CN110309115A - Fusion calculates the railway power distribution network magnanimity information processing method with off-line calculation in real time - Google Patents
Fusion calculates the railway power distribution network magnanimity information processing method with off-line calculation in real time Download PDFInfo
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- CN110309115A CN110309115A CN201810208708.4A CN201810208708A CN110309115A CN 110309115 A CN110309115 A CN 110309115A CN 201810208708 A CN201810208708 A CN 201810208708A CN 110309115 A CN110309115 A CN 110309115A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/18—File system types
- G06F16/182—Distributed file systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24553—Query execution of query operations
- G06F16/24561—Intermediate data storage techniques for performance improvement
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2455—Query execution
- G06F16/24568—Data stream processing; Continuous queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2471—Distributed queries
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses fusions to calculate the railway power distribution network magnanimity information processing method with off-line calculation in real time, its step include the access of railway power distribution network magnanimity monitoring data, monitoring data handle in real time, off line data analysis and processing result distributed storage, when railway power distribution network magnanimity monitoring data access, distributed post for connecting railway power distribution network monitoring information source is installed in the computer cluster of (SuSE) Linux OS and subscribes to message system, magnanimity monitoring data are quickly and efficiently accessed in distributed type assemblies.The present invention is when executing magnanimity monitoring data processing task, processing in real time, independent off line data analysis selectively can be carried out individually to data according to actual treatment demand or simultaneously be calculated in real time and off-line calculation, it is able to satisfy the process demand of different occasions, promotes flexibility and the treatment effeciency of railway monitoring system for power distribution networks.
Description
Technical field
The present invention relates to distribution network monitoring technical field, specifically fusion calculates the railway power distribution network with off-line calculation in real time
Magnanimity information processing method.
Background technique
Railway transportation is the main artery of the turnover of China's substance, flow of personnel or even national economy, be in recent years adaptation speed-raising,
Heavy haul transport and economic construction need, and electric railway development is very fast, and the traction power supply monitoring of Raising speed railway increases big
AT institute is measured, plans online disconnecting switch and is included in monitoring comprehensively, while railway power monitoring is also by communication, signal, optical fiber repeater
Case becomes, ring network cabinet is included in monitoring, both all high and low pressure circuits is included in the monitoring of railway distribution net, monitoring circuit is no longer needle
Monitoring to single rail track, but a plurality of route United Dispatching monitors.Therefore, the more traditional general fast railway prison of monitoring information amount
Measurement information amount increases severely in geometric progression.In face of this big data process demand, traditional monitoring information processing manner increasingly cannot
Adapt to the needs of railway distribution network monitoring.
Maturation has been approached by the development in more than ten years using Hadoop as the batch processing frame of representative, it is highly reliable by its
Property, high scalability and the advantages that high fault tolerance, batch processing calculate mode had been obtained in smart grid monitoring field it is preliminary
Using.However, batch processing frame is limited to itself computation model, data processing suitable for the processing of large-scale off-line data
Real-time and throughput be increasingly difficult to meet current railway power distribution network Dispatching monitor and control system demand.
Spark Computational frame is referred to as next-generation big data and handles engine, shows up prominently in the extremely short time, and to burn
The gesture of original sweeps across industry.Spark is mainly reflected in the improvement for the Hadoop computing platform for once igniting big data Industrial Revolution
The following aspects: faster, handling capacity is bigger for Spark processing speed, can be competent at railway power distribution network monitoring data stream process task；
Spark expansion interface abundant brings more powerful ease for use, and Spark not merely supports traditional batch application, more supports
Interactive inquiry, streaming computing, machine learning, figure calculate etc. a variety of applications, meet power distribution automation operation expanding demand and
Intelligent requirements；Finally, the elasticity distribution formula data set of micro- batch processing ensure that the data consistency in micro- batch of frame, it is railway
Power distribution network monitoring information stream provides extremely strong fault-tolerant guarantee.
With the continuous expansion of power grid scale and capacity, equipment collection point and sample frequency sharply increase, and data volume is in
Geometric growth, the real-time processing that traditional monitoring information processing method is no longer satisfied this mass data access need
Ask, propose thus it is a kind of by railway power distribution network magnanimity real-time monitoring information by distributed post subscribe to message system be transferred to
Spark Computational frame, after Spark carries out calculating in real time and off-line calculation fusion treatment to monitoring information, calculated result unloading
To the fast parallel New Method for Processing of distributed file system and distributed data base.
Summary of the invention
The purpose of the present invention is to provide melting for the flexibility for being able to ascend railway monitoring system for power distribution networks and treatment effeciency
The railway power distribution network magnanimity information processing method calculated in real time with off-line calculation is closed, to solve mentioned above in the background art ask
Topic.
To achieve the above object, the invention provides the following technical scheme:
Fusion calculates the railway power distribution network magnanimity information processing method with off-line calculation in real time, the specific steps are as follows:
(1) railway power distribution network magnanimity monitoring data access:
Distributed post for connecting railway power distribution network monitoring information source is installed in the computer cluster of (SuSE) Linux OS
Message system is subscribed to, magnanimity monitoring data are quickly and efficiently accessed in distributed type assemblies；
(2) monitoring data are handled in real time:
Hadoop computing platform and distributed data base, the distributed file system under Hadoop computing platform are built in the cluster
For unloading results of intermediate calculations, distributed data base is used for the reliable memory of calculated result；Installation configuration Spark calculation block
Frame, comprising the real-time streams computing module that is handled in real time for stream calculation and for offline micro- batch of off line data analysis in the frame
Processing module；Configuration connection distributed post subscribes to the data-interface of message system, railway distribution in real-time streams computing module
Net monitoring data calculate knot after distributed post subscription message system is transferred to and is handled in real time in real-time streams computing module
Fruit can dump in distributed file system, facilitate subsequent calculating to use, or directly store into distributed data base；
(3) off line data analysis:
The data-interface that connection distributed post subscribes to message system is configured in Hadoop computing platform, is first ordered by distributed post
It reads message system monitoring data is transmitted in distributed file system and carry out intermediate storage, then by offline micro- batch processing module pair
Intermediate storage data extract, and after completing off line data analysis, calculated result can continue to be stored in distributed field system
It is called in system for subsequent calculating, or storage is into distributed data base；
(4) processing result distributed storage:
Configure the distributed data base built, established in distributed data base towards column family can the data of infinite extension deposit
Table is stored up, for storing the railway power distribution network dispatching and monitoring magnanimity monitoring data handled in real time with after off line data analysis respectively,
In simplifying in structure type for table, design line unit is device number attribute, redesigns three column families, respectively implementor name attribute, electricity
Stream attribute and voltage properties, wherein first column family is device name, other two column family is respectively the collected three-phase of equipment
Electric current and voltage real value, to cope with the more mass data storage of railway power distribution network dispatching and monitoring, distributed data base is patrolled
Volume table can dynamically increase the column family of the monitoring item attribute of corresponding equipment and the quantity of column according to actual needs；By calculated result
It is transmitted in distributed data base, more database servers is called to carry out data preservation, realize and carried out using cloud computing technology
The distributed column family reliable memory of railway distributing monitoring system big data.
As further scheme of the invention: railway power distribution network monitoring data flow through distributed hair in the step (2)
Cloth subscribes to message system and is transferred to real-time streams computing module, and according to the processing interval being arranged, real-time streams computing module will be inputted
Monitoring data be divided into sectional discrete data queue, then each section of discrete data is all converted into Spark calculation block
Elasticity distribution formula data set in frame.
Compared with prior art, the beneficial effects of the present invention are:
The present invention subscribes to message system using distributed post and Spark Computational frame cloud computing technology handles data, realizes real
When calculate and the fusion treatment of off-line calculation.Message system is subscribed to by distributed post, and monitoring data are accessed into Spark calculating
In frame, selectively monitoring data are handled in real time according to process demand or off line data analysis, processing result can deposit
Into distributed file system or distributed data base, realize railway monitoring system for power distribution networks mass data calculate in real time with from
The fusion treatment of line computation is able to satisfy the process demand of different occasions, promotes flexibility and the place of railway monitoring system for power distribution networks
Manage efficiency.
Detailed description of the invention
Fig. 1 is the fusion treatment process schematic of the method for the present invention.
Specific embodiment
The technical solution of the patent is explained in further detail With reference to embodiment.
Referring to Fig. 1, fusion calculates the railway power distribution network magnanimity information processing method with off-line calculation, specific steps in real time
It is as follows:
(1) railway power distribution network monitoring data access phase
It is installed on each computer for connecting railway power distribution network monitoring information source in the computer cluster of (SuSE) Linux OS
Distributed post subscribe to message system and configure good corresponding system environment variable, magnanimity monitoring data are quickly and efficiently connect
Enter in distributed type assemblies, distributed post subscribes to data transmission bauds, handling capacity and the error resilience performance of message system better than tradition
Socket mode, and have the characteristics that high real-time and support multilingual exploitation, when two systems be in state of isolation without
When method communicates, the distribution subscription message processing facility that distributed post subscribes to message system can realize producers and consumers
Between seamless interfacing, achieve the purpose that cross-platform data real-time Transmission.
(2) monitoring data real-time processing stage
Hadoop computing platform and distributed data base are built in the cluster, and the distributed file system under platform is used for unloading
Results of intermediate calculations, distributed data base are used for the reliable memory of calculated result；Installation configuration Spark Computational frame, the frame
In offline micro- batch processing module comprising the real-time streams computing module that is handled in real time for stream calculation and for off line data analysis；
The data that connection distributed post subscription message system is first configured in Hadoop computing platform and real-time streams computing module connect
Mouthful, railway power distribution network monitoring data are subscribed to message system through distributed post and are transferred in real-time streams computing module, according to having set
The monitoring data of input are divided into sectional discrete data queue by the processing interval set, stream calculation module, and then each section
Discrete data is all converted into the elasticity distribution formula data set in Spark Computational frame, in this way, real-time streams computing module is to monitoring
The processing of data flow has been transformed into the processing of the elasticity distribution formula data set to Spark Computational frame；After completing processing in real time,
Calculated result can dump in distributed file system, facilitate subsequent calculating to use, or directly store to distributed data
In library；
(3) the off line data analysis stage
Different from real-time processing stage before, first subscribing to message system through distributed post in the off line data analysis stage will be supervised
Measured data, which is transmitted in distributed file system, carries out intermediate storage, then by offline micro- batch processing module to intermediate storing data into
Row extracts, and offline micro- batch processing module is according to the data mining of railway monitoring system for power distribution networks, data visualization, multidimensional data point
The business demands such as analysis and decision assistant can carry out depth analysis to the adapted TV university data of accumulation, complete off-line data processing
Afterwards, calculated result can continue to be stored in distributed file system and call for subsequent calculating, or distributed data is arrived in storage
In library；
(4) the processing result distributed storage stage
Configure the distributed data base built, established in distributed data base towards column family can the data of infinite extension deposit
Table is stored up, for storing the railway power distribution network dispatching and monitoring magnanimity monitoring data handled in real time with after off line data analysis respectively,
In simplifying in structure type for table, design line unit is device number attribute, redesigns three column families, respectively implementor name attribute, electricity
Stream attribute and voltage properties, wherein first column family is device name, other two column family is respectively the collected three-phase of equipment
Electric current and voltage real value, to cope with the more mass data storage of railway power distribution network dispatching and monitoring, distributed data base is patrolled
Volume table can dynamically increase the column family of the monitoring item attribute of corresponding equipment and the quantity of column according to actual needs；By calculated result
It is transmitted in distributed data base, more database servers is called to carry out data preservation, realize and carried out using cloud computing technology
The distributed column family reliable memory of railway distributing monitoring system big data.
The present invention, can be according to actual treatment demand selectively logarithm when executing magnanimity monitoring data processing task
According to carrying out individually processing in real time, independent off line data analysis or simultaneously calculated in real time and off-line calculation, it is able to satisfy different fields
The process demand of conjunction promotes flexibility and the treatment effeciency of railway monitoring system for power distribution networks.
The preferred embodiment of the patent is described in detail above, but this patent is not limited to above-mentioned embodiment party
Formula within the knowledge of one of ordinary skill in the art can also be under the premise of not departing from this patent objective
Various changes can be made.
Claims (2)
1. fusion calculates the railway power distribution network magnanimity information processing method with off-line calculation in real time, which is characterized in that specific steps
It is as follows:
(1) railway power distribution network magnanimity monitoring data access:
Distributed post for connecting railway power distribution network monitoring information source is installed in the computer cluster of (SuSE) Linux OS
Message system is subscribed to, magnanimity monitoring data are quickly and efficiently accessed in distributed type assemblies；
(2) monitoring data are handled in real time:
Hadoop computing platform and distributed data base, the distributed file system under Hadoop computing platform are built in the cluster
For unloading results of intermediate calculations, distributed data base is used for the reliable memory of calculated result；Installation configuration Spark calculation block
Frame, comprising the real-time streams computing module that is handled in real time for stream calculation and for offline micro- batch of off line data analysis in the frame
Processing module；Configuration connection distributed post subscribes to the data-interface of message system, railway distribution in real-time streams computing module
Net monitoring data calculate knot after distributed post subscription message system is transferred to and is handled in real time in real-time streams computing module
Fruit can dump in distributed file system, facilitate subsequent calculating to use, or directly store into distributed data base；
(3) off line data analysis:
The data-interface that connection distributed post subscribes to message system is configured in Hadoop computing platform, is first ordered by distributed post
It reads message system monitoring data is transmitted in distributed file system and carry out intermediate storage, then by offline micro- batch processing module pair
Intermediate storage data extract, and after completing off line data analysis, calculated result can continue to be stored in distributed field system
It is called in system for subsequent calculating, or storage is into distributed data base；
(4) processing result distributed storage:
Configure the distributed data base built, established in distributed data base towards column family can the data of infinite extension deposit
Table is stored up, for storing the railway power distribution network dispatching and monitoring magnanimity monitoring data handled in real time with after off line data analysis respectively,
In simplifying in structure type for table, design line unit is device number attribute, redesigns three column families, respectively implementor name attribute, electricity
Stream attribute and voltage properties, wherein first column family is device name, other two column family is respectively the collected three-phase of equipment
Electric current and voltage real value, to cope with the more mass data storage of railway power distribution network dispatching and monitoring, distributed data base is patrolled
Volume table can dynamically increase the column family of the monitoring item attribute of corresponding equipment and the quantity of column according to actual needs；By calculated result
It is transmitted in distributed data base, more database servers is called to carry out data preservation, realize and carried out using cloud computing technology
The distributed column family reliable memory of railway distributing monitoring system big data.
2. fusion according to claim 1 calculates the railway power distribution network magnanimity information processing method with off-line calculation in real time,
It is characterized in that, in the step (2) railway power distribution network monitoring data flow through distributed post subscribe to message system be transferred to reality
When stream calculation module, according to the processing interval being arranged, the monitoring data of input are divided into one section one section by real-time streams computing module
Discrete data queue, then each section of discrete data is all converted into the elasticity distribution formula data set in Spark Computational frame.
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| CN113128840A (en) * | 2021-03-24 | 2021-07-16 | 国网湖北省电力有限公司 | Equipment state evaluation method, system and storage medium |
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