CN109710505A - A kind of disk failure prediction technique, device, terminal and storage medium - Google Patents
A kind of disk failure prediction technique, device, terminal and storage medium Download PDFInfo
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- CN109710505A CN109710505A CN201910002208.XA CN201910002208A CN109710505A CN 109710505 A CN109710505 A CN 109710505A CN 201910002208 A CN201910002208 A CN 201910002208A CN 109710505 A CN109710505 A CN 109710505A
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Abstract
The present invention provides a kind of disk failure prediction technique, device, terminal and storage medium, comprising: acquisition magnetic disc characteristic information and historical failure data；The characteristic information and historical failure data are pre-processed using sklearn；Prediction model is created according to pretreated characteristic information and historical failure data using using supervised learning algorithm；By the failure predication result that magnetic disc characteristic information input prediction model is obtained to corresponding disk.The present invention can carry out failure predication to disk, know the health status of disk in advance, convenient for taking relevant counter-measure in time, reduce risk and harm.
Description
Technical field
The invention belongs to server the field of test technology, and in particular to a kind of disk failure prediction technique, device, terminal and
Storage medium.
Background technique
With the arrival of big data era, enterprise and the personal data volume that need to be stored are increasing, and disk is as data
Carrier, reliability, stability, stability are just receiving various tests.If disk failures will lead to loss of data,
The loss for being difficult to restore even is brought to user.
In the prior art, lack the relevant technologies predicted disk failure, can only be taken in the practical failure of disk
Corresponding counter-measure.To the disk on critical server generally according to usual experience, according to the operating time of disk, periodically
Be maintained or replaced, higher cost.And when chance failure occurs for disk, often bring about great losses to user.
Summary of the invention
In view of the deficiencies of the prior art, the present invention provides a kind of disk failure prediction technique, device, terminal and storage and is situated between
Matter, to solve the above technical problems.
In a first aspect, the embodiment of the present application provides a kind of disk failure prediction technique, which comprises
Acquire magnetic disc characteristic information and historical failure data；
The characteristic information and historical failure data are pre-processed using sklearn；
Mould is predicted according to pretreated characteristic information and historical failure data creation using using supervised learning algorithm
Type；
By the failure predication result that magnetic disc characteristic information input prediction model is obtained to corresponding disk.
With reference to first aspect, in the first embodiment of first aspect, the acquisition magnetic disc characteristic information and history
Fault data includes:
Destination server BMC is logged according to destination server BMC IP；
Utilize the magnetic disc characteristic information of information collection tool acquisition destination server；
It is journal file by the magnetic disc characteristic information preservation of acquisition.
With reference to first aspect, described that the feature is believed using sklearn in second of embodiment of first aspect
Breath and historical failure data carry out pretreatment
Quantitative characteristic binary conversion treatment is carried out to the characteristic information；
The processing of qualitative features one-hot coding is carried out to the historical failure data.
With reference to first aspect, in the third embodiment of first aspect, it is described using supervised learning algorithm according to
Pretreated characteristic information and historical failure data creation prediction model include:
Training data and simultaneously defined feature value are generated according to the characteristic information；
Using the historical failure data as the label value of the training dataset；
Tentative prediction model is created according to the training dataset and label value using supervised learning algorithm；
Tentative prediction model is assessed to obtain prediction model using metrics.
Second aspect, the embodiment of the present application provide a kind of disk failure prediction meanss, and described device includes:
Data acquisition unit is configured to acquisition magnetic disc characteristic information and historical failure data；
Pretreatment unit is configured to locate the characteristic information and historical failure data in advance using sklearn
Reason；
Model creating unit, be configured to using using supervised learning algorithm according to pretreated characteristic information and going through
History fault data creates prediction model；
As a result acquiring unit is configured to obtaining magnetic disc characteristic information input prediction model into the failure of corresponding disk
Prediction result.
In conjunction with second aspect, in the first embodiment of second aspect, the data acquisition unit includes:
Target login module is configured to log in destination server BMC according to destination server BMC IP；
Information acquisition module is configured to the magnetic disc characteristic information using information collection tool acquisition destination server；
Information preservation module, being configured to the magnetic disc characteristic information preservation that will be acquired is journal file.
In conjunction with second aspect, in second of embodiment of second aspect, the pretreatment unit includes:
Quantitative Treatment module is configured to carry out quantitative characteristic binary conversion treatment to the characteristic information；
Qualitative processing module is configured to carry out the processing of qualitative features one-hot coding to the historical failure data.
In conjunction with second aspect, in the third embodiment of second aspect, the model creating unit includes:
Data training module is configured to generate training data and simultaneously defined feature value according to the characteristic information；
Label setup module is configured to using the historical failure data as the label value of the training dataset；
Preliminary creation module is configured to be created using supervised learning algorithm according to the training dataset and label value
Tentative prediction model；
Model evaluation module is configured to be assessed to obtain prediction model to tentative prediction model using metrics.
The third aspect provides a kind of terminal, comprising:
Processor, memory, wherein
The memory is used to store computer program,
The processor from memory for calling and running the computer program, so that terminal executes above-mentioned terminal
Method.
Fourth aspect provides a kind of computer storage medium, instruction is stored in the computer readable storage medium,
When run on a computer, so that computer executes method described in above-mentioned various aspects.
5th aspect, provides a kind of computer program product comprising instruction, when run on a computer, so that
Computer executes method described in above-mentioned various aspects.
The beneficial effects of the present invention are,
Disk failure prediction technique, device, terminal and storage medium provided by the invention are based on machine learning algorithm, build
The prediction model of vertical server disk failure introduces input label value, by existing disk failure number when modeling using algorithm
According to being added in prediction model, save and calculate the time, and the accuracy of the prediction model obtained is high, by the fault condition of disk into
Row is accumulative to be added in prediction model, and the accuracy of assessment can be continuously improved.The present invention can carry out failure predication to disk,
The health status of disk is known in advance, convenient for taking relevant counter-measure in time, reduces risk and harm.
In addition, design principle of the present invention is reliable, structure is simple, has very extensive application prospect.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, for those of ordinary skill in the art
Speech, without creative efforts, is also possible to obtain other drawings based on these drawings.
Fig. 1 is the schematic flow chart of the method for the application one embodiment.
Fig. 2 is the schematic flow chart of the method for the application one embodiment.
Fig. 3 is a kind of structural schematic diagram of terminal provided in an embodiment of the present invention.
Specific embodiment
Technical solution in order to enable those skilled in the art to better understand the present invention, below in conjunction with of the invention real
The attached drawing in example is applied, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described implementation
Example is only a part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, this field is common
Technical staff's every other embodiment obtained without making creative work, all should belong to protection of the present invention
Range.
The Key Term occurred in the application is explained below.
Fig. 1 is the schematic flow chart of the method for the application one embodiment.Wherein, Fig. 1 executing subject can be one kind
Disk failure prediction meanss.
As shown in Figure 1, this method 100 includes:
Step 110, magnetic disc characteristic information and historical failure data are acquired；
Step 120, the characteristic information and historical failure data are pre-processed using sklearn；
Step 130, it is created using using supervised learning algorithm according to pretreated characteristic information and historical failure data
Build prediction model；
Step 140, by the way that magnetic disc characteristic information input prediction model to be obtained to the failure predication result of corresponding disk.
Optionally, as the application one embodiment, the acquisition magnetic disc characteristic information and historical failure data include:
Destination server BMC is logged according to destination server BMC IP；
Utilize the magnetic disc characteristic information of information collection tool acquisition destination server；
It is journal file by the magnetic disc characteristic information preservation of acquisition.
Optionally, described to utilize sklearn to the characteristic information and historical failure number as the application one embodiment
Include: according to pretreatment is carried out
Quantitative characteristic binary conversion treatment is carried out to the characteristic information；
The processing of qualitative features one-hot coding is carried out to the historical failure data.
Optionally, described to utilize supervised learning algorithm according to pretreated feature as the application one embodiment
Information and historical failure data creation prediction model include:
Training data and simultaneously defined feature value are generated according to the characteristic information；
Using the historical failure data as the label value of the training dataset；
Tentative prediction model is created according to the training dataset and label value using supervised learning algorithm；
Tentative prediction model is assessed to obtain prediction model using metrics.
In order to facilitate the understanding of the present invention, below with the principle of inventive disk failure prediction method, in conjunction with the embodiments
In to disk carry out failure predication process, disk failure prediction technique provided by the invention is further described.
As shown in Fig. 2, specifically, the disk failure prediction technique includes:
S1, acquisition magnetic disc characteristic information and historical failure data.
Pass through the information collection tool of tide company --- " InspurDiagLogCollect " tool acquisition different vendor,
The magnetic disc characteristic information of different model forms training dataset.The information collection tool is divided into Linux editions and Windows editions, can
To operate in different platform, remote collection and local acquisition are supported.The behaviour of server disk information is obtained under Linux environment
Make method are as follows: execute sh run.sh running tool " InspurDiagLogCollect " under a linux operating system, generate one
A journal file comprising magnetic disc characteristic information.To data set definition characteristic value obtained in journal file, training data is formed
Collection.Specific characteristic value is defined as follows shown in table:
The BMC of destination server is logged in when remote collection by the BMC IP of destination server, and then obtains remote service
The magnetic disc characteristic information of device.
S2, the characteristic information and historical failure data are pre-processed using sklearn.
Data prediction is carried out using the library preproccessing in sklearn, quantitative spy is carried out to characteristic information
Binary conversion treatment is levied, the processing of qualitative features one-hot coding is carried out to historical failure data.
S3, it is predicted using using supervised learning algorithm according to pretreated characteristic information and historical failure data creation
Model.
According to the characteristic information training dataset of multiple disk groups, training number is inputted using historical failure data as label value
According to collection, and this process be it is continual, i.e., new fault data is constantly inputted to training dataset.Using in sklearn
Supervised study RandomForestClassifier algorithm pretreated characteristic information and label value are trained, create
Build tentative prediction model.Tentative prediction model is assessed using metrics module, obtains prediction model after assessment is normal.
S4, the failure predication result by the way that magnetic disc characteristic information input prediction model to be obtained to corresponding disk.
By the characteristic information input prediction model for the disk for needing to predict, prediction model output carries out disk health status
Assessment, obtains prediction result, and prediction result output is " prediction label value ".Prediction result is embodied by " YES " or " NO ".
The device includes:
Data acquisition unit is configured to acquisition magnetic disc characteristic information and historical failure data；
Pretreatment unit is configured to locate the characteristic information and historical failure data in advance using sklearn
Reason；
Model creating unit, be configured to using using supervised learning algorithm according to pretreated characteristic information and going through
History fault data creates prediction model；
As a result acquiring unit is configured to obtaining magnetic disc characteristic information input prediction model into the failure of corresponding disk
Prediction result.
Optionally, as the application one embodiment, the data acquisition unit includes:
Target login module is configured to log in destination server BMC according to destination server BMC IP；
Information acquisition module is configured to the magnetic disc characteristic information using information collection tool acquisition destination server；
Information preservation module, being configured to the magnetic disc characteristic information preservation that will be acquired is journal file.
Optionally, as the application one embodiment, the pretreatment unit includes:
Quantitative Treatment module is configured to carry out quantitative characteristic binary conversion treatment to the characteristic information；
Qualitative processing module is configured to carry out the processing of qualitative features one-hot coding to the historical failure data.
Optionally, as the application one embodiment, the model creating unit includes:
Data training module is configured to generate training data and simultaneously defined feature value according to the characteristic information；
Label setup module is configured to using the historical failure data as the label value of the training dataset；
Preliminary creation module is configured to be created using supervised learning algorithm according to the training dataset and label value
Tentative prediction model；
Model evaluation module is configured to be assessed to obtain prediction model to tentative prediction model using metrics.
Fig. 3 is a kind of structural schematic diagram of terminal installation 300 provided in an embodiment of the present invention, which can be with
For executing disk failure prediction technique provided by the embodiments of the present application.
Wherein, which may include: processor 310, memory 320 and communication unit 330.These components
It is communicated by one or more bus, it will be understood by those skilled in the art that the structure of server shown in figure is not
The restriction to the application is constituted, it is also possible to hub-and-spoke configuration either busbar network, can also include more than illustrating
Or less component, perhaps combine certain components or different component layouts.
Wherein, which can be used for executing instruction for storage processor 310, and memory 320 can be by any class
The volatibility or non-volatile memories terminal or their combination of type are realized, such as static random access memory (SRAM), electricity
Erasable Programmable Read Only Memory EPROM (EEPROM), Erasable Programmable Read Only Memory EPROM (EPROM), programmable read only memory
(PROM), read-only memory (ROM), magnetic memory, flash memory, disk or CD.When executing instruction in memory 320
When being executed by processor 310, so that terminal 300 some or all of is able to carry out in following above method embodiment step.
Processor 310 is the control centre for storing terminal, utilizes each of various interfaces and the entire electric terminal of connection
A part by running or execute the software program and/or module that are stored in memory 320, and calls and is stored in storage
Data in device, to execute the various functions and/or processing data of electric terminal.The processor can be by integrated circuit
(Integrated Circuit, abbreviation IC) composition, such as the IC that can be encapsulated by single are formed, can also be by more of connection
The encapsulation IC of identical function or different function and form.For example, processor 310 can only include central processing unit
(Central Processing Unit, abbreviation CPU).In the application embodiment, CPU can be single operation core, can also
To include multioperation core.
Communication unit 330, for establishing communication channel, so that the storage terminal be allow to be led to other terminals
Letter.It receives the user data of other terminals transmission or sends user data to other terminals.
The application also provides a kind of computer storage medium, wherein the computer storage medium can be stored with program, the journey
Sequence may include step some or all of in each embodiment provided by the present application when executing.The storage medium can for magnetic disk,
CD, read-only memory (English: read-only memory, referred to as: ROM) or random access memory (English:
Random access memory, referred to as: RAM) etc..
Therefore, the application is based on machine learning algorithm, establishes the prediction model of server disk failure, builds using algorithm
When mould, input label value is introduced, existing disk failure data are added in prediction model, saved and calculate the time, and obtain
Prediction model accuracy it is high, by the fault condition of disk carry out it is accumulative be added in prediction model, can be continuously improved and comment
The accuracy estimated.The present invention can carry out failure predication to disk, know the health status of disk, in advance convenient for taking phase in time
The counter-measure of pass, reduces risk and harm, the attainable technical effect of the present embodiment institute may refer to it is described above, this
Place repeats no more.
It is required that those skilled in the art can be understood that the technology in the embodiment of the present application can add by software
The mode of general hardware platform realize.Based on this understanding, the technical solution in the embodiment of the present application substantially or
Say that the part that contributes to existing technology can be embodied in the form of software products, which is stored in
Such as USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory in one storage medium
The various media that can store program code such as (RAM, Random Access Memory), magnetic or disk, including it is several
Instruction is used so that a terminal (can be personal computer, server or second terminal, the network terminal etc.) is held
Row all or part of the steps of the method according to each embodiment of the present invention.
Same and similar part may refer to each other between each embodiment in this specification.Implement especially for terminal
For example, since it is substantially similar to the method embodiment, so being described relatively simple, related place is referring in embodiment of the method
Explanation.
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, it can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of device or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.
Although by reference to attached drawing and combining the mode of preferred embodiment to the present invention have been described in detail, the present invention
It is not limited to this.Without departing from the spirit and substance of the premise in the present invention, those of ordinary skill in the art can be to the present invention
Embodiment carry out various equivalent modifications or substitutions, and these modifications or substitutions all should in covering scope of the invention/appoint
What those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, answer
It is included within the scope of the present invention.Therefore, protection scope of the present invention is answered described is with scope of protection of the claims
It is quasi-.
Claims (10)
1. a kind of disk failure prediction technique, which is characterized in that the described method includes:
Acquire magnetic disc characteristic information and historical failure data；
The characteristic information and historical failure data are pre-processed using sklearn；
Prediction model is created according to pretreated characteristic information and historical failure data using using supervised learning algorithm；
By the failure predication result that magnetic disc characteristic information input prediction model is obtained to corresponding disk.
2. the method according to claim 1, wherein the acquisition magnetic disc characteristic information and historical failure data packet
It includes:
Destination server BMC is logged according to destination server BMC IP；
Utilize the magnetic disc characteristic information of information collection tool acquisition destination server；
It is journal file by the magnetic disc characteristic information preservation of acquisition.
3. the method according to claim 1, wherein described utilize sklearn to the characteristic information and history
Fault data carries out pretreatment
Quantitative characteristic binary conversion treatment is carried out to the characteristic information；
The processing of qualitative features one-hot coding is carried out to the historical failure data.
4. the method according to claim 1, wherein described utilize supervised learning algorithm according to pretreated
Characteristic information and historical failure data creation prediction model include:
Training data and simultaneously defined feature value are generated according to the characteristic information；
Using the historical failure data as the label value of the training dataset；
Tentative prediction model is created according to the training dataset and label value using supervised learning algorithm；
Tentative prediction model is assessed to obtain prediction model using metrics.
5. a kind of disk failure prediction meanss, which is characterized in that described device includes:
Data acquisition unit is configured to acquisition magnetic disc characteristic information and historical failure data；
Pretreatment unit is configured to pre-process the characteristic information and historical failure data using sklearn；
Model creating unit is configured to utilize using supervised learning algorithm according to pretreated characteristic information and history event
Hinder data creation prediction model；
As a result acquiring unit is configured to obtaining magnetic disc characteristic information input prediction model into the failure predication of corresponding disk
As a result.
6. device according to claim 5, which is characterized in that the data acquisition unit includes:
Target login module is configured to log in destination server BMC according to destination server BMC IP；
Information acquisition module is configured to the magnetic disc characteristic information using information collection tool acquisition destination server；
Information preservation module, being configured to the magnetic disc characteristic information preservation that will be acquired is journal file.
7. device according to claim 5, which is characterized in that the pretreatment unit includes:
Quantitative Treatment module is configured to carry out quantitative characteristic binary conversion treatment to the characteristic information；
Qualitative processing module is configured to carry out the processing of qualitative features one-hot coding to the historical failure data.
8. device according to claim 5, which is characterized in that the model creating unit includes:
Data training module is configured to generate training data and simultaneously defined feature value according to the characteristic information；
Label setup module is configured to using the historical failure data as the label value of the training dataset；
Preliminary creation module is configured to be created using supervised learning algorithm according to the training dataset and label value preliminary
Prediction model；
Model evaluation module is configured to be assessed to obtain prediction model to tentative prediction model using metrics.
9. a kind of terminal characterized by comprising
Processor；
The memory executed instruction for storage processor；
Wherein, the processor is configured to perform claim requires the described in any item methods of 1-4.
10. a kind of computer readable storage medium for being stored with computer program, which is characterized in that the program is executed by processor
Shi Shixian method for example of any of claims 1-4.
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110119339A (en) * | 2019-05-07 | 2019-08-13 | 上海电气集团股份有限公司 | Appraisal procedure, system, equipment and the storage medium of the health status of industrial equipment |
| CN110580932A (en) * | 2019-08-29 | 2019-12-17 | 华中科技大学 | Memory cell quality measurement method applied to wear leveling |
| CN110598802A (en) * | 2019-09-26 | 2019-12-20 | 腾讯科技（深圳）有限公司 | Memory detection model training method, memory detection method and device |
| CN110674019A (en) * | 2019-08-30 | 2020-01-10 | 中国人民财产保险股份有限公司 | Method and device for predicting system fault and electronic equipment |
| CN110780646A (en) * | 2019-09-21 | 2020-02-11 | 苏州浪潮智能科技有限公司 | Memory quality early warning method based on MES system |
| 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 |
| CN111400964A (en) * | 2020-03-16 | 2020-07-10 | 中国人民解放军海军航空大学 | Fault occurrence time prediction method and device |
| CN111476400A (en) * | 2020-03-11 | 2020-07-31 | 珠海格力电器股份有限公司 | Circuit fault prediction method, device, equipment and computer readable medium |
| 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 |
| CN113778797A (en) * | 2021-08-30 | 2021-12-10 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Mechanical hard disk monitoring method and device, computer equipment and storage medium |
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2019
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110119339A (en) * | 2019-05-07 | 2019-08-13 | 上海电气集团股份有限公司 | Appraisal procedure, system, equipment and the storage medium of the health status of industrial equipment |
| CN110580932A (en) * | 2019-08-29 | 2019-12-17 | 华中科技大学 | Memory cell quality measurement method applied to wear leveling |
| CN110580932B (en) * | 2019-08-29 | 2021-04-06 | 华中科技大学 | Memory cell quality measurement method applied to wear leveling |
| CN110674019A (en) * | 2019-08-30 | 2020-01-10 | 中国人民财产保险股份有限公司 | Method and device for predicting system fault and electronic equipment |
| CN110674019B (en) * | 2019-08-30 | 2023-02-07 | 中国人民财产保险股份有限公司 | Method and device for predicting system fault and electronic equipment |
| CN110780646A (en) * | 2019-09-21 | 2020-02-11 | 苏州浪潮智能科技有限公司 | Memory quality early warning method based on MES system |
| CN110598802A (en) * | 2019-09-26 | 2019-12-20 | 腾讯科技（深圳）有限公司 | Memory detection model training method, memory detection method and device |
| CN110598802B (en) * | 2019-09-26 | 2021-07-27 | 腾讯科技（深圳）有限公司 | Memory detection model training method, memory detection method and device |
| 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 |
| CN111476400A (en) * | 2020-03-11 | 2020-07-31 | 珠海格力电器股份有限公司 | Circuit fault prediction method, device, equipment and computer readable medium |
| CN111400964A (en) * | 2020-03-16 | 2020-07-10 | 中国人民解放军海军航空大学 | Fault occurrence time prediction method and device |
| CN111400964B (en) * | 2020-03-16 | 2023-12-22 | 中国人民解放军海军航空大学 | Fault occurrence time prediction method and device |
| 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 |
| CN113778797A (en) * | 2021-08-30 | 2021-12-10 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Mechanical hard disk monitoring method and device, computer equipment and storage medium |
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Application publication date: 20190503 |