CN109309594B - Method, device, equipment and storage medium for analyzing power failure of communication equipment - Google Patents
Method, device, equipment and storage medium for analyzing power failure of communication equipment Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0604—Management of faults, events, alarms or notifications using filtering, e.g. reduction of information by using priority, element types, position or time
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/069—Management of faults, events, alarms or notifications using logs of notifications; Post-processing of notifications
Abstract
The invention provides a method, a device, equipment and a storage medium for power failure analysis of communication equipment. The method comprises the following steps: acquiring log key information after the fault of the communication equipment is recovered; cleaning the log key information to obtain the alarm information of the out-of-service activities and the cleaned data set corresponding to the cleaning alarm information; extracting characteristic information in the cleaned data set; and analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model to obtain out-of-service alarm information and power failure probability. The log key information can be actively acquired, the power failure probability of the communication equipment can be rapidly analyzed through the failure analysis model, and the analysis efficiency of the power failure is improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of communication equipment, in particular to a method, a device, equipment and a storage medium for power failure analysis of communication equipment.
Background
For the power supply problem of wireless communication equipment, the prior art mainly analyzes based on a dynamic loop monitoring system, 60% of communication equipment in the existing network wireless communication equipment cannot monitor and analyze the power supply fault through the dynamic loop, the result after fault processing is mainly analyzed in a manner of enduring manual work, the data accuracy in power supply fault probability analysis is low, and the analysis result cannot be timely applied to power supply fault diagnosis.
The fault log of the wireless communication equipment only exists at the rear end of the equipment, the passive query is performed, an engineer needs to log in the equipment side to perform query in a command mode after a fault occurs, and the power failure analysis efficiency is low.
Disclosure of Invention
The embodiment of the invention provides a method, a device, equipment and a storage medium for analyzing a power failure of communication equipment, which solve the technical problem of low power failure analysis efficiency in the power failure analysis method of the communication equipment in the prior art.
In a first aspect, an embodiment of the present invention provides a method for analyzing a power failure of a communication device, where the method includes:
acquiring log key information after the fault of the communication equipment is recovered;
cleaning the log key information to obtain service quitting activity alarm information and a cleaned data set corresponding to the cleaning alarm information;
extracting characteristic information in the cleaned data set;
and analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model to obtain out-of-service alarm information and power failure probability.
Further, before analyzing and processing the feature information of the cleaned data set by using the trained fault analysis model to obtain the fallback warning information and the power failure probability, the method further includes:
constructing the fault analysis model by using a Bayesian algorithm;
training the fault analysis model by adopting a training set to obtain a trained fault analysis model;
wherein the fault analysis model at least comprises: the power supply system comprises a first model and a second model, wherein the first model is a single-cell out-of-service time and power supply abnormity probability model, and the second model is a multi-cell out-of-service time and power supply abnormity probability model.
Further, the method, after analyzing and processing the feature information of the cleaned data set by using the trained fault analysis model to obtain the fallback warning information and the power failure probability, further includes:
and correcting the acquired out-of-service alarm information and the power failure probability by using a Laplace algorithm.
Further, the method, after analyzing and processing the feature information of the cleaned data set by using the trained fault analysis model to obtain the fallback warning information and the power failure probability, further includes:
acquiring historical time of the communication equipment fault and the characteristic information;
determining the shortest time from the current time according to the historical time;
and analyzing and processing the power failure probability, the shortest time and the characteristic information by using a preset artificial neural network model, and judging whether the power fails.
Further, the method as described above, after determining whether the power supply fails, further includes:
if the power failure probability obtained by the trained failure analysis model is greater than or equal to a preset probability threshold value and the judgment result obtained by the artificial neural network model is a power failure, determining that the power supply of the communication equipment fails;
and if the power failure probability obtained by the trained failure analysis model is smaller than a preset probability threshold value and the judgment result obtained by the artificial neural network model is that the power supply is not failed, determining that the power supply of the communication equipment is not failed.
Further, the method as described above, further comprising:
acquiring actual data of the power supply work of the communication equipment;
judging whether the conclusion that the power supply fails or the power supply does not fail is correct or not according to the actual data;
and if the conclusion is correct, performing model optimization on the trained fault analysis model and the artificial neural network model by using data corresponding to the conclusion until the artificial neural network model reaches a preset optimization level threshold.
Further, the method as described above, the log key information comprising the following information: cell activity alarm information, cell clearing alarm information and cell alarm log information;
the cell alarm log information includes the following information: site identification, cell identification, alarm name and alarm occurrence time;
the characteristic information at least comprises the following characteristic information: cell quit information, log power supply abnormal information, time sequence information and base station and cell association information;
the power failure probability includes at least one of: single cell power failure probability, two cell power failure probability, multiple cell power failure probability.
In a second aspect, an embodiment of the present invention provides an apparatus for analyzing a power failure of a communication device, including:
the acquisition module is used for acquiring the log key information after the fault of the communication equipment is recovered;
the cleaning module is used for cleaning the log key information to obtain service quitting activity alarm information and a cleaned data set corresponding to the cleaning alarm information;
the extraction module is used for extracting the characteristic information in the cleaned data set;
and the analysis module is used for analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model so as to obtain out-of-service alarm information and power failure probability.
Further, the apparatus as described above, further comprising:
the building module is used for building the fault analysis model by using a Bayesian algorithm;
the training module is used for training the fault analysis model by adopting a training set to obtain a trained fault analysis model;
wherein the fault analysis model at least comprises: the power supply system comprises a first model and a second model, wherein the first model is a single-cell out-of-service time and power supply abnormity probability model, and the second model is a multi-cell out-of-service time and power supply abnormity probability model.
Further, the apparatus as described above, further comprising:
and the correction module is used for correcting the acquired out-of-service alarm information and the power failure probability by using the Laplace algorithm.
Further, the apparatus as described above, further comprising: the device comprises a determining module and a judging module;
the acquisition module is further configured to acquire historical time of the communication device failure and the characteristic information;
the determining module is further configured to determine the shortest time from the current time according to the historical time;
and the judging module is used for analyzing and processing the power failure probability, the shortest time and the characteristic information by utilizing a preset artificial neural network model and judging whether the power fails.
Further, in the apparatus described above, the determining module is further configured to determine that the power supply of the communication device fails if the power supply failure probability obtained by using the trained failure analysis model is greater than or equal to a preset probability threshold and the determination result obtained by using the artificial neural network model is a power supply failure; and if the power failure probability obtained by the trained failure analysis model is smaller than a preset probability threshold value and the judgment result obtained by the artificial neural network model is that the power supply is not failed, determining that the power supply of the communication equipment is not failed.
Further, the apparatus as described above, further comprising: an optimization module;
the acquisition module is also used for acquiring actual data of the power supply work of the communication equipment;
the judging module is also used for judging whether the conclusion that the power supply fails or the power supply does not fail is correct or not according to the actual data;
and the optimization module is used for performing model optimization on the trained fault analysis model and the artificial neural network model by using the data corresponding to the conclusion if the conclusion is correct until the artificial neural network model reaches a preset optimization level threshold.
Further, the apparatus as described above, the log key information includes the following information: cell activity alarm information, cell clearing alarm information and cell alarm log information;
the cell alarm log information includes the following information: site identification, cell identification, alarm name and alarm occurrence time;
the characteristic information at least comprises the following characteristic information: cell quit information, log power supply abnormal information, time sequence information and base station and cell association information;
the power failure probability includes at least one of: single cell power failure probability, two cell power failure probability, multiple cell power failure probability.
In a third aspect, an embodiment of the present invention provides an electronic device, including:
a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any of the first aspects.
In a fourth aspect, the present invention provides a readable storage medium, on which a computer program is stored, the computer program being executed by a processor to implement the method according to any one of the first aspect.
The embodiment of the invention provides a method, a device, equipment and a storage medium for analyzing power failure of communication equipment, which are characterized in that log key information after the failure of the communication equipment is recovered is obtained; cleaning the log key information to obtain the alarm information of the out-of-service activities and the cleaned data set corresponding to the cleaning alarm information; extracting characteristic information in the cleaned data set; and analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model to obtain out-of-service alarm information and power failure probability. The log key information can be actively acquired, the power failure probability of the communication equipment can be rapidly analyzed through the failure analysis model, and the analysis efficiency of the power failure is improved.
It should be understood that what is described in the summary above is not intended to limit key or critical features of embodiments of the invention, nor is it intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a method for analyzing a power failure of a communication device according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for analyzing a power failure of a communication device according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a device for analyzing a power failure of a communication device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for analyzing a power failure of a communication device according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and the embodiments of the present invention are illustrative only and are not intended to limit the scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, and in the above-described drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example one
Fig. 1 is a flowchart of a method for analyzing a power failure of a communication device according to an embodiment of the present invention, and as shown in fig. 1, an execution main body of the embodiment is a device for analyzing a power failure of a communication device, and the device for analyzing a power failure of a communication device may be integrated in an electronic device such as a computer, a notebook computer, or a server.
Step 101, obtaining the log key information after the fault recovery of the communication equipment.
Specifically, in this embodiment, the log key information after the fault recovery of the communication device may be obtained from the network element management system by sending a request command to the network element management system (EMS for short). The log key information after the failure recovery of the communication device may also be stored in advance, and the log key information after the failure recovery of the communication device is acquired from the storage area, which is not limited in this embodiment.
The log key information may include: cell activity alarm information, cell clear alarm information, cell alarm log information and the like. The cell alarm log information may include: site identification, cell identification, alarm name, alarm occurrence time and the like.
And 102, cleaning the log key information to obtain service quitting activity alarm information and a cleaned data set corresponding to the cleaning alarm information.
Specifically, in this embodiment, the obtained log key information is cleaned, and the alarm information of the out-of-service activity and the clearing alarm information are obtained, where the alarm information of the out-of-service activity and the clearing alarm information are corresponding information related to the non-turbo flash.
And the service quitting activity alarm information and the clearing alarm information which are not subjected to over-frequency flash break form a data set after cleaning.
And 103, extracting characteristic information in the cleaned data set.
Specifically, in this embodiment, a feature extraction algorithm is used to extract feature information in the cleaned data set. Wherein, the characteristic information may include: cell quit information, log power supply abnormal information, time series information, base station and cell associated information and the like.
And 104, analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model to obtain out-of-service alarm information and power failure probability.
Specifically, in this embodiment, a fault analysis model is first constructed, then a training set of the fault analysis model is formed after log key information of a plurality of communication devices after fault recovery is cleaned and feature extraction is performed, and the fault analysis model is trained to obtain a trained fault analysis model.
The fault analysis model can be constructed by adopting a Bayesian algorithm.
In this embodiment, the power failure probability may be a single cell power failure probability, a two-cell power failure probability, or a multiple-cell power failure probability.
According to the method, the device, the equipment and the storage medium for analyzing the power failure of the communication equipment, the log key information after the failure recovery of the communication equipment is obtained; cleaning the log key information to obtain the alarm information of the out-of-service activities and the cleaned data set corresponding to the cleaning alarm information; extracting characteristic information in the cleaned data set; and analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model to obtain out-of-service alarm information and power failure probability. The log key information can be actively acquired, the power failure probability of the communication equipment can be rapidly analyzed through the failure analysis model, and the analysis efficiency of the power failure is improved.
Example two
As shown in fig. 2, the method for analyzing power failure of communication equipment provided in this embodiment is further detailed in steps 101 to 104 on the basis of the method for analyzing power failure of communication equipment provided in the first embodiment of the present invention, and includes other steps.
Step 201, a Bayesian algorithm is used for constructing a fault analysis model.
Further, in this embodiment, a bayesian algorithm is used to construct the fault analysis model. The bayesian algorithm may be a naive bayes algorithm.
The naive bayes algorithm assumes that under the conditions determined by classification, the features used for classification are conditionally independent, i.e. formula (1) holds.
Wherein x is(1),X(2)＝x(2),…,X(n)For one division of the sample space, p (X)(1)) Greater than or equal to zero.
Based on Bayes' theorem and conditional independence of classification characteristics, it is assumed that formula (2) holds
Formula (2) is a fault analysis model constructed by a bayesian algorithm, and P (Y ═ c)k) According to ckThe value of (a) represents a power failure probability or a non-failure probability.
Wherein, the fault analysis model at least comprises: the power supply system comprises a first model and a second model, wherein the first model is a single-cell out-of-service time and power supply abnormity probability model, and the second model is a multi-cell out-of-service time and power supply abnormity probability model.
Further, in this embodiment, the training samples in the training set are feature information extracted after cleaning the log key information after the failure recovery of a certain communication device.
Step 203, obtaining the log key information after the failure recovery of the communication equipment.
Further, in this embodiment, the log key information includes the following information: cell activity alarm information, cell clear alarm information, and cell alarm log information. The cell alarm log information includes the following information: site identification, cell identification, alarm name and alarm occurrence time.
And step 204, cleaning the log key information to obtain the service quitting activity alarm information and a cleaned data set corresponding to the cleaning alarm information.
And step 205, extracting characteristic information in the cleaned data set.
Further, in this embodiment, the feature information at least includes the following feature information: the system comprises cell quit information, log power supply abnormal information, time sequence information and base station and cell association information.
And step 206, analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model to obtain out-of-service alarm information and power failure probability.
Further, in this embodiment, the fault analysis model at least includes: the power supply system comprises a first model and a second model, wherein the first model is a single-cell out-of-service time and power supply abnormity probability model, and the second model is a multi-cell out-of-service time and power supply abnormity probability model. Therefore, the trained fault analysis model is used for analyzing and processing the characteristic information of the cleaned data set, and the obtained power failure probability comprises at least one of the following: single cell power failure probability, two cell power failure probability, multiple cell power failure probability.
And step 207, correcting the acquired out-of-service alarm information and the power failure probability by using a Laplace algorithm.
Further, in this embodiment, after the bayesian algorithm is used to construct the fault analysis model, the maximum likelihood estimation may be used to estimate that the probability value estimated is zero, which may affect the calculation result of the posterior probability and cause the classification to have a deviation. The correction coefficient λ is added to the fault analysis model, and the fault analysis model is shown in formula (3).
Wherein K is 1,2, …, K, j is 1,2, …, n, l is 1,2, …, sj。
Wherein the value of the correction coefficient lambda is 1.
Specifically, in this embodiment, the historical time of the failure of the communication device may include historical time of a failure that has occurred in the history, and if a failure occurs once, the historical time of a failure is the historical time of a failure once, and if the failure occurs multiple times, the historical time of multiple failures is included.
Wherein the feature information is the feature information in step 205.
Step 209, determining the shortest time from the current time according to the historical time.
Further, in this embodiment, the historical time with the shortest time interval from the current time is determined in the historical times, and the difference between the historical time with the shortest time interval from the current time and the current time is calculated to obtain the shortest time from the current time.
And step 210, analyzing and processing the power failure probability, the shortest time and the characteristic information by using a preset artificial neural network model, and judging whether the power fails.
Further, in this embodiment, the power failure probability, the shortest time and the feature information are input into the artificial neural network model, the power failure probability is used as a prediction factor in the artificial neural network model, the shortest time and the feature information are used as a prediction variable in the artificial neural network model, and the artificial neural network model determines whether the power fails according to the prediction factor and the prediction variable.
The hidden layer selected by the artificial neural network can be 30 layers or more, and the activation function is used by relu and sigmoid in a crossed mode.
Step 211, if the power failure probability obtained by using the trained failure analysis model is greater than or equal to the preset probability threshold and the determination result obtained by using the artificial neural network model is a power failure, determining that the power of the communication device fails, and if the power failure probability obtained by using the trained failure analysis model is less than the preset probability threshold and the determination result obtained by using the artificial neural network model is a power failure, determining that the power of the communication device does not fail.
Further, in this embodiment, a final determination of whether the power supply has a fault is performed according to results output by the fault analysis model and the artificial neural network model. If the power failure probability obtained by the trained failure analysis model is greater than or equal to a preset probability threshold value and the judgment result obtained by the artificial neural network model is a power failure, the direction of the results of the two model analyses is consistent, namely the power fails, the power failure of the communication equipment is determined, and if the power failure probability obtained by the trained failure analysis model is smaller than the preset probability threshold value and the judgment result obtained by the artificial neural network model is a power failure, the direction of the results of the two model analyses is consistent, namely the power failure does not occur, the power failure of the communication equipment is determined.
The preset probability threshold may be determined through multiple experiments, for example, may be 70%, or other values, which is not limited in this embodiment.
Step 212, actual data of the power operation of the communication device is obtained.
Specifically, in this embodiment, actual data of the power supply operation may be acquired from the communication device, and the actual data includes information such as the operating state of the power supply at each stage, whether a fault occurs, and the time when the fault occurs.
And step 213, judging whether the conclusion that the power supply fails or the power supply does not fail is correct according to the actual data.
Further, in this embodiment, the conclusion that the power supply fails or does not fail in step 211 is compared with whether the power supply in the actual data fails, so as to determine whether the conclusion is correct.
And 214, if the conclusion is correct, performing model optimization on the trained fault analysis model and the trained artificial neural network model by using the data corresponding to the conclusion until the artificial neural network model reaches a preset optimization level threshold.
The predetermined optimization level threshold of the artificial neural network may be 30 or other values, which is not limited in this embodiment.
The method for analyzing the power failure of the communication device according to the embodiment includes the steps of constructing a failure analysis model by using a Bayesian algorithm, training the failure analysis model by using a training set to obtain the trained failure analysis model, obtaining log key information after failure recovery of the communication device, cleaning the log key information to obtain a cleaned data set corresponding to the alarm information of the out-of-service activity and the alarm information removal, extracting feature information in the cleaned data set, analyzing the feature information in the cleaned data set by using the trained failure analysis model to obtain out-of-service alarm information and power failure probability, and correcting the obtained out-of-service alarm information and power failure probability by using a Laplace algorithm, so that the accuracy of analyzing the power failure probability by using the trained failure analysis model can be improved.
In the method for analyzing a power failure of a communication device provided by this embodiment, because the bayesian algorithm is used to construct the failure analysis model, and the training set is used to train the failure analysis model, so as to obtain the trained failure analysis model, the failure analysis model at least includes: the power failure analysis system comprises a first model and a second model, wherein the first model is a single-cell out-of-service time and power supply abnormity probability model, the second model is a multi-cell out-of-service time and power supply abnormity probability model, and in addition, when the power failure of the communication equipment is analyzed, the out-of-service information and the power supply abnormity information of the single cell and the multi-cell are also extracted, so the analysis of the power failure of the communication equipment in the whole network can be carried out.
The method for analyzing power failure of communication equipment provided in this embodiment, after analyzing and processing feature information of a cleaned data set by using a trained failure analysis model to obtain fallback warning information and power failure probability, obtains historical time and feature information of a failure of the communication equipment, determines a shortest time from a current time according to the historical time, analyzes and processes the power failure probability, the shortest time and the feature information by using a preset artificial neural network model, determines whether the power fails, determines that the power of the communication equipment fails if the power failure probability obtained by using the trained failure analysis model is greater than or equal to a preset probability threshold and the determination result obtained by using the artificial neural network model is a power failure, and determines that the power of the communication equipment fails if the power failure probability obtained by using the trained failure analysis model is less than the preset probability threshold, and if the judgment result obtained by utilizing the artificial neural network model is that the power supply is not in fault, determining that the power supply of the communication equipment is not in fault. Whether the communication equipment has power failure or not can be determined under the joint analysis action of the trained fault analysis model and the artificial neural network model, and the accuracy of an analysis result can be further improved.
In the method for analyzing the power failure of the communication device provided by the embodiment, the actual data of the power operation of the communication device is obtained; judging whether the conclusion that the power supply fails or the power supply does not fail is correct or not according to the actual data; if the conclusion is correct, model optimization is carried out on the trained fault analysis model and the artificial neural network model by using the data corresponding to the conclusion until the artificial neural network model reaches a preset optimization level threshold, the trained fault analysis model and the artificial neural network model can be optimized by using the data corresponding to the conclusion, and then the trained fault analysis model and the trained artificial neural network model can be in a continuous optimization process, so that the accuracy of fault analysis can be further improved along with the increase of the number of times of fault analysis.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a device for analyzing a power failure of a communication device according to a third embodiment of the present invention, and as shown in fig. 3, the device for analyzing a power failure of a communication device according to the present embodiment includes: an acquisition module 31, a cleaning module 32, an extraction module 33 and an analysis module 34.
The obtaining module 31 is configured to obtain the log key information after the failure recovery of the communication device. And the cleaning module 32 is used for cleaning the log key information to obtain the service quitting activity alarm information and the cleaned data set corresponding to the cleaning alarm information. And the extracting module 33 is configured to extract feature information in the cleaned data set. And the analysis module 34 is configured to analyze and process the feature information of the cleaned data set by using the trained fault analysis model to obtain out-of-service alarm information and power failure probability.
The apparatus for analyzing power failure of a communication device provided in this embodiment may implement the technical solution of the method embodiment shown in fig. 1, and the implementation principle and the technical effect are similar, which are not described herein again.
Fig. 4 is a schematic structural diagram of a power failure analysis apparatus for communication equipment according to a fourth embodiment of the present invention, and as shown in fig. 4, the power failure analysis apparatus for communication equipment according to the present embodiment further includes, on the basis of the power failure analysis apparatus for communication equipment according to a third embodiment of the present invention: a construction module 41, a training module 42, a modification module 43, a determination module 44, a judgment module 45 and an optimization module 46.
Further, the building module 41 is configured to build the fault analysis model by using a bayesian algorithm. And a training module 42, configured to train the fault analysis model by using a training set to obtain a trained fault analysis model. Wherein, the fault analysis model at least comprises: the power supply system comprises a first model and a second model, wherein the first model is a single-cell out-of-service time and power supply abnormity probability model, and the second model is a multi-cell out-of-service time and power supply abnormity probability model.
Further, the correcting module 43 is configured to correct the acquired out-of-service alarm information and the power failure probability by using a laplacian algorithm.
Further, the obtaining module 31 is further configured to obtain historical time and characteristic information of the communication device failure. The determining module 44 is further configured to determine the shortest time from the current time according to the historical time. And the judging module 45 is configured to analyze and process the power failure probability, the shortest time and the feature information by using a preset artificial neural network model, and judge whether the power fails.
Further, the determining module 44 is further configured to determine that the power supply of the communication device fails if the power supply failure probability obtained by using the trained failure analysis model is greater than or equal to a preset probability threshold and the determination result obtained by using the artificial neural network model is a power supply failure; and if the power failure probability obtained by using the trained failure analysis model is smaller than a preset probability threshold value and the judgment result obtained by using the artificial neural network model is that the power supply is not failed, determining that the power supply of the communication equipment is not failed.
Further, the obtaining module 31 is further configured to obtain actual data of the power operation of the communication device. The judging module 45 is further configured to judge whether the power failure or the power failure conclusion is correct according to the actual data. And the optimizing module 46 is configured to, if the conclusion is correct, perform model optimization on the trained fault analysis model and the trained artificial neural network model by using data corresponding to the conclusion until the artificial neural network model reaches a preset optimization level threshold.
Further, in this embodiment, the log key information includes the following information: cell activity alarm information, cell clearing alarm information and cell alarm log information; the cell alarm log information includes the following information: site identification, cell identification, alarm name and alarm occurrence time; the characteristic information at least includes the following characteristic information: cell quit information, log power supply abnormal information, time sequence information and base station and cell association information; the power failure probability includes at least one of: single cell power failure probability, two cell power failure probability, multiple cell power failure probability.
The apparatus for analyzing power failure of a communication device provided in this embodiment may implement the technical solution of the method embodiment shown in fig. 2, and the implementation principle and the technical effect are similar, which are not described herein again.
EXAMPLE five
Fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention, as shown in fig. 5, in this embodiment, the electronic device includes: a memory 51, a processor 52 and a computer program.
The computer program is stored in the memory 51 and configured to be executed by the processor 52 to implement the method for analyzing a power failure of a communication device according to the first embodiment of the present invention or the method for analyzing a power failure of a communication device according to the second embodiment of the present invention.
The relevant description may be understood by referring to the relevant description and effect corresponding to the steps in fig. 1 to fig. 2, and redundant description is not repeated here.
EXAMPLE six
A sixth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method for analyzing a power failure of a communication device according to the first embodiment of the present invention or the method for analyzing a power failure of a communication device according to the second embodiment of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing module, or each of the modules may exist alone physically, or two or more modules are integrated into one module. The integrated module can be realized in a hardware form, and can also be realized in a form of hardware and a software functional module.
Program code for implementing the methods of the present invention may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on 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.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (9)
1. A method of power failure analysis for a communication device, comprising:
acquiring log key information after the fault of the communication equipment is recovered;
cleaning the log key information to obtain service quitting activity alarm information and a cleaned data set corresponding to the cleaning alarm information;
extracting characteristic information in the cleaned data set;
analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model to obtain out-of-service alarm information and power failure probability;
acquiring historical time of the communication equipment fault and the characteristic information;
determining the shortest time from the current time according to the historical time;
and analyzing and processing the power failure probability, the shortest time and the characteristic information by using a preset artificial neural network model, and judging whether the power fails.
2. The method of claim 1, wherein before analyzing the feature information of the cleaned data set by using the trained fault analysis model to obtain the fallback warning information and the power failure probability, the method further comprises:
constructing the fault analysis model by using a Bayesian algorithm;
training the fault analysis model by adopting a training set to obtain a trained fault analysis model;
wherein the fault analysis model at least comprises: the power supply system comprises a first model and a second model, wherein the first model is a single-cell out-of-service time and power supply abnormity probability model, and the second model is a multi-cell out-of-service time and power supply abnormity probability model.
3. The method of claim 1, wherein after analyzing and processing the feature information of the cleaned data set by using the trained fault analysis model to obtain the fallback warning information and the power failure probability, further comprising:
and correcting the acquired out-of-service alarm information and the power failure probability by using a Laplace algorithm.
4. The method of claim 1, wherein after determining whether the power supply fails, further comprising:
if the power failure probability obtained by the trained failure analysis model is greater than or equal to a preset probability threshold value and the judgment result obtained by the artificial neural network model is a power failure, determining that the power supply of the communication equipment fails;
and if the power failure probability obtained by the trained failure analysis model is smaller than a preset probability threshold value and the judgment result obtained by the artificial neural network model is that the power supply is not failed, determining that the power supply of the communication equipment is not failed.
5. The method of claim 4, further comprising:
acquiring actual data of the power supply work of the communication equipment;
judging whether the conclusion that the power supply fails or the power supply does not fail is correct or not according to the actual data;
and if the conclusion is correct, performing model optimization on the trained fault analysis model and the artificial neural network model by using data corresponding to the conclusion until the artificial neural network model reaches a preset optimization level threshold.
6. The method of any of claims 1-3, wherein the log key information comprises the following information: cell activity alarm information, cell clearing alarm information and cell alarm log information;
the cell alarm log information includes the following information: site identification, cell identification, alarm name and alarm occurrence time;
the characteristic information at least comprises the following characteristic information: cell quit information, log power supply abnormal information, time sequence information and base station and cell association information;
the power failure probability includes at least one of: single cell power failure probability, two cell power failure probability, multiple cell power failure probability.
7. An apparatus for power failure analysis of a communication device, comprising:
the acquisition module is used for acquiring the log key information after the fault of the communication equipment is recovered;
the cleaning module is used for cleaning the log key information to obtain service quitting activity alarm information and a cleaned data set corresponding to the cleaning alarm information;
the extraction module is used for extracting the characteristic information in the cleaned data set;
the analysis module is used for analyzing and processing the characteristic information of the cleaned data set by using the trained fault analysis model so as to obtain out-of-service alarm information and power failure probability;
the acquisition module is further configured to acquire historical time of the communication device failure and the characteristic information;
the determining module is further used for determining the shortest time from the current time according to the historical time;
and the judging module is used for analyzing and processing the power failure probability, the shortest time and the characteristic information by utilizing a preset artificial neural network model and judging whether the power fails.
8. An electronic device, comprising:
a memory, a processor, and a computer program;
wherein the computer program is stored in the memory and configured to be executed by the processor to implement the method of any one of claims 1-6.
9. A readable storage medium, having stored thereon a computer program for execution by a processor to perform the method of any one of claims 1-6.
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| CN111880981A (en) * | 2020-07-30 | 2020-11-03 | 北京浪潮数据技术有限公司 | Fault repairing method and related device for docker container |
| CN113282000A (en) * | 2021-04-30 | 2021-08-20 | 科华数据股份有限公司 | Fault diagnosis method and device of data center and dynamic loop monitoring system |
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