CN110647446A - Log fault association and prediction method, device, equipment and storage medium - Google Patents
Log fault association and prediction method, device, equipment and storage medium Download PDFInfo
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- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
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Abstract
The utility model discloses a log fault association and prediction method, device, equipment and storage medium, belonging to the technical field of communication, the method comprises: acquiring a historical service log, and carrying out pareto analysis on the historical service log to obtain a training log sample; carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event; processing the real-time service log according to the prediction model, and predicting the probability and time of occurrence of the associated fault event; through pareto analysis, high-frequency events are abandoned as system disturbances, and data processing and training are accelerated; through Bayesian operation, the incidence relation of log faults can be given in a probability mode, the unbalanced probability distribution given by a machine is changed from manual judgment, decision making of operation and maintenance personnel is assisted, labor intensity is reduced, and working efficiency is improved.
Description
Technical Field
The present disclosure relates to the field of communications technologies, and in particular, to a log fault association and prediction method, apparatus, device, and storage medium.
Background
In daily system operation and maintenance in-process, no matter be IT system or CT system, often need operation and maintenance personnel, research and development personnel to fix a position the problem according to the log when meetting the problem, along with the system is more and more complicated, often a problem needs the people of a plurality of subsystems to fix a position, and is inefficient, and human cost is huge, and research and development often needs to fix a position, solve the problem on duty, and the result causes three dissatisfaction: dissatisfaction of customers (waiting to solve the problem too slowly), dissatisfaction of research and development (waiting too much overtime), dissatisfaction of leadership (low overall efficiency of departments).
Disclosure of Invention
The method, the device, the equipment and the storage medium for correlating and predicting the log faults discard high-frequency events as system disturbance through pareto analysis, and speed up data processing and training; through Bayesian operation, the incidence relation of log faults can be given in a probability mode, the unbalanced probability distribution given by a machine is changed from manual judgment, decision making of operation and maintenance personnel is assisted, labor intensity is reduced, and working efficiency is improved.
The technical scheme adopted for solving the technical problems is as follows:
according to one aspect of the present disclosure, there is provided a log failure correlation and prediction method, including:
acquiring a historical service log, and carrying out pareto analysis on the historical service log to obtain a training log sample;
carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event;
and processing the real-time service log according to the prediction model, and predicting the probability and time of the occurrence of the associated fault event.
Optionally, the obtaining a historical service log, and performing pareto analysis on the historical service log to obtain a training log sample includes:
acquiring a historical service log, and classifying the historical service log according to the specification and the characteristics of the service log;
carrying out pareto analysis on the historical service logs to obtain the occurrence frequency of each type of service logs, and carrying out positive sequence arrangement according to the frequency;
and filtering the high-frequency service log according to a preset quantile value to obtain a training log sample.
Optionally, the high-frequency service log is system disturbance information.
Optionally, the associated event includes a log event and an associated fault event.
Optionally, the obtaining a prediction model of the associated event by performing bayesian operation on the training log samples through a preset time window includes:
filtering the training log samples through a preset time window to obtain the prior probability of the associated fault event, the probability of the corresponding log event occurring in the corresponding time window before the associated fault event occurs, and the probability of the associated fault event occurring;
calculating the posterior probability of the associated fault event through a Bayesian formula, and calculating the interval time from the log event to the associated fault event;
setting a confidence threshold, comparing the posterior probability of all log events with the confidence threshold, screening out the log events with the posterior probability larger than the confidence threshold, and storing the log events and the corresponding interval time into a rule base to form a prediction model of the associated events.
Optionally, the time window is 5 minutes, 15 minutes or half an hour in duration.
Optionally, the processing the real-time service log according to the prediction model, and predicting the probability and time of the occurrence of the associated fault event further includes:
and acquiring the real-time occurrence probability and time of the associated fault event, and updating the prediction model.
According to another aspect of the present disclosure, there is provided a log failure correlation and prediction apparatus, including:
the pareto analysis module is used for acquiring a historical service log, and carrying out pareto analysis on the historical service log to acquire a training log sample;
the Bayesian operation module is used for carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event;
and the prediction module is used for processing the real-time service log according to the prediction model and predicting the probability and time of the occurrence of the associated fault event.
According to yet another aspect herein, there is provided an electronic device comprising a memory, a processor, and at least one application stored in the memory and configured to be executed by the processor, the application configured to perform the log failure correlation and prediction method described above.
According to yet another aspect herein, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the log failure correlation and prediction method described above.
The embodiment of the invention discloses a log fault association and prediction method, a log fault association and prediction device, log fault association equipment and a log fault storage medium, wherein the method comprises the following steps: acquiring a historical service log, and carrying out pareto analysis on the historical service log to obtain a training log sample; carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event; processing the real-time service log according to the prediction model, and predicting the probability and time of occurrence of the associated fault event; through pareto analysis, high-frequency events are abandoned as system disturbances, and data processing and training are accelerated; through Bayesian operation, the incidence relation of log faults can be given in a probability mode, the unbalanced probability distribution given by a machine is changed from manual judgment, decision making of operation and maintenance personnel is assisted, labor intensity is reduced, and working efficiency is improved.
Drawings
Fig. 1 is a flowchart of a log fault association and prediction method according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method of step S10 of FIG. 1;
FIG. 3 is a flowchart of a method of step S20 of FIG. 1;
FIG. 4 is a flowchart of another log failure correlation and prediction method according to an embodiment of the present invention;
fig. 5 is a block diagram illustrating an exemplary structure of a log failure correlation and prediction apparatus according to a second embodiment of the present invention.
The objects, features, and advantages described herein will be further explained with reference to the accompanying drawings.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer and more obvious, the present invention is further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not restrictive.
Example one
As shown in fig. 1, in this embodiment, a log fault association and prediction method includes:
s10, acquiring a historical service log, and carrying out pareto analysis on the historical service log to obtain a training log sample;
s20, carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event;
and S30, processing the real-time service log according to the prediction model, and predicting the probability and time of the occurrence of the associated fault event.
In the embodiment, the high-frequency event is discarded as system disturbance through pareto analysis, so that data processing and training are accelerated; through Bayesian operation, the incidence relation of log faults can be given in a probability mode, the unbalanced probability distribution given by a machine is changed from manual judgment, decision making of operation and maintenance personnel is assisted, labor intensity is reduced, and working efficiency is improved.
In this embodiment, the pareto analysis, also called ABC Classification (Activity Based Classification), shall be called ABC Classification inventory control, also called 80-to-20 rule. Specifically, in this embodiment, pareto analysis may be performed on log samples to obtain the occurrence frequency of each type of log, the logs may be arranged in a positive order according to the number of times, and then 20% of the log types after ranking may be discarded according to a set score, which means that more than 80% of the logs may be processed less according to the pareto rule.
In this embodiment, bayesian inference is used as a method commonly used in the field of artificial intelligence, log fault association can be given in a probabilistic manner, rules are not required to be defined after analysis by professionals, only fault types and fault occurrence time need to be labeled, and the method has a significant advantage under an uncertain condition (a small data set) (a large data set can not be obtained for training in all cases).
In the present embodiment, in order to ensure the timeliness of the prediction model (rule base) in consideration of the imbalance of the distribution of various log events in different phases of the software lifecycle (the probability distribution of the log events in the installation deployment state and in the running state, and in the commissioning and smooth running phases is certainly different), in step S10, a concept of a large time window is introduced, and 15 days, 30 days and 60 days are taken as the large time window, i.e. a training data set, according to the configuration, and the large time window is obtained from the historical logs of the last 15 days, 30 days and 60 days.
In this embodiment, when obtaining the historical service log, the fault type and the fault time of the historical log information are labeled first, so as to perform bayesian operation.
As shown in fig. 2, in the present embodiment, the step S10 includes:
s11, acquiring historical service logs, and classifying the historical service logs according to the specifications and the characteristics of the service logs;
s12, carrying out pareto analysis on the historical service logs to obtain the occurrence frequency of each type of service logs, and carrying out positive sequence arrangement according to the frequency;
and S13, filtering the high-frequency service log according to a preset quantile value to obtain a training log sample.
In this embodiment, the quantile value is 80%, the logs are arranged in the positive order of the times, that is, in the order from less to more, and the log types located 20% after ranking are represented as more times and higher in frequency, these services are system disturbances, and according to the pareto analysis method, these services account for 80% of the total traffic, which means that more than 80% of the log data are excluded from the training set, so that the load of the analysis engine is greatly reduced, and the generation of the rule is accelerated.
In this embodiment, the high-frequency service log is system disturbance information.
In this embodiment, the associated events include log events and associated fault events.
As shown in fig. 3, in the present embodiment, the step S20 includes:
s21, filtering the training log samples through a preset time window to obtain the prior probability of the associated fault event, the probability of the corresponding log event occurring in the corresponding time window before the associated fault event occurs, and the probability of the associated fault event occurring;
s22, calculating the posterior probability of the associated fault event through a Bayes formula, and calculating the interval time from the log event to the associated fault event;
s23, setting a confidence threshold, comparing the posterior probability of all log events with the confidence threshold, screening out the log events with the posterior probability larger than the confidence threshold, and storing the log events and the corresponding interval time into a rule base to form a prediction model of the associated events.
In this embodiment, the time window is a small time window with a duration of 5 minutes, 15 minutes, or half an hour. The whole sample period is filtered through a set time window (5 minutes, 15 minutes or half hour) to obtain a prior probability P (A), the probability P (B) of a corresponding log event occurring in the corresponding time window before the occurrence of the associated fault event, the probability P (A | B) of the occurrence of the associated fault event, the posterior probability P (B | A) is obtained through a Bayesian formula, and the average interval time from the event A to the fault B is calculated. The log events whose P (B | A) of the log events is greater than the lower limit are filtered according to a set confidence threshold (e.g., 80%) and stored in the rule base together with the interval time.
In this embodiment, after a certain kind of fault-related log appears, the corresponding information is reported: "XXX Log occurs at XXX time, XX% probability will occur XXX fault after XX duration. "
As shown in fig. 4, in this embodiment, after step S30, the method further includes:
and S40, acquiring the real-time occurrence probability and time of the associated fault event, and updating the prediction model. So as to ensure the timeliness of the prediction model.
Example two
As shown in fig. 5, a log failure correlation and prediction apparatus includes:
the pareto analysis module 10 is configured to obtain a historical service log, perform pareto analysis on the historical service log, and obtain a training log sample;
the Bayesian operation module 20 is used for carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event;
and the prediction module 30 is configured to process the real-time service log according to the prediction model, and predict the probability and time of the occurrence of the associated fault event.
In the embodiment, the high-frequency event is discarded as system disturbance through pareto analysis, so that data processing and training are accelerated; through Bayesian operation, the incidence relation of log faults can be given in a probability mode, the unbalanced probability distribution given by a machine is changed from manual judgment, decision making of operation and maintenance personnel is assisted, labor intensity is reduced, and working efficiency is improved.
In this embodiment, the pareto analysis, also called ABC Classification (Activity Based Classification), shall be called ABC Classification inventory control, also called 80-to-20 rule. Specifically, in this embodiment, pareto analysis may be performed on log samples to obtain the occurrence frequency of each type of log, the logs may be arranged in a positive order according to the number of times, and then 20% of the log types after ranking may be discarded according to a set score, which means that more than 80% of the logs may be processed less according to the pareto rule.
In this embodiment, bayesian inference is used as a method commonly used in the field of artificial intelligence, log fault association can be given in a probabilistic manner, rules are not required to be defined after analysis by professionals, only fault types and fault occurrence time need to be labeled, and the method has a significant advantage under an uncertain condition (a small data set) (a large data set can not be obtained for training in all cases).
In the embodiment, in order to ensure the timeliness of the prediction model (rule base) in consideration of the imbalance of the distribution of various log events in different stages of the software life cycle (the probability distribution of the log events in the installation deployment state and the running state of the software and in the trial running and smooth running stages is certainly different), the concept of a large time window is introduced, and the large time window, namely a training data set, is obtained from the historical logs of the last 15 days, 30 days and 60 days according to the configuration, wherein the large time window is 15 days, 30 days and 60 days.
In this embodiment, when obtaining the historical service log, the fault type and the fault time of the historical log information are labeled first, so as to perform bayesian operation.
In this embodiment, after a certain kind of fault-related log appears, the corresponding information is reported: "XXX Log occurs at XXX time, XX% probability will occur XXX fault after XX duration. "
EXAMPLE III
In this embodiment, an electronic device includes a memory, a processor, and at least one application program stored in the memory and configured to be executed by the processor, where the application program is configured to perform the log fault correlation and prediction method of the first embodiment.
Example four
Embodiments of the present invention provide a storage medium having stored thereon a computer program that, when executed by a processor, implements a method embodiment as described in any of the above log failure correlation and prediction method embodiments.
It should be noted that the above device, apparatus, and storage medium embodiments and method embodiments belong to the same concept, and specific implementation processes thereof are described in detail in the method embodiments, and technical features in the method embodiments are correspondingly applicable in the device embodiments, and are not described herein again.
The embodiment of the invention discloses a log fault association and prediction method, a log fault association and prediction device, log fault association equipment and a log fault storage medium, wherein the method comprises the following steps: acquiring a historical service log, and carrying out pareto analysis on the historical service log to obtain a training log sample; carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event; processing the real-time service log according to the prediction model, and predicting the probability and time of occurrence of the associated fault event; through pareto analysis, high-frequency events are abandoned as system disturbances, and data processing and training are accelerated; through Bayesian operation, the incidence relation of log faults can be given in a probability mode, the unbalanced probability distribution given by a machine is changed from manual judgment, decision making of operation and maintenance personnel is assisted, labor intensity is reduced, and working efficiency is improved.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and are not to be construed as limiting the scope of the invention. Any modifications, equivalents and improvements which may occur to those skilled in the art without departing from the scope and spirit of the present invention are intended to be within the scope of the claims.
Claims (10)
1. A log failure correlation and prediction method comprises the following steps:
acquiring a historical service log, and carrying out pareto analysis on the historical service log to obtain a training log sample;
carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event;
and processing the real-time service log according to the prediction model, and predicting the probability and time of the occurrence of the associated fault event.
2. The log fault association and prediction method of claim 1, wherein the obtaining of the historical traffic log and the performing of pareto analysis on the historical traffic log to obtain the training log samples comprises:
acquiring a historical service log, and classifying the historical service log according to the specification and the characteristics of the service log;
carrying out pareto analysis on the historical service logs to obtain the occurrence frequency of each type of service logs, and carrying out positive sequence arrangement according to the frequency;
and filtering the high-frequency service log according to a preset quantile value to obtain a training log sample.
3. The log fault correlation and prediction method of claim 2, wherein the high frequency service log is system disturbance information.
4. The log failure correlation and prediction method of claim 1, wherein the correlation events comprise log events and correlation failure events.
5. The log fault association and prediction method according to claim 4, wherein the obtaining of the prediction model of the association event by performing the bayesian operation on the training log samples through a preset time window comprises:
filtering the training log samples through a preset time window to obtain the prior probability of the associated fault event, the probability of the corresponding log event occurring in the corresponding time window before the associated fault event occurs, and the probability of the associated fault event occurring;
calculating the posterior probability of the associated fault event through a Bayesian formula, and calculating the interval time from the log event to the associated fault event;
setting a confidence threshold, comparing the posterior probability of all log events with the confidence threshold, screening out the log events with the posterior probability larger than the confidence threshold, and storing the log events and the corresponding interval time into a rule base to form a prediction model of the associated events.
6. The log failure correlation and prediction method of claim 5, wherein the time window is 5 minutes, 15 minutes or half an hour long.
7. The log failure correlation and prediction method of claim 1, wherein the processing the real-time service log according to the prediction model and predicting the probability and time of occurrence of the correlated failure event further comprises:
and acquiring the real-time occurrence probability and time of the associated fault event, and updating the prediction model.
8. A log failure correlation and prediction apparatus, comprising:
the pareto analysis module is used for acquiring a historical service log, and carrying out pareto analysis on the historical service log to acquire a training log sample;
the Bayesian operation module is used for carrying out Bayesian operation on the training log samples through a preset time window to obtain a prediction model of the associated event;
and the prediction module is used for processing the real-time service log according to the prediction model and predicting the probability and time of the occurrence of the associated fault event.
9. An electronic device comprising a memory, a processor, and at least one application stored in the memory and configured to be executed by the processor, wherein the application is configured to perform the log failure correlation and prediction method of any of claims 1-7.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements a log fault correlation and prediction method according to any one of claims 1 to 7.
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