CN110022226A - A kind of data collection system and acquisition method based on object-oriented - Google Patents
A kind of data collection system and acquisition method based on object-oriented Download PDFInfo
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- H—ELECTRICITY
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- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- 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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Abstract
The invention discloses a kind of data collection system and acquisition method based on object-oriented, are related to power system of data acquisition field.Currently, insufficient in terms of the expansion occurred during electric energy acquisition, reusability and flexibility.The present invention includes gateway cluster, the preposition cluster of communication, operational processor cluster, data/address bus, storage service module, mass data analysis module, data memory module；The technical program uses distributed elastic architecture design, with technologies such as stream process, message-oriented middleware, distributed storage and parallel computations, reconstruct electric power data acquisition system, it is greatly promoted from storage capacity, calculated performance, data processing speed and intellectual analysis etc., provides powerful guarantee for support Power marketing intellectual analysis, service business innovation, expansion professional application and raising electric service level etc..
Description
Technical field
The present invention relates to power system of data acquisition field more particularly to a kind of data collection systems based on object-oriented
And acquisition method.
Background technique
The communication protocol of acquisition system very disunity due to various expansions at present, causes to increase in electric energy acquisition communication process
A large amount of unnecessary specification conversion works seriously constrain electric under the following smart grid to realizing that interoperability brings difficulty
Measure the height intelligent Application of information.Meanwhile traditional communication protocol is mainly the data type agreement of service-oriented, for electric energy
The acquisition tasks demand to become more diverse in acquisition, gradually shown in terms of its expansion, reusability and flexibility it is some not
Foot.
When acquiring data, lower of traditional 1376.1 specifications allow to freeze according to regular progress data acquisition, such as day
The positive active total electric flux of knot, it is necessary to it goes to acquire by defined 0DF005 coding, the expansion that occurs during electric energy acquisition,
It is insufficient in terms of reusability and flexibility.
Summary of the invention
The technical problem to be solved in the present invention and the technical assignment of proposition are to be improved and improved to prior art,
A kind of data collection system and acquisition method based on object-oriented is provided, improves expansion, reusability and flexibility to reach
Purpose.For this purpose, the present invention takes following technical scheme.
A kind of data collection system based on object-oriented, including gateway cluster, the preposition cluster of communication, operational processor collection
Group, data/address bus, storage service module, mass data analysis module, data memory module；
Gateway cluster accesses electric power data acquisition system, and maintenance terminal communication link and original for that will acquire equipment
The transmitting-receiving of message, the acquisition equipment include specially becoming negative control terminal, Distribution transformer, low pressure concentrator；
Preposition cluster is communicated, is connected with gateway cluster, for the distribution scheduling of initial data message, and by original datagram
Text pushes in Distributed Message Queue；It communicates preposition cluster and device address domain algorithm realization plan is based on to the distribution scheduling of message
Slightly distribute, realizes that the dynamic of distribution policy adjusts by the operation conditions of each node of monitoring business processor cluster；Pass through heartbeat
The operation conditions of each node of handshake mechanism monitoring front operational processor, for node, newly-increased, node failure and malfunctioning node are extensive
Multiple three kinds of scenes are respectively according to " newly-increased node distribution policy ", " distribution policy when node failure " and " malfunctioning node recovery time-division
Hair strategy carries out dynamic adjustment, and the terminal address on this node is distributed to specified services processor section by address field algorithm
Point reduces program to server memory configuration requirement with reduction and load balance system file；
Operational processor cluster is connected with preposition module is communicated, parses for communication protocol, and and Distributed Message Queue
It interacts；Downbound request and composition downlink frame are obtained from message queue, the initial data message for communicating preposition is advised
It about parses and pushes to parsing result in Distributed Message Queue,
Data bus module is used to support the timing and persistence of uplink and downlink communication interactive information；Using high-throughput
Distributed Kafka message queue, make full use of Kafka service theme and theme subregion, publisher's theme and main website are answered
It is associated with cluster, operational processor cluster, the downbound request data and terminal uplink data that unified management master station application generates
Transmit/receive；
It is put in storage service module, is stored in relevant database for obtaining batch data from message queue；Using distributed big
The mode that data framework Hadoop and traditional Relational DataBase Oracle are combined is to adapt to the analysis and storage of mass data；
Mass data analysis module realizes the real-time of business datum by the big data frame based on distributed file system
It calculates, off-line analysis, provides technical support for the excavation of further depth；
Data memory module provides basic number for storing whole business datums, file data, initial data for system
According to support and calculate service；It is divided into main storage facility located at processing plant, calamity for library, history library, data publication library, and does by business and storage time limit
Point library strategy is to reduce the access pressure in one point data library.
Storage to mass data, according to different data using attribute by relevant database divide for main storage facility located at processing plant,
Calamity ensures that acquisition data safety is stable, reduces the data access pressure of storage facility located at processing plant, promotes number for library, history library, data publication library
Point library strategy is done according to efficiency for issuing, and by business and storage time limit to reduce the access pressure in one point data library.
As optimization technique means: prepositive communication computer, which draws all terminal device address domains in scene by certain regular partition, is
Multiple sections, device address obtain corresponding group address domain section by the quantity modulus of downlink Topic；Downlink Topic and address field
There are mapping relations, management of the front service processor node to address field section, namely the pipes to downlink Topic between section
Reason；Initialization address domain allocation strategy presses the preposition processing service node quantity modulus of business, and node increases newly, node failure and failure
Respectively according to " newly-increased node distribution policy ", " distribution policy when node failure " and " malfunctioning node recovery time-division when node restores
Hair strategy " realizes that dynamic adjusts, and distribution information is timely updated to Zookeeper distribution service, to reduce in program
Load is deposited, program cluster extended capability is improved.
As optimization technique means: A) node is newly-increased/dilatation when distribution policy are as follows:
A01) Topic distributed each operational processor node is ranked up according to Topic coding, and calculates the section
The currently processed Topic sum of point；
A02) operational processor node is ranked up according to Topic sum；
A03 the average value of Topic can be handled by) calculating each operational processor node, and Topic sum is divided by operational processor
Node total number；
A04) the extra Topic of all operational processor nodes by Topic quantity in node greater than AvgTopic takes out,
Take out rule: A05) preferential selection step A02) Topic encodes biggish Topic in the biggish operational processor node of sequence；
A06 the Topic taken out in step A04) preferentially) is distributed into newly-increased operational processor node, makes newly-increased node
Topic number about average value；Modulus distribution is carried out to all nodes if still having unappropriated Topic；
A07 the Topic information being assigned away) is deleted in other nodes.
As optimization technique means: B) node failure when distribution policy are as follows:
B01) front service processor node is ranked up according to Topic quantity；
B02) Topic sum obtains currently running each divided by currently running front service processor node sum
The average value of operational processor node processing Topic；
B03 it) the Topic quantity that can be increased newly according to the currently running each operational processor node of mean value calculation: is counted by b
The counted existing Topic number of mean value-；
B04) by because Topic to be distributed caused by node failure according to node it is newly-increased/dilatation when distribution policy calculating
Value is successively distributed to the small operational processor node that sorts.
As optimization technique means: C) malfunctioning node restore when distribution policy are as follows:
C01 allocation strategy when) the operational processor node restored is according to initialization loads corresponding Topic；
C02 these Topic information for being returned to recovery nodes) are deleted from other operational processor node timings.
As optimization technique means: storage service module is adopted i.e. school, the in real time mode repaired, fortune to acquisition data
It with stream process technology, realizes and acquisition load, electric energy indicating value data is checked in real time, are verified, and problem data is marked
Note, repairs abnormal load data；For problem data by power estimation, ARIMA algorithm and marketing distribution electricity into
Row is repaired, and guarantees the reasonability, consistency, logicality of data, by finding, marking invalid and distortion data in time, improves system
The system quality of data；The real time monitoring and analyzing of electric energy data, alarm event is realized with stream process technology；The stream process skill
Art is using the frame calculated in real time, and using Hbase+Storm, the real-time Computational frame of Storm is responsible for obtaining from message queue former
Beginning data and message data enter HBase distributed data base.
As optimization technique means: mass data analysis module is real by big data distributed memory parallel computation frame
Now by the hour to acquisition success rate index, all types of user electricity and load, line loss calculation, distribution transforming operational monitoring, mobile operator
Channel quality monitoring, the online rate of terminal are counted, to meet unit business control demands at different levels；Quasi real time analytical framework uses
Hive+Spark, Spark off-line calculation frame, which are realized, imports the statistics that Hive data warehouse executes mass data for initial data
Analysis business and data mining.
Another goal of the invention of the invention is to provide a kind of collecting method based on object-oriented, and feature exists
In:
1) when acquisition main website needs to be configured terminal, measurement point, calls survey operation together, comprising the following steps:
101) the main storage facility located at processing plant of Oracle is main to store whole business datums, archives number from marketing system synchronous foundation data
According to, initial data, data query is provided for acquisition main website；
102) acquisition main website initiates downbound request, and different Key can be arranged according to different operation type and be published to Kafka service
Downlink Topic in, and by operational order id deposit Redis caching in；
103) message in downlink Topic is partitioned storage according to Key and algorithm, and different subregions can define different excellent
Class downlink is arranged in first grade, the multidomain treat-ment as the highest multidomain treat-ment control class downbound request of configuration preference level, priority are taken second place
It requests, the multidomain treat-ment of other priority calls survey/relaying class downbound request together；
104) operational processor node loaded from Redis cache server with the archive information of synchronous designated terminal, from
The message of Kafka service subscription down queue is executed according to different Partition priority, forms downbound request message frame,
It is distributed to the preposition cluster of communication, and downlink message is pushed in the message Topic in Kafka；
105) it communicates preposition cluster and is sent to communication gate cluster by scheduling distribution policy；
106) downbound request is sent to terminal device by communication gate；
107) terminal returns to operating result, carries out packet parsing by operational processor through communication gate, the preposition cluster of communication
And operating result is returned into the corresponding operational order id of the terminal in Redis；
108) acquisition main website obtains operating result according to the corresponding operational order id of the terminal from Redis；
2) when needing data acquisition for electric energy, comprising the following steps:
201) by task class data, abnormal events, the formal of message is sent to gateway cluster accordingly；
202 gateway clusters are distributed to the preposition cluster of communication by load balancing；
203) original message data is distributed to operational processor cluster according to scheduling distribution policy；
204) device node loads and the archive information of synchronous designated terminal, parsing uplink original from Redis cache server
Beginning message data, and the information such as parsing result, original message data are pushed in corresponding Kafka message queue；That is, parsing
As a result it pushes in reported data Topic, original message data pushes to message Topic；
205) library service obtains former from Kafka service subscription message, the real-time Computational frame of Storm from Kafka message queue
Beginning message data, electric energy data etc. are stored in HBase distributed data base；Spark off-line calculation frame imports initial data
Hive data warehouse executes complicated statistical analysis and data mining；Data loading service is by original message data, electric flux number
Relevant database is stored according to batch；
206) from cloud platform quick search data acquisition for electric energy detail, acquisition success rate etc.；
207) the main storage facility located at processing plant of le is main to store whole business datums, file data, original from marketing system synchronous foundation data
Beginning data provide data query for acquisition main website；
3) when the completion of Yao Jinhang electric energy data, include the following steps
301) electric energy data is sent with message in form to communication gate cluster by multiple communication modes；
302) communication gate cluster is sent to the preposition cluster of communication according to load balancing distribution policy；
303) preposition cluster is distributed to operational processor cluster by dispatching distribution policy；
304) operational processor node loads and the archive information of synchronous designated terminal, solution from Redis cache server
Uplink original message data is analysed, and the information such as parsing result, original message data are pushed into corresponding Kafka message queue
In；That is, parsing result pushes in reported data Topic, original message data pushes to message Topic；
305) stream calculation services Storm from Kafka service subscription message, obtains electric energy data in real time, is stored in real time
Task data in HBase distributed data gets table ready；
306) when mending trick for real-time leak source, Spark RDD timing executes leak source audit task, i.e., is mended according to leak source and recruit plan
Leak source audit slightly is carried out to getting table in HBase ready, and corresponding leak source is formed according to terminal called state and is requested, and pushes to Kafka
It issues in the downlink Topic of service for operational processor acquisition, is recruited to realize that real-time leak source is mended；
307) when mending trick for manual leak source, Spark RDD timed task reads benefit from oracle database and recruits plan
Leak source audit task is slightly executed afterwards, and corresponding leak source is formed according to terminal called situation and is requested, and pushes to the confession of Kafka messaging service
Operational processor acquisition issues, and realizes that leak source is mended and recruits.
As optimization technique means: in the acquisition for terminal affair, being defined according to the order of importance and emergency of event not at the same level
Other acquisition module specifies and different reports frequency；Channel resource can not only be more reasonably distributed, reduces terminal handler not
Necessary expense, while administrative staff's analysis can be assisted, processing anomalous event, promote the efficiency of management.
As optimization technique means: data acquisition include the acquisition to special parameter evidence, low pressure I type concentrator data acquisition,
The acquisition of low pressure II type concentrator data；
One) special parameter is according to acquisition:
When specially day is freezed active energy in change acquisition, it is 1 that terminal, which acquires ammeter and executes frequency, the frequency of terminal reported data
It is 12 hours, data classification is day freezing data, and data item includes working as previous quadrant reactive energy indicating value data block, current four-quadrant
Limit reactive energy indicating value data block, current forward direction active energy indicating value data block, current reversed active energy indicating value data block；
When specially becoming 96 point load curve of acquisition, it is 15 minutes that terminal, which acquires ammeter and the execution frequency of reported data, data
It is classified as real time data, data item includes voltage data block, current data block, active power, when previous quadrant reactive energy shows
Value Data block, current four-quadrant reactive energy indicating value data block, positive active total electric flux, power factor；
Two) low pressure I type concentrator data acquire:
When day is freezed active energy in the acquisition of low pressure I type concentrator, it is 1 that terminal, which acquires ammeter and executes frequency, and terminal reports
The frequency of data is 12 hours, and data classification is day freezing data, and acquisition and reported data item include current positive active energy
Indicating value data block, current reversed active energy indicating value data block；
When low pressure I type concentrator acquires 96 point load curve, it is 6 small that terminal, which acquires ammeter and the execution frequency of reported data,
When, data classification is minute freezing data, if installation is three-phase electric energy meter, data item is voltage data block, current data
Block, power factor (PF), active power, positive active total electric flux, if installation is single-phase electric energy meter, data item be A phase voltage,
A phase current, power factor (PF), positive active total electric flux, active power, N line current；
Three) low pressure II type concentrator data acquire:
It is same with I type concentrator when day is freezed active energy in the acquisition of low pressure II type concentrator, that is, issue same acquisition scheme
Template and report plan template；
When low pressure II type concentrator acquires 96 point load curve, it is 15 that terminal, which acquires ammeter and the execution frequency of reported data,
Minute, data classification is real time data, if installation is three-phase electric energy meter, data item be voltage data block, current data block,
Power factor (PF), active power, positive active total electric flux, if installation is single-phase electric energy meter, data item is A phase voltage, A phase
Electric current, power factor (PF), positive active total electric flux, active power, N line current.
The utility model has the advantages that
One, the technical program uses distributed elastic architecture design, with stream process, message-oriented middleware, distributed storage
With the technologies such as parallel computation, electric power data acquisition system is reconstructed, from storage capacity, calculated performance, data processing speed and intelligence
Analysis etc. is greatly promoted, and for support Power marketing intellectual analysis, service business innovation, is expanded professional application and is mentioned
High electric service level etc. provides powerful guarantee.
Two, the technical program is based on object-oriented communications protocol characteristic, more sets of data acquisition methods can be set, relative to tradition
Acquisition mode be obviously improved effect in terms of the efficiency of data acquisition, flexibility and loss:
1, when the acquisition scheme of basic data is divided into acquisition scheme and reports scheme, terminal acquisition ammeter is defined respectively
Rule and the rule that reports of terminal data, have the advantages that following two aspect:
(1) flexible data acquisition modes (adopt in real time, be packaged and adopt), the period of reported data and frequency are realized, is realized
Local communication traffic, which avoids the peak hour, reduces acquisition leak source obvious action；
(2) alternative configuration data acquisition and data report, for the partial data item (example of support equipment local service
As clock patrols survey, local mend is copied), it can accomplish only to adopt not reporting, effectively improve the diversity and data acquisition of field device service
Quality.
2, different acquisition data item, such as three-phase meter acquisition current data block and voltage be can configure for electric energy meter type
Data block (A, B, C), single-phase meter only acquire A phase current and A phase voltage, all acquire A, B, C three-phase electricity relative to all tables of tradition
Pressure and electric current, effectively reduce the loss of flow.
3, for the acquisition of terminal affair, the acquisition module of different stage can be defined according to the order of importance and emergency of event, specified
Different reports frequency, can not only more reasonably distribute channel resource, reduces the unnecessary expense of terminal handler, while can
To assist administrative staff's analysis, processing anomalous event, promote the efficiency of management.
The technical program realizes that dynamic adjusts, and with reduction and load balance system file, reduces program to server memory
Configuration requirement.
Mass data storage framework focuses on " memorization, Yun Hua, the division of labor are specialized ", realize electric energy data integrated fusion and
Efficiently management.
Analysis to mass data, it is real by the big data frame based on distributed file system using big data cloud platform
The real-time calculating of existing business datum, off-line analysis.
Off-line analysis frame preferably uses Hive+Spark, and the realization of Spark off-line calculation frame imports initial data
The statistical analysis business and data mining of Hive data warehouse execution mass data.
Storage to mass data, according to different data using attribute by relevant database divide for main storage facility located at processing plant,
Calamity ensures that acquisition data safety is stable, reduces the data access pressure of storage facility located at processing plant, promotes number for library, history library, data publication library
Point library strategy is done according to efficiency for issuing, and by business and storage time limit to reduce the access pressure in one point data library.
Detailed description of the invention
Fig. 1 is structure of the invention block diagram；
Fig. 2 is main website setting of the invention, the flow chart for calling survey together；
Fig. 3 is the flow chart of data acquisition for electric energy of the invention；
Fig. 4 is the flow chart of the invention to electric energy data completion；
Specific embodiment
Technical solution of the present invention is described in further detail below in conjunction with Figure of description.
As shown in Figure 1, a kind of data collection system based on object-oriented includes gateway cluster, the preposition cluster of communication, industry
Business processor cluster, data/address bus, storage service module, mass data analysis module, data memory module.
The technical program use distributed elastic architecture design, with stream process, message-oriented middleware, distributed storage with simultaneously
The technologies such as row calculating, reconstruct electric power data acquisition system, from storage capacity, calculated performance, data processing speed and intellectual analysis
Etc. greatly promoted, for support Power marketing intellectual analysis, service business innovation, expand professional application and improve supply
Electric service level etc. provides powerful guarantee.
The technical program has the following characteristics that
1. reconstructing signal procedure using resilient infrastructure, meets and constantly increase userbase and acquisition demand: using big data
Technology redesigns the framework of power information acquisition system, using distributed elastic architecture design: first is that communication gate
It is cached using message as bus with acquisition front end processor, carries out message communication；Second is that front end processor operational processor only to message into
Professional etiquette about parses, and writes data into message caching；Third is that storage architecture focuses on " memorization, Yun Hua, the division of labor are specialized ", introduce
NOSQL storage, data volume is big, storage management ability is various, and cloud platform streaming computing and off-line analysis service cluster carry out event
The operation such as analysis, data check, reparation, relational database use Attribute transposition standby for main storage facility located at processing plant, calamity according to different data
Library, history library, data publication library.Fourth is that all acquisition data are put in storage by storage service cluster is unified.
Communication gate service is mainly responsible for the specially acquisition equipment such as change negative control terminal, Distribution transformer, low pressure concentrator and connects
Enter electric power data acquisition system, and the transmitting-receiving of maintenance terminal communication link and original message.
Communicate the preposition distribution scheduling for servicing and being responsible for initial data message.
It communicates preposition service to distribute the distribution scheduling of message based on device address domain algorithm implementation strategy, before monitoring
The operation conditions for setting each node of operational processor realizes the dynamic adjustment of distribution policy.Its specific algorithm is as follows:
A. all terminal device address domains in scene are drawn by certain regular partition is several sections, for example device address is pressed
Quantity (100) modulus of row Topic obtains 100 group address domain sections.In this way, between downlink Topic and address field section
There are mapping relations, management of the front service processor node to the management in address field section namely to downlink Topic.
B. initialization address domain allocation strategy presses the preposition processing service node quantity modulus of business, and node is newly-increased, node is former
Respectively according to " newly-increased node distribution policy ", " distribution policy when node failure " and " malfunctioning node when barrier and malfunctioning node restore
Distribution policy when recovery " realizes that dynamic adjusts, and distribution information is timely updated to Zookeeper distribution service.This
Distribution policy purpose is to reduce program internal memory load, raising program cluster extended capability.
C. " distribution policy when node increases (dilatation) newly ":
1) Topic each operational processor node distributed be ranked up according to Topic coding (such as: by greatly to
It is small), and calculate the currently processed Topic sum of the node.
2) operational processor node (without newly-increased node) is ranked up (such as: descending) according to Topic sum；
3) average value (assuming that the value is labeled as AvgTopic) of Topic can be handled by calculating each operational processor node:
Topic sum divided by operational processor node total number (containing newly-increased node), cast out by decimal place.
4) the extra Topic of all operational processor nodes by Topic quantity in node greater than AvgTopic takes out, and takes
Regular out: preferential selection step 2 Topic in biggish operational processor node that sorts encodes biggish Topic.
5) Topic taken out in step 4 is preferentially distributed to newly-increased operational processor node, makes newly-increased node
Topic number about average value；Modulus distribution is carried out to all nodes if still having unappropriated Topic.
6) the Topic information being assigned away is deleted in other nodes.
D. " distribution policy when node failure ":
1) (such as descending) is ranked up according to Topic quantity to front service processor node.
2) Topic sum obtains currently running each industry divided by currently running front service processor node sum
The average value of business processor node processing Topic；
3) it the Topic quantity that can be increased newly according to the currently running each operational processor node of mean value calculation: is calculated by b
The existing Topic number of mean value-obtained；
4) according to the calculated value of step c the small industry that sorts will be successively distributed to because of Topic to be distributed caused by node failure
Business processor node；
E. " distribution policy when malfunctioning node restores ":
1) allocation strategy when operational processor node restored is according to initialization loads corresponding Topic；
2) these Topic information for being returned to recovery nodes are deleted from other operational processor node timings.
Front service processing, which services, is responsible for communication protocol parsing, and interacts with Distributed Message Queue.I.e. from message
Queue obtains downbound request and composition downlink frame, will communicate preposition initial data message and carry out specification parsing and by parsing result
It pushes in Distributed Message Queue.
Preposition service node is communicated to be shaken hands by timing heartbeat between operational processor node to obtain all business processings
The operating status of device node, and the terminal address on this node is distributed to specified services processor node by address field algorithm.
Distributed Message Queue is responsible for supporting the timing and persistence of uplink and downlink communication interactive information as data/address bus.
The specific distributed Kafka message queue for using high-throughput, the theme and theme subregion for making full use of Kafka to service will
Publisher's theme and master station application service cluster, operational processor service cluster are associated, and unified management master station application generates
Downbound request data and terminal uplink data transmit/receive.
Service cluster is put in storage to be responsible for obtaining batch data deposit relevant database from message queue.
Processing cluster realizes industry by the big data frame based on distributed file system using big data cloud platform in real time
The real-time calculating for data of being engaged in, off-line analysis provide technical support for the excavation of further depth.
2, " adopting i.e. school " carried out to acquisition data, repaired in real time, promoted the quality of data: using stream process technology, realize
Acquisition load, electric energy indicating value data are checked in real time, are verified, and problem data is marked, to abnormal load data
It is repaired；It is repaired for problem data by power estimation, ARIMA algorithm and marketing distribution electricity, guarantees data
Reasonability, consistency, logicality improve system data quality by finding, marking invalid and distortion data in time.Meanwhile it transporting
The real time monitoring and analyzing of electric energy data, alarm event is realized with stream process technology.The stream process technology calculates in real time
Frame, preferably use Hbase+Storm, the real-time Computational frame of Storm be responsible for from message queue obtain initial data and
Message data enters HBase distributed data base；
3, by distributive parallel computation framework, realize that mass data quasi real time counts: by big data distributed memory
Parallel computation frame is realized and is run by the hour to acquisition success rate index, all types of user electricity and load, line loss calculation, distribution transforming
Monitoring, the monitoring of mobile operator channel quality, the online rate of terminal etc. are counted, and unit business control demands at different levels are met.It is quasi-
Real-time analytical framework preferably uses Hive+Spark, and Spark off-line calculation frame, which is realized, imports Hive data for initial data
The statistical analysis business and data mining of warehouse execution mass data.
4, flexible data store strategy is constructed, realize " storage on demand ", meet various dimensions query demand: analysis is not of the same trade or business
Be engaged in data application demand, play commercial data base (Oracle), cache database (Redis), distributed data base (HBase) and
Data warehouse (Hive) strong point designs multi-level storage machine system, improves query performance, promotes data application efficiency.
Commercial data base uses Oracle12c database version, and in conjunction with InfiniBand high speed network and SSD, (solid-state is hard
Disk) data storing platform for building OLTP (Transaction Processing) business of support high-throughput high concurrent is stored, it is mainly responsible for
Whole business datums, file data, initial data are stored, master data is provided for system and supports and calculate service.Relationship type number
It can segment as main storage facility located at processing plant, calamity according to library for library, history library, data publication library, ensure that acquisition data safety is stable, reduce storage facility located at processing plant
Data access pressure, promoted data publication efficiency, and by business and storage the time limit do point library strategy to reduce one point data library
Access pressure.
Cache database (Redis) is a high performance Key-Value database, and the high-Redis of performance can support to surpass
Cross 100K+ read-write frequency per second.Not only support the data of simple Key-Value type, at the same also provide list, set,
The storage of the data structures such as zset, hash.
Distributed data base HBase is a high reliability, high-performance, towards column, telescopic distributed memory system.
Hive is a Tool for Data Warehouse based on Hadoop, the data file of structuring can be mapped as a number
According to library table, simple MapReduce is fast implemented by class SQL statement and is counted, the statistical analysis of data warehouse is very suitable for.
Data between cloud data platform and relevant database synchronize preferably real using Sqoop data transfer tool
It is existing.
5, it is based on object-oriented communications protocol characteristic, more sets of data acquisition methods can be devised, relative to traditional acquisition
Mode is obviously improved effect in terms of the efficiency of data acquisition, flexibility and loss, described in detail below:
The acquisition scheme of basic data is divided into acquisition scheme and reports scheme by a, defines terminal acquisition ammeter respectively
The rule that rule and terminal data report, has the advantages that following two aspect:
(1) flexible data acquisition modes (adopt in real time, be packaged and adopt), the period of reported data and frequency are realized, is realized
Local communication traffic, which avoids the peak hour, reduces acquisition leak source obvious action；
(2) alternative configuration data acquisition and data report, for the partial data item (example of support equipment local service
As clock patrols survey, local mend is copied), it can accomplish only to adopt not reporting, effectively improve the diversity and data acquisition of field device service
Quality.
B can configure different acquisition data item, such as three-phase meter acquisition current data block and voltage for electric energy meter type
Data block (A, B, C), single-phase meter only acquire A phase current and A phase voltage, all acquire A, B, C three-phase electricity relative to all tables of tradition
Pressure and electric current, effectively reduce the loss of flow.
Acquisition of the c for terminal affair can define the acquisition module of different stage according to the order of importance and emergency of event, specify not
With report frequency, can not only more reasonably distribute channel resource, reduce the unnecessary expense of terminal handler, while can be with
It assists administrative staff's analysis, processing anomalous event, promote the efficiency of management.
The data acquisition modes of object-oriented protocol terminal can be divided into acquisition scheme and report scheme, and acquisition scheme defines
Terminal acquires the rule of ammeter, the rule of schema definition terminal reported data is reported, shown in template sample following table.
Table 1
Object-oriented terminal affair is divided into each rank according to event significance level, and each rank defines respective acquisition
Frequency and reported data item, template sample are as shown in the table.
The collecting method of data collection system based on object-oriented, comprising:
One: acquisition main website is configured terminal, measurement point, calls the process for surveying operation together, as shown in Figure 2.
1) the main storage facility located at processing plant of .Oracle is main to store whole business datums, archives number from marketing system synchronous foundation data
According to, initial data, data query is provided for acquisition main website.
2) acquires main website and initiates downbound request, and different Key can be arranged according to different operation type and be published to Kafka service
Downlink Topic in, and by operational order id deposit Redis caching in.
3) message in downlink Topic is partitioned storage according to Key and algorithm, and different subregions can define different excellent
Class downlink is arranged in first grade, the multidomain treat-ment as the highest multidomain treat-ment control class downbound request of configuration preference level, priority are taken second place
It requests, the multidomain treat-ment of other priority calls survey/relaying class downbound request together.
4) operational processor node loaded from Redis cache server with the archive information of synchronous designated terminal, from
The message of Kafka service subscription down queue is executed according to different Partition priority, forms downbound request message frame,
It is distributed to the preposition cluster of communication, and downlink message is pushed in the message Topic in Kafka.
5) communicates preposition cluster and is sent to communication gate cluster by scheduling distribution policy.
6) downbound request is sent to terminal device by communication gate.
7) terminal returns to operating result, carries out packet parsing simultaneously by operational processor through communication gate, the preposition cluster of communication
Operating result is returned into the corresponding operational order id of the terminal in Redis.
8) acquires main website and obtains operating result according to the corresponding operational order id of the terminal from Redis.
Two: the process of data acquisition for electric energy, as shown in Figure 3；
1) by task class data, abnormal events, the formal of message is sent to gateway cluster acquisition terminal accordingly；
2) gateway cluster is distributed to the preposition cluster of communication by load balancing；
3) communication is preposition is distributed to operational processor cluster for original message data according to scheduling distribution policy；
4) operational processor node loads and the archive information of synchronous designated terminal, parsing from Redis cache server
Uplink original message data, and the information such as parsing result, original message data are pushed in corresponding Kafka message queue；
That is, parsing result pushes in reported data Topic, original message data pushes to message Topic.
5) service of stream process storage is from Kafka service subscription message, and the real-time Computational frame of Storm is from Kafka message queue
Obtain the deposit HBase distributed data base such as original message data, electric energy data.Spark off-line calculation frame is by original number
Complicated statistical analysis and data mining are executed according to Hive data warehouse is imported.Data loading service is by original message data, electricity
Energy datum batch deposit relevant database.
6) acquires main website from cloud platform quick search data acquisition for electric energy detail, acquisition success rate etc..
7) the main storage facility located at processing plant of .Oracle is main to store whole business datums, archives number from marketing system synchronous foundation data
According to, initial data, data query is provided for acquisition main website.
Performance indicator:
Calculate time-consuming index: all kinds of off-line calculation business of big data cloud platform complete (such as acquisition quality point within half an hour
The typical services such as analysis, the analysis of industry load trend)；All kinds of real-time stream calculation business treatment scales per second reach 20,000 (such as loads
The typical services such as specificity analysis, the maintenance of terminal communications status).
Communication process index communicates preposition cluster single node TCP link access amount and is up to 400,000；Acquire preposition cluster
Single node distribution processor message per second is up to 30,000；Data storage service overall data warehouse-in efficiency reaches 60,000 per second.
Supplementing Data can be carried out by mending the strategy recruited for electric energy data missing, improve acquisition success rate；By big
Data cloud platform can realize the quick completion of leak source.Benefit trick can be divided into real-time leak source and mend trick and main website manual leak source benefit trick two
Point, the specific process such as to electric energy data completion.
Three: to the flow chart of electric energy data completion, as shown in Figure 4.
1) acquisition terminal is sent electric energy data with message to communication gate cluster by multiple communication modes in form.
2) communication gate cluster is sent to the preposition cluster of communication according to load balancing distribution policy.
3) communicates preposition cluster and is distributed to operational processor cluster by dispatching distribution policy.
4) operational processor node loads and the archive information of synchronous designated terminal, parsing from Redis cache server
Uplink original message data, and the information such as parsing result, original message data are pushed in corresponding Kafka message queue；
That is, parsing result pushes in reported data Topic, original message data pushes to message Topic.
5) stream calculation services Storm from Kafka service subscription message, obtains electric energy data in real time, is stored in real time
Task data in HBase distributed data gets table ready.
6) .Spark RDD timing execute leak source audit task, i.e., according to leak source mend recruit strategy to got ready in HBase table into
The audit of row leak source, and corresponding leak source is formed according to terminal called state and is requested, it pushes in the downlink Topic of Kafka service and supplies
Operational processor acquisition issues, and recruits to realize that real-time leak source is mended.
On the other hand, acquisition main website also can trigger manual leak source and mend trick.
1), which will be mended, recruits in tactful (such as districts and cities' unit, user type, data type) deposit oracle database.
2) .Spark RDD timed task executes leak source audit task after reading benefit trick strategy in oracle database,
And corresponding leak source is formed according to terminal called situation and is requested, it pushes to Kafka messaging service and is issued for operational processor acquisition, it is real
Existing leak source, which is mended, recruits.
Four: the technical program can be according to terminal type, device type, the type (electricity, load etc.) for acquiring data, electric energy
The difference of table type, we need to select different acquisition methods when implementation.
1) specially become collecting method
When specially day is freezed active energy in change acquisition, it is 1 that terminal, which acquires ammeter and executes frequency, the frequency of terminal reported data
It is 12 hours, data classification is day freezing data, and data item includes working as previous quadrant reactive energy indicating value data block, current four-quadrant
Limit reactive energy indicating value data block, current forward direction active energy indicating value data block, current reversed active energy indicating value data block.
When specially becoming 96 point load curve of acquisition, it is 15 minutes that terminal, which acquires ammeter and the execution frequency of reported data, data
It is classified as real time data, data item includes voltage data block, current data block, active power, when previous quadrant reactive energy shows
Value Data block, current four-quadrant reactive energy indicating value data block, positive active total electric flux, power factor.
2) low pressure I type concentrator collecting method
When day is freezed active energy in the acquisition of low pressure I type concentrator, it is 1 that terminal, which acquires ammeter and executes frequency, and terminal reports
The frequency of data is 12 hours, and data classification is day freezing data, and acquisition and reported data item include current positive active energy
Indicating value data block, current reversed active energy indicating value data block.
When low pressure I type concentrator acquires 96 point load curve, it is 6 small that terminal, which acquires ammeter and the execution frequency of reported data,
When, data classification is minute freezing data, if installation is three-phase electric energy meter, data item is voltage data block, current data
Block, power factor (PF), active power, positive active total electric flux, if installation is single-phase electric energy meter, data item be A phase voltage,
A phase current, power factor (PF), positive active total electric flux, active power, N line current.
3) low pressure II type concentrator collecting method
It is completely the same with I type concentrator when day is freezed active energy in the acquisition of low pressure II type concentrator, that is, it issues and similarly adopts
Collect plan template and reports plan template.
When low pressure II type concentrator acquires 96 point load curve, it is 15 that terminal, which acquires ammeter and the execution frequency of reported data,
Minute, data classification is real time data, if installation is three-phase electric energy meter, data item be voltage data block, current data block,
Power factor (PF), active power, positive active total electric flux, if installation is single-phase electric energy meter, data item is A phase voltage, A phase
Electric current, power factor (PF), positive active total electric flux, active power, N line current.
4) event scheme designs
The event scheme designed in the present invention can almost cover all terminal affairs, select three events in the present embodiment
As case:
A kind of collecting method based on object-oriented shown in figure 1 above -4 is specific embodiments of the present invention,
Substantive distinguishing features of the present invention and progress are embodied, it can be carried out under the inspiration of the present invention using needs according to actual
The equivalent modifications of shape, structure etc., the column in the protection scope of this programme.
Claims (10)
1. a kind of data collection system based on object-oriented, it is characterised in that: including gateway cluster, the preposition cluster of communication, industry
Business processor cluster, data/address bus, storage service module, mass data analysis module, data memory module；
Gateway cluster, for equipment access electric power data acquisition system, and maintenance terminal communication link and original message will to be acquired
Transmitting-receiving, the acquisition equipment include specially become negative control terminal, Distribution transformer, low pressure concentrator；
Preposition cluster is communicated, is connected with gateway cluster, is pushed away for the distribution scheduling of initial data message, and by initial data message
It send into Distributed Message Queue；Preposition cluster is communicated to the distribution scheduling of message based on device address domain algorithm implementation strategy point
Hair realizes that the dynamic of distribution policy adjusts by the operation conditions of each node of monitoring business processor cluster；It is shaken hands by heartbeat
The operation conditions of each node of mechanism monitors front service processor, for node, newly-increased, node failure and malfunctioning node restore three
Kind scene according to " newly-increased node distribution policy ", " distribution policy when node failure " and " distributes plan when malfunctioning node recovery respectively
Dynamic adjustment is slightly carried out, and the terminal address on this node is distributed to specified services processor node by address field algorithm, with
Reduction and load balance system file reduce program to server memory configuration requirement；
Operational processor cluster is connected with preposition module is communicated, parses for communication protocol, and carries out with Distributed Message Queue
Interaction；Downbound request and composition downlink frame are obtained from message queue, preposition initial data message will be communicated and carry out specification solution
It analyses and pushes to parsing result in Distributed Message Queue,
Data bus module is used to support the timing and persistence of uplink and downlink communication interactive information；Using point of high-throughput
Cloth Kafka message queue, the theme and theme subregion for making full use of Kafka to service, by publisher's theme and master station application collection
Group, operational processor cluster are associated, and the receipts of downbound request data and terminal uplink data that unified management master station application generates/
Hair；
It is put in storage service module, is stored in relevant database for obtaining batch data from message queue；Using distributed big data
The mode that frame Hadoop and traditional Relational DataBase Oracle are combined is to adapt to the analysis and storage of mass data；
Mass data analysis module, by by the big data frame of distributed file system realize business datum it is real-time based on
It calculates, off-line analysis, provides technical support for the excavation of further depth；
Data memory module provides master data branch for storing whole business datums, file data, initial data for system
Hold and calculate service；It is divided into main storage facility located at processing plant, calamity for library, history library, data publication library, and does a point library by business and storage time limit
Strategy is to reduce the access pressure in one point data library.
2. a kind of data collection system based on object-oriented according to claim 1, it is characterised in that: prepositive communication computer
It is multiple sections that all terminal device address domains in scene, which are drawn by certain regular partition, and the quantity of downlink Topic is pressed in device address
Modulus obtains corresponding group address domain section；There are mapping relations, front service processors between downlink Topic and address field section
Management of the node to address field section, namely the management to downlink Topic；Initialization address domain allocation strategy presses the preposition place of business
Service node quantity modulus is managed, respectively according to " newly-increased node distributes plan when node is newly-increased, node failure and malfunctioning node restore
Slightly ", " distribution policy when node failure " and " distribution policy when malfunctioning node restores " realizes that dynamic adjusts, timely by information is distributed
It is updated to Zookeeper distribution service, to reduce program internal memory load, improves program cluster extended capability.
3. a kind of data collection system based on object-oriented according to claim 2, it is characterised in that: A) node is new
Distribution policy when increasing/dilatation are as follows:
A01) Topic distributed each operational processor node is ranked up according to Topic coding, and is calculated the node and worked as
The Topic sum of pre-treatment；
A02) operational processor node is ranked up according to Topic sum；
A03 the average value of Topic can be handled by) calculating each operational processor node, and Topic sum is divided by operational processor node
Sum；
A04) the extra Topic of all operational processor nodes by Topic quantity in node greater than AvgTopic takes out, and takes out
Rule: A05) preferential selection step A02) Topic encodes biggish Topic in the biggish operational processor node of sequence；
A06 the Topic taken out in step A04) preferentially) is distributed into newly-increased operational processor node, makes newly-increased node
Topic number about average value；Modulus distribution is carried out to all nodes if still having unappropriated Topic；
A07 the Topic information being assigned away) is deleted in other nodes.
4. a kind of data collection system based on object-oriented according to claim 3, it is characterised in that: B) node failure
When distribution policy are as follows:
B01) front service processor node is ranked up according to Topic quantity；
B02) Topic sum obtains currently running each business divided by currently running front service processor node sum
The average value of processor node processing Topic；
B03 it) the Topic quantity that can be increased newly according to the currently running each operational processor node of mean value calculation: is calculated by b
The existing Topic number of mean value-；
B04) by because Topic to be distributed caused by node failure according to node it is newly-increased/dilatation when distribution policy calculated value according to
It is secondary to be distributed to the small operational processor node that sorts.
5. a kind of data collection system based on object-oriented according to claim 4, it is characterised in that: C) malfunctioning node
Distribution policy when recovery are as follows:
C01 allocation strategy when) the operational processor node restored is according to initialization loads corresponding Topic；
C02 these Topic information for being returned to recovery nodes) are deleted from other operational processor node timings.
6. a kind of data collection system based on object-oriented according to claim 5, it is characterised in that: storage service mould
Block adopts acquisition data, and i.e. school, the mode repaired is realized and is shown acquisition load, electric energy with stream process technology in real time
Value Data is checked in real time, is verified, and problem data is marked, and is repaired to abnormal load data；For problem
Data are repaired by power estimation, ARIMA algorithm and marketing distribution electricity, guarantee the reasonability, consistency, logic of data
Property, by finding, marking invalid and distortion data in time, improve system data quality；Electric flux is realized with stream process technology
The real time monitoring and analyzing of data, alarm event；The stream process technology is using the frame calculated in real time, using Hbase+
The real-time Computational frame of Storm, Storm is responsible for obtaining initial data from message queue and message data enters HBase distribution number
According to library.
7. a kind of data collection system based on object-oriented according to claim 6, it is characterised in that: mass data point
Module is analysed by big data distributed memory parallel computation frame, is realized by the hour to acquisition success rate index, all types of user electricity
Amount and load, line loss calculation, distribution transforming operational monitoring, the monitoring of mobile operator channel quality, the online rate of terminal are counted, with full
Foot unit business at different levels manage demand；Quasi real time analytical framework uses Hive+Spark, and the realization of Spark off-line calculation frame will be former
Beginning data import the statistical analysis business and data mining that Hive data warehouse executes mass data.
8. a kind of data of the data collection system based on object-oriented described in -7 any claims are adopted according to claim 1
Set method, it is characterised in that:
1) when acquisition main website needs to be configured terminal, measurement point, calls survey operation together, comprising the following steps:
101) the main storage facility located at processing plant of Oracle is main to store whole business datums, file data, original from marketing system synchronous foundation data
Beginning data provide data query for acquisition main website；
102) acquisition main website initiates downbound request, and different Key can be arranged according to different operation type and be published under Kafka service
In row Topic, and will be in operational order id deposit Redis caching；
103) message in downlink Topic is partitioned storage according to Key and algorithm, and different subregions can define different preferential
Grade, the multidomain treat-ment setting class downlink as the highest multidomain treat-ment control class downbound request of configuration preference level, priority are taken second place are asked
It asks, the multidomain treat-ment of other priority calls survey/relaying class downbound request together；
104) operational processor node loaded from Redis cache server with the archive information of synchronous designated terminal, from Kafka
The message of service subscription down queue is executed according to different Partition priority, forms downbound request message frame, distribution
It gives communication preposition cluster, and downlink message is pushed in the message Topic in Kafka；
105) it communicates preposition cluster and is sent to communication gate cluster by scheduling distribution policy；
106) downbound request is sent to terminal device by communication gate；
107) terminal returns to operating result, carries out packet parsing by operational processor through communication gate, the preposition cluster of communication and incites somebody to action
Operating result returns to the corresponding operational order id of the terminal in Redis；
108) acquisition main website obtains operating result according to the corresponding operational order id of the terminal from Redis；
2) when needing data acquisition for electric energy, comprising the following steps:
201) by task class data, abnormal events, the formal of message is sent to gateway cluster accordingly；
202 gateway clusters are distributed to the preposition cluster of communication by load balancing；
203) original message data is distributed to operational processor cluster according to scheduling distribution policy；
204) device node loads and the archive information of synchronous designated terminal, the parsing original report of uplink from Redis cache server
Literary data, and the information such as parsing result, original message data are pushed in corresponding Kafka message queue；That is, parsing result
It pushes in reported data Topic, original message data pushes to message Topic；
205) library service obtains original report from Kafka message queue from Kafka service subscription message, the real-time Computational frame of Storm
Literary data, electric energy data etc. are stored in HBase distributed data base；Initial data is imported Hive by Spark off-line calculation frame
Data warehouse executes complicated statistical analysis and data mining；Original message data, electric energy data are criticized in data loading service
Amount deposit relevant database；
206) from cloud platform quick search data acquisition for electric energy detail, acquisition success rate etc.；
207) the main storage facility located at processing plant of le is main to store whole business datums, file data, original number from marketing system synchronous foundation data
According to, for acquisition main website data query is provided；
3) when the completion of Yao Jinhang electric energy data, include the following steps
301) electric energy data is sent with message in form to communication gate cluster by multiple communication modes；
302) communication gate cluster is sent to the preposition cluster of communication according to load balancing distribution policy；
303) preposition cluster is distributed to operational processor cluster by dispatching distribution policy；
304) operational processor node loaded from Redis cache server with the archive information of synchronous designated terminal, in parsing
Row original message data, and the information such as parsing result, original message data are pushed in corresponding Kafka message queue；That is,
Parsing result pushes in reported data Topic, and original message data pushes to message Topic；
305) stream calculation services Storm from Kafka service subscription message, obtains electric energy data in real time, is stored in HBase points in real time
Task data in cloth data gets table ready；
306) when mending trick for real-time leak source, Spark RDD timing executes leak source audit task, i.e., it is tactful right to be recruited according to leak source benefit
It gets table in HBase ready and carries out leak source audit, and corresponding leak source is formed according to terminal called state and is requested, push to Kafka service
Downlink Topic in issued for operational processor acquisition, recruited to realize that real-time leak source is mended；
307) when mending trick for manual leak source, Spark RDD timed task is after reading benefit trick strategy in oracle database
Leak source audit task is executed, and corresponding leak source is formed according to terminal called situation and is requested, pushes to Kafka messaging service for business
Processor acquisition issues, and realizes that leak source is mended and recruits.
9. a kind of collecting method based on object-oriented according to claim 8, it is characterised in that: for terminal
When the acquisition of event, the acquisition module of different stage is defined according to the order of importance and emergency of event, is specified and different is reported frequency.
10. a kind of collecting method based on object-oriented according to claim 8, it is characterised in that: data acquisition
Including acquisition, the acquisition of low pressure I type concentrator data, the acquisition of low pressure II type concentrator data to special parameter evidence；
One) special parameter is according to acquisition:
When specially day is freezed active energy in change acquisition, it is 1 that terminal, which acquires ammeter and executes frequency, and the frequency of terminal reported data is 12
Hour, data classification be day freezing data, data item include work as previous quadrant reactive energy indicating value data block, current four-quadrant without
Function electric energy indicating value data block, current forward direction active energy indicating value data block, current reversed active energy indicating value data block；
When specially becoming 96 point load curve of acquisition, it is 15 minutes that terminal, which acquires ammeter and the execution frequency of reported data, data classification
For real time data, data item includes voltage data block, current data block, active power, when previous quadrant reactive energy indicating value number
According to block, current four-quadrant reactive energy indicating value data block, positive active total electric flux, power factor；
Two) low pressure I type concentrator data acquire:
When day is freezed active energy in the acquisition of low pressure I type concentrator, it is 1 that terminal, which acquires ammeter and executes frequency, terminal reported data
Frequency be 12 hours, data classification is day freezing data, and acquisition and reported data item include current positive active energy indicating value
Data block, current reversed active energy indicating value data block；
When low pressure I type concentrator acquires 96 point load curve, it is 6 hours that terminal, which acquires ammeter and the execution frequency of reported data,
Data classification is minute freezing data, if installation is three-phase electric energy meter, data item be voltage data block, current data block,
Power factor (PF), active power, positive active total electric flux, if installation is single-phase electric energy meter, data item is A phase voltage, A phase
Electric current, power factor (PF), positive active total electric flux, active power, N line current；
Three) low pressure II type concentrator data acquire:
It is same with I type concentrator when day is freezed active energy in the acquisition of low pressure II type concentrator, that is, issue same acquisition scheme template
With report plan template；
When low pressure II type concentrator acquires 96 point load curve, it is 15 points that terminal, which acquires ammeter and the execution frequency of reported data,
Clock, data classification are real time data, if installation is three-phase electric energy meter, data item is voltage data block, current data block, function
Rate factor, active power, positive active total electric flux, if installation is single-phase electric energy meter, data item is A phase voltage, A phase electricity
Stream, power factor (PF), positive active total electric flux, active power, N line current.
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