CN105657039A - Big data based power plant equipment fault fast positioning system and method - Google Patents
Big data based power plant equipment fault fast positioning system and method Download PDFInfo
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Abstract
The invention relates to a big data based power plant equipment fault fast positioning system and method. The invention aims at providing a big data based power plant equipment fault fast positioning system and method; the system and method can meet the requirements of high throughput capacity, low time delay and complex computation to the system while processing complex events. The big data based power plant equipment fault fast positioning system comprises a fault detection unit, an event filtering unit, a fault positioning unit and a fault statistics analysis unit, wherein the fault detection unit is used for recognizing faults after the equipment malfunctions through the inputting of information of an agent report with respect to the network resource change; the event filtering unit is used for filtering excessive information, wherein the event filtering unit is used for filtering unimportant and unconcerned events and repeated alarming noise through the adoption of a filter and a threshold mechanism so as to find out the event needing to be processed; the fault positioning unit is used for determining the position of the equipment in fault in the network, even specific to a software system in fault; and the fault statistics analysis unit is used for recording, counting and analyzing the fault and/or system.
Description
Technical field
The present invention relates to the big data technique field of industry, in particular to a kind of electric power factory equipment fault fast positioning system and method based on big data.
Background technology
Along with the fast development of computer information technology, the Internet produces substantial amounts of data, and is quickly increasing with speed more than annual twice. Software application produces mass data endlessly, to such an extent as to cannot rely on artificial inspection and therefrom excavate the information that fraction people are interested and can be used. In business intelligence field, mass data implies unpredictable potential, effectively utilizes these data to be likely to produce more rich income in real time, and better meets customer demand.
The big data of industry are the keys that futurity industry is made the most of the advantage in global market competition. Being no matter German Industrial 4.0, American industry the Internet or " made in China 2025 ", the practice processes of various countries' manufacturing industry innovation strategy is all collection and the feature analysis of the big data of industry, and as the carefree environment that future manufacturing systems is built. Historical background based on industry 4.0, changed by the innovative technology of the business model of deep anatomy futurity industry and Intelligent Service system, how to pass through the analysis of the big data of industry and forecast demand, prediction manufacture should be spent, integrate industrial chain and value chain, find the value breach of user, find and manage sightless problem, it is achieved providing the user the products & services of customization.
The traditional method processing mass data is by the history data store of generation in data base, or generates journal file, certain interval of time, uses Distributed Architecture such as Hadoop and carries out batch processing, obtains analyzing result. But, quick growth along with data, all data can not be intactly stored in data base by traditional system timely and effectively, real-time analyzing and processing can not be effectively taking place, it is difficult to meet the application of much business and wishes quickly to obtain the demand of data processed result, additionally, business application to initial data and dare not interest, and more pay close attention to from initial data extraction and infer high-caliber business intelligence. Therefore, system is required to by filtering, and polymerization associates these real time datas, thus the result that detection is analyzed and abnormal conditions inform colony interested rapidly, in order to meet these demands, the real-time process performance of system is extremely important.
Equipment fault refers to the abnormal running of the industrial system that all mistakes relevant with certain fault cause.One fault is the direct or indirect reason of some mistakes, and mistake is the performance of fault, and inefficacy is the gross effect of fault. The mistake of certain parts, not necessarily owing to inside exists fault, is more likely owing to the propagation of fault causes in industrial system environment. Close relationship is had between fault, inefficacy and alarm event.
The latest development of industry real-time system is introduced into complex event processing techniques CEP (ComplexEventProcessing), for detecting the AD HOC existed between the data continuously reached, there is the features such as high-throughput, low delay and complicated calculations.
Complicated event detection is with event for driving, and the mass data of the generation of information system is processed in real time, can be used for the specific behavior pattern occurred in detection system, thus carrying out mode excavation or event prediction etc.
Complex event processing system receives from different pieces of information source, different types of event, the required data stream processed is very big, but requirement of real-time is higher, the data message that relation is complicated in the face of magnanimity, system needs quickly to calculate and decision-making, and the handling capacity of system is had higher requirement by this.
In the face of distributed Internet of things system, the fault of equipment finds and is accurately positioned application in time, and demand is based on the Fault Locating Method of Complex event processing.
Summary of the invention
It is an object of the invention to provide a kind of electric power factory equipment fault fast positioning system based on big data, this system is used in shop equipment O&M, for also be able to during to the process of complicated event to meet to the high-throughput of system, low delay, complicated calculations requirement.
It is a further object of the present invention to provide a kind of electric power factory equipment failure fast positioning method based on big data, it is adaptable to said system.
The object of the present invention is achieved like this:
A kind of electric power factory equipment fault fast positioning system based on big data, including: fault detection unit, event filtering unit, failure location unit and fault statistics analytic unit.
Wherein, fault detection unit, by inputting the information that proxy-reporting changes about Internet resources, after fault occurs by Fault Identification out; Event filtering unit, for filtering excessive information, described event filtering unit utilizes filter and threshold mechanism, filters out inessential and unconcerned event, repeats alarm noise etc., finds out the event to be processed that needs; Failure location unit, for determining the device location of fault in network, even down to the software system broken down; Fault statistics analytic unit, for being recorded fault and/or system, statistics and analysis.
Described failure location unit includes: event information acquisition layer, event-monitoring analysis layer, system display layer.
Described event information acquisition layer, by the event information of distributed event acquisition agent acquisition distributed network system (DNS), and writing events flow database.
Described event-monitoring analysis layer, including: the automatic transform subblock of detected rule and flow of event are monitored and analyze submodule. Record in rule database is automatically converted by the automatic transform subblock of described detected rule according to the flow of event filtering model based on set, generates SQL statement; The monitoring of described flow of event and analysis submodule read the SQL statement generated, the record in scan event flow database, monitor, analyze the fault of distributed system.
Described system display layer, including: fault alarm submodule, fault inquiry submodule, detected rule configuration submodule and event information real time inspection submodule.The distributed network system (DNS) fault that described fault alarm submodule real-time exhibition time monitoring analysis layer is oriented; Described fault inquiry submodule receives the classified inquiry information of the defeated people of user, shows Query Result after inquiry Mishap Database; The input of user, to the open distinct interface of different user, is write rule database by described detected rule configuration submodule; The flow of event real-time exhibition that time acquisition layer is collected by described event information real time inspection submodule is on system interface.
Further, the occurrence frequency of fault, which fault impact are provided by described fault statistics analytic unit service and/or the discrimination of fault is recorded by electric power factory equipment fault fast positioning system, statistics and analysis.
The present invention also provides a kind of electric power factory equipment failure fast positioning method based on big data suitable in said system simultaneously, the method is to receive the flow of event from different pieces of information source by distributed complex event real-time detecting system, user can define event rules interested, by complex event processing techniques, event is filtered, assembles, the complicated calculations such as connection, mate defined event schema, thus carrying out early warning or corresponding actions.
Specifically, a kind of electric power factory equipment failure fast positioning method based on big data suitable in said system of the present invention, comprise the following steps:
4.1 fault detects: by inputting the information that proxy-reporting changes about Internet resources, after fault occurs by Fault Identification out;
4.2 event filterings: by arranging filter and threshold mechanism, filter out inessential and unconcerned event, repeat alarm noise etc., find out the event to be processed that needs;
4.3 fault location: determine the device location of fault in network, even down to the software system broken down; Including:
4.3.1 event information collection: by the event information of distributed event acquisition agent acquisition distributed network system (DNS), and writing events flow database;
4.3.2 event-monitoring analysis, including:
4.3.2.1 detected rule is changed automatically: according to the flow of event filtering model based on set, the record in rule database is converted automatically, generates SQL statement;
4.3.2.2 flow of event monitoring and analysis: read the SQL statement generated, the record in scan event flow database, monitor, analyze the fault of distributed system;
4.3.3 system shows, including:
4.3.3.1 fault alarm: the distributed network system (DNS) fault that real-time exhibition time monitoring analysis layer is oriented;
4.3.3.2 fault inquiry: receive the classified inquiry information of the defeated people of user, show Query Result after inquiry Mishap Database;
4.3.3.3 detected rule configuration: to the open distinct interface of different user, the input of user is write rule database;
4.3.3.4 event information real time inspection: the flow of event real-time exhibition collected by time acquisition layer is on system interface;
4.4 fault statistics analyses: fault and/or system are recorded, statistics and analysis.
Complex event processing techniques binding events drives framework and event stream processing, the requirement of real-time of data is higher, therefore the present invention as above is based on the electric power factory equipment failure fast positioning method of big data, described distributed complex event real-time detecting system, Complex event processing engine Esper is adopted to be combined with distributed real-time Computational frame Storm, to realize high-throughput, low delay, complicated calculations for target.
Further, described step 4.4 fault statistics analysis, including the service that the occurrence frequency of fault, which fault impact are provided and/or the discrimination of fault is recorded by electric power factory equipment fault fast positioning system, statistics and analysis.
The present invention is tested based on the function of the electric power factory equipment fault fast positioning system of big data and applies by we, this system has higher fault detect accuracy rate and ageing faster, further demonstrates the effectiveness of the Fault Locating Method based on Complex event processing.
Accompanying drawing explanation
By embodiment of the invention below description taken together with the accompanying drawings, it is shown that the further advantage of the present invention and feature, this embodiment provides by way of example, but is not limited to this, wherein:
Fig. 1 show the present invention structural representation based on the electric power factory equipment fault fast positioning system of big data.
Fig. 2 show the present invention structural representation based on the failure location unit of the electric power factory equipment fault fast positioning system of big data.
Fig. 3 show the present invention workflow schematic diagram based on the electric power factory equipment fault fast positioning system of big data.
Detailed description of the invention
Shown in Fig. 1, Fig. 2, a kind of electric power factory equipment fault fast positioning system based on big data of the present invention, including: fault detection unit, event filtering unit, failure location unit and fault statistics analytic unit.
Wherein, fault detection unit, by inputting the information that proxy-reporting changes about Internet resources, after fault occurs by Fault Identification out; Event filtering unit, for filtering excessive information, described event filtering unit utilizes filter and threshold mechanism, filters out inessential and unconcerned event, repeats alarm noise etc., finds out the event to be processed that needs; Failure location unit, for determining the device location of fault in network, even down to the software system broken down; Fault statistics analytic unit, for being recorded fault and/or system, statistics and analysis.
Described failure location unit includes: event information acquisition layer, event-monitoring analysis layer, system display layer.
Described event information acquisition layer, by the event information of distributed event acquisition agent acquisition distributed network system (DNS), and writing events flow database.
Described event-monitoring analysis layer, including: the automatic transform subblock of detected rule and flow of event are monitored and analyze submodule. Record in rule database is automatically converted by the automatic transform subblock of described detected rule according to the flow of event filtering model based on set, generates SQL statement; The monitoring of described flow of event and analysis submodule read the SQL statement generated, the record in scan event flow database, monitor, analyze the fault of distributed system.
Described system display layer, including: fault alarm submodule, fault inquiry submodule, detected rule configuration submodule and event information real time inspection submodule. The distributed network system (DNS) fault that described fault alarm submodule real-time exhibition time monitoring analysis layer is oriented; Described fault inquiry submodule receives the classified inquiry information of the defeated people of user, shows Query Result after inquiry Mishap Database; The input of user, to the open distinct interface of different user, is write rule database by described detected rule configuration submodule; The flow of event real-time exhibition that time acquisition layer is collected by described event information real time inspection submodule is on system interface.
Detected rule configuration, fault alarm and display all complete on the main frame that responsible system shows.
Further, the occurrence frequency of fault, which fault impact are provided by described fault statistics analytic unit service and/or the discrimination of fault is recorded by electric power factory equipment fault fast positioning system, statistics and analysis.
The present invention also provides a kind of electric power factory equipment failure fast positioning method based on big data suitable in said system simultaneously, the method is to receive the flow of event from different pieces of information source by distributed complex event real-time detecting system, user can define event rules interested, by complex event processing techniques, event is filtered, assembles, the complicated calculations such as connection, mate defined event schema, thus carrying out early warning or corresponding actions.
Specifically include following steps:
S1 fault detect: by inputting the information that proxy-reporting changes about Internet resources, after fault occurs by Fault Identification out;
S2 event filtering: by arranging filter and threshold mechanism, filters out inessential and unconcerned event, repeats alarm noise etc., find out the event to be processed that needs;
S3 fault location: determine the device location of fault in network, even down to the software system broken down; Including:
S3.1 event information gathers: by the event information of distributed event acquisition agent acquisition distributed network system (DNS), and writing events flow database;
S3.2 event-monitoring is analyzed, including:
S3.2.1 detected rule is changed automatically: according to the flow of event filtering model based on set, the record in rule database is converted automatically, generates SQL statement;
The monitoring of S3.2.2 flow of event and analysis: read the SQL statement generated, the record in scan event flow database, monitor, analyze the fault of distributed system; S3.3 system shows, including:
S3.3.1 fault alarm: the distributed network system (DNS) fault that real-time exhibition time monitoring analysis layer is oriented;
S3.3.2 fault inquiry: receive the classified inquiry information of the defeated people of user, show Query Result after inquiry Mishap Database;
S3.3.3 detected rule configures: to the open distinct interface of different user, the input of user is write rule database;
S3.3.4 event information real time inspection: the flow of event real-time exhibition collected by time acquisition layer is on system interface;
S4 fault statistics is analyzed: fault and/or system are recorded, statistics and analysis.
Detailed description below.
1, the input of data stream:
Distributed complex event real-time detecting system receives the flow of event from different pieces of information source, user can define event rules interested, by complex event processing techniques, event is filtered, assembles, the complicated calculations such as connection, mate defined event schema, thus carrying out early warning or corresponding actions. Realized the real-time of complicated event detection by distributed real-time Computational frame, take measures fast and effectively, improve risk preventing ability.
The input data of system mainly include real-time event stream, historical data and rule three major types, and the data stream input designing general distributed complex event real-time detecting system is most important.
(1) flow of event definition
Native system design general purpose event class is abstract in Event, comprises Event Timestamp, and a Map stores all properties title and property value.
Event Collector, according to user-defined event type and attribute, carries out unified format conversion general purpose event type. Owing to Storm actively obtains data input from multiple data sources, mainly there are three kinds of modes: be directly connected to access, message queue, remote procedure call.The present invention mainly realizes by the second way.
(2) rule definition
The present invention adopts Esper Complex event processing engine to carry out Event Pattern Match, and rule, by EPL language definition, is broadly divided into two big classes: statement action statement and query query statement.
Wherein statement is the detection pattern to event definition, is mainly directed towards application, when pattern match, triggers audiomonitor execution action, including updating database operation; And the data in Esper human window can be inquired about by query, it is mainly directed towards user, it is thus achieved that real time execution result, carries out showing interface, or Query Result is sent to user.
Statement rule includes uniquely identifying ruleId, Rule content ruleContext, rule remarks ruleComment, rule state ruleStatus, event set Set<String>eventidSet.
Query rule, except comprising the identical attribute of statement, increases user and collects Set<String>useridSet, Query Result informs to user interested.
(3) stream data definition
Event Collector receives the data stream of input in real time, and user needs to formulate type and the attribute of flow of event. The Topology of Storm can be understood as the directed graph structure of data stream transmitting, each Bolt node is it needs to be determined that inlet flow or output stream, wherein go up the output stream of a Spout or Bolt node, it is the inlet flow of current Bolt node, therefore, need uniform data stream definition standard between inlet flow and output stream, and in Topology, the data stream ID of transmission is unique.
Data stream D, including assembly ID and data stream D. Wherein assembly D is the D of Spout or Bolt of Storm, and data stream III is defaulted as " default ", it is possible to arrange unique data stream D title.
For event stream processing, as shown in said procedure, event type describes EventTypeDescriptor and includes event title name, event attribute fields, additionally the unique identified event stream of streamld, it may be determined that this event is from which flow of event.
The inlet flow of Spout is to input from externally obtained data stream, specify incoming event class inputEventType, after Spout can carry out the operations such as type conversion, output stream, therefore, output stream includes event type title and event attribute mapping table, and Property Name is major key, and attribute type is value. The data stream of design Spout is as follows:
The inlet flow of Bolt is a upper Spout or Bolt node, therefore only need to know inlet flow type, can obtain event attribute information from Topology. After Bolt node carries out logical calculated, output stream, therefore, similar with the design of the output stream of Spout, including event type title and event attribute mapping table.
2, the real-time monitoring modular of complicated event
The real-time detection module of distributed complex event is mainly in conjunction with the distributed real-time Computational frame of Storm and Esper Complex event processing engine, owing to Esper is lightweight engine, and the advantage of Storm is Stream Processing, both effectively combine, bringing out the best in each other, Storm uses Esper as logical calculated node, from without paying close attention to loaded down with trivial details logical code, only need to define rule and flow of event interface, make whole processing procedure simpler clearly. In order to realize versatility, design general flow of event input interface, regular input interface, historical data input interface and generic logic and process class, be implemented as follows.
(1) flow of event input class: EventSpout
EventSpout reads flow of event from external equipment, as memory database Redis or message queue Kafka, EventSpout comprise event attribute fields, output stream catcher collector, database access interface eventsRep.Main method includes:
Open method, starts Storm, Spout and loads topological structure contextual information.
NextTuple method, is mainly used in reading message and launching, reads message data streams collector.emit (List<Obejct>tuple) as called JedisPop from Redis.
DeclareOutputFields method, the attribute of statement output stream.
Ack method, when message is by complete process, receives the process logic confirmed, as deleted the message being successfully processed from message queue.
Fail method, for the situation that message transmission is overtime or failed, carries out retransmitting operation.
Close method, needs other resources closed before closing Spout.
(2) rule input class: RuleSpout
RuleSpout loading rule storehouse, and rule is carried out packet transmission, basic skills is similar with EventSpout, nextTuple method writes core code, design topology, use DirtectGrouping strategy to directly transmit rule to formulating Bolt, or carry out FieldsGrouping according to attribute, it is also possible to AllGrouping broadcast distribution.
(3) historical data input class: DataSpout
DataSpout is used for static data, and basic skills is similar with EventSpout. NextTuple method accesses data from data base, and is transmitted to Bolts.
Static data includes: original historical data is carried out pretreated statistical result; Complex event processing testing result, the machine if system is delayed, it is possible to load window data when testing result is run as Esper, strengthen system reliability.
(4) generic logic processes class EsperBolt
The design of EsperBolt class uses typical Builder pattern, is separated with expression by the structure of complicated Bolt object, uses EsperBolt to realize multiple different expression. EsperBolt mainly includes inlet flow and output stream configuration, and loading rule, logical process transfers to Esper engine to process. Inner classes Builder can build inlet flow fabricant InputsBuilder, output stream fabricant OutputsBuilder, statement rule fabricant StatementsBuilder, inquires about fabricant QuerysBuilder, stores fabricant KeysBuilder. EsperBolt class is as follows:
Privately owned attribute mainly includes three major types: the configuration of EsperBolt, including inlet flow, output stream, rule, inquiry, data, storage major key definition; The framework of Esper engine, including EPServiceProvider, EPRuntime, EPAdminstrator etc.; Data base exchange method EventsRepository.
Prepare method can configure the configuration of topological structure contextual information and this EsperBolt, most importantly configures Esper, and initializes, and starts engine EPRuntime, loading rule statements and querys, and corresponding audiomonitor performs operation.
Execute method is the Bolt logic often receiving that an event is carried out, and mainly event is sent to Esper engine, or passes to next Bolt.
DeclareOutputFields method is as the term suggests stating the attribute of output stream exactly.
Update method, owing to EsperBolt inherits UpdateListener, when Event Pattern Match in Esper engine, will trigger update method, testing result will be updated database manipulation.
(5) incoming data stream, it is determined that data distribution policy
Abstract service logic, in XIVIL configuration file, defines flow of event Attribute domain EventSpout by Spring, rules properties territory RuleSpout, with historical data form DataSpout, it is determined that they flow to the distribution policy of logical processing nodes EsperBolt, as follows:
3, the combination of real-time stream and historical data processes
The combination of real-time event stream and historical data is used for solving two aspect difficult problems:
(1) owing to rule is ever-changing, Esper engine uses EPL statement to describe multiple event schema rule, the coupling of some rule needs in conjunction with historical data, monitoring rules such as money transfer transactions, if current turnover is more than the maximum of first trimester transfer amounts, then it is assumed that this transaction has certain risk.This rule needs, in conjunction with historical data, to calculate the maximum of first trimester transfer amounts, thus carrying out rule match. Esper provides the combination of EPL and SQL, uses sql:database_name [" parameterized_sql_query "] to allow access data base, it is achieved mutual with historical data. Consider that Esper frequently accesses the inefficiency of data base, therefore adopt and historical data is carried out preprocess method.
Rule is decomposed, it is necessary to calculate the maximum of first trimester device history data, minima and meansigma methods.
DataSpout loads first trimester historical data from data base, is input to BatchBolt, calculates the maximum of first trimester device attribute real time data, for EsperBolt rules engines processes, statistical result is updated in data base simultaneously.
(2) for rule that window is longer, Esper is not suitable for storage mass data by the limitation of Java Virtual Machine, the such as statistics total number of events of a year, should not arrange time window is 1 year, often receives an event and all counts, but arranges parameter, often receive an event, whether Check-Out Time satisfies condition, if meeting, then counting adds one. And for example, user's visit capacity of statistics server every day, monthly user's visit capacity, it is possible to use every day user's visit capacity, more accumulative draw monthly user's visit capacity. The condition of this rule-like is identical, and only time window is different, then long window rules can utilize the matching result of short window rules. For the long-time window rules that user specifies, it is possible to rule is decomposed, historical data is carried out pretreatment, reduce the pressure of Complex event processing engine, more effective perform detection. At present, artificial long window rules is decomposed is relied on.
Claims (6)
1. the electric power factory equipment fault fast positioning system based on big data, it is characterised in that including:
Fault detection unit, by inputting the information that proxy-reporting changes about Internet resources, after fault occurs by Fault Identification out;
Event filtering unit, for filtering excessive information, described event filtering unit utilizes filter and threshold mechanism, filters out inessential and unconcerned event, repeats alarm noise, finds out and need event to be processed;
Failure location unit, for determining the device location of fault in network; And
Fault statistics analytic unit, for being recorded fault and/or system, statistics and analysis;
Wherein:
Described failure location unit includes: event information acquisition layer, event-monitoring analysis layer, system display layer;
Described event information acquisition layer, by the event information of distributed event acquisition agent acquisition distributed network system (DNS), and writing events flow database;
Described event-monitoring analysis layer, including: the automatic transform subblock of detected rule and flow of event are monitored and analyze submodule; Record in rule database is automatically converted by the automatic transform subblock of described detected rule according to the flow of event filtering model based on set, generates SQL statement; The monitoring of described flow of event and analysis submodule read the SQL statement generated, the record in scan event flow database, monitor, analyze the fault of distributed system;
Described system display layer, including: fault alarm submodule, fault inquiry submodule, detected rule configuration submodule and event information real time inspection submodule; The distributed network system (DNS) fault that described fault alarm submodule real-time exhibition time monitoring analysis layer is oriented;Described fault inquiry submodule receives the classified inquiry information of the defeated people of user, shows Query Result after inquiry Mishap Database; The input of user, to the open distinct interface of different user, is write rule database by described detected rule configuration submodule; The flow of event real-time exhibition that time acquisition layer is collected by described event information real time inspection submodule is on system interface.
2. electric power factory equipment fault fast positioning system as claimed in claim 1, it is characterized in that: described fault statistics analytic unit for fault and/or system are recorded, statistics and analysis, including: service that the occurrence frequency of fault, which fault impact are provided and/or the discrimination of fault is recorded by electric power factory equipment fault fast positioning system, statistics and analysis.
3. the electric power factory equipment failure fast positioning method based on big data, it is characterized in that: the method is to receive the flow of event from different pieces of information source by distributed complex event real-time detecting system, user can define event rules interested, by complex event processing techniques, event is filtered, assembles, the complicated calculations such as connection, mate defined event schema, thus carrying out early warning or corresponding actions.
4. electric power factory equipment failure fast positioning method as claimed in claim 3, it is characterised in that the method comprises the following steps:
4.1 fault detects: by inputting the information that proxy-reporting changes about Internet resources, after fault occurs by Fault Identification out;
4.2 event filterings: by arranging filter and threshold mechanism, filter out inessential and unconcerned event, repeat alarm noise, find out the information needing event filtering to be processed excessive, find out and need event to be processed;
4.3 fault location: determine the device location of fault in network; Including:
4.3.1 event information collection: by the event information of distributed event acquisition agent acquisition distributed network system (DNS), and writing events flow database;
4.3.2 event-monitoring analysis, including:
4.3.2.1 detected rule is changed automatically: according to the flow of event filtering model based on set, the record in rule database is converted automatically, generates SQL statement;
4.3.2.2 flow of event monitoring and analysis: read the SQL statement generated, the record in scan event flow database, monitor, analyze the fault of distributed system;
4.3.3 system shows, including:
4.3.3.1 fault alarm: the distributed network system (DNS) fault that real-time exhibition time monitoring analysis layer is oriented;
4.3.3.2 fault inquiry: receive the classified inquiry information of the defeated people of user, show Query Result after inquiry Mishap Database;
4.3.3.3 detected rule configuration: to the open distinct interface of different user, the input of user is write rule database;
4.3.3.4 event information real time inspection: the flow of event real-time exhibition collected by time acquisition layer is on system interface;
4.4 fault statistics analyses: fault and/or system are recorded, statistics and analysis.
5. the electric power factory equipment failure fast positioning method as described in claim 3 or 4, it is characterised in that: described distributed complex event real-time detecting system, adopt Complex event processing engine Esper to be combined with distributed real-time Computational frame Storm.
6. electric power factory equipment failure fast positioning method as claimed in claim 4, it is characterized in that: described step 4.4 fault statistics analysis, including: service that the occurrence frequency of fault, which fault impact are provided and/or the discrimination of fault is recorded by electric power factory equipment fault fast positioning system, statistics and analysis.
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|---|---|---|---|---|
| CN106199421A (en) * | 2016-06-27 | 2016-12-07 | 北京协同创新研究院 | A kind of method for early warning based on the big data of industry and system |
| CN106341281A (en) * | 2016-11-10 | 2017-01-18 | 福州智永信息科技有限公司 | Distributed fault detection and recovery method of linux server |
| CN106445790A (en) * | 2016-10-12 | 2017-02-22 | 北京集奥聚合科技有限公司 | Counting and account-checking method and device used in distributed real-time computing system |
| CN106934563A (en) * | 2017-04-07 | 2017-07-07 | 国网河南省电力公司检修公司 | A kind of grid equipment accident treatment decision making device and method based on data analysis |
| CN108009257A (en) * | 2017-12-08 | 2018-05-08 | 武汉虹信技术服务有限责任公司 | A kind of wireless RF data screening plant and method based on streaming computing |
| CN109344117A (en) * | 2018-10-10 | 2019-02-15 | 四川新网银行股份有限公司 | A kind of risk detecting system based on concurrent |
| CN110275899A (en) * | 2019-04-18 | 2019-09-24 | 智链万源(北京)数字科技有限公司 | Internet of things data method for stream processing, system and device |
| CN111224813A (en) * | 2019-11-10 | 2020-06-02 | 辽宁金晟科技股份有限公司 | Intelligent network analysis system |
| CN111858262A (en) * | 2019-04-29 | 2020-10-30 | 安图斯科技股份有限公司 | Warning lamp control method and electronic device |
| CN112583623A (en) * | 2019-09-30 | 2021-03-30 | 中兴通讯股份有限公司 | Filtering information configuration method and system |
| CN112610564A (en) * | 2020-11-09 | 2021-04-06 | 上海中联重科桩工机械有限公司 | Monitoring system and maintenance system of hydraulic motor and vehicle |
| CN112866364A (en) * | 2021-01-07 | 2021-05-28 | 中国重型机械研究院股份公司 | Industrial internet cloud platform |
| CN113486030A (en) * | 2021-06-29 | 2021-10-08 | 北京安盟信息技术股份有限公司 | Real-time database synchronization method and device based on industrial protocol analysis |
| US20210369995A1 (en) * | 2020-05-27 | 2021-12-02 | GE Precision Healthcare LLC | Methods and systems for a medical gas quality monitor |
| CN116311828A (en) * | 2023-05-11 | 2023-06-23 | 武汉科迪智能环境股份有限公司 | Alarm management method, alarm management device, computer equipment and computer readable storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130077367A1 (en) * | 2011-09-23 | 2013-03-28 | Huibin Zhu | Systems and methods for use in grid fault event control |
| CN103209094A (en) * | 2013-03-11 | 2013-07-17 | 中国科学院信息工程研究所 | System and method for fault positioning on basis of events |
| CN104852473A (en) * | 2015-06-01 | 2015-08-19 | 国网上海市电力公司 | Monitoring method based on transformer station digital model |
| CN105160483A (en) * | 2015-09-10 | 2015-12-16 | 国家电网公司 | Secondary apparatus fault diagnosis method based on data excavation |
-
2016
- 2016-02-15 CN CN201610086123.0A patent/CN105657039A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130077367A1 (en) * | 2011-09-23 | 2013-03-28 | Huibin Zhu | Systems and methods for use in grid fault event control |
| CN103209094A (en) * | 2013-03-11 | 2013-07-17 | 中国科学院信息工程研究所 | System and method for fault positioning on basis of events |
| CN104852473A (en) * | 2015-06-01 | 2015-08-19 | 国网上海市电力公司 | Monitoring method based on transformer station digital model |
| CN105160483A (en) * | 2015-09-10 | 2015-12-16 | 国家电网公司 | Secondary apparatus fault diagnosis method based on data excavation |
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| CN106199421B (en) * | 2016-06-27 | 2018-03-02 | 北京协同创新研究院 | A kind of method for early warning and system based on industrial big data |
| CN106199421A (en) * | 2016-06-27 | 2016-12-07 | 北京协同创新研究院 | A kind of method for early warning based on the big data of industry and system |
| CN106445790A (en) * | 2016-10-12 | 2017-02-22 | 北京集奥聚合科技有限公司 | Counting and account-checking method and device used in distributed real-time computing system |
| CN106341281A (en) * | 2016-11-10 | 2017-01-18 | 福州智永信息科技有限公司 | Distributed fault detection and recovery method of linux server |
| CN106934563A (en) * | 2017-04-07 | 2017-07-07 | 国网河南省电力公司检修公司 | A kind of grid equipment accident treatment decision making device and method based on data analysis |
| CN108009257A (en) * | 2017-12-08 | 2018-05-08 | 武汉虹信技术服务有限责任公司 | A kind of wireless RF data screening plant and method based on streaming computing |
| CN108009257B (en) * | 2017-12-08 | 2020-09-11 | 武汉虹信技术服务有限责任公司 | Wireless radio frequency data screening device and method based on stream computing |
| CN109344117A (en) * | 2018-10-10 | 2019-02-15 | 四川新网银行股份有限公司 | A kind of risk detecting system based on concurrent |
| CN110275899A (en) * | 2019-04-18 | 2019-09-24 | 智链万源(北京)数字科技有限公司 | Internet of things data method for stream processing, system and device |
| CN111858262A (en) * | 2019-04-29 | 2020-10-30 | 安图斯科技股份有限公司 | Warning lamp control method and electronic device |
| CN112583623B (en) * | 2019-09-30 | 2023-02-07 | 中兴通讯股份有限公司 | Filtering information configuration method and system |
| CN112583623A (en) * | 2019-09-30 | 2021-03-30 | 中兴通讯股份有限公司 | Filtering information configuration method and system |
| CN111224813A (en) * | 2019-11-10 | 2020-06-02 | 辽宁金晟科技股份有限公司 | Intelligent network analysis system |
| US20210369995A1 (en) * | 2020-05-27 | 2021-12-02 | GE Precision Healthcare LLC | Methods and systems for a medical gas quality monitor |
| CN112610564A (en) * | 2020-11-09 | 2021-04-06 | 上海中联重科桩工机械有限公司 | Monitoring system and maintenance system of hydraulic motor and vehicle |
| CN112866364A (en) * | 2021-01-07 | 2021-05-28 | 中国重型机械研究院股份公司 | Industrial internet cloud platform |
| CN113486030A (en) * | 2021-06-29 | 2021-10-08 | 北京安盟信息技术股份有限公司 | Real-time database synchronization method and device based on industrial protocol analysis |
| CN113486030B (en) * | 2021-06-29 | 2022-08-19 | 北京安盟信息技术股份有限公司 | Real-time database synchronization method and device based on industrial protocol analysis |
| CN116311828A (en) * | 2023-05-11 | 2023-06-23 | 武汉科迪智能环境股份有限公司 | Alarm management method, alarm management device, computer equipment and computer readable storage medium |
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