CN107357730A - A kind of system fault diagnosis restorative procedure and device - Google Patents

A kind of system fault diagnosis restorative procedure and device Download PDF

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Publication number
CN107357730A
CN107357730A CN201710580322.1A CN201710580322A CN107357730A CN 107357730 A CN107357730 A CN 107357730A CN 201710580322 A CN201710580322 A CN 201710580322A CN 107357730 A CN107357730 A CN 107357730A
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fault
data
type
mrow
failure
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CN107357730B (en
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王慧锋
王晓通
张凯顺
郭锋
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Suzhou Langchao Intelligent Technology Co Ltd
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Zhengzhou Yunhai Information Technology Co Ltd
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/36Preventing errors by testing or debugging software
    • G06F11/362Software debugging
    • G06F11/366Software debugging using diagnostics

Abstract

Provided herein is a kind of system fault diagnosis restorative procedure and device, it is related to cloud computation data center technology.A kind of system fault diagnosis restorative procedure disclosed herein, including:Extract the feature of the fault data of each fault type respectively by historical failure data, establish fault diagnosis model;When system jam, by fault diagnosis model described in the data input of current failure, analysis determines the fault type of current failure;Troubleshooting strategy corresponding to the fault type of current failure is called, carries out fault restoration.A kind of system fault diagnosis prosthetic device is also provided herein.

Description

A kind of system fault diagnosis restorative procedure and device
Technical field
The present invention relates to cloud computation data center technology, and in particular to a kind of O&M automation plateform system failure automation Diagnosis and the scheme repaired.
Background technology
With the propulsion of company information process, business module quantity is increased sharply, and O&M difficulty also increases therewith.And business mould The complexity of block, while also cause system failure processing to become more difficult.How automated diagnostic and repair system failure, drop Low O&M cost, reduce the system failure becomes particularly important to the loss that company brings.
The content of the invention
The technical problem to be solved by the invention is to provide a kind of system fault diagnosis restorative procedure and device, Ke Yiti High system fault diagnosis efficiency.
In order to solve the above-mentioned technical problem, the invention discloses a kind of system fault diagnosis restorative procedure, including:
Extract the feature of the fault data of each fault type respectively by historical failure data, establish fault diagnosis model;
When system jam, by fault diagnosis model described in the data input of current failure, analysis determines current event The fault type of barrier;
Troubleshooting strategy corresponding to the fault type of current failure is called, carries out fault restoration.
Alternatively, in the above method, the fault data for extracting each fault type respectively by historical failure data Feature, establishing fault diagnosis model includes:
The feature of the fault data of different faults type is extracted from historical failure data using contrastive pattern, by extracting Feature establish fault diagnosis model.
Alternatively, it is described to extract different faults type from historical failure data using contrastive pattern in the above method The feature of fault data includes:
Contrastive pattern P support sup (P, D is calculated using equation belowi), contrastive pattern P=I1I2I3…I|P|, be Frequently occurred in a kind of data set of fault type, non-frequently pattern, is expressed as this in the data set of other fault types The feature of the fault data of kind fault type:
sup(P,Di)=| S | S ∈ DiAnd P occurs in S } |/| Di|;i∈[1,k]
In formula, D i represent the fault data collection of i-th kind of fault type, and k is the total number of the type of fault data;
And sup (P, Di) more than the first threshold value of support α it is less than the threshold value of support second.
Alternatively, in the above method, the fault diagnosis model of establishing includes:
Fault diagnosis model Φ is established according to equation below:
Φ={ F1,F2,...Fk};
Fi=f (P) | P ∈ Ti};
wiFor function f (Pi) weights;
Ti={ P1,P2…PnIt is the i-th class fault data collection DiThe set of modes of (i ∈ [1, k]), n=k.
Alternatively, the above method also includes:
Troubleshooting strategy corresponding to the fault type of current failure is called, after carrying out fault restoration, if failing effective Solves the failure, then fault data is sent into operation maintenance personnel carries out manual intervention processing.
A kind of system fault diagnosis prosthetic device is also provided herein, including:
First module, extract the feature of the fault data of each fault type respectively by historical failure data, establish failure Diagnostic model;
Second unit, it is when system jam, fault diagnosis model described in the data input of current failure, analysis is true The fault type of settled prior fault;
Third unit, troubleshooting strategy corresponding to the fault type of current failure is called, carry out fault restoration.
Alternatively, in said apparatus, the first module extracts the event of each fault type by historical failure data respectively Hinder the feature of data, establishing fault diagnosis model includes:
The feature of the fault data of different faults type is extracted from historical failure data using contrastive pattern, by extracting Feature establish fault diagnosis model.
Alternatively, it is described to extract different faults type from historical failure data using contrastive pattern in said apparatus The feature of fault data includes:
Contrastive pattern P support sup (P, D is calculated using equation belowi), contrastive pattern P=I1I2I3…I|P|, be Frequently occurred in a kind of data set of fault type, non-frequently pattern, is expressed as this in the data set of other fault types The feature of the fault data of kind fault type:
sup(P,Di)=| S | S ∈ DiAnd P occurs in S } |/| Di|;i∈[1,k]
In formula, D i represent the fault data collection of i-th kind of fault type, and k is the total number of the type of fault data;
And sup (P, Di) more than the first threshold value of support α it is less than the threshold value of support second.
Alternatively, it is described fault diagnosis model is established by the feature extracted to refer in said apparatus:
Fault diagnosis model Φ is established according to equation below:
Φ={ F1,F2,...Fk};
Fi=f (P) | P ∈ Ti};
wiFor function f (Pi) weights;
Ti={ P1,P2…PnIt is the i-th class fault data collection DiThe set of modes of (i ∈ [1, k]), n=k.
Alternatively, in said apparatus, the third unit, troubleshooting plan corresponding to the fault type of current failure is called Slightly, after carrying out fault restoration, if not can effectively solve the problem that the failure, fault data is sent into operation maintenance personnel is manually done Pretreatment.
On the one hand effect that technical scheme realizes that fault data efficiently accesses by establishing multiple index, the opposing party Face solves the problems, such as that artificial fault data classification is difficult by semi-supervised learning in machine learning, so as to realize for number of faults According to efficient access and classification automatically is carried out, the time of man-made fault investigation and processing cost is reduced, reduces the loss of company.
Brief description of the drawings
Fig. 1 is system fault diagnosis restorative procedure flow chart in the embodiment of the present invention.
Embodiment
For the object, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with accompanying drawing to skill of the present invention Art scheme is described in further detail.It should be noted that in the case where not conflicting, in embodiments herein and embodiment Feature can arbitrarily be mutually combined.
The present embodiment provides a kind of system fault diagnosis restorative procedure, as shown in figure 1, main include following operation:
Step 100:Extract the feature of the fault data of each fault type respectively by historical failure data, establish failure and examine Disconnected model;
In the step, the purpose for extracting the feature of fault data is to carry out failure modes.The failure being referred to herein Type can include disk sort failure (such as the failure such as Insufficient disk space), CPU classes failure (such as CPU fully loaded etc. failure), Service class failure (such as the failure such as type of service exception).
In the present embodiment, it is contemplated that contrastive pattern has inborn advantage when describing Different categories of samples feature, therefore establishes The contrastive pattern of various fault types can be chosen as different faults type when fault diagnosis model and progress fault restoration The feature of fault data.
Specifically extracting mode is as follows, and for convenience of describing, we use D={ D1,D2,…,DkRepresent fault data collection Close, DkRepresent the fault data collection of kth class fault type.Contrastive pattern P=I1I2I3…I|P|Description is in a kind of fault data Frequently occur, the non-frequently pattern in other classes.With support (sup (P, the D of patterni)) weigh pattern P in data set Di The frequent degree of middle appearance, computational methods can use formula 1 as follows.
sup(P,Di)=| S | S ∈ DiAnd P occurs in S } |/| Di| formula 1
Wherein, i ∈ [1, k]), and contrastive pattern P support sup (P, Di) to meet to require as follows:
sup(P,Di)>α;sup(P,Dj)<β(j∈[1,k]∧j!=i);
That is sup (P, Di) be more than and support the first threshold value α and be less than the threshold value of support second, wherein, support the One threshold value α is that the fault data of various fault types concentrates support minimum value, and the threshold value of support second is various failures The fault data of type concentrates support maximum.The foundation for establishing fault diagnosis model is fault data warehouse, according to above-mentioned Mode obtains the feature of fault data in fault data warehouse.
Give certain class fault data DiThe set of modes T of (i ∈ [1, k])i={ P1,P2…Pn, fault diagnosis model Φ= {F1,F2,...Fk, wherein Fi={ f (P) | P ∈ T } represents the mathematical modeling of the i-th class fault data, whereinBy data mining algorithm, such as DPMiner algorithms, MDSP-CGC algorithms etc., just Set of modes T can be obtained.Function f (Pi) in weight wiIt can be tried to achieve by corresponding weights learning algorithm.To sum up, can be with Obtain the mathematical modeling Φ of fault diagnosis.
Step 200:When system jam, by the data input fault diagnosis model of current failure, it is analyzed to identify and works as The fault type of prior fault;
The step is when system breaks down, and collection fault message (obtains the number of current failure by fault log According to), input fault diagnostic model Φ, judgement obtains fault type.
Step 300:Troubleshooting strategy corresponding with the fault type of current failure is called, carries out fault restoration.
In the step, after obtaining fault type, can in the method for built-in certain failure of solution in calling system, i.e., with this Troubleshooting strategy corresponding to fault type carries out corresponding repair and operated.If built-in fault handling method (i.e. with the event Troubleshooting strategy corresponding to barrier type) it not can effectively solve the problem that the failure, then fault message can be sent to related O&M Personnel, manual intervention solve.Wherein, existing any side can be used for troubleshooting strategy corresponding to different faults type Formula, the present embodiment are no longer specifically limited to this.
The present embodiment also provides a kind of system fault diagnosis prosthetic device, including at least following each unit.
First module, extract the feature of the fault data of each fault type respectively by historical failure data, establish failure Diagnostic model;
Alternatively, first module, the failure of different faults type is extracted from historical failure data using contrastive pattern The feature of data, fault diagnosis model is established by the feature extracted.
Specifically, contrastive pattern P support sup (P, D is calculated using equation belowi), contrastive pattern P=I1I2I3… I|P|, to be frequently occurred in a kind of data set of fault type, the non-frequently pattern in the data set of other fault types, It is expressed as the feature of the fault data of this kind of fault type:
sup(P,Di)=| S | S ∈ DiAnd P occurs in S } |/| Di|;i∈[1,k]
In formula, D i represent the fault data collection of i-th kind of fault type, and k is the total number of the type of fault data;
And sup (P, Di) more than the first threshold value of support α it is less than the threshold value of support second.
Afterwards, establishing fault diagnosis model by the feature of the fault data of each fault type extracted includes:
Fault diagnosis model Φ is established according to equation below:
Φ={ F1,F2,...Fk};
Fi=f (P) | P ∈ Ti};
wiFor function f (Pi) weights;
Ti={ P1,P2…PnIt is the i-th class fault data collection DiThe set of modes of (i ∈ [1, k]), n=k.
Second unit, it is when system jam, fault diagnosis model described in the data input of current failure, analysis is true The fault type of settled prior fault;
Third unit, troubleshooting strategy corresponding to the fault type of current failure is called, carry out fault restoration.
It is noted that troubleshooting strategy corresponding to the fault type of current failure is called, after carrying out fault restoration, if The failure is not can effectively solve the problem that, then fault data is sent into operation maintenance personnel carries out manual intervention processing.And it is referred to herein Different faults type corresponding to troubleshooting strategy can use existing any-mode, the present embodiment no longer special limit to this System.
In addition, said apparatus can realize the system fault diagnosis restorative procedure described in above-described embodiment, therefore, to the present apparatus Some concrete operations details can be found in the corresponding contents of above method embodiment, will not be repeated here.
One of ordinary skill in the art will appreciate that all or part of step in the above method can be instructed by program Related hardware is completed, and described program can be stored in computer-readable recording medium, such as read-only storage, disk or CD Deng.Alternatively, all or part of step of above-described embodiment can also be realized using one or more integrated circuits.Accordingly Ground, each module/unit in above-described embodiment can be realized in the form of hardware, can also use the shape of software function module Formula is realized.The application is not restricted to the combination of the hardware and software of any particular form.
It is described above, it is only the preferred embodiments of the present invention, is not intended to limit the scope of the present invention.It is all this Within the spirit and principle of invention, any modification, equivalent substitution and improvements done etc., the protection model of the present invention should be included in Within enclosing.

Claims (10)

  1. A kind of 1. system fault diagnosis restorative procedure, it is characterised in that including:
    Extract the feature of the fault data of each fault type respectively by historical failure data, establish fault diagnosis model;
    When system jam, by fault diagnosis model described in the data input of current failure, analysis determines current failure Fault type;
    Troubleshooting strategy corresponding to the fault type of current failure is called, carries out fault restoration.
  2. 2. the method as described in claim 1, it is characterised in that described to extract each fault type respectively by historical failure data Fault data feature, establishing fault diagnosis model includes:
    The feature of the fault data of different faults type is extracted from historical failure data using contrastive pattern, by the spy extracted Sign establishes fault diagnosis model.
  3. 3. method as claimed in claim 2, it is characterised in that described to be extracted not from historical failure data using contrastive pattern Feature with the fault data of fault type includes:
    Contrastive pattern P support sup (P, D is calculated using equation belowi), contrastive pattern P=I1I2I3…I|P|, it is in one kind Frequently occurred in the data set of fault type, the non-frequently pattern in the data set of other fault types, be expressed as this kind event Hinder the feature of the fault data of type:
    sup(P,Di)=| S | S ∈ DiAnd P occurs in S } |/| Di|;i∈[1,k]
    In formula, Di represents the fault data collection of i-th kind of fault type, and k is the total number of the type of fault data;
    And sup (P, Di) more than the first threshold value of support α it is less than the threshold value of support second.
  4. 4. method as claimed in claim 3, it is characterised in that the fault diagnosis model of establishing includes:
    Fault diagnosis model Φ is established according to equation below:
    Φ={ F1,F2,...Fk};
    Fi=f (P) | P ∈ Ti};
    <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>P</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mo>|</mo> <mi>p</mi> <mo>|</mo> </mrow> </msubsup> <msub> <mi>w</mi> <mi>i</mi> </msub> <msub> <mi>I</mi> <mi>i</mi> </msub> <mo>,</mo> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mi>i</mi> </msub> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>&amp;rsqb;</mo> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    wiFor function f (Pi) weights;
    Ti={ P1,P2…PnIt is the i-th class fault data collection DiThe set of modes of (i ∈ [1, k]), n=k.
  5. 5. the method as described in any one of Claims 1-4, it is characterised in that this method also includes:
    Troubleshooting strategy corresponding to the fault type of current failure is called, after carrying out fault restoration, if not can effectively solve the problem that The failure, then fault data is sent to operation maintenance personnel and carries out manual intervention processing.
  6. A kind of 6. system fault diagnosis prosthetic device, it is characterised in that including:
    First module, extract the feature of the fault data of each fault type respectively by historical failure data, establish fault diagnosis Model;
    Second unit, when system jam, by fault diagnosis model described in the data input of current failure, analysis determines to work as The fault type of prior fault;
    Third unit, troubleshooting strategy corresponding to the fault type of current failure is called, carry out fault restoration.
  7. 7. device as claimed in claim 6, it is characterised in that the first module is extracted respectively respectively by historical failure data The feature of the fault data of fault type, establishing fault diagnosis model includes:
    The feature of the fault data of different faults type is extracted from historical failure data using contrastive pattern, by the spy extracted Sign establishes fault diagnosis model.
  8. 8. device as claimed in claim 7, it is characterised in that described to be extracted not from historical failure data using contrastive pattern Feature with the fault data of fault type includes:
    Contrastive pattern P support sup (P, D is calculated using equation belowi), contrastive pattern P=I1I2I3…I|P|, it is in one kind Frequently occurred in the data set of fault type, the non-frequently pattern in the data set of other fault types, be expressed as this kind event Hinder the feature of the fault data of type:
    sup(P,Di)=| S | S ∈ DiAnd P occurs in S } |/| Di|;i∈[1,k]
    In formula, Di represents the fault data collection of i-th kind of fault type, and k is the total number of the type of fault data;
    And sup (P, Di) more than the first threshold value of support α it is less than the threshold value of support second.
  9. 9. device as claimed in claim 8, it is characterised in that described fault diagnosis model is established by the feature extracted to refer to:
    Fault diagnosis model Φ is established according to equation below:
    Φ={ F1,F2,...Fk};
    Fi=f (P) | P ∈ Ti};
    <mrow> <mi>f</mi> <mrow> <mo>(</mo> <mi>P</mi> <mo>)</mo> </mrow> <mo>=</mo> <msubsup> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mrow> <mo>|</mo> <mi>p</mi> <mo>|</mo> </mrow> </msubsup> <msub> <mi>w</mi> <mi>i</mi> </msub> <msub> <mi>I</mi> <mi>i</mi> </msub> <mo>,</mo> <mrow> <mo>(</mo> <msub> <mi>w</mi> <mi>i</mi> </msub> <mo>&amp;Element;</mo> <mo>&amp;lsqb;</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>&amp;rsqb;</mo> <mo>)</mo> </mrow> <mo>;</mo> </mrow>
    wiFor function f (Pi) weights;
    Ti={ P1,P2…PnIt is the i-th class fault data collection DiThe set of modes of (i ∈ [1, k]), n=k.
  10. 10. the device as described in any one of claim 6 to 9, it is characterised in that
    The third unit, troubleshooting strategy corresponding to the fault type of current failure is called, after carrying out fault restoration, if not The failure is can effectively solve the problem that, then fault data is sent into operation maintenance personnel carries out manual intervention processing.
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