CN110413500A - Failure analysis methods and device based on big data fusion - Google Patents
Failure analysis methods and device based on big data fusion Download PDFInfo
- Publication number
- CN110413500A CN110413500A CN201910704775.XA CN201910704775A CN110413500A CN 110413500 A CN110413500 A CN 110413500A CN 201910704775 A CN201910704775 A CN 201910704775A CN 110413500 A CN110413500 A CN 110413500A
- Authority
- CN
- China
- Prior art keywords
- data
- log
- fusing
- daily record
- sample
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 80
- 230000004927 fusion Effects 0.000 title claims abstract description 59
- 238000000605 extraction Methods 0.000 claims abstract description 59
- 238000000034 method Methods 0.000 claims description 37
- 238000013507 mapping Methods 0.000 claims description 30
- 238000004891 communication Methods 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 17
- 241001269238 Data Species 0.000 claims description 15
- 238000005065 mining Methods 0.000 claims description 9
- 239000000284 extract Substances 0.000 claims description 6
- 230000006399 behavior Effects 0.000 claims description 3
- 230000008901 benefit Effects 0.000 description 7
- 238000012423 maintenance Methods 0.000 description 7
- 238000012360 testing method Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 239000012466 permeate Substances 0.000 description 4
- 230000001502 supplementing effect Effects 0.000 description 4
- 230000002159 abnormal effect Effects 0.000 description 3
- 235000013399 edible fruits Nutrition 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 239000000155 melt Substances 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 1
- 238000009223 counseling Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000001537 neural effect Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/008—Reliability or availability analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
- G06F11/079—Root cause analysis, i.e. error or fault diagnosis
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Health & Medical Sciences (AREA)
- Computer Hardware Design (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Debugging And Monitoring (AREA)
Abstract
The invention discloses a kind of failure analysis methods and device based on big data fusion, wherein the failure analysis methods based on big data fusion include: the daily record data for obtaining multiple applications；According to user identifier, fusion treatment is carried out to the daily record data of multiple applications, obtains multiple Log fusing data；Feature extraction is carried out to multiple Log fusing data, obtains the corresponding feature extraction of multiple Log fusing data as a result, and using the corresponding feature extraction of multiple Log fusing data as a result, building log sample set；Log sample in log sample set is analyzed, failure analysis result is obtained.The program carries out fusion treatment to the daily record data of the corresponding multiple applications of the full link of business, can more fully it be reflected the case where each application based on log sample constructed by Log fusing data, comprehensive analysis during business full link to Trouble cause is realized, accident analysis efficiency and fault location precision are effectively improved.
Description
Technical field
The present invention relates to technical field of data processing, and in particular to it is a kind of based on big data fusion failure analysis methods and
Device.
Background technique
With the continuous development of Internet technology, people are convenient to install using in the terminal devices such as smart phone, PAD
Various applications paid, done shopping, entertained, and apply in the process of running, it is possible that dodge move back, be connected to the network mistake
The various problems such as lose, collapse.Some is applied generally by business operation maintenance personnel or diagnostic tool in the prior art
Generated daily record data is analyzed in operational process, thus find the application there may be the problem of, then determine correspond to
Solution.For example, application publication number provides one kind in a mobile device for the Chinese patent application of CN 104598369A
The software supervision method of realization grabs software log relevant to the operating condition of application software to be monitored in a mobile device
Information, when failure characterization field in software log information, by the predetermined amount of time before the appearance of fault signature field
The software log information of middle crawl is sent to the equipment for fault message integration outside mobile device.
However, what existing this accident analysis mode was based on is the daily record data individually applied, and individually apply
The data content recorded in daily record data is limited, can only reflect that this applies the partial picture of itself unilaterally, can not comprehensively, precisely
Ground positions physical fault Producing reason.
Summary of the invention
In view of the above problems, it proposes on the present invention overcomes the above problem or at least be partially solved in order to provide one kind
State the failure analysis methods and device based on big data fusion of problem.
According to an aspect of the invention, there is provided a kind of failure analysis methods based on big data fusion, this method packet
It includes:
Obtain the daily record data of multiple applications；
According to user identifier, fusion treatment is carried out to the daily record data of multiple applications, obtains multiple Log fusing data；
Feature extraction is carried out to multiple Log fusing data, obtains the corresponding feature extraction knot of multiple Log fusing data
Fruit, and using the corresponding feature extraction of multiple Log fusing data as a result, building log sample set；
Log sample in log sample set is analyzed, failure analysis result is obtained.
Further, the daily record data for obtaining multiple applications further comprises:
For each application, obtains this and apply and remember in corresponding applications client, application service end and application database end
The daily record data of record.
Further, according to user identifier, fusion treatment is carried out to the daily record data of multiple applications, multiple logs is obtained and melts
Closing data further comprises:
From the daily record data of multiple applications search include same subscriber mark and log generate the time belong to it is same
The daily record data of time window obtains multiple log data sets；
For each log data set, all daily record datas in the log data set are permeated a Log fusing number
According to.
Further, feature extraction is carried out to multiple Log fusing data, obtains the corresponding spy of multiple Log fusing data
Sign extracts result:
For each Log fusing data, according to pre-set multiple data characteristics dimensions, from the Log fusing data
It is middle to extract characteristic corresponding with multiple data characteristics dimensions；
Mapping processing is carried out to the corresponding characteristic of multiple data characteristics dimensions, it is corresponding to obtain the Log fusing data
Feature extraction result.
Further, mapping processing is carried out to the corresponding characteristic of multiple data characteristics dimensions, obtains the Log fusing
The corresponding feature extraction result of data further comprises:
For the corresponding characteristic of each data characteristics dimension, logical mappings corresponding with the data characteristics dimension are obtained
Rule；
According to logical mappings rule corresponding with the data characteristics dimension, mapping processing is carried out to this feature data, is obtained
The corresponding characteristic value of this feature data；
The corresponding characteristic value of the corresponding characteristic of multiple data characteristics dimensions is combined, the Log fusing number is obtained
According to corresponding feature extraction result.
Further, using the corresponding feature extraction of multiple Log fusing data as a result, constructing log sample set into one
Step includes:
For each Log fusing data, if the log rank data in the Log fusing data are error level, benefit
With the corresponding feature extraction of Log fusing data as a result, building obtain include negative sample label log sample；
It is corresponding using the Log fusing data if the log rank data in the Log fusing data are not error level
Feature extraction as a result, building obtain include positive sample label log sample；
To it is all include positive sample label log sample and it is all include negative sample label log sample
This is summarized, and log sample set is obtained.
Further, the log sample in log sample set is analyzed, obtains failure analysis result and further wraps
It includes:
Using clustering algorithm and/or association rules mining algorithm, the log sample in log sample set is analyzed,
Obtain failure analysis result.
Further, before the daily record data for obtaining multiple applications, this method further include:
The data format of the daily record data of multiple applications is registered in journal format library.
Further, according to user identifier, fusion treatment is carried out to the daily record data of multiple applications, obtains multiple logs
Before fused data, this method further include:
From screened out in the daily record data of multiple applications the daily record data generated in debugging process and pressure test sequence and
Repeat existing daily record data；
For the daily record data of each application, using the resource data of the application to the missing in the daily record data of the application
Data are supplemented；
The data representation format of the daily record data of unified multiple applications.
According to another aspect of the present invention, a kind of fail analysis device based on big data fusion, the device packet are provided
It includes:
Module is obtained, suitable for obtaining the daily record data of multiple applications；
Fusion Module, is suitable for according to user identifier, carries out fusion treatment to the daily record data of multiple applications, obtains multiple days
Will fused data；
Characteristic extracting module is suitable for carrying out feature extraction to multiple Log fusing data, obtains multiple Log fusing data
Corresponding feature extraction result；
Sample constructs module, is suitable for using the corresponding feature extraction of multiple Log fusing data as a result, building log sample
Set；
Analysis module obtains failure analysis result suitable for analyzing the log sample in log sample set.
Further, module is obtained to be further adapted for:
For each application, obtains this and apply and remember in corresponding applications client, application service end and application database end
The daily record data of record.
Further, Fusion Module is further adapted for:
From the daily record data of multiple applications search include same subscriber mark and log generate the time belong to it is same
The daily record data of time window obtains multiple log data sets；
For each log data set, all daily record datas in the log data set are permeated a Log fusing number
According to.
Further, characteristic extracting module is further adapted for:
For each Log fusing data, according to pre-set multiple data characteristics dimensions, from the Log fusing data
It is middle to extract characteristic corresponding with multiple data characteristics dimensions；
Mapping processing is carried out to the corresponding characteristic of multiple data characteristics dimensions, it is corresponding to obtain the Log fusing data
Feature extraction result.
Further, characteristic extracting module is further adapted for:
For the corresponding characteristic of each data characteristics dimension, logical mappings corresponding with the data characteristics dimension are obtained
Rule；
According to logical mappings rule corresponding with the data characteristics dimension, mapping processing is carried out to this feature data, is obtained
The corresponding characteristic value of this feature data；
The corresponding characteristic value of the corresponding characteristic of multiple data characteristics dimensions is combined, the Log fusing number is obtained
According to corresponding feature extraction result.
Further, sample building module is further adapted for:
For each Log fusing data, if the log rank data in the Log fusing data are error level, benefit
With the corresponding feature extraction of Log fusing data as a result, building obtain include negative sample label log sample；
It is corresponding using the Log fusing data if the log rank data in the Log fusing data are not error level
Feature extraction as a result, building obtain include positive sample label log sample；
To it is all include positive sample label log sample and it is all include negative sample label log sample
This is summarized, and log sample set is obtained.
Further, analysis module is further adapted for:
Using clustering algorithm and/or association rules mining algorithm, the log sample in log sample set is analyzed,
Obtain failure analysis result.
Further, the device further include: registration module, suitable for the data format of the daily record data of multiple applications is registered
Into journal format library.
Further, device further include: preprocessing module was debugged suitable for screening out from the daily record data of multiple applications
The existing daily record data of daily record data and repetition generated in journey and pressure test sequence；For the log number of each application
According to being supplemented using the resource data of the application the missing data in the daily record data of the application；Unified multiple applications
The data representation format of daily record data.
According to another aspect of the invention, provide a kind of calculating equipment, comprising: processor, memory, communication interface and
Communication bus, processor, memory and communication interface complete mutual communication by communication bus；
Memory makes processor execution is above-mentioned to melt based on big data for storing an at least executable instruction, executable instruction
The corresponding operation of the failure analysis methods of conjunction.
In accordance with a further aspect of the present invention, a kind of computer storage medium is provided, at least one is stored in storage medium
Executable instruction, executable instruction execute processor such as the above-mentioned corresponding behaviour of failure analysis methods based on big data fusion
Make.
The technical solution provided according to the present invention merges the daily record data of the corresponding multiple applications of the full link of business
Processing, can more fully be reflected the case where each application based on log sample constructed by Log fusing data；By right
Log sample in log sample set is analyzed, and can automatically and quickly be obtained accurately failure analysis result, be realized
Comprehensive analysis during business full link to Trouble cause, effectively improves accident analysis efficiency and failure is fixed
Position precision, optimizes accident analysis mode, and business operation maintenance personnel is safeguarded as early as possible according to failure analysis result,
Reduce the loss of failure bring.
The above description is only an overview of the technical scheme of the present invention, in order to better understand the technical means of the present invention,
And it can be implemented in accordance with the contents of the specification, and in order to allow above and other objects of the present invention, feature and advantage can
It is clearer and more comprehensible, the followings are specific embodiments of the present invention.
Detailed description of the invention
By reading the following detailed description of the preferred embodiment, various other advantages and benefits are common for this field
Technical staff will become clear.The drawings are only for the purpose of illustrating a preferred embodiment, and is not considered as to the present invention
Limitation.And throughout the drawings, the same reference numbers will be used to refer to the same parts.In the accompanying drawings:
Fig. 1 shows the process signal of the failure analysis methods according to an embodiment of the invention based on big data fusion
Figure；
Fig. 2 shows the process signals of the failure analysis methods according to another embodiment of the present invention based on big data fusion
Figure；
Fig. 3 shows the structural block diagram of the fail analysis device according to an embodiment of the present invention based on big data fusion；
Fig. 4 shows a kind of structural schematic diagram for calculating equipment according to an embodiment of the present invention.
Specific embodiment
Exemplary embodiments of the present disclosure are described in more detail below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
It is fully disclosed to those skilled in the art.
Fig. 1 shows the process signal of the failure analysis methods according to an embodiment of the invention based on big data fusion
Figure, as shown in Figure 1, this method comprises the following steps:
Step S101 obtains the daily record data of multiple applications.
Under normal conditions, in practical business scene, meeting during the full link of business of the business from starting to finishing
It is related to multiple applications.By taking online shopping business scenario as an example, from querying commodity information to shopping consulting, place an order, then to ordering
The shopping consulting of the shopping application, offer shopping counseling services that can be related to offer shopping service during the link singly paid is answered
With and provide payment services payment application etc., that is to say, that the corresponding application of the full link of online shopping business include purchase
Object application, shopping consulting application and payment application.
In the present invention, in order to obtain more comprehensive daily record data, to carry out accident analysis, in step S101
In can obtain in real time the daily record data of each application from the corresponding multiple applications of the full link of business, multiple applications include but not
Be limited to: shopping application, payment application, browser application, music application, e-book reading application, is beaten at shopping consulting application
Vehicle application, application of making a reservation, communications applications etc..Those skilled in the art can determine according to actual needs needs to obtain daily record data
Using and application quantity, be not specifically limited herein.
Step S102 carries out fusion treatment to the daily record data of multiple applications, obtains multiple logs and melt according to user identifier
Close data.
In view of multiple applications and a large amount of user can be related to during the full link of business, then according to user identifier pair
The daily record data of multiple applications carries out fusion treatment, can not only easily realize data fusion, and also by user identifier
Easily Log fusing data can be managed.Wherein, user identifier can set for User ID, terminal device ID, terminal
Standby IP, terminal device MAC Address or cell-phone number etc..It specifically, can will include identical use in the daily record data of multiple applications
Family mark and log generate the time and belong to the daily record data of window at the same time and permeate Log fusing data.Ability
Field technique personnel can according to actual needs be configured the length of time window, for example, can set time window to 30 seconds
Or 1 minute etc..
Step S103 carries out feature extraction to multiple Log fusing data, obtains the corresponding spy of multiple Log fusing data
Sign is extracted as a result, and using the corresponding feature extraction of multiple Log fusing data as a result, building log sample set.
For the ease of feature extraction, feature extraction library can be preset, include pre-set more in feature extraction library
A data characteristic dimension, multiple data characteristics dimensions can include: user identifier dimension, application identities dimension, city dimension, user
Gender dimension, age of user dimension, weather dimension, execution operation dimension, personality preference dimension and/or taste preference dimension etc..
Specifically, multiple Log fusing data can be carried out special according to multiple data characteristics dimensions pre-set in feature extraction library
Sign is extracted, and obtains the corresponding feature extraction of multiple Log fusing data as a result, then corresponding using multiple Log fusing data
Then feature extraction summarizes all log samples, obtains log sample set as a result, obtain multiple log samples.
Step S104 analyzes the log sample in log sample set, obtains failure analysis result.
When needing to carry out accident analysis, so that it may utilize parser, be carried out to the log sample in log sample set
Analysis searches failure Producing reason, is quickly obtained failure analysis result.Specifically, it can analyze between each log sample
Difference, excavate the incidence relation between each log sample, obtain failure analysis result.
Failure analysis methods provided in this embodiment based on big data fusion, multiple applications corresponding to the full link of business
Daily record data carry out fusion treatment, can more fully be reflected based on log sample constructed by Log fusing data each
Using the case where；By analyzing the log sample in log sample set, accurately event can be automatically and quickly obtained
Hinder analysis as a result, realizing comprehensive analysis during business full link to Trouble cause, effectively improves failure
Analysis efficiency and fault location precision, optimize accident analysis mode, so that business operation maintenance personnel is according to accident analysis knot
Fruit can be safeguarded as early as possible, reduce the loss of failure bring.
Fig. 2 shows the process signals of the failure analysis methods according to another embodiment of the present invention based on big data fusion
Figure, as shown in Fig. 2, this method comprises the following steps:
The data format of the daily record data of multiple applications is registered in journal format library by step S201.
Since different applications may record its daily record data using different data formats, in order to rapidly right
The daily record data of multiple applications is identified and is handled, and the data format by the daily record data of multiple applications is needed to be registered to log
In format library.By taking some application as an example, some daily record data of the application be " 001,1558256738,30.24.138.4,
2088702172789691,330100 ,-, 95955F33-BFBD-48BA-A630-866D2DAE482D, 2019234,10.01,
20, Info ", then the data format registered in journal format library of daily record data for the application can are as follows:
" logVersionId, time, serverIp, userId, city, traceId, deviceIp, appId, appVersion,
Age, loglevel ", the corresponding multiple data items Chinese of the data format are expressed as " registration log format version number, log
The time is generated, application service end IP address, User ID, ID, terminal device IP, application ID, application version number, year are tracked in city
Age, log rank ".
Step S202 obtains the daily record data of multiple applications.
Under normal conditions, multiple applications can be related to during a full link of business, and an application can have correspondence
Applications client, application service end and application database end, wherein applications client is installed on the smart phone of user, PAD
In equal terminal devices, application service end can be server cluster, and connection is established at applications client and application service end, application service
End provides corresponding service for applications client, and application database end is mainly that application service end provides reading and writing data service, that
In order to obtain more comprehensive daily record data, it can be directed to each application, this is obtained and apply corresponding applications client, answer
With the daily record data recorded in server-side and application database end.
Step S203 pre-processes the daily record data of multiple applications.
It may include to be generated in debugging process and pressure test sequence in daily record data acquired in step S202
Daily record data, it is also possible to include duplicate daily record data, there is likely to be data content missings in some daily record datas
Situation, and the data representation format of the daily record data of multiple applications is also not quite similar, then for the ease of to daily record data into
Row fusion treatment, before carrying out fusion treatment, can first to the daily record datas of multiple applications carry out data cleansing, Supplementing Data,
The pretreatment such as data normalizing.
During data cleansing, produced from being screened out in debugging process and pressure test sequence in the daily record data of multiple applications
The raw existing daily record data of daily record data and repetition.Specifically, it is generated in debugging process or pressure test sequence
Daily record data can include that corresponding debugging mark or pressure mark are known, then packet can be screened out from the daily record data of multiple applications
Daily record data containing debugging mark includes the daily record data for pressing mark to know and the existing daily record data of repetition.
During Supplementing Data, for the daily record data of each application, this is answered using the resource data of the application
Missing data in daily record data is supplemented.For example, for the daily record data of some application, in conjunction with it in journal format
The data format registered in library it is found that in the daily record data of the application the corresponding data content of " application version number " data items as
Sky illustrates that its missing data is the corresponding data content of " application version number " data items, then having using application record
The resource data of application version number is to the corresponding data content of " application version number " data items in the daily record data of the application
It is supplemented.
During data normalizing, the data representation format of the daily record data of unified multiple applications.For example, some applications
The data representation format that log in daily record data generates the time is " * * divides * * seconds when * * * * * * * days * * of month * ", some applications
Daily record data in log generate the time data representation format be " * * divides * * seconds when * * * */* * month/* days * * of * ", also
Application daily record data in log generate the time data representation format be the other timestamp of Millisecond, then can be by log
The data representation format for generating the time is all unified for the other timestamp of Millisecond.
Step S204, searching from the daily record data of multiple applications includes same subscriber mark and log generates time category
In the daily record data of window at the same time, multiple log data sets are obtained.
Wherein, user identifier can be User ID, terminal device ID, terminal device IP, terminal device MAC Address or hand
Machine number etc..Searching from the daily record data of multiple applications includes same subscriber mark and log generates the time when belonging to same
Between window daily record data, will include the log that the same user identifier and log generate that the time belongs to window at the same time
Data are divided in the same log data set, to obtain multiple log data sets.
Step S205 permeates all daily record datas in the log data set a for each log data set
Log fusing data.
Specifically, it can be set in advance according to the data format of the daily record data for the multiple applications registered in journal format library
Data fusion format is set, which includes the data items of all daily record datas, as data fusion format can be
" userId, appId, city, usergender, age, telephonenumber, weather, performance, time,
Loglevel, serverIp, traceId, deviceId, deviceMAC, deviceIp, appVersion ... ... ", data are melted
The corresponding multiple data items Chinese of qualified formula are expressed as " User ID, application ID, city, user's gender, age, cell-phone number, day
Gas executes operation, and log generates time, log rank, and application service end IP address tracks ID, terminal device ID, terminal device
MAC Address, terminal device IP, application version number ... ... ".For each log data set, according to pre-set data fusion
Format permeates all daily record datas in the log data set a Log fusing data.It is wrapped in the Log fusing data
The content of data items containing all daily record datas in the log data set.
Step S206, for each Log fusing data, according to pre-set multiple data characteristics dimensions, from the log
Characteristic corresponding with multiple data characteristics dimensions is extracted in fused data.
It wherein, include that pre- first pass through is manually specified and/or that the mode of machine learning is arranged is multiple in feature extraction library
Data characteristics dimension, multiple data characteristics dimensions can include: user identifier dimension, application identities dimension, city dimension, Yong Huxing
Other dimension, age of user dimension, weather dimension, execution operation dimension, personality preference dimension and/or taste preference dimension etc..It examines
Considering in Log fusing data can include a large amount of data content, and not all data content is all to accident analysis
Useful data, then each Log fusing data can be directed to, according to pre-set multiple data characteristicses in feature extraction library
Dimension extracts characteristic corresponding with multiple data characteristics dimensions, thus easily from log from the Log fusing data
The data useful to accident analysis are extracted in fused data, and remove the data useless to accident analysis.
By taking user identifier is User ID as an example, characteristic corresponding with user identifier dimension is in Log fusing data
The corresponding data content of " User ID " data items, characteristic corresponding with application identities dimension are in Log fusing data
The corresponding data content of " application ID " data items, characteristic corresponding with city dimension are in Log fusing data " city "
The corresponding data content of data items.
Assuming that the corresponding multiple data items Chinese of data fusion format are expressed as " User ID, application ID, city, user
Gender, age, cell-phone number, weather execute operation, and log generates time, log rank ... ... ", some Log fusing data is
" 2088702172789691,2019234,330100, female, 20,139****6688, it is fine, it logs in, 1558256738,
Error ... ... ", if user identifier is User ID, multiple data characteristics dimensions include: user identifier dimension, application identities dimension
Degree, user's gender dimension, age of user dimension, executes operation dimension and weather dimension at city dimension, then from the Log fusing number
It is " 2088702172789691 " according to middle obtained characteristic corresponding with user identifier dimension of extracting, with application identities dimension
Corresponding characteristic is " 2019234 ", and characteristic corresponding with city dimension is " 330100 ", with user's gender dimension pair
The characteristic answered is " female ", and characteristic corresponding with age of user dimension is " 20 ", spy corresponding with operation dimension is executed
Levying data is " login ", and characteristic corresponding with weather dimension is " fine ".
Step S207 carries out mapping processing to the corresponding characteristic of multiple data characteristics dimensions, obtains the Log fusing
The corresponding feature extraction result of data.
For the ease of being analyzed using parser, also need to carry out the corresponding characteristic of multiple data characteristics dimensions
Mapping processing, is mapped to the data that algorithm can be identified, be handled for characteristic, so that it is corresponding to obtain the Log fusing data
Feature extraction result.Specifically, it for the corresponding characteristic of each data characteristics dimension, obtains and the data characteristics dimension pair
The logical mappings rule answered maps this feature data according to logical mappings rule corresponding with the data characteristics dimension
Processing, obtains the corresponding characteristic value of this feature data；The corresponding characteristic of multiple data characteristics dimensions is reflected completing
It penetrates after processing, the corresponding characteristic value of the corresponding characteristic of multiple data characteristics dimensions is combined, the log is obtained and melts
Close the corresponding feature extraction result of data.Wherein, feature extraction result can be the result of vector form.
Assuming that and user identifier dimension corresponding logical mappings rule, logical mappings corresponding with application identities dimension rule
Then and the corresponding logical mappings rule of city dimension and logical mappings rule corresponding with age of user dimension all defines:
The corresponding characteristic value of characteristic is characterized data itself；Logical mappings rule corresponding with user's gender dimension defines: if
Characteristic is " female ", then corresponding characteristic value is 0, if characteristic is " male ", corresponding characteristic value is 1；With execute behaviour
Make the corresponding logical mappings rule of dimension to define: if characteristic is " login ", corresponding characteristic value is 1, if characteristic
According to for " access homepage ", then corresponding characteristic value is 2, if characteristic is " access secondary page ", corresponding characteristic value is
3, if characteristic is " exiting ", corresponding characteristic value is 4, etc.；Logical mappings rule regulation corresponding with weather dimension
: if characteristic is " fine ", and corresponding characteristic value is 1, if characteristic is " rain ", corresponding characteristic value is 2, if special
Levying data is " cloudy ", then corresponding characteristic value is 3, if characteristic is " strong wind ", corresponding characteristic is 4, etc..
So according to above-mentioned logical mappings rule, mapping processing is carried out to characteristic exemplified by step S206, obtain it is corresponding
After characteristic value, obtained characteristic value can be combined according to built-up sequence, for example, when built-up sequence is (user's mark
Know, application identities, city, user's gender, age of user executes operation, weather) when, then the obtained Log fusing number
It can be (2088702172789691,2019234,330100,0,20,1,1) according to corresponding feature extraction result.
Step S208, using the corresponding feature extraction of multiple Log fusing data as a result, building log sample set.
Log rank data can be provided with, usually in daily record data to be to operate normally shape for distinguishing daily record data
It is being generated under state or generated under abnormal operating condition, in concrete scene, log rank data may include normal level,
Warning level and error level, wherein normal level is lower than warning level, and warning level is lower than error level.For journal stage
Other data can be assumed that it is the log number generated under normal operating conditions for the daily record data of normal level and warning level
According to, for log rank data be error level daily record data then think that it is the log number generated under abnormal operating condition
According to.
Since each daily record data can include log rank data, then the Log fusing number obtained for fusion
According to, can according in the corresponding log data set of Log fusing data in all daily record datas highest-ranking log rank data come
Determine the log rank data of Log fusing data.Assuming that some Log fusing data is directed to, in corresponding log data set
It include 10 daily record datas, wherein the log rank data of 6 daily record datas are normal level, the log of 4 daily record datas
Rank data are warning level, and warning level is highest-ranking log rank data in this 10 daily record datas, then should
The log rank data of Log fusing data are determined as warning level.
Wherein, for each Log fusing data, if the log rank data in the Log fusing data are error level,
Then using the corresponding feature extraction of Log fusing data as a result, building obtain include negative sample label log sample；If
Log rank data in the Log fusing data are not error level, then utilize the corresponding feature extraction of Log fusing data
As a result, building obtain include positive sample label log sample；Then to it is all include positive sample label log sample
This and it is all include that the log sample of negative sample label is summarized, obtain log sample set.Specifically, include
The log sample of negative sample label is negative sample, and the log sample for including positive sample label is positive sample, can use " 1 " table
Show positive sample label, indicates negative sample label with " 0 ".
Log sample can be the sample of vector form, and sample label can be indicated in the dimension of some in vector.In
When constructing log sample, one can be increased on the basis of the feature extraction result of vector form for indicating the dimension of sample label
Degree, building obtain log sample.Assuming that indicating positive sample label with " 1 ", negative sample label, some Log fusing are indicated with " 0 "
Log rank data in data are error level, and the corresponding feature extraction result of the Log fusing data is
(2088702172789691,2019234,330100,0,20,1,1) increase after each dimension in feature extraction result
One for indicating the dimension of sample label, then the log sample constructed be represented by (2088702172789691,
2019234,330100,0,20,1,1,0).
Step S209, using clustering algorithm and/or association rules mining algorithm, to the log sample in log sample set
It is analyzed, obtains failure analysis result.
When needing to carry out accident analysis, using clustering algorithm and/or association rules mining algorithm, to log sample set
The log sample generated in the time range (such as 10 minutes) before and after failure generation in conjunction is analyzed, these logs are analyzed
Difference in sample between positive sample and negative sample excavates the general character between the incidence relation and negative sample between negative sample,
Obtain failure analysis result, for business operation maintenance personnel comprehensively, accurately positioning failure Producing reason, to be tieed up as early as possible
Shield.Wherein, clustering algorithm can be K-means (K mean value) clustering algorithm, hierarchical clustering algorithm, SOM (self-organizing map neural net
Network, Self-organizing Maps) clustering algorithm or FCM (fuzzy, Fuzzy C-Means) clustering algorithm etc., association rule
Then mining algorithm can be Apriori algorithm, FP-growth algorithm, DHP algorithm or PARTITION algorithm etc., art technology
Personnel can select clustering algorithm and association rules mining algorithm according to actual needs, herein without limitation.
For example, at a time receiving the prompt messages of monitoring mechanism sending, point out to purchase in prompt messages
The purchase order amount of object application drops to the 30% of original purchase order amount within the unit time, then using clustering algorithm
And/or association rules mining algorithm, the log sample generated in 10 minutes before and after the moment in log sample set is carried out
Analysis.Skilled person would appreciate that the content of log sample is actually the data that algorithm can be identified, be handled
Form, but use natural language to the content of log sample in following example of the invention with description in order to facilitate understanding
It is indicated.Assuming that the log sample of required analysis is as follows:
Sample 1:(user 1, shopping consulting application, Hangzhou, female, 20, rain logs in, mistake)；
Sample 2:(user 2, shopping consulting application, Beijing, male, 32, it is fine, it logs in, normal)；
Sample 3:(user 3, shopping consulting application, Hangzhou, male, 29, rain logs in, mistake)；
Sample 4:(user 4, shopping consulting application, Hangzhou, male, 19, rain logs in, mistake)；
Sample 5:(user 5, shopping application, Beijing, male, 39, it is fine, it logs in, normal)；
Sample 6:(user 6, shopping application, Shanghai, male, 29, yin logs in, normal)；
Sample 7:(user 7, shopping application, Beijing, female, 21, it is fine, it logs in, normal)；
Sample 8:(user 8, shopping application, Hangzhou, male, 29, rain accesses homepage, normal)；
Sample 9:(user 9, payment application, Hangzhou, male, 29, rain logs in, normal) ...
By analyzing above-mentioned log sample, obtained failure analysis result be " shopping consulting application, Hangzhou,
Rain logs in, mistake, 100% ", the use safeguarded according to all application service ends by being located at Hangzhou of the failure analysis result
It is 100% that family, which occurs abnormal probability when logging in and doing shopping consulting application, and business operation maintenance personnel can according to the failure analysis result
Failure Producing reason is positioned to shopping consulting and is applied at the application service end of Hangzhou setting, illustrates the shopping of shopping application
It may be to apply to make by the application service end in the application service end failure of Hangzhou setting due to shopping consulting that order volume, which die-offs,
Caused by the user of maintenance can not normally log in shopping consulting application, then business operation maintenance personnel can be by the application service
It is overhauled to solve the failure at end.
Failure analysis methods provided in this embodiment based on big data fusion, by corresponding multiple to the full link of business
The daily record data of application carries out the pretreatment such as data cleansing, Supplementing Data, data normalizing, effectively improves the number of daily record data
According to quality, daily record data is enabled to carry out fusion treatment more conveniently；The time is generated to more according to user identifier and log
The daily record data of a application carries out fusion treatment, can not only easily realize data fusion, and can also by user identifier
It is enough that easily Log fusing data are managed；The log sample constructed based on Log fusing data can be more fully
Ground reflects the case where each application, by analyzing it, can automatically and quickly obtain accurately failure analysis result,
Realize comprehensive analysis during business full link to Trouble cause, effectively improve accident analysis efficiency and
Fault location precision.
Fig. 3 shows the structural block diagram of the fail analysis device according to an embodiment of the present invention based on big data fusion, such as
Shown in Fig. 3, which includes: to obtain module 310, Fusion Module 320, characteristic extracting module 330, sample building 340 and of module
Analysis module 350.
It obtains module 310 to be suitable for: obtaining the daily record data of multiple applications.
Optionally, module 310 is obtained to be further adapted for: for each application, obtain this using corresponding applications client,
The daily record data recorded in application service end and application database end.
Fusion Module 320 is suitable for: according to user identifier, carrying out fusion treatment to the daily record data of multiple applications, obtains more
A Log fusing data.
Optionally, Fusion Module 320 is further adapted for: searching from the daily record data of multiple applications includes same subscriber
Mark and log generate the daily record data for the time belonging to window at the same time, obtain multiple log data sets；For each day
Will data group permeates all daily record datas in the log data set a Log fusing data.
Characteristic extracting module 330 is suitable for: carrying out feature extraction to multiple Log fusing data, obtains multiple Log fusing numbers
According to corresponding feature extraction result.
Optionally, characteristic extracting module 330 is further adapted for: each Log fusing data is directed to, according to pre-set
Multiple data characteristics dimensions extract characteristic corresponding with multiple data characteristics dimensions from the Log fusing data；To more
The corresponding characteristic of a data characteristic dimension carries out mapping processing, obtains the corresponding feature extraction knot of the Log fusing data
Fruit.
Optionally, characteristic extracting module 330 is further adapted for: it is directed to the corresponding characteristic of each data characteristics dimension,
Obtain logical mappings rule corresponding with the data characteristics dimension；It is advised according to logical mappings corresponding with the data characteristics dimension
Then, mapping processing is carried out to this feature data, obtains the corresponding characteristic value of this feature data；It is corresponding to multiple data characteristics dimensions
The corresponding characteristic value of characteristic be combined, obtain the corresponding feature extraction result of the Log fusing data.
Sample constructs module 340, is suitable for using the corresponding feature extraction of multiple Log fusing data as a result, building log sample
This set.
Optionally, sample building module 340 is further adapted for: for each Log fusing data, if the Log fusing number
Log rank data in are error level, then using the corresponding feature extraction of Log fusing data as a result, building obtains
It include the log sample of negative sample label；If the log rank data in the Log fusing data are not error level, benefit
With the corresponding feature extraction of Log fusing data as a result, building obtain include positive sample label log sample；To all
Include positive sample label log sample and it is all include that the log sample of negative sample label is summarized, obtain
Log sample set.
Analysis module 350 is suitable for: analyzing the log sample in log sample set, obtains failure analysis result.
Optionally, analysis module 350 is further adapted for: clustering algorithm and/or association rules mining algorithm is utilized, to log
Log sample in sample set is analyzed, and failure analysis result is obtained.
Optionally, which may also include that registration module 360, suitable for by the data format of the daily record data of multiple applications
It is registered in journal format library.
Optionally, which may also include that preprocessing module 370, suitable for screening out tune from the daily record data of multiple applications
The existing daily record data of daily record data and repetition generated in examination process and pressure test sequence；For the log of each application
Data supplement the missing data in the daily record data of the application using the resource data of the application；Unified multiple applications
Daily record data data representation format.
Fail analysis device provided in this embodiment based on big data fusion, by corresponding multiple to the full link of business
The daily record data of application carries out the pretreatment such as data cleansing, Supplementing Data, data normalizing, effectively improves the number of daily record data
According to quality, daily record data is enabled to carry out fusion treatment more conveniently；The time is generated to more according to user identifier and log
The daily record data of a application carries out fusion treatment, can not only easily realize data fusion, and can also by user identifier
It is enough that easily Log fusing data are managed；The log sample constructed based on Log fusing data can be more fully
Ground reflects the case where each application, by analyzing it, can automatically and quickly obtain accurately failure analysis result,
Realize comprehensive analysis during business full link to Trouble cause, effectively improve accident analysis efficiency and
Fault location precision.
The present invention also provides a kind of nonvolatile computer storage media, computer storage medium is stored at least one can
It executes instruction, the failure analysis methods based on big data fusion in above-mentioned any means embodiment can be performed in executable instruction.
Fig. 4 shows a kind of structural schematic diagram for calculating equipment according to an embodiment of the present invention, the specific embodiment of the invention
The specific implementation for calculating equipment is not limited.
As shown in figure 4, the calculating equipment may include: processor (processor) 402, communication interface
(Communications Interface) 404, memory (memory) 406 and communication bus 408.
Wherein:
Processor 402, communication interface 404 and memory 406 complete mutual communication by communication bus 408.
Communication interface 404, for being communicated with the network element of other equipment such as client or other servers etc..
Processor 402 can specifically execute the above-mentioned accident analysis side based on big data fusion for executing program 410
Correlation step in method embodiment.
Specifically, program 410 may include program code, which includes computer operation instruction.
Processor 402 may be central processor CPU or specific integrated circuit ASIC (Application
Specific Integrated Circuit), or be arranged to implement the integrated electricity of one or more of the embodiment of the present invention
Road.The one or more processors that equipment includes are calculated, can be same type of processor, such as one or more CPU；It can also
To be different types of processor, such as one or more CPU and one or more ASIC.
Memory 406, for storing program 410.Memory 406 may include high speed RAM memory, it is also possible to further include
Nonvolatile memory (non-volatile memory), for example, at least a magnetic disk storage.
Program 410 specifically can be used for so that processor 402 execute in above-mentioned any means embodiment based on big data
The failure analysis methods of fusion.The specific implementation of each step may refer to the above-mentioned failure based on big data fusion in program 410
Corresponding description in corresponding steps and the unit in embodiment is analyzed, this will not be repeated here.Those skilled in the art can be clear
Recognize to Chu, for convenience and simplicity of description, the equipment of foregoing description and the specific work process of module, can refer to aforementioned
Corresponding process description in embodiment of the method, details are not described herein.
Algorithm and display are not inherently related to any particular computer, virtual system, or other device provided herein.
Various general-purpose systems can also be used together with teachings based herein.As described above, it constructs required by this kind of system
Structure be obvious.In addition, the present invention is also not directed to any particular programming language.It should be understood that can use various
Programming language realizes summary of the invention described herein, and the description done above to language-specific is to disclose this hair
Bright preferred forms.
In the instructions provided here, numerous specific details are set forth.It is to be appreciated, however, that implementation of the invention
Example can be practiced without these specific details.In some instances, well known method, structure is not been shown in detail
And technology, so as not to obscure the understanding of this specification.
Similarly, it should be understood that in order to simplify the disclosure and help to understand one or more of the various inventive aspects, In
Above in the description of exemplary embodiment of the present invention, each feature of the invention is grouped together into single implementation sometimes
In example, figure or descriptions thereof.However, the disclosed method should not be interpreted as reflecting the following intention: i.e. required to protect
Shield the present invention claims features more more than feature expressly recited in each claim.More precisely, as following
Claims reflect as, inventive aspect is all features less than single embodiment disclosed above.Therefore,
Thus the claims for following specific embodiment are expressly incorporated in the specific embodiment, wherein each claim itself
All as a separate embodiment of the present invention.
Those skilled in the art will understand that can be carried out adaptively to the module in the equipment in embodiment
Change and they are arranged in one or more devices different from this embodiment.It can be the module or list in embodiment
Member or component are combined into a module or unit or component, and furthermore they can be divided into multiple submodule or subelement or
Sub-component.Other than such feature and/or at least some of process or unit exclude each other, it can use any
Combination is to all features disclosed in this specification (including adjoint claim, abstract and attached drawing) and so disclosed
All process or units of what method or apparatus are combined.Unless expressly stated otherwise, this specification is (including adjoint power
Benefit require, abstract and attached drawing) disclosed in each feature can carry out generation with an alternative feature that provides the same, equivalent, or similar purpose
It replaces.
In addition, it will be appreciated by those of skill in the art that although some embodiments described herein include other embodiments
In included certain features rather than other feature, but the combination of the feature of different embodiments mean it is of the invention
Within the scope of and form different embodiments.For example, in the following claims, embodiment claimed is appointed
Meaning one of can in any combination mode come using.
Various component embodiments of the invention can be implemented in hardware, or to run on one or more processors
Software module realize, or be implemented in a combination thereof.It will be understood by those of skill in the art that can be used in practice
Microprocessor or digital signal processor (DSP) realize one of some or all components according to embodiments of the present invention
A little or repertoire.The present invention is also implemented as setting for executing some or all of method as described herein
Standby or program of device (for example, computer program and computer program product).It is such to realize that program of the invention deposit
Storage on a computer-readable medium, or may be in the form of one or more signals.Such signal can be from because of spy
It downloads and obtains on net website, be perhaps provided on the carrier signal or be provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and ability
Field technique personnel can be designed alternative embodiment without departing from the scope of the appended claims.In the claims,
Any reference symbol between parentheses should not be configured to limitations on claims.Word "comprising" does not exclude the presence of not
Element or step listed in the claims.Word "a" or "an" located in front of the element does not exclude the presence of multiple such
Element.The present invention can be by means of including the hardware of several different elements and being come by means of properly programmed computer real
It is existing.In the unit claims listing several devices, several in these devices can be through the same hardware branch
To embody.The use of word first, second, and third does not indicate any sequence.These words can be explained and be run after fame
Claim.
Claims (10)
1. a kind of failure analysis methods based on big data fusion, which comprises
Obtain the daily record data of multiple applications；
According to user identifier, fusion treatment is carried out to the daily record data of multiple applications, obtains multiple Log fusing data；
Feature extraction is carried out to multiple Log fusing data, obtains the corresponding feature extraction of multiple Log fusing data as a result, simultaneously
Using the corresponding feature extraction of multiple Log fusing data as a result, building log sample set；
Log sample in the log sample set is analyzed, failure analysis result is obtained.
2. according to the method described in claim 1, wherein, the daily record data for obtaining multiple applications further comprises:
For each application, this is obtained using recording in corresponding applications client, application service end and application database end
Daily record data.
3. it is described according to user identifier according to the method described in claim 1, wherein, the daily record data of multiple applications is carried out
Fusion treatment, obtaining multiple Log fusing data further comprises:
Searching from the daily record data of multiple applications includes same subscriber mark and the log generation time belongs at the same time
The daily record data of window obtains multiple log data sets；
For each log data set, all daily record datas in the log data set are permeated a Log fusing data.
4. it is described that feature extraction is carried out to multiple Log fusing data according to the method described in claim 1, wherein, it obtains more
The corresponding feature extraction result of a Log fusing data further comprises:
It is mentioned from the Log fusing data for each Log fusing data according to pre-set multiple data characteristics dimensions
Take characteristic corresponding with multiple data characteristics dimensions；
Mapping processing is carried out to the corresponding characteristic of multiple data characteristics dimensions, obtains the corresponding feature of Log fusing data
Extract result.
5. described to be reflected to the corresponding characteristic of multiple data characteristics dimensions according to the method described in claim 4, wherein
Processing is penetrated, obtaining the corresponding feature extraction result of the Log fusing data further comprises:
For the corresponding characteristic of each data characteristics dimension, logical mappings rule corresponding with the data characteristics dimension are obtained
Then；
According to logical mappings rule corresponding with the data characteristics dimension, mapping processing is carried out to this feature data, obtains the spy
Levy the corresponding characteristic value of data；
The corresponding characteristic value of the corresponding characteristic of multiple data characteristics dimensions is combined, the Log fusing data pair are obtained
The feature extraction result answered.
6. method according to claim 1-5, wherein described to utilize the corresponding feature of multiple Log fusing data
It extracts as a result, building log sample set further comprises:
For each Log fusing data, if the log rank data in the Log fusing data are error level, utilizing should
The corresponding feature extraction of Log fusing data as a result, building obtain include negative sample label log sample；
If the log rank data in the Log fusing data are not error level, the corresponding spy of the Log fusing data is utilized
Sign extract as a result, building obtain include positive sample label log sample；
To it is all include positive sample label log sample and it is all include negative sample label log sample into
Row summarizes, and obtains log sample set.
7. method according to claim 1-6, wherein the log sample in the log sample set
It is analyzed, obtaining failure analysis result further comprises:
Using clustering algorithm and/or association rules mining algorithm, the log sample in the log sample set is analyzed,
Obtain failure analysis result.
8. a kind of fail analysis device based on big data fusion, described device include:
Module is obtained, suitable for obtaining the daily record data of multiple applications；
Fusion Module, is suitable for according to user identifier, carries out fusion treatment to the daily record data of multiple applications, obtains multiple logs and melt
Close data；
Characteristic extracting module is suitable for carrying out feature extraction to multiple Log fusing data, it is corresponding to obtain multiple Log fusing data
Feature extraction result；
Sample constructs module, is suitable for using the corresponding feature extraction of multiple Log fusing data as a result, building log sample set；
Analysis module obtains failure analysis result suitable for analyzing the log sample in the log sample set.
9. a kind of calculating equipment, comprising: processor, memory, communication interface and communication bus, the processor, the storage
Device and the communication interface complete mutual communication by the communication bus；
The memory executes the processor as right is wanted for storing an at least executable instruction, the executable instruction
Ask the corresponding operation of failure analysis methods based on big data fusion described in any one of 1-7.
10. a kind of computer storage medium, an at least executable instruction, the executable instruction are stored in the storage medium
Execute processor such as the failure analysis methods corresponding behaviour of any of claims 1-7 based on big data fusion
Make.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910704775.XA CN110413500B (en) | 2019-07-31 | 2019-07-31 | Fault analysis method and device based on big data fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910704775.XA CN110413500B (en) | 2019-07-31 | 2019-07-31 | Fault analysis method and device based on big data fusion |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110413500A true CN110413500A (en) | 2019-11-05 |
| CN110413500B CN110413500B (en) | 2024-01-09 |
Family
ID=68364871
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910704775.XA Active CN110413500B (en) | 2019-07-31 | 2019-07-31 | Fault analysis method and device based on big data fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110413500B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111984714A (en) * | 2020-08-13 | 2020-11-24 | 孙小丽 | Information generation method based on intelligent online communication and big data and cloud service platform |
| CN113254255A (en) * | 2021-07-15 | 2021-08-13 | 苏州浪潮智能科技有限公司 | Cloud platform log analysis method, system, device and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101159617A (en) * | 2007-11-22 | 2008-04-09 | 中国电信股份有限公司 | Two dimensional fault management method and system of combining whole network and whole service |
| CN105653444A (en) * | 2015-12-23 | 2016-06-08 | 北京大学 | Internet log data-based software defect failure recognition method and system |
| CN106446228A (en) * | 2016-10-08 | 2017-02-22 | 中国工商银行股份有限公司 | Collection analysis method and device for WEB page data |
| EP3336636A1 (en) * | 2016-12-19 | 2018-06-20 | Palantir Technologies Inc. | Machine fault modelling |
-
2019
- 2019-07-31 CN CN201910704775.XA patent/CN110413500B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101159617A (en) * | 2007-11-22 | 2008-04-09 | 中国电信股份有限公司 | Two dimensional fault management method and system of combining whole network and whole service |
| CN105653444A (en) * | 2015-12-23 | 2016-06-08 | 北京大学 | Internet log data-based software defect failure recognition method and system |
| CN106446228A (en) * | 2016-10-08 | 2017-02-22 | 中国工商银行股份有限公司 | Collection analysis method and device for WEB page data |
| EP3336636A1 (en) * | 2016-12-19 | 2018-06-20 | Palantir Technologies Inc. | Machine fault modelling |
Non-Patent Citations (4)
| Title |
|---|
| LI ZHONG: "Incentive Analysis of Power Failure in Distribution Network Based on Multi-source Data Fusion", 《IEEE XPLORE》 * |
| 廖明;陈明;周冀;向小华;李芳;焦叶芬;: "基于大数据融合算法的DNS日志分析系统", 电信科学, no. 05 * |
| 陈楠;陈东辉;邓莉;: "基于ELK的用户访问行为分析技术", 气象科技进展, no. 01 * |
| 魏晨辉;张展;向琳;王书婷;左德承;: "基于高端容错计算机的故障日志分析系统", 清华大学学报(自然科学版), no. 1 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111984714A (en) * | 2020-08-13 | 2020-11-24 | 孙小丽 | Information generation method based on intelligent online communication and big data and cloud service platform |
| CN113254255A (en) * | 2021-07-15 | 2021-08-13 | 苏州浪潮智能科技有限公司 | Cloud platform log analysis method, system, device and medium |
| CN113254255B (en) * | 2021-07-15 | 2021-10-29 | 苏州浪潮智能科技有限公司 | Cloud platform log analysis method, system, device and medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110413500B (en) | 2024-01-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP2460258B1 (en) | Method and device for auto-generating goose signal connection topology from substation level | |
| CN102597993B (en) | Managing application state information by means of uniform resource identifier (URI) | |
| CN109344170B (en) | Stream data processing method, system, electronic device and readable storage medium | |
| CN113094200A (en) | Application program fault prediction method and device | |
| CN107704396A (en) | The method of testing and device of application program | |
| CN109993189A (en) | A kind of network failure method for early warning, device and medium | |
| CN109213773A (en) | A kind of diagnostic method, device and the electronic equipment of online failure | |
| CN111859076B (en) | Data crawling method, device, computer equipment and computer readable storage medium | |
| CN110413500A (en) | Failure analysis methods and device based on big data fusion | |
| CN114185770A (en) | Method and device for generating test data, computer equipment and storage medium | |
| CN114531334A (en) | Intention processing method and device, electronic equipment and readable storage medium | |
| CN105099804B (en) | Detection method, server and the terminal of unit failure | |
| CN108345658A (en) | Algorithm calculates decomposing process, server and the storage medium of track | |
| CN117827784A (en) | Noise log filtering method and system | |
| CN115994079A (en) | Test method, test device, electronic apparatus, storage medium, and program product | |
| CN112583663B (en) | Automatic network performance testing method based on domestic operating system | |
| CN109189679A (en) | Interface test method and system, electronic equipment, storage medium | |
| CN113190746A (en) | Recommendation model evaluation method and device and electronic equipment | |
| WO2016114748A1 (en) | Data comparison | |
| CN112363939A (en) | Method, system and equipment for quickly generating fuzzy test network protocol template | |
| CN108196853A (en) | For disposing the method for Android system SDK, storage medium, equipment and system | |
| CN110333897A (en) | A kind of interface allocation method, device, medium and electronic equipment | |
| CN117370203B (en) | Automatic test method, system, electronic equipment and storage medium | |
| CN116136817A (en) | Information processing method and device based on cloud service, electronic equipment and storage medium | |
| CN114443602A (en) | Multi-cloud log processing method, storage method and computer storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |