing on the operation and maintenance data;
wherein the data processing mode comprises at least one of the following steps:
analyzing data;
data cleaning;
filtering data;
data derivatization;
data aggregation;
converting data; and
and (5) data characteristic analysis.
According to a second aspect of an embodiment of the present application, there is provided an apparatus for processing a network device failure, the apparatus including:
The system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an operation and maintenance data set, and the operation and maintenance data set comprises operation and maintenance data of at least one network device;
the detection module is used for carrying out abnormal detection on the operation and maintenance data set, determining abnormal operation and maintenance data in the operation and maintenance data set and determining equipment faults based on the abnormal operation and maintenance data set;
the positioning module is used for positioning the root cause of the equipment fault and determining the root cause of the fault;
a matching module for determining at least one candidate treatment strategy for the root cause of the fault based on a predetermined knowledge graph;
a pre-treatment module for testing the at least one candidate treatment strategy using digital twinning techniques, determining a target treatment strategy from the at least one candidate treatment strategy according to test results of each candidate treatment strategy;
and the execution module is used for executing the target treatment strategy.
According to a third aspect of embodiments of the present application, there is provided an electronic device comprising a memory, a processor and a computer program stored on the memory, the processor implementing the steps of the method as provided in the first aspect when the program is executed.
According to a fourth aspect of embodiments of the present application, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method as provided by the first aspect.
The technical scheme provided by the embodiment of the application has the beneficial effects that:
by adding a detection mechanism to the fault processing by adopting a digital twin technology, the problem that the equipment fault is possibly more serious due to the fact that the detection mechanism is lacking when the fault processing is carried out by using a fixed strategy in the prior art is solved, and the accuracy of the equipment fault processing is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are required to be used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flow chart of a network device fault handling method according to an embodiment of the present application;
fig. 2 is a schematic flow chart of acquiring a candidate treatment policy according to an embodiment of the present application;
FIG. 3 is a flowchart illustrating a method for determining a target treatment policy according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a network device fault handling apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an electronic device for network device fault handling according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described below with reference to the drawings in the present application. It should be understood that the embodiments described below with reference to the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application, and the technical solutions of the embodiments of the present application are not limited.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and "comprising," when used in this specification, specify the presence of stated features, information, data, steps, operations, elements, and/or components, but do not preclude the presence or addition of other features, information, data, steps, operations, elements, components, and/or groups thereof, all of which may be included in the present specification. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein indicates that at least one of the items defined by the term, e.g., "a and/or B" may be implemented as "a", or as "B", or as "a and B".
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the embodiments of the present application will be described in further detail with reference to the accompanying drawings.
The application provides a network equipment fault processing method, a network equipment fault processing device, electronic equipment and a computer readable storage medium, and aims to solve the technical problems in the prior art.
The technical solutions of the embodiments of the present application and technical effects produced by the technical solutions of the present application are described below by describing several exemplary embodiments. It should be noted that the following embodiments may be referred to, or combined with each other, and the description will not be repeated for the same terms, similar features, similar implementation steps, and the like in different embodiments.
Fig. 1 is a schematic flow chart of network equipment fault handling according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
s100, acquiring an operation and maintenance data set, wherein the operation and maintenance data set comprises operation and maintenance data of at least one network device.
In the embodiment of the application, after receiving the alarm data, operation and maintenance data of all network devices are acquired, and the operation and maintenance data of all network devices form an operation and maintenance data set.
In the embodiment of the application, the operation and maintenance data may include at least one of historical alarm data, real-time alarm data, historical performance index data, real-time performance index data, log data, topology data, work order data, service data and resource data, and Guan Yun data are accessed and stored by at least one mode of a Kafka distributed system and FTP (File Transfer Protocol ) to obtain an operation and maintenance data set.
S200, performing anomaly detection on the operation and maintenance data set, determining the abnormal operation and maintenance data in the operation and maintenance data set, and determining equipment faults based on the abnormal operation and maintenance data.
In the embodiment of the application, the acquired operation and maintenance data set is subjected to data anomaly detection to determine the abnormal data in the operation and maintenance data set, and the faults of the equipment are determined according to the equipment information reflected by the abnormal data.
In the embodiment of the application, the abnormality detection of the data can be realized by using an AI algorithm including machine learning, and the abnormality detection of the data can be that the abnormality detection is performed on the performance index data and the abnormality detection is performed on the log data.
S300, performing root cause positioning on equipment faults, and determining fault root causes.
In the embodiment of the application, for the determined equipment fault, the root cause of the fault, namely the root cause of the fault, is found and used for solving the equipment fault by adopting a corresponding treatment strategy.
By analyzing and integrating various data sources, the specific cause of the system fault is determined, namely the fault root cause is determined.
In the embodiment of the application, the root cause of the fault can be determined through various root cause positioning algorithms, such as at least one of an RCA (Root Cause Analysis ) algorithm, a centrality algorithm and pattern learning.
The RCA algorithm determines potential alarm master-slave relation by mining and association analysis on alarm data and combining an alarm propagation diagram to generate an alarm relation rule base, so that quick positioning of the root cause of the fault can be supported.
And the centrality algorithm carries out real-time alarm aggregation based on centrality calculation by using event dimension through alarm resource topological association and alarm time segmentation, and positions the root cause of the fault.
The mode learning can compare and match the current alarm with the history mode through a machine learning algorithm and a mode matching technology, find the similar root cause mode, and determine the root cause of the fault.
The root cause of the fault can be comprehensively decided by combining multidimensional topology assistance through at least one of an RCA algorithm, a centrality algorithm and a mode learning method.
Multidimensional topology assistance is to model and represent each component, node and connection relationship and establish a multidimensional topology model of the network device. When a fault occurs, the fault path can be tracked according to topology assistance, so that the fault root cause is positioned.
S400, determining at least one candidate treatment strategy of the fault root cause based on a predetermined knowledge graph.
In the embodiment of the application, after determining the root cause of the fault, determining the treatment strategy corresponding to the root cause of the fault according to the corresponding relation between the root cause of the fault and the treatment strategy in the knowledge graph, and taking the treatment strategy as a candidate treatment strategy.
The knowledge graph contains the corresponding relation between the fault root cause and the treatment strategy. There may be multiple solutions (i.e., multiple treatment strategies) for one root cause of a fault, so one root cause of a fault may correspond to multiple candidate treatment strategies in a knowledge-graph.
S500, testing at least one candidate treatment strategy by using a digital twin technology, and determining a target treatment strategy from the at least one candidate treatment strategy according to the test result of each candidate treatment strategy.
In an embodiment of the present application, the digital twin technique is a technique of associating a digital representation of an entity system or a physical system with an actual system. The running state and behavior of the entity system are simulated and monitored in real time by establishing a virtual digital model corresponding to the entity system.
In the embodiment of the application, a corresponding digital twin model can be constructed according to the equipment with faults, the candidate treatment strategies are applied to the digital twin model for testing, and one candidate treatment strategy is determined as a target treatment strategy according to the test result of each candidate treatment strategy. Through the digital twin technology, the pretreatment mechanism for fault processing is realized, and the problem that when the fault root in the knowledge graph cannot solve the equipment fault due to the corresponding treatment strategy is avoided, the treatment strategy cannot be directly used for solving the equipment fault, but rather, the more serious equipment fault is generated.
S600, executing a target treatment strategy.
In the embodiment of the application, the equipment information is modified correspondingly according to the content in the target treatment strategy.
The acquired target treatment strategy may be performed by an associated RPA (Robotic Process Automation, robotic flow automation) robot.
Specifically, the target treatment policy is automatically extracted using NLP (Natural Language Processing ); the RPA robot performs treatment flow arrangement on the target treatment strategy; and starting and executing the well-arranged treatment flow of the RPA robot to realize equipment fault processing and simultaneously acquiring the equipment fault processing execution result information.
The method comprises the steps of providing further guidance for fault equipment processing, connecting equipment faults, a located fault root cause, a matched treatment strategy, a digital twin technology pre-treatment-based result, an RPA (remote procedure for assembly) programmed treatment strategy and fault processing execution result information in series to form an equipment fault processing scheme related to the fault root cause, reporting the equipment fault processing scheme to an operation and maintenance system in at least one mode of WeChat, short message and mailbox, and providing samples for fault root cause location and fault processing according to the fault processing scheme to update a knowledge graph.
If the device failure processing result still does not reach the situation expected by the user after the target processing strategy is executed, a one-key rollback function is provided, namely, the version before execution can be rolled back.
In some embodiments, step S200 may further include:
s210, the operation and maintenance data comprise at least one of log data and performance index data;
performing anomaly detection on the operation and maintenance data set, determining abnormal operation and maintenance data in the operation and maintenance data set, and determining equipment faults based on the abnormal operation and maintenance data, wherein the equipment faults comprise at least one of the following steps:
for any performance index data, determining a corresponding abnormality detection algorithm according to the type of the performance index data, and performing abnormality detection on the performance index data according to the abnormality detection algorithm, the static threshold strategy and the abnormality aggregation strategy;
for any one of the log data, performing anomaly detection on the log data according to at least one of the quantity change degree, the keywords and the log mode of the log data;
and predicting the change trend of the performance index data by a machine learning algorithm for any performance index data, and carrying out early warning on the performance index data by combining an early warning strategy.
In the embodiment of the application, at least one of log data and performance index data is acquired to detect abnormal data.
When the performance index data is subjected to anomaly detection, a corresponding anomaly detection algorithm is determined according to the type of the performance index data, for example, a Holt-windows algorithm can be selected for anomaly detection of time series data, an N-Sigma algorithm can be selected for anomaly detection of continuous numerical data, a discovery algorithm is used for periodic data, an EWMA algorithm is used for ladder type data, and a ball algorithm is used for fluctuation type data. The corresponding anomaly detection algorithm can be combined with the static threshold strategy and the anomaly aggregation strategy to carry out comprehensive anomaly detection on the performance index data.
The algorithm upper limit, the algorithm lower limit and the algorithm fitting value of each time sequence point in the performance index data can be output through a corresponding abnormality detection algorithm, whether the current time sequence point is abnormal or not is judged by combining a static threshold, for example, the actual upper limit takes the minimum value of the static threshold upper limit and the algorithm upper limit, the actual lower limit takes the maximum value of the static threshold lower limit and the algorithm lower limit, then whether the actual value of the time sequence point in the performance index data is between the actual lower limit and the actual upper limit is judged, and if the time sequence point is normal in description, the time sequence point is not abnormal.
Because anomalies in individual nodes in performance index data can be considered occasional events, equipment failure cannot be determined. Therefore, an abnormal aggregation policy, such as a continuous number policy in the abnormal aggregation policy, can be introduced, that is, when the number of times that one node continuously generates an abnormality reaches a certain threshold, it can be determined as an equipment failure, for example, if one node continuously generates an abnormality 5 times, it can be determined as an equipment failure; a time window policy, i.e. when the number of anomalies occurring in a node within a certain time window reaches a certain threshold, may determine that it is a device failure, e.g. if a node has anomalies occurring 5 times within 10 minutes, it may determine that it is a device failure.
When the abnormality detection is performed on the log data, the abnormality detection may be performed on the log data based on at least one data information of the number change degree, the keyword, and the log pattern of the log data.
Abnormal fluctuations in the amount of log data, such as a sharp increase or decrease in the amount of log data, may be detected using at least one of statistical methods, time series analysis techniques.
Keywords can be extracted from the log data by at least one of text mining and NLP technology, and keywords different from normal log data can be identified by at least one of word frequency statistics, TF-IDF algorithm and word embedding method, so as to judge whether the data has abnormality.
The log schema refers to a specific schema or structure that repeatedly appears in log data.
The new log pattern or the abnormal log pattern may be identified by analyzing the structure and pattern of the log data, and the log data inconsistent with the normal pattern may be found and identified using at least one of a sequence pattern mining, a cluster analysis, and an association rule mining method.
The occurrence of a failure often has a deterioration in precursor information such as performance index data, so that a failure that will occur to the device can be predicted by predicting the trend of the performance index data.
Meanwhile, the trend of the performance index data can be predicted through at least one machine learning algorithm in Prophet and MLP (Multi-layer Perceptron), and meanwhile, the degradation of the performance index data is early warned in advance by combining with a warning strategy such as a static threshold configured empirically or a dynamic threshold learned automatically.
Therefore, by detecting abnormality of the data, not only the failure of the device but also the failure can be determined in advance.
Step S210 further includes:
s211, determining the severity level of the equipment fault.
In embodiments of the present application, equipment failures may be classified into three categories, critical, and generally, by severity. The severity of the equipment failure is the severity of the data anomaly. For example, the value range of a in the data is [0-100], the normal value is [0-50], but the actual value is (50-70), the equipment fault can be judged to be the general grade, the actual value is the important grade when (70-90), the actual value is the serious grade when (90, 100).
As shown in fig. 2, before step S400, the method further includes:
s410, determining the corresponding relation between the fault root cause and the treatment strategy.
In the embodiment of the application, the corresponding relation between the fault root cause and the treatment strategy is determined through the treatment strategy in the prior equipment fault treatment process.
S420, constructing a knowledge graph according to the corresponding relation between the fault root cause and the treatment strategy, wherein the knowledge graph comprises at least one fault root cause, the treatment strategy with the corresponding relation with the fault root cause and the weight of the corresponding treatment strategy.
And storing the corresponding relation between the determined fault root and the treatment strategies in a knowledge graph, wherein one fault root possibly corresponds to a plurality of treatment strategies, corresponding weights are set for each treatment strategy, the weight of the treatment strategy represents the accuracy of the treatment strategy for processing the corresponding fault root, and the larger the weight of the treatment strategy is, the higher the accuracy of the treatment strategy for processing the corresponding fault root is.
As shown in fig. 3, step S500 further includes:
s510, creating a digital twin model of the fault equipment by using a digital twin technology.
In the embodiment of the application, the digital twin model can carry out virtual simulation and analysis on an actual system, can display the running condition of the system, forecast the behavior of the system and carry out optimization and decision support.
A digital twin model may be created from real-time data, historical data, and twin data, the twin data being virtual copy data of the network device. In addition, normal nodes of the device may be displayed green, and failed nodes of the device may be displayed red, orange, and yellow by severity level.
S520, carrying out dynamic simulation and analysis on at least one candidate treatment strategy in a digital twin model of the fault equipment according to the sequence of the weights of the candidate strategies of the fault root cause from large to small.
Because in the knowledge graph, the larger the weight of the treatment strategy is, the higher the accuracy of the treatment strategy in treating the corresponding fault root causes is. Thus, when testing candidate treatment strategies using digital twinning techniques, first a candidate treatment strategy of significant weight is selected for testing.
The candidate treatment strategy is input into the digital twin model, and dynamic characteristic simulation under the action of a single influence factor and a plurality of influence factors of the system is carried out in a certain time step, so that dynamic characteristic parameters of the digital twin model in the simulation process are obtained. Meanwhile, the model parameters can be updated and adjusted as required to adapt to the real-time change of the network equipment.
S530, taking the dynamic simulation and analysis results of each candidate treatment strategy as test results, and determining a target treatment strategy from at least one candidate treatment strategy according to the test results of each candidate treatment strategy.
After dynamic simulation is carried out through the digital twin model, the simulation result of the system can be obtained. The simulation results can be presented in at least one form of visualization technology, charts and dynamic displays. And analyzing the simulation result, and taking the analysis result as a test result.
The result of executing the candidate treatment strategy to handle the equipment fault can be subjected to dynamic analog simulation analysis by setting an objective function. The objective function may be f=w 1 G 1 +w 2 G 2 +…+w i G i + …, where w i As node coefficient, G i Related data for the fault node, such as the self-alarming quantity of the fault node and fault associationAt least one of a node alarm amount, a total log amount, a response time delay, and an index usage rate, wherein the alarm amount is an alarm amount adjusted according to a candidate treatment policy.
Setting a range of test success function results, and if the output result of the objective function is within the range, recognizing that the candidate treatment strategy can solve the equipment fault problem, and taking the candidate treatment strategy as the objective treatment strategy; if the output result of the objective function is not within the above range, the candidate treatment strategy is determined to be incapable of solving the equipment failure problem. Meanwhile, when the objective function result is within the range, the fault node in the digital twin model is restored to green for visualizing the analysis result.
After step S530, further includes:
and S531, if the weight of the target treatment strategy is not the highest weight, increasing the weight of the target treatment strategy in the knowledge graph.
The target treatment strategy passes the simulation test in the digital twin model, so the target treatment strategy is effective in solving the corresponding equipment fault, and the weight of the candidate treatment strategy corresponding to the target treatment strategy in the knowledge graph is increased, so that the treatment strategy can be preferentially selected when the same fault root cause is met.
Before step S100, the method further includes:
s110, carrying out data processing on the operation and maintenance data;
wherein the data processing mode comprises at least one of the following steps:
analyzing data;
data cleaning;
filtering data;
data derivatization;
data aggregation;
converting data; and
and (5) data characteristic analysis.
In the embodiment of the application, the data analysis can be to analyze the original operation and maintenance data, and convert the original operation and maintenance data into a data format with strong readability and structuring, so that the subsequent processing and analysis are convenient;
the data cleaning may be cleaning and preprocessing of the data, including removing at least one of duplicate data, processing missing values, and processing outliers to ensure quality and accuracy of the data;
The data filtering can be to screen and filter the data according to specific conditions and rules, and only the data meeting the conditions is reserved so as to reduce the data quantity and improve the analysis efficiency;
data derivatization may be the generation of new derivatized data by computing and converting the original data. For example, a new index is obtained by calculating the difference or ratio of the two indexes, or a new sequence pair is obtained and the missing value is complemented by the differential operation of time sequence data;
the data aggregation may be to combine and aggregate multiple data to generate aggregate statistics. For example, fine-grained data is aggregated into coarse-grained data by averaging;
the data conversion may be a format conversion of the data to accommodate different analysis and application requirements. For example, converting data from one database format to another, or converting data to at least one visualization form in a chart, image; and
the data feature analysis may be the analysis and extraction of features of the data to reveal rules and associations of the data. For example, at least one of statistical characteristics, frequency characteristics, time domain characteristics, and frequency domain characteristics of data is extracted by at least one of statistical analysis, machine learning method, and data waveform characteristic analysis divides the motion data into different data fluctuation types of periodic type, fluctuation type, and step type, wherein the periodic type, fluctuation type, and step type data are data that exhibit periodic, fluctuation, and step type changes, respectively, in accordance with a change in data over time.
In order to solve the technical problems, the embodiment of the application also provides a network equipment fault processing device. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a network device fault handling apparatus according to an embodiment of the present application.
The acquiring module 100 is configured to perform acquiring an operation and maintenance data set, where the operation and maintenance data set includes operation and maintenance data of at least one network device.
In the embodiment of the application, after receiving the alarm data, operation and maintenance data of all network devices are acquired, and the operation and maintenance data of all network devices form an operation and maintenance data set.
In the embodiment of the application, the operation and maintenance data may include at least one of historical alarm data, real-time alarm data, historical performance index data, real-time performance index data, log data, topology data, work order data, service data and resource data, and Guan Yun data are accessed and stored by at least one mode of a Kafka distributed system and FTP (File Transfer Protocol ) to obtain an operation and maintenance data set.
The detection module 200 is configured to perform anomaly detection on the operation and maintenance data set, determine abnormal operation and maintenance data in the operation and maintenance data set, and determine equipment failure based on the abnormal operation and maintenance data.
In the embodiment of the application, the acquired operation and maintenance data set is subjected to data anomaly detection to determine the abnormal data in the operation and maintenance data set, and the faults of the equipment are determined according to the equipment information reflected by the abnormal data.
In the embodiment of the application, the abnormality detection of the data can be realized by using an AI algorithm including machine learning, and the abnormality detection of the data can be that the abnormality detection is performed on the performance index data and the abnormality detection is performed on the log data.
And the positioning module 300 is used for performing root cause positioning on equipment faults and determining fault root causes.
In the embodiment of the application, for the determined equipment fault, the root cause of the fault, namely the root cause of the fault, is found and used for solving the equipment fault by adopting a corresponding treatment strategy.
By analyzing and integrating various data sources, the specific cause of the system fault is determined, namely the fault root cause is determined.
In the embodiment of the application, the root cause of the fault can be determined through various root cause positioning algorithms, such as at least one of an RCA (Root Cause Analysis ) algorithm, a centrality algorithm and pattern learning.
The RCA algorithm determines potential alarm master-slave relation by mining and association analysis on alarm data and combining an alarm propagation diagram to generate an alarm relation rule base, so that quick positioning of the root cause of the fault can be supported.
And the centrality algorithm carries out real-time alarm aggregation based on centrality calculation by using event dimension through alarm resource topological association and alarm time segmentation, and positions the root cause of the fault.
The mode learning can compare and match the current alarm with the history mode through a machine learning algorithm and a mode matching technology, find the similar root cause mode, and determine the root cause of the fault.
The root cause of the fault can be comprehensively decided by combining multidimensional topology assistance through at least one of an RCA algorithm, a centrality algorithm and a mode learning method.
Multidimensional topology assistance is to model and represent each component, node and connection relationship and establish a multidimensional topology model of the network device. When a fault occurs, the fault path can be tracked according to topology assistance, so that the fault root cause is positioned.
A matching module 400 for determining at least one candidate treatment strategy for the root cause of the fault based on a predetermined knowledge-graph.
In the embodiment of the application, after determining the root cause of the fault, determining the treatment strategy corresponding to the root cause of the fault according to the corresponding relation between the root cause of the fault and the treatment strategy in the knowledge graph, and taking the treatment strategy as a candidate treatment strategy.
The knowledge graph contains the corresponding relation between the fault root cause and the treatment strategy. There may be multiple solutions (i.e., multiple treatment strategies) for one root cause of a fault, so one root cause of a fault may correspond to multiple candidate treatment strategies in a knowledge-graph.
A pre-treatment module 500 for testing at least one candidate treatment strategy using digital twinning techniques, determining a target treatment strategy from the at least one candidate treatment strategy based on test results of each candidate treatment strategy.
In an embodiment of the present application, the digital twin technique is a technique of associating a digital representation of an entity system or a physical system with an actual system. The running state and behavior of the entity system are simulated and monitored in real time by establishing a virtual digital model corresponding to the entity system.
In the embodiment of the application, a corresponding digital twin model can be constructed according to the equipment with faults, the candidate treatment strategies are applied to the digital twin model for testing, and one candidate treatment strategy is determined as a target treatment strategy according to the test result of each candidate treatment strategy. Through the digital twin technology, the pretreatment mechanism for fault processing is realized, and the problem that when the fault root in the knowledge graph cannot solve the equipment fault due to the corresponding treatment strategy is avoided, the treatment strategy cannot be directly used for solving the equipment fault, but rather, the more serious equipment fault is generated.
An execution module 600 for executing the target treatment policy.
In the embodiment of the application, the equipment information is modified correspondingly according to the content in the target treatment strategy.
The acquired target treatment strategy may be performed by an associated RPA (Robotic Process Automation, robotic flow automation) robot.
Specifically, the target treatment policy is automatically extracted using NLP (Natural Language Processing ); the RPA robot performs treatment flow arrangement on the target treatment strategy; and starting and executing the well-arranged treatment flow of the RPA robot to realize equipment fault processing and simultaneously acquiring the equipment fault processing execution result information.
The method comprises the steps of providing further guidance for fault equipment processing, connecting equipment faults, a located fault root cause, a matched treatment strategy, a digital twin technology pre-treatment-based result, an RPA (remote procedure for assembly) programmed treatment strategy and fault processing execution result information in series to form an equipment fault processing scheme related to the fault root cause, reporting the equipment fault processing scheme to an operation and maintenance system in at least one mode of WeChat, short message and mailbox, and providing samples for fault root cause location and fault processing according to the fault processing scheme to update a knowledge graph.
If the device failure processing result still does not reach the situation expected by the user after the target processing strategy is executed, a one-key rollback function is provided, namely, the version before execution can be rolled back.
The embodiment of the application provides an electronic device, which comprises a memory, a processor and a computer program stored on the memory, wherein the processor executes the computer program to realize the steps of a network device fault processing method, and compared with the related technology, the steps of the network device fault processing method can be realized: by adding a detection mechanism to the fault processing by adopting a digital twin technology, the problem that the equipment fault is possibly more serious due to the fact that the detection mechanism is lacking when the fault processing is carried out by using a fixed strategy in the prior art is solved, and the accuracy of the equipment fault processing is improved.
In an alternative embodiment, there is provided an electronic device, as shown in fig. 5, the electronic device 4000 shown in fig. 5 includes: a processor 4001 and a memory 4003. Wherein the processor 4001 is coupled to the memory 4003, such as via a bus 4002. Optionally, the electronic device 4000 may further comprise a transceiver 4004, the transceiver 4004 may be used for data interaction between the electronic device and other electronic devices, such as transmission of data and/or reception of data, etc. It should be noted that, in practical applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 is not limited to the embodiment of the present application.
The processor 4001 may be a CPU (Central Processing Unit ), general purpose processor, DSP (Digital Signal Processor, data signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gate Array, field programmable gate array) or other programmable logic device, transistor logic device, hardware components, or any combination thereof. Which may implement or perform the various exemplary logic blocks, modules and circuits described in connection with this disclosure. The processor 4001 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 4002 may include a path to transfer information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus or an EISA (Extended Industry Standard Architecture ) bus, or the like. The bus 4002 can be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Memory 4003 may be, but is not limited to, ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, RAM (Random Access Memory ) or other type of dynamic storage device that can store information and instructions, EEPROM (Electrically Erasable Programmable Read Only Memory ), CD-ROM (Compact Disc Read Only Memory, compact disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, blu-ray discs, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store a computer program and that can be Read by a computer.
The memory 4003 is used for storing a computer program for executing an embodiment of the present application, and is controlled to be executed by the processor 4001. The processor 4001 is configured to execute a computer program stored in the memory 4003 to realize the steps shown in the foregoing method embodiment.
Among them, the electronic device package may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example, and should not impose any limitations on the functionality and scope of use of embodiments of the present disclosure.
Embodiments of the present application provide a computer readable storage medium having a computer program stored thereon, which when executed by a processor, implements the steps of the foregoing method embodiments and corresponding content. Compared with the prior art, can realize: by adding a detection mechanism to the fault processing by adopting a digital twin technology, the problem that the equipment fault is possibly more serious due to the fact that the detection mechanism is lacking when the fault processing is carried out by using a fixed strategy in the prior art is solved, and the accuracy of the equipment fault processing is improved.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The terms "first," "second," "third," "fourth," "1," "2," and the like in the description and in the claims and in the above figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate, such that the embodiments of the application described herein may be implemented in other sequences than those illustrated or otherwise described.
It should be understood that, although various operation steps are indicated by arrows in the flowcharts of the embodiments of the present application, the order in which these steps are implemented is not limited to the order indicated by the arrows. In some implementations of embodiments of the application, the implementation steps in the flowcharts may be performed in other orders as desired, unless explicitly stated herein. Furthermore, some or all of the steps in the flowcharts may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or phases may be performed at the same time, or each of these sub-steps or phases may be performed at different times, respectively. In the case of different execution time, the execution sequence of the sub-steps or stages can be flexibly configured according to the requirement, which is not limited by the embodiment of the present application.
The foregoing is merely an optional implementation manner of some of the implementation scenarios of the present application, and it should be noted that, for those skilled in the art, other similar implementation manners based on the technical ideas of the present application are adopted without departing from the technical ideas of the scheme of the present application, and the implementation manner is also within the protection scope of the embodiments of the present application.
Claims (10)
1. A method for network device failure handling, comprising:
acquiring an operation and maintenance data set, wherein the operation and maintenance data set comprises operation and maintenance data of at least one network device;
performing anomaly detection on the operation and maintenance data set, determining abnormal operation and maintenance data in the operation and maintenance data set, and determining equipment faults based on the abnormal operation and maintenance data set;
performing root cause positioning on the equipment faults, and determining fault root causes;
determining at least one candidate treatment strategy for the root cause of the fault based on a predetermined knowledge graph;
testing the at least one candidate treatment strategy by using a digital twin technology, and determining a target treatment strategy from the at least one candidate treatment strategy according to the test result of each candidate treatment strategy;
executing the target treatment strategy.
2. The method of claim 1, wherein the operation data comprises at least one of log data and performance index data;
The anomaly detection is performed on the operation and maintenance data set, the anomaly operation and maintenance data in the operation and maintenance data set is determined, and equipment faults are determined based on the anomaly operation and maintenance data, and the anomaly detection comprises at least one of the following steps:
for any performance index data, determining a corresponding abnormality detection algorithm according to the type of the performance index data, and performing abnormality detection on the performance index data according to the abnormality detection algorithm, a static threshold strategy and an abnormality aggregation strategy;
for any one of log data, performing anomaly detection on the log data according to at least one of the quantity change degree, keywords and log mode of the log data;
and predicting the variation trend of any performance index data through a machine learning algorithm, and carrying out early warning on the performance index data by combining an early warning strategy.
3. The method of claim 2, wherein the anomaly detection of the operation and maintenance data set, determining abnormal operation and maintenance data in the operation and maintenance data set, and determining equipment failure based on the abnormal operation and maintenance data, further comprises:
a severity level of the equipment failure is determined.
4. The method of claim 1, further comprising, prior to determining at least one candidate treatment strategy for the root cause of the fault based on a predetermined knowledge-graph:
determining the corresponding relation between the fault root cause and the treatment strategy;
and constructing a knowledge graph according to the corresponding relation between the fault root cause and the treatment strategy, wherein the knowledge graph comprises at least one fault root cause, the treatment strategy with the corresponding relation with the fault root cause and the weight of the corresponding treatment strategy.
5. The method of claim 1, wherein the at least one candidate treatment strategy is tested using a digital twinning technique, and wherein determining a target treatment strategy from the at least one candidate treatment strategy based on test results for each candidate treatment strategy comprises:
creating a digital twin model of the fault device using digital twin techniques;
according to the sequence from the big to the small of the weights of the candidate strategies of the fault root, carrying out dynamic analog simulation and analysis on the at least one candidate treatment strategy in a digital twin model of the fault equipment;
and taking the dynamic simulation and analysis results of each candidate treatment strategy as test results, and determining a target treatment strategy from the at least one candidate treatment strategy according to the test results of each candidate treatment strategy.
6. The method of claim 5, the determining a target treatment policy from the at least one candidate treatment policy, further comprising thereafter:
if the weight of the target treatment strategy is not the highest weight, the weight of the target treatment strategy in the knowledge graph is increased.
7. The method of claim 1, wherein the anomaly detection of the operation and maintenance data set further comprises:
carrying out data processing on the operation and maintenance data;
wherein the data processing mode comprises at least one of the following steps:
analyzing data;
data cleaning;
filtering data;
data derivatization;
data aggregation;
converting data; and
and (5) data characteristic analysis.
8. A network device failure handling apparatus, comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an operation and maintenance data set, and the operation and maintenance data set comprises operation and maintenance data of at least one network device;
the detection module is used for carrying out abnormal detection on the operation and maintenance data set, determining abnormal operation and maintenance data in the operation and maintenance data set and determining equipment faults based on the abnormal operation and maintenance data set;
the positioning module is used for positioning the root cause of the equipment fault and determining the root cause of the fault;
A matching module for determining at least one candidate treatment strategy for the root cause of the fault based on a predetermined knowledge graph;
a pre-treatment module for testing the at least one candidate treatment strategy using digital twinning techniques, determining a target treatment strategy from the at least one candidate treatment strategy according to test results of each candidate treatment strategy;
and the execution module is used for executing the target treatment strategy.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to carry out the steps of the method according to any one of claims 1-7.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method according to any of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117349102A (en) * | 2023-12-05 | 2024-01-05 | 网思科技股份有限公司 | Digital twin operation and maintenance data quality inspection method, system and medium |
| CN117560706A (en) * | 2024-01-12 | 2024-02-13 | 亚信科技（中国）有限公司 | Root cause analysis method, root cause analysis device, electronic equipment and storage medium |
| CN117880055A (en) * | 2024-03-12 | 2024-04-12 | 灵长智能科技(杭州)有限公司 | Network fault diagnosis method, device, equipment and medium based on transmission layer index |
| CN118152786A (en) * | 2024-05-10 | 2024-06-07 | 中国矿业大学 | Knowledge graph-based equipment fault auxiliary decision-making method, system and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117349102A (en) * | 2023-12-05 | 2024-01-05 | 网思科技股份有限公司 | Digital twin operation and maintenance data quality inspection method, system and medium |
| CN117349102B (en) * | 2023-12-05 | 2024-03-15 | 网思科技股份有限公司 | Digital twin operation and maintenance data quality inspection method, system and medium |
| CN117560706A (en) * | 2024-01-12 | 2024-02-13 | 亚信科技（中国）有限公司 | Root cause analysis method, root cause analysis device, electronic equipment and storage medium |
| CN117560706B (en) * | 2024-01-12 | 2024-03-22 | 亚信科技（中国）有限公司 | Root cause analysis method, root cause analysis device, electronic equipment and storage medium |
| CN117880055A (en) * | 2024-03-12 | 2024-04-12 | 灵长智能科技(杭州)有限公司 | Network fault diagnosis method, device, equipment and medium based on transmission layer index |
| CN117880055B (en) * | 2024-03-12 | 2024-05-31 | 灵长智能科技(杭州)有限公司 | Network fault diagnosis method, device, equipment and medium based on transmission layer index |
| CN118152786A (en) * | 2024-05-10 | 2024-06-07 | 中国矿业大学 | Knowledge graph-based equipment fault auxiliary decision-making method, system and storage medium |
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