CN108234176A - A kind of monitoring system and its method - Google Patents
A kind of monitoring system and its method Download PDFInfo
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- CN108234176A CN108234176A CN201611191725.9A CN201611191725A CN108234176A CN 108234176 A CN108234176 A CN 108234176A CN 201611191725 A CN201611191725 A CN 201611191725A CN 108234176 A CN108234176 A CN 108234176A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
- H04L41/0631—Management of faults, events, alarms or notifications using root cause analysis; using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
- H04L41/06—Management of faults, events, alarms or notifications
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- Computer Networks & Wireless Communication (AREA)
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Abstract
The invention belongs to Network Monitoring Technology fields, and in particular to the monitoring system and its method using depth analysis mode of a kind of network O＆M.It includes dispensing unit, data cell and alarm unit, and dispensing unit is configured for parameter rule, and the parameter rule of configuration is sent respectively to acquisition client and abnormal determination module；Data cell includes acquisition client, transmission module and data distribution module, it acquires client and is used for gathered data, and gathered data is transferred to data distribution module by transmission module, data distribution module is used for the gathered data that will be received, is distributed to abnormal determination module and is handled；Alarm unit includes abnormal determination module, exception database and alarm execution module, after abnormal determination module is for analyzing single monitoring data according to alarm rule, obtain exception information, and exception information is sent to exception database, alarm execution module is used to obtain the information in exception database, performs alarm.
Description
Technical field
The invention belongs to Network Monitoring Technology fields, and in particular to the monitoring system and its method of a kind of network O＆M.
Background technology
With the development of network technology, more and more clients are in the scheme for considering or adopting business clustering.However
After operation system clustering, not only increase the working strength of operation and maintenance, but also system can be made to become more numerous and diverse.Effective system
System and application monitoring system become the behaviour in service for understanding service resources, have found that it is likely that the hidden danger for leading to the system failure in time, real
The key of existing system operation guarantee.
On the other hand, by means of Centralized Monitoring solution, user can properly and timely understand the operation shape of system
State finds to influence the bottleneck of total system operation, the necessary system optimization of help system developer progress and configuration change, very
The most upgrading of system and dilatation offer foundation.It is quick that strong monitoring and diagnostic tool may also help in operation maintenance personnel
Ground analyzes application and trouble reason, they are freed from the labour of numerous and diverse repetition.
Monitoring system is entire O＆M link or even a most important ring in the entire product life cycle, and monitoring system can
Failure is found with timely early warning in advance, provides full and accurate data afterwards for tracing orientation problem.Because monitoring is related to operation
Many aspects, so monitoring system should be easy to use.
Existing monitoring system is general using first being stored in data set, then to being sentenced based on centrally stored data execution
Disconnected rule, this processing mode cause server very big load pressure in the case where data volume is big.It is and existing to be based on
The abnormal of threshold rule judges, can not adapt to O＆M variation, especially for abnormal caused by the normal fluctuation of monitor control index, often
Often cause false alarm.
Invention content
For shortcoming existing for above-mentioned monitoring system, the present invention propose a kind of network O＆M monitoring system and its
Method.The monitoring method carries out data acquisition by acquiring client, and by collected data, is passed by file distributing unit
It is defeated to arrive different processing units, carry out data processing.After obtaining exception information, abnormal alarm is carried out.
The present invention adopts the following technical scheme that：
A kind of monitoring system, it includes configuration module, data module and alarm module,
Configuration module is configured for parameter rule, and the parameter rule of configuration is sent respectively to acquisition client and abnormal determination
Unit；
Data module includes acquisition client, transmission unit and file distributing unit, and acquisition client is used for gathered data, and handle
Gathered data is transferred to file distributing unit by transmission unit, and file distributing unit is used for the gathered data that will be received, point
Abnormality determination unit is dealt into be handled；
Alarm module includes abnormality determination unit, exception database and alarm execution unit, abnormality determination unit and is used for according to report
After police regulations then analyze single monitoring data, exception information is obtained, and exception information is sent to exception database, alarmed
Execution unit is used to obtain the information in exception database, performs alarm.
Further, the deployed environment of abnormality determination unit is disposed for clustering.
Further, data module further includes purpose data classifying unit, and file distributing unit divides the gathered data received
Purpose data classifying unit is issued, purpose data classifying unit is used to carry out data collected multidimensional data according to project rule is merged
Processing is collected, and data return to file distributing unit by treated.
Further, alarm module further includes alarm feedback unit, and alarm feedback unit is used to judge whether false alarm simultaneously
Feed back to exception database.
Further, alarm module further includes deep learning analytic unit, and deep learning analytic unit is used to analyze exception
Information in database, and analysis result is fed back into exception database.
A kind of monitoring method based on any of the above-described monitoring system, includes the following steps,
S1, parameter rule configuration, including acquisition project configuration and alarm rule configuration, the parameter rule for acquiring project configuration is sent
Acquisition client is given, the parameter rule of alarm rule configuration is sent to abnormality determination unit；
Gathered data is transferred to file distributing unit by S2, acquisition client for gathered data by transmission unit；
S3, the gathered data that file distributing unit will receive are sent to abnormality determination unit and are handled, abnormality determination unit
After being analyzed according to alarm rule single monitoring data, exception information is obtained, and exception information is sent to exception
Database；
S4, alarm execution unit obtain the information in exception database, perform alarm.
Further, the deployed environment of abnormality determination unit is disposed for clustering in step S3.
Further, step S3 further includes file distributing unit and the gathered data received is distributed to purpose data classifying list
Member, purpose data classifying unit carry out data according to merging project rule to collect processing, and data return to data by treated
Dispatching Unit.
Further, step S4 further includes alarm feedback unit and judges whether false alarm and feed back to exception database.
Further, step S3 further includes the information in deep learning analytic unit analysis exception database, and will analysis
As a result exception database is fed back to.
The present invention has the following advantages that relative to the prior art：Overcome original monitoring analysis server standalone processes pressure
Excessive, the problems such as Single Point of Faliure, Real-time and Concurrent processing single monitoring data and distributed type assemblies processing structure greatly promote monitoring
Data-handling efficiency；It realizes collecting for data, the different monitoring derived data of same monitored object is collected in advance, reduce
The computational load of abnormality determination unit improves statistic property, simultaneously as the data of acquisition are multiple dimensions, uses multidimensional
Data carry out abnormal determination, improve the information content of exception information so that single abnormal determination accuracy rate higher；It is anti-based on alarm
Feedback and deep learning analysis ability, can be adjusted according to the judgement of operation maintenance personnel, while be generated by the trend generated extremely
Alarm, pinpoints the problems in advance, provides association exception information, improves the accuracy rate of whole anomalous identification, reduces rate of false alarm.Spirit is provided
Interface living allows operation maintenance personnel that can search the problems in operation by inquiring various data analyses.
Description of the drawings
Fig. 1 is the structure chart of monitoring system of the present invention；
Fig. 2 is alarm schematic diagram.
Specific embodiment
To further illustrate each embodiment, the present invention is provided with attached drawing.These attached drawings are that the invention discloses one of content
Point, mainly to illustrate embodiment, and the associated description of specification can be coordinated to explain the operation principles of embodiment.Cooperation ginseng
These contents are examined, those of ordinary skill in the art will be understood that other possible embodiments and advantages of the present invention.In figure
Component be not necessarily to scale, and similar element numbers are conventionally used to indicate similar component.
In conjunction with the drawings and specific embodiments, the present invention is further described.
As shown in fig.1, proposing a kind of structure chart of monitoring system for the present invention, it includes configuration module 1, data module
2nd, alarm module 3 and display module 4.
Configuration module 1 is used to parameter be configured, and for the parameter of configuration to be sent on corresponding execution unit.Configuration module 1
Including regular dispensing unit 11 and configuration Dispatching Unit 12, regular dispensing unit 11 divides for two parts：Acquire project configuration and report
Police regulations are then configured.Project configuration is acquired, management different acquisition client 21 needs the data class acquired, by monitoring purpose acquisition
Corresponding data.Alarm rule is configured, arrangement abnormalities criterion and abnormal informant person's range.Dispatching Unit 12 is configured, uses
Distribution efficiency is configured in improving.Configuration in the multiple place deployment of the whole network, cached configuration database, then by configuration data point
It is dealt on corresponding execution unit.
Data module 2 includes acquisition client 21, transmission unit 22, file distributing unit 23 and purpose data classifying unit 24,
Client 21 is acquired for gathered data, and gathered data is transferred to file distributing unit 23, data by transmission unit 22
Dispatching Unit 23 is used for the gathered data that will be received, and is distributed at abnormality determination unit 31 and purpose data classifying unit 24
Reason.
Whether data model is powerful, if flexibly, most important for monitoring system.Monitoring data uses an acquisition number
The data model structure of multiple dimension datas is recorded on.Multi dimensional analysis can be carried out to some monitoring project in this way, and
Data processing quantity is reduced, improves data store query efficiency.The composition of monitoring data structure is in the embodiment:Time adopts
Collect data name, multigroup KV（Key-Value, key assignments）Inquire data, multigroup KV gathered datas.
Client 21 is acquired, three kinds of acquisition modes are provided.It is respectively：Actively acquisition, offer api interface and plug-in unit acquisition branch
It holds, in acquisition by the different latitude assignment of a gathered data.It actively acquires, the main hardware information for acquiring book server,
Purpose is used for monitoring server operating status；Api interface is to acquire being reported certainly to other application or service for the offer of client 21
The api interface of body state, it is therefore an objective to which, for the monitoring and test of application state, this interface can be easier to realize application monitoring
The numerical value of internal detection, such as the data base querying response data acquisition of application；Plug-in unit acquires, and is that monitor supervision platform is manageable
Expanding element is detected, is mainly used for monitoring other application and service, this clock monitor mode can be managed by monitoring configuration platform is unified
Reason issues, and can at any time be opened or closed on configuration platform, realizes unified and flexible monitoring objective.
Transmission unit 22 enhances gathered data efficiency of transmission and success rate.22 designed lines of transmission unit select excellent function,
Coordinate the parsing of DNS subregions, global network monitoring application routing can be built.The efficiency of transmission of gathered data is greatly improved.Transmission
Unit 22 can be transmission agency unit, in cross operator, when Network status are bad, the quick monitoring data that uploads to be to number
According to Dispatching Unit 23.
File distributing unit 23 returns the data distribution that acquisition client 21 reports to abnormality determination unit 31 and data
Collection unit 24 is handled, and according to the needs that monitoring data is handled, monitoring data, which is sent to, needs the unit for handling it to carry out
Processing.Purpose data classifying unit 24 is for handling data according to merging project rule, and data are sent to by treated
File distributing unit 23.
Purpose data classifying unit 24 for one group of monitoring data is calculated merge after generate new monitoring data, and by new prison
It controls data to return in file distributing unit 23, abnormality determination unit 31 is then passed to for abnormal determination, by purpose data classifying list
Member is embedded into gathered data flow, has collected reprocessing so that data processing is faster, more simply.
Alarm module 3 includes abnormality determination unit 31, exception database 32, alarm execution unit 33, alarm feedback unit
34 and deep learning analytic unit 35.After abnormality determination unit 31 to data according to alarm rule for carrying out processing analysis, obtain
Exception database 32 is sent to exception information, and by exception information, alarm execution unit 33 is used to obtain in exception database
Information, perform alarm, alarm feedback unit 34 is for judging whether false alarm and feeding back to exception database, deep learning point
Analysis unit 35 is used to analyze the abnormal data in exception database, and analysis result is fed back to exception database.The unit can
Wrong report caused by the wrong report generated for filtering threshold rule and monitoring data fluctuate, and can be according to the fluctuation exception of early period
Information judges whether failure and generates alarm.
Abnormality determination unit 31 is that main abnormal generates unit, it is responsible for performing abnormality judgment criterions in alarm rule, production
Raw abnormal data.Abnormal determination receives gathered data information by two channels, is gone through according to single monitoring data information and correspondence
History record performs abnormal determination rule, generates exception information.Abnormal determination can receive what data distribution interface unit was sent
Monitoring data can also receive the data that third party gives, such as log system, the number that provider system is given by kafka
According to.The deployed environment of abnormality determination unit 31 is disposed for clustering, to reduce single-point server stress, improves treatment effeciency.
Alarm execution unit 33 is responsible for, according to the exception information and alarm rule received, judging and performing notice.It performs
Notice can call multiple interfaces, such as：Mail notification connects to people, short massage notice to people, Advise By Wire to people and the processing of calling standard
Mouthful.Wherein, standard Processing Interface is the type information realized according to problem, according to problem-solving process standard, can be touched automatically
Send out the execution of this flow.Simultaneously when people is notified, it is to need to pay close attention to that can explicitly point out, it is desired nonetheless to handle, provide
Treatment advice.Judge it is according to abnormal origin server, node, service covering relation, abnormal priority in terms of notice, go back
Have personnel and server, node, application service, exception rules concern relation.Comprehensive analysis and judgement informant person's range, no
It is different with the mode and content of announcement of personnel's notice, including being associated with content, such as associated exception, suggestion for operation etc..
The upper use pattern matching way of the design of deep learning analytic unit 35, analyzes all exception informations, reduces and judge rule
Wrong report, filters the exception without artificial treatment, and according to early period caused by the false alarm and system fluctuation that then generate
Fault message caused by fluctuation judges to cause big failure exception below, needs manpower intervention and alarm.It, can by the unit
Alarm is generated to press the trend generated extremely, pinpoints the problems in advance, association exception information is provided.And by indirect shadow between exception
The relationship of sound generates alarm, finds coverage, provides solution.
Display module 4 is used to show the information of the monitoring system.These information can be monitor control index data, warning message
And exception information.Display module 4 includes index display unit 41, alarm indication unit 42 and abnormal show unit 43.
Index display unit 41：Different interfaces are provided, can be formulated in interface according to personnel's business and inquire different indexs,
Judgement is analyzed for personnel and is searched problem.
Alarm indication unit 42：Warning message is provided to check.As shown in fig.2, for alarm schematic diagram.
Abnormal show unit 43：Using different icons, show the trend that this Operation Network generates extremely, facilitate personnel couple
According to search problem.
Alarm module 3 further includes alarm feedback unit, and alarm feedback unit is used to judge whether false alarm and feed back to different
Regular data library.
A kind of monitoring method using depth analysis mode is proposed based on the above-mentioned monitoring system present invention, it includes following
Step：
S1, parameter configuration, including acquisition project configuration and alarm rule configuration, the parameter for acquiring project configuration is sent to acquisition visitor
Family end 21, the parameter of alarm rule configuration are sent to abnormality determination unit 31.
Gathered data is transferred to data distribution by S2, acquisition client 21 for gathered data by transmission unit 22
Unit 23.Wherein, the mode of collection client gathered data includes：Actively acquisition, api interface acquisition and plug-in unit acquire three kinds of acquisitions
Mode.
S3, the gathered data that file distributing unit 23 will receive are distributed to abnormality determination unit 31 and purpose data classifying list
Member 24 is handled, and purpose data classifying unit 24 is handled data according to merging project rule, and data are sent out by treated
File distributing unit 23 is given, it, will be abnormal after abnormality determination unit 31 analyzes single monitoring data according to alarm rule
Data are sent to exception database.Display module 4 includes index display unit 41, alarm indication unit 42 and abnormal show unit
43。
S4, alarm execution unit 33 obtain the abnormal data in exception database, carry out alarm execution.Alarm execution unit
33 modes for carrying out alarm execution include mail notification, short massage notice, Advise By Wire and standard Processing Interface are called to be handled.
Alarm feedback unit 34 is used to judge whether false alarm and feeds back to exception database.Deep learning analytic unit 35 analyzes exception
Abnormal data in database, and analysis result is fed back into exception database.
Although specifically showing and describing the present invention with reference to preferred embodiment, those skilled in the art should be bright
In vain, it is not departing from the spirit and scope of the present invention that the appended claims are limited, it in the form and details can be right
The present invention makes a variety of changes, and is protection scope of the present invention.
Claims (10)
1. a kind of monitoring system, it is characterised in that：It includes configuration module, data module and alarm module,
The configuration module is configured for parameter rule, and the parameter rule of configuration is sent respectively to acquisition client and exception
Identifying unit；
The data module includes acquisition client, transmission unit and file distributing unit, and acquisition client is used for gathered data,
And gathered data is transferred to file distributing unit by transmission unit, file distributing unit is used for the acquisition number that will be received
According to being distributed to abnormality determination unit and handled；
The alarm module includes abnormality determination unit, exception database and alarm execution unit, the abnormality determination unit and uses
After being analyzed according to alarm rule single monitoring data, exception information is obtained, and exception information is sent to abnormal number
According to library, the alarm execution unit is used to obtain the information in exception database, performs alarm.
2. monitoring system as described in claim 1, it is characterised in that：The deployed environment of the abnormality determination unit is clustering portion
Administration.
3. monitoring system as described in claim 1, it is characterised in that：The data module further includes purpose data classifying unit, data
The gathered data received is distributed to purpose data classifying unit by Dispatching Unit, and purpose data classifying unit is used for collected multidimensional number
Data collect with processing according to according to merging project rule, and will treated that data return to file distributing unit.
4. monitoring system as described in claim 1, it is characterised in that：The alarm module further includes alarm feedback unit, alarm
Feedback unit is used to judge whether false alarm and feeds back to exception database.
5. monitoring system as claimed in claim 4, it is characterised in that：The alarm module further includes deep learning analytic unit,
Deep learning analytic unit is used to analyze the information in exception database, and analysis result is fed back to exception database.
6. a kind of monitoring method based on any one of the claim 1-5 monitoring systems, it is characterised in that：Including following step
Suddenly,
S1, parameter rule configuration, including acquisition project configuration and alarm rule configuration, the parameter rule for acquiring project configuration is sent
Acquisition client is given, the parameter rule of alarm rule configuration is sent to abnormality determination unit；
Gathered data is transferred to file distributing unit by S2, acquisition client for gathered data by transmission unit；
S3, the gathered data that file distributing unit will receive are sent to abnormality determination unit and are handled, abnormality determination unit
After being analyzed according to alarm rule single monitoring data, exception information is obtained, and exception information is sent to exception
Database；
S4, alarm execution unit obtain the information in exception database, perform alarm.
7. monitoring method as claimed in claim 6, it is characterised in that：The deployed environment of abnormality determination unit in the step S3
It is disposed for clustering.
8. monitoring method as claimed in claim 6, it is characterised in that：The step S3 further includes file distributing unit and will receive
To gathered data be distributed to purpose data classifying unit, purpose data classifying unit carries out data according to merging project rule to collect place
Reason, and data return to file distributing unit by treated.
9. monitoring method as claimed in claim 6, it is characterised in that：The step S4 further includes alarm feedback unit judgement
No false alarm simultaneously feeds back to exception database.
10. monitoring method as claimed in claim 9, it is characterised in that：The step S3 further includes deep learning analytic unit
The information in exception database is analyzed, and analysis result is fed back into exception database.
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| CN201611191725.9A CN108234176A (en) | 2016-12-21 | 2016-12-21 | A kind of monitoring system and its method |
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| CN109660407A (en) * | 2019-01-18 | 2019-04-19 | 鑫涌算力信息科技（上海）有限公司 | Distributed system monitoring system and method |
| CN110555608A (en) * | 2019-08-23 | 2019-12-10 | 山东科技大学 | Comprehensive efficiency multistage early warning method for main well hoisting system |
| CN110858170A (en) * | 2018-08-23 | 2020-03-03 | 阿里巴巴集团控股有限公司 | Sandbox component, data abnormity monitoring method, equipment and storage medium |
| CN111679958A (en) * | 2020-06-11 | 2020-09-18 | 上海安畅网络科技股份有限公司 | Server monitoring system |
| CN113391900A (en) * | 2021-06-18 | 2021-09-14 | 长春吉星印务有限责任公司 | Abnormal event processing method and system in discrete production environment |
| CN113727213A (en) * | 2021-08-26 | 2021-11-30 | 四川华能氢能科技有限公司 | Remote online monitoring system for hydrogen production device and implementation method thereof |
| CN113920767A (en) * | 2021-10-22 | 2022-01-11 | 南京智慧交通信息股份有限公司 | Operation and maintenance alarming method, system, device and computer readable storage medium |
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| CN113727213A (en) * | 2021-08-26 | 2021-11-30 | 四川华能氢能科技有限公司 | Remote online monitoring system for hydrogen production device and implementation method thereof |
| CN113920767A (en) * | 2021-10-22 | 2022-01-11 | 南京智慧交通信息股份有限公司 | Operation and maintenance alarming method, system, device and computer readable storage medium |
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