CN107644256A - A kind of method that diagnosis rule storehouse is formed based on machine learning mode - Google Patents
A kind of method that diagnosis rule storehouse is formed based on machine learning mode Download PDFInfo
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- CN107644256A CN107644256A CN201710828211.8A CN201710828211A CN107644256A CN 107644256 A CN107644256 A CN 107644256A CN 201710828211 A CN201710828211 A CN 201710828211A CN 107644256 A CN107644256 A CN 107644256A
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Abstract
The present invention is more particularly directed to a kind of method that diagnosis rule storehouse is formed based on machine learning mode.This forms the method in diagnosis rule storehouse based on machine learning mode, obtains fault message and solution first, extracts diagnosis rule storehouse field；Then random forests algorithm is used, automatic identification failure simultaneously excavates phenomenon of the failure and the relation of diagnosis rule；Experts' evaluation finally is carried out to the failure automatically identified, effective phenomenon of the failure and processing scheme are generated into diagnosis rule, are stored in diagnosis rule storehouse.This forms the method in diagnosis rule storehouse based on machine learning mode, one diagnosis rule storehouse is formed according to the rule of fault routine and treating method, when failure occurs again, the information checked in diagnosis rule storehouse can find corresponding solution, substantially increase the efficiency of malfunction elimination.
Description
Technical field
The present invention relates to Computer Applied Technology field, more particularly to one kind forms diagnosis rule based on machine learning mode
The method in storehouse.
Background technology
With the development in epoch, the improvement of people's living standards, the life style and working method of people are all become
Change, computer has become equipment irreplaceable in people's daily life.
When computer breaks down, technical staff can only be very time-consuming by checking that log information is investigated.And
There is a lot, log information enormous amount because computer operating system component is relatively complicated, the reason for caused failure.When
, it is necessary to which technical staff checks that log information is analyzed manually when computer breaks down, corresponding fault message is found, is solved
The failure problems of appearance, therefore it is extremely difficult that technical staff, which wants rapid determination failure cause,.
Prior art is without the general diagnosis rule storehouse of use, the artificial row that can only be wasted time and energy when computer breaks down
Look into.For such case, the present invention devises a kind of method that diagnosis rule storehouse is formed based on machine learning mode.
The content of the invention
The defects of present invention is in order to make up prior art, there is provided a kind of simply efficiently to be formed based on machine learning mode
The method in diagnosis rule storehouse.
The present invention is achieved through the following technical solutions：
A kind of method that diagnosis rule storehouse is formed based on machine learning mode, it is characterised in that comprise the following steps：
（1）Failure training set is chosen, and obtains fault message and solution, extracts diagnosis rule storehouse field；
（2）Machine learning algorithm is trained, and using random forests algorithm, automatic identification failure simultaneously excavates phenomenon of the failure and diagnosis rule
Relation；
（3）Expert judging typing diagnosis rule, experts' evaluation is carried out to the failure that automatically identifies, by effective phenomenon of the failure and
Processing scheme generates diagnosis rule, is stored in diagnosis rule storehouse.
The step（1）In, it is by curstomer's site, research and development department, test organization and O＆M people that failure training set, which is chosen,
The fault message and solution that member obtains, extract diagnosis rule storehouse field；Meanwhile the data in training set are accurately positioned
Specific equipment, analyse in depth failure cause.
Diagnosis rule storehouse field includes failure title, machine models, operating system, trouble location, fault model, therefore
Hinder type, daily record rank, daily record details, keyword, log path, problem description and solution.
When data in training set are accurately positioned CPU and memory failure, CPU events and internal memory event, parsing are read
Mcelog, position failure CPU and core position；PCIE failures are positioned, read PCIE events, according to the machine silk-screen table of comparisons,
Allot corresponding slot Information；CallTrace failure error-reporting routine sections are positioned, analyze CallTrace event logs, excavate function
Call stack, analyse in depth failure cause.
The step（2）In, machine learning algorithm training, using random forests algorithm, generate be made up of decision tree it is gloomy
Woods, merger processing is carried out to fault message, phenomenon of the failure voted by more decision trees, failure judgement, and take phase
The solution answered.
Beneficial effects of the present invention：This forms the method in diagnosis rule storehouse based on machine learning mode, according to fault routine
Rule and treating method formed a diagnosis rule storehouse, when failure occurs again, the information checked in diagnosis rule storehouse is i.e.
Corresponding solution can be found, substantially increases the efficiency of malfunction elimination.
Brief description of the drawings
Accompanying drawing 1 forms the method schematic diagram in diagnosis rule storehouse for the present invention based on machine learning mode.
Embodiment
In order that technical problems, technical solutions and advantages to be solved are more clearly understood, tie below
Drawings and examples are closed, the present invention will be described in detail.It should be noted that specific embodiment described herein is only used
To explain the present invention, it is not intended to limit the present invention.
This forms the method in diagnosis rule storehouse based on machine learning mode, including：Failure training set is chosen, and machine learning is calculated
Method is trained and expert judging typing diagnosis rule three parts.
The failure training set chooses the event for referring to obtain by curstomer's site, research and development department, test organization, operation maintenance personnel
Hinder information and solution, extract diagnosis rule storehouse field, ensure the completeness and accuracy of fault diagnosis；In training set
Data are accurately positioned specific equipment, such as when positioning CPU and memory failure, read CPU events and internal memory event, parsing
Mcelog, position failure CPU and core position；PCIE failures are positioned, read PCIE events, according to the machine silk-screen table of comparisons,
Allot corresponding slot Information；CallTrace failure error-reporting routine sections are positioned, analyze CallTrace event logs, excavate function
Call stack, analyse in depth failure cause.
Diagnosis rule storehouse field includes failure title, machine models, operating system, trouble location, fault model, failure classes
Type, daily record rank, daily record details, keyword, log path, problem description, solution.
The machine learning algorithm training refers to use random forests algorithm, realizes the automatic identification of failure, excavate failure
The relation of phenomenon and diagnosis rule.Based on random forests algorithm rule, the forest being made up of decision tree is generated, fault message is entered
Row merger is handled, and phenomenon of the failure is voted by more decision trees, failure judgement, taken appropriate measures.Using machine
The method automatic identification failure of learning algorithm training, the thing system of giving being accomplished manually is automatically performed, can save O＆M
Cost, improve operating efficiency.
The expert judging typing diagnosis rule refers to carry out experts' evaluation to the failure automatically identified, by effective event
Hinder phenomenon and processing scheme generation diagnosis rule, be stored in diagnosis rule storehouse.When failure occurs again, failure is checked
Information in rule base can find corresponding solution, substantially increase the efficiency of malfunction elimination.
Claims (5)
- A kind of 1. method that diagnosis rule storehouse is formed based on machine learning mode, it is characterised in that comprise the following steps：（1）Failure training set is chosen, and obtains fault message and solution, extracts diagnosis rule storehouse field；（2）Machine learning algorithm is trained, and using random forests algorithm, automatic identification failure simultaneously excavates phenomenon of the failure and diagnosis rule Relation；（3）Expert judging typing diagnosis rule, experts' evaluation is carried out to the failure that automatically identifies, by effective phenomenon of the failure and Processing scheme generates diagnosis rule, is stored in diagnosis rule storehouse.
- 2. the method according to claim 1 that diagnosis rule storehouse is formed based on machine learning mode, it is characterised in that：It is described Step（1）In, the selection of failure training set is the failure by curstomer's site, research and development department, test organization and operation maintenance personnel acquisition Information and solution, extract diagnosis rule storehouse field；It is deep meanwhile the data in training set are accurately positioned specific equipment Enter analyzing failure cause.
- 3. the method according to claim 1 or 2 that diagnosis rule storehouse is formed based on machine learning mode, it is characterised in that： Diagnosis rule storehouse field includes failure title, machine models, operating system, trouble location, fault model, fault type, Daily record rank, daily record details, keyword, log path, problem description and solution.
- 4. the method according to claim 2 that diagnosis rule storehouse is formed based on machine learning mode, it is characterised in that：Training When the data of concentration are accurately positioned CPU and memory failure, CPU events and internal memory event are read, parses mcelog, positions failure CPU and core position；PCIE failures are positioned, read PCIE events, according to the machine silk-screen table of comparisons, match corresponding slot letter Breath；CallTrace failure error-reporting routine sections are positioned, analyze CallTrace event logs, excavate function call stack, are analysed in depth Failure cause.
- 5. the method according to claim 1 that diagnosis rule storehouse is formed based on machine learning mode, it is characterised in that：It is described Step（2）In, machine learning algorithm training, using random forests algorithm, the forest being made up of decision tree is generated, to fault message Merger processing is carried out, phenomenon of the failure is voted by more decision trees, failure judgement, and take corresponding solution.
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| CN109800127A (en) * | 2019-01-03 | 2019-05-24 | 众安信息技术服务有限公司 | A kind of system fault diagnosis intelligence O&M method and system based on machine learning |
| CN110321243A (en) * | 2018-03-29 | 2019-10-11 | 国际商业机器公司 | Use method, system and the storage medium of the system maintenance of the unified cognition basic reason analysis for multiple fields |
| CN111274056A (en) * | 2018-11-20 | 2020-06-12 | 河南许继仪表有限公司 | Self-learning method and device for intelligent electric energy meter fault library |
| CN112307076A (en) * | 2019-08-02 | 2021-02-02 | 深圳中集智能科技有限公司 | Production equipment operation and maintenance system and method based on cloud and end fusion |
| CN115543665A (en) * | 2022-09-23 | 2022-12-30 | 超聚变数字技术有限公司 | Memory reliability evaluation method and device and storage medium |
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