CN109634828A - Failure prediction method, device, equipment and storage medium - Google Patents
Failure prediction method, device, equipment and storage medium Download PDFInfo
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- CN109634828A CN109634828A CN201811543066.XA CN201811543066A CN109634828A CN 109634828 A CN109634828 A CN 109634828A CN 201811543066 A CN201811543066 A CN 201811543066A CN 109634828 A CN109634828 A CN 109634828A
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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
- G06F11/3452—Performance evaluation by statistical analysis
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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
- G06F11/3466—Performance evaluation by tracing or monitoring
- G06F11/3476—Data logging
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
Abstract
The invention discloses a kind of failure prediction methods, comprising: the daily record data that acquisition server generates in current operational process；The decision tree prediction model pre-established is called, the decision tree prediction model is the model being trained to the sample data comprising history log data and fault message；The daily record data is input in the decision tree prediction model, the prediction result predicted server failure is generated.The present invention analyzes the daily record data of current server operating status, obtains the prediction result to server failure using the decision tree prediction model pre-established, so that the failure for server is prevented, significantly improves the efficiency of operation and maintenance.Disclosed herein as well is a kind of fault prediction device, equipment and computer readable storage mediums, are equally able to achieve above-mentioned technical effect.
Description
Technical field
The present invention relates to information technology fields, can more particularly to a kind of failure prediction method, device, equipment and computer
Read storage medium.
Background technique
With the development of information technology, the scale of data center is increasing, although the single machine failure rate of server with
The progress of technology is gradually decreasing, but in large-scale data center, the occurrence frequency of server failure is still very high.
Current server failure system, mostly just realizes the function of being analyzed the failure having occurred and that, does not have
The scheme of failure predication is carried out according to the health condition of equipment, Frequent Troubles region and period.Therefore, how above-mentioned purpose is realized
It is those skilled in the art's urgent problem to be solved.
Summary of the invention
The object of the present invention is to provide a kind of failure prediction method, device, equipment and computer readable storage mediums, with solution
The problem of failure that certainly server may not occur in the prior art is predicted.
In order to solve the above technical problems, the present invention provides a kind of failure prediction method, comprising:
The daily record data that acquisition server generates in current operational process；
Call the decision tree prediction model that pre-establishes, the decision tree prediction model be to comprising history log data with
And the model that the sample data of fault message is trained；
The daily record data is input in the decision tree prediction model, generation predicts server failure pre-
Survey result.
Wherein, the daily record data that the acquisition server generates in current operational process includes:
When meeting preset trigger condition, daily record data that acquisition server generates in current operational process.
Wherein, after the daily record data that the acquisition server generates in current operational process further include:
The daily record data of generation is stored into MongoDB database, the data in the MongoDB database are made
For sample data.
Wherein, described that the daily record data is input in the decision tree prediction model, generate to server failure into
Row prediction prediction result include:
The daily record data is input in the decision tree prediction model, generate to the probability of malfunction of server component and
The prediction result that fault signature is predicted.
Wherein, the daily record data is input in the decision tree prediction model described, is generated to server failure
After the prediction result predicted, further includes:
The prediction result is shown by the patterned page.
To achieve the above object, this application provides a kind of fault prediction devices, comprising:
Data acquisition module, the daily record data generated in current operational process for acquisition server；
Model calling module, for calling the decision tree prediction model pre-established, the decision tree prediction model is pair
The model that sample data comprising history log data and fault message is trained；
Result-generation module is generated for the daily record data to be input in the decision tree prediction model to service
The prediction result that device failure is predicted.
Wherein, further includes:
Data memory module, for after the daily record data that the acquisition server generates in current operational process,
The daily record data of generation is stored into MongoDB database, the data in the MongoDB database are as sample number
According to.
Wherein, further includes:
Pattern displaying unit is generated for the daily record data to be input in the decision tree prediction model to clothes
After the prediction result that business device failure is predicted, the prediction result is shown by the patterned page.
To achieve the above object, this application provides a kind of failure predication equipment, comprising:
Memory, for storing computer program；
Processor realizes the step of aforementioned disclosed any failure prediction method when for executing the computer program
Suddenly.
To achieve the above object, this application provides a kind of computer readable storage medium, the computer-readable storages
Computer program is stored on medium, the computer program realizes that aforementioned disclosed any failure is pre- when being executed by processor
The step of survey method.
Failure prediction method provided by the present invention, the log number generated in current operational process by acquisition server
According to；The decision tree prediction model pre-established is called, the decision tree prediction model is to comprising history log data and event
The model that the sample data of barrier information is trained；The daily record data is input in the decision tree prediction model,
Generate the prediction result predicted server failure.The present invention is using the decision tree prediction model pre-established, to current
The daily record data of operation condition of server is analyzed, and the prediction result to server failure is obtained, to be the event of server
Barrier is prevented, and the efficiency of operation and maintenance is significantly improved.Disclosed herein as well is a kind of fault prediction device, failure predications to set
Standby and computer readable storage medium is equally able to achieve above-mentioned technical effect.
It should be understood that the above general description and the following detailed description are merely exemplary, this can not be limited
Application.
Detailed description of the invention
It, below will be to embodiment or existing for the clearer technical solution for illustrating the embodiment of the present invention or the prior art
Attached drawing needed in technical description is briefly described, it should be apparent that, the accompanying drawings in the following description is only this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is a kind of flow chart of specific embodiment of failure prediction method provided herein；
Fig. 2 is the flow chart of another specific embodiment of failure prediction method provided herein；
Fig. 3 is the flow chart of another specific embodiment of failure prediction method provided herein；
Fig. 4 is the structural block diagram of fault prediction device provided in an embodiment of the present invention；
Fig. 5 is a kind of structural block diagram of specific embodiment of failure predication equipment provided in an embodiment of the present invention；
Fig. 6 is the structural block diagram of another specific embodiment of failure predication equipment provided in an embodiment of the present invention.
Specific embodiment
Current server failure system, mostly just realizes the function of being analyzed the failure having occurred and that, does not have
The scheme of failure predication is carried out according to the health condition of equipment, Frequent Troubles region and period.Core of the invention is to provide one
Kind failure prediction method, device, equipment and computer readable storage medium, it is possible to server in the prior art to solve
The problem of failure of generation is predicted.
In order to enable those skilled in the art to better understand the solution of the present invention, with reference to the accompanying drawings and detailed description
The present invention is described in further detail.Obviously, described embodiments are only a part of the embodiments of the present invention, rather than
Whole embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making creative work premise
Under every other embodiment obtained, shall fall within the protection scope of the present invention.
In order to solve the above technical problems, the present invention provides a kind of failure prediction method, shown in Figure 1, method includes:
S101: the daily record data that acquisition server generates in current operational process；
In the present embodiment, in acquisition server operational process with interior daily record data and with outer daily record data.Wherein, it adopts
Mode set can be acquisition in real time, can also carry out taken at regular intervals according to preset period of time, it is not limited here.
S102: calling the decision tree prediction model pre-established, and the decision tree prediction model is to including history log
The model that data and the sample data of fault message are trained；
Further, the decision tree prediction model pre-established is called.It is understood that decision tree prediction model base
It is obtained in the sample data training collected in advance, sample data may include a large amount of history log datas and corresponding failure letter
Breath.By being trained to a large amount of sample data, training can be optimized to the decision tree prediction model.By a large amount of
Sample data is trained, and after generating decision tree prediction model, it is pre- that the daily record data generated in real time is input to the decision tree
It surveys in model, corresponding prediction result can be obtained.
S103: the daily record data is input in the decision tree prediction model, is generated and is carried out in advance to server failure
The prediction result of survey.
Specifically, the daily record data of current server operational process is inputted decision tree prediction model by the present embodiment, thus
Server failure is predicted using decision tree prediction model, obtains prediction result.
Failure prediction method provided by the present invention, the log number generated in current operational process by acquisition server
According to；The decision tree prediction model pre-established is called, the decision tree prediction model is to comprising history log data and event
The model that the sample data of barrier information is trained；The daily record data is input in the decision tree prediction model,
Generate the prediction result predicted server failure.The present invention is using the decision tree prediction model pre-established, to current
The daily record data of operation condition of server is analyzed, and the prediction result to server failure is obtained, to be the event of server
Barrier is prevented, and the efficiency of operation and maintenance is significantly improved.
The embodiment of the present application discloses a kind of failure prediction method, and relative to a upper embodiment, the present embodiment is to technical side
Case has made further instruction and optimization.It is specific:
Referring to fig. 2, the flow chart of another failure prediction method provided by the embodiments of the present application, as shown in Figure 2, comprising:
S201: when meeting preset trigger condition, daily record data that acquisition server generates in current operational process；
Preset trigger condition is the specified condition being acquired to daily record data.It is alternatively possible to for the timing week of setting
Phase, after the period for reaching setting, the daily record data generated in current operational process to server is acquired.Default touching
Clockwork spring part can also be to receive the triggering command that user is inputted by interface, this does not influence the realization of the application.
S202: calling the decision tree prediction model pre-established, and the decision tree prediction model is to including history log
The model that data and the sample data of fault message are trained；
It should be noted that the present embodiment is after collecting the daily record data in server operational process, by daily record data
It stores to MongoDB database, and further utilize sample data using the data in MongoDB database as sample data
It is trained, establishes decision tree prediction model.
S203: the daily record data is input in the decision tree prediction model, generates the failure to server component
The prediction result that probability and fault signature are predicted.
In specific implementation, after daily record data being inputted in decision tree prediction model, the analysis of decision tree prediction model is obtained
The failure predication result of current server afterwards.Specifically, failure predication result may include server component probability of malfunction,
Fault signature, fault type.Whether may be broken down by all parts in the available current server of prediction result
Probability, and the feature or type that may break down.
Further, the prediction that the present embodiment predicts the probability of malfunction and fault signature of server component in generation
It as a result can also include: to be shown by the patterned page to the prediction result after.
Referring to the flow chart of another specific embodiment of failure prediction method Fig. 3 provided herein, the process
Include:
Step S301: the daily record data that acquisition server generates in current operational process；
Step S302: calling the decision tree prediction model pre-established, and the decision tree prediction model is to including history
The model that the sample data of daily record data and fault message is trained；
Step S303: the daily record data is input in the decision tree prediction model, generate to server failure into
The prediction result of row prediction；
Step S304: the prediction result is shown by the patterned page.
After the prediction result for obtaining predicting server failure, it can further pass through the patterned page pair
The prediction result is shown.It is understood that prediction result is utilized graphical page exhibition after generating prediction result
Show, can provide more intuitive as a result, further improving the usage experience of user for user or maintenance personnel.
Fault prediction device provided in an embodiment of the present invention is introduced below, fault prediction device described below with
Above-described failure prediction method can correspond to each other reference.
Fig. 4 is the structural block diagram of fault prediction device provided in an embodiment of the present invention, can be with referring to Fig. 4 fault prediction device
Include:
Data acquisition module 100, the daily record data generated in current operational process for acquisition server；
Model calling module 200, for calling the decision tree prediction model pre-established, the decision tree prediction model is
The model that sample data comprising history log data and fault message is trained；
Result-generation module 300 is generated for the daily record data to be input in the decision tree prediction model to clothes
The prediction result that business device failure is predicted.
Further, fault prediction device provided in an embodiment of the present invention can also include:
Data memory module, for after the daily record data that the acquisition server generates in current operational process,
The daily record data of generation is stored into MongoDB database, the data in the MongoDB database are as sample number
According to.
Fault prediction device provided in this embodiment can also include:
Image display module is input in the decision tree prediction model for the daily record data, is generated to server
After the prediction result that failure is predicted, the prediction result is shown by the patterned page.
The fault prediction device of the present embodiment is for realizing failure prediction method above-mentioned, therefore in fault prediction device
The embodiment part of the visible failure prediction method hereinbefore of specific embodiment, for example, data acquisition module 100, model tune
With module 200, result-generation module 300 is respectively used to realize step S101, S102 and S103 in above-mentioned failure prediction method,
So specific embodiment is referred to the description of corresponding various pieces embodiment, details are not described herein.
The application passes through the daily record data that acquisition server generates in current operational process；Call the decision pre-established
Prediction model is set, the decision tree prediction model is to instruct to the sample data comprising history log data and fault message
The model got；The daily record data is input in the decision tree prediction model, generates and server failure is carried out in advance
The prediction result of survey.The present invention is using the decision tree prediction model pre-established, to the log number of current server operating status
According to being analyzed, the prediction result to server failure is obtained, so that the failure for server is prevented, significantly improves fortune
The efficiency of row maintenance.
Present invention also provides a kind of failure predication equipment, referring to Fig. 5, a kind of electronic equipment provided by the embodiments of the present application
Structural block diagram, as shown in Figure 5, comprising:
Memory 11, for storing computer program；
Aforementioned disclosed any failure prediction method may be implemented in processor 12 when for executing the computer program
The step of.
Specifically, memory 11 includes non-volatile memory medium, built-in storage.The non-volatile memory medium is stored with
Operating system and computer-readable instruction, the built-in storage are the operating system and computer-readable in non-volatile memory medium
The operation of instruction provides environment.Processor 12 can be a central processing unit (Central in some embodiments
Processing Unit, CPU), controller, microcontroller, microprocessor or other data processing chips, mentioned for electronic equipment
For calculate and control ability,
On the basis of the above embodiments, preferably, referring to Fig. 6, the electronic equipment further include:
Input interface 13 is connected with processor 12, for obtaining computer program, parameter and the instruction of external importing, warp
The control of processor 12 is saved into memory 11.The input interface 13 can be connected with input unit, receive user and be manually entered
Parameter or instruction.The input unit can be the touch layer covered on display screen, be also possible to be arranged in terminal enclosure by
Key, trace ball or Trackpad are also possible to keyboard, Trackpad or mouse etc..
Display unit 14 is connected with processor 12, for the data of the processing of video-stream processor 12 and for showing visually
The user interface of change.The display unit 14 can be light-emitting diode display, liquid crystal display, touch-control liquid crystal display and OLED
(Organic Light-Emitting Diode, Organic Light Emitting Diode) touches device etc..
The network port 15 is connected with processor 12, for being communicatively coupled with external each terminal device.The communication connection
The used communication technology can be cable communicating technology or wireless communication technique, such as mobile high definition chained technology (MHL), general
Universal serial bus (USB), high-definition media interface (HDMI), adopting wireless fidelity technology (WiFi), Bluetooth Communication Technology, low-power consumption bluetooth
The communication technology, communication technology based on IEEE802.11s etc..
Fig. 6 illustrates only the electronic equipment with component 11-15, it will be appreciated by persons skilled in the art that Fig. 6 is shown
Structure do not constitute the restriction to electronic equipment, may include more certain than illustrating less perhaps more components or combination
Component or different component layouts.
Present invention also provides a kind of computer readable storage medium, the storage medium may include: USB flash disk, mobile hard disk,
Read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic
The various media that can store program code such as dish or CD.Computer program, the calculating are stored on the storage medium
Machine program realizes the step of aforementioned disclosed any failure prediction method when being executed by processor.
The present invention carries out the daily record data of current server operating status using the decision tree prediction model pre-established
Analysis, obtains the prediction result to server failure, so that the failure for server is prevented, significantly improves operation and maintenance
Efficiency.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with it is other
The difference of embodiment, same or similar part may refer to each other between each embodiment.For being filled disclosed in embodiment
For setting, since it is corresponded to the methods disclosed in the examples, so being described relatively simple, related place is referring to method part
Explanation.
Professional further appreciates that, unit described in conjunction with the examples disclosed in the embodiments of the present disclosure
And algorithm steps, can be realized with electronic hardware, computer software, or a combination of the two, in order to clearly demonstrate hardware and
The interchangeability of software generally describes each exemplary composition and step according to function in the above description.These
Function is implemented in hardware or software actually, the specific application and design constraint depending on technical solution.Profession
Technical staff can use different methods to achieve the described function each specific application, but this realization is not answered
Think beyond the scope of this invention.
The step of method described in conjunction with the examples disclosed in this document or algorithm, can directly be held with hardware, processor
The combination of capable software module or the two is implemented.Software module can be placed in random access memory (RAM), memory, read-only deposit
Reservoir (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technology
In any other form of storage medium well known in field.
Failure prediction method provided by the present invention, device, equipment and computer readable storage medium are carried out above
It is discussed in detail.Used herein a specific example illustrates the principle and implementation of the invention, above embodiments
Illustrate to be merely used to help understand method and its core concept of the invention.It should be pointed out that for the common skill of the art
, without departing from the principle of the present invention, can be with several improvements and modifications are made to the present invention for art personnel, these change
It is also fallen within the protection scope of the claims of the present invention into modification.
Claims (10)
1. a kind of failure prediction method characterized by comprising
The daily record data that acquisition server generates in current operational process；
The decision tree prediction model pre-established is called, the decision tree prediction model is to comprising history log data and event
The model that the sample data of barrier information is trained；
The daily record data is input in the decision tree prediction model, the prediction knot predicted server failure is generated
Fruit.
2. failure prediction method as described in claim 1, which is characterized in that the acquisition server is in current operational process
The daily record data of generation includes:
When meeting preset trigger condition, daily record data that acquisition server generates in current operational process.
3. failure prediction method as claimed in claim 2, which is characterized in that in the acquisition server in current operational process
After the daily record data of middle generation further include:
The daily record data of generation is stored into MongoDB database, the data in the MongoDB database are as sample
Notebook data.
4. failure prediction method as described in any one of claims 1 to 3, which is characterized in that described that the daily record data is defeated
Enter into the decision tree prediction model, generating the prediction result predicted server failure includes:
The daily record data is input in the decision tree prediction model, the probability of malfunction and failure to server component are generated
The prediction result that feature is predicted.
5. failure prediction method as claimed in claim 4, which is characterized in that it is described the daily record data is input to it is described
In decision tree prediction model, after the prediction result that server failure is predicted in generation, further includes:
The prediction result is shown by the patterned page.
6. a kind of fault prediction device characterized by comprising
Data acquisition module, the daily record data generated in current operational process for acquisition server；
Model calling module, for calling the decision tree prediction model pre-established, the decision tree prediction model be to comprising
The model that history log data and the sample data of fault message are trained；
Result-generation module is generated for the daily record data to be input in the decision tree prediction model to server event
Hinder the prediction result predicted.
7. fault prediction device as claimed in claim 6, which is characterized in that further include:
Data memory module, for that will give birth to after the daily record data that the acquisition server generates in current operational process
At the daily record data store into MongoDB database, the data in the MongoDB database are as sample data.
8. fault prediction device as claimed in claim 6, which is characterized in that further include:
Pattern displaying unit is generated for the daily record data to be input in the decision tree prediction model to server
After the prediction result that failure is predicted, the prediction result is shown by the patterned page.
9. a kind of failure predication equipment characterized by comprising
Memory, for storing computer program；
Processor, realizing the failure prediction method as described in any one of claim 1 to 5 when for executing the computer program
Step.
10. a kind of computer readable storage medium, which is characterized in that be stored with computer on the computer readable storage medium
Program realizes the step of the failure prediction method as described in any one of claim 1 to 5 when the computer program is executed by processor
Suddenly.
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Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110348684A (en) * | 2019-06-06 | 2019-10-18 | 阿里巴巴集团控股有限公司 | Service call risk model generation method, prediction technique and respective device |
| CN110555004A (en) * | 2019-07-30 | 2019-12-10 | 北京奇艺世纪科技有限公司 | Service monitoring method and device, computer equipment and storage medium |
| CN110851342A (en) * | 2019-11-08 | 2020-02-28 | 中国工商银行股份有限公司 | Fault prediction method, device, computing equipment and computer readable storage medium |
| CN111309562A (en) * | 2020-02-27 | 2020-06-19 | 上海上大鼎正软件股份有限公司 | Server failure prediction method, device, equipment and storage medium |
| CN111338836A (en) * | 2020-02-24 | 2020-06-26 | 北京奇艺世纪科技有限公司 | Method, device, computer equipment and storage medium for processing fault data |
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| CN111581062A (en) * | 2020-05-18 | 2020-08-25 | 聚好看科技股份有限公司 | Service fault processing method and server |
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| CN112579327A (en) * | 2019-09-27 | 2021-03-30 | 阿里巴巴集团控股有限公司 | Fault detection method, device and equipment |
| CN113014412A (en) * | 2019-12-20 | 2021-06-22 | 中国移动通信集团陕西有限公司 | Method and system for predicting delay time of downtime fault service |
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| CN117114352A (en) * | 2023-09-15 | 2023-11-24 | 北京阿帕科蓝科技有限公司 | Vehicle maintenance method, device, computer equipment and storage medium |
| CN117278383A (en) * | 2023-11-21 | 2023-12-22 | 航天科工广信智能技术有限公司 | Internet of things fault investigation scheme generation system and method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104156562A (en) * | 2014-07-15 | 2014-11-19 | 清华大学 | Failure predication system and failure predication method for background operation and maintenance system of bank |
| US20150135012A1 (en) * | 2013-11-08 | 2015-05-14 | Accenture Global Services Limited | Network node failure predictive system |
| CN105930934A (en) * | 2016-04-27 | 2016-09-07 | 北京物思创想科技有限公司 | Prediction model demonstration method and device and prediction model adjustment method and device |
| CN108491305A (en) * | 2018-03-09 | 2018-09-04 | 网宿科技股份有限公司 | A kind of detection method and system of server failure |
| CN108959039A (en) * | 2018-07-18 | 2018-12-07 | 郑州云海信息技术有限公司 | A kind of method and device of virtual-machine fail prediction |
-
2018
- 2018-12-17 CN CN201811543066.XA patent/CN109634828A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150135012A1 (en) * | 2013-11-08 | 2015-05-14 | Accenture Global Services Limited | Network node failure predictive system |
| CN104156562A (en) * | 2014-07-15 | 2014-11-19 | 清华大学 | Failure predication system and failure predication method for background operation and maintenance system of bank |
| CN105930934A (en) * | 2016-04-27 | 2016-09-07 | 北京物思创想科技有限公司 | Prediction model demonstration method and device and prediction model adjustment method and device |
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