CN108959356A - A kind of intelligence adapted TV university Data application system Data Mart method for building up - Google Patents
A kind of intelligence adapted TV university Data application system Data Mart method for building up Download PDFInfo
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- CN108959356A CN108959356A CN201810426004.4A CN201810426004A CN108959356A CN 108959356 A CN108959356 A CN 108959356A CN 201810426004 A CN201810426004 A CN 201810426004A CN 108959356 A CN108959356 A CN 108959356A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
- G06F2216/03—Data mining
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The present invention discloses a kind of intelligent adapted TV university Data application system Data Mart method for building up, a series of basic datas such as user power utilization data, grid operation data are analyzed and are excavated by different data means, Data Mart is built up, is provided for each application module using data.It is core that Data Mart, which is based on data processing, carries out data acquisition, data storage, data cleansing, data analysis, finally realizes the purpose that data analysis result visualizes, and realizes that big data application system is inquired and retrieved to the high speed of data.
Description
Technical field
The present invention relates to field of power distribution more particularly to multi-source heterogeneous adapted TV university data processing aspects, are big data skills
Application of the art in power distribution network.
Background technique
With the arriving of big data era, all trades and professions are all being faced with huge data pressure.Power industry also not example
Outside, the storage, management, analysis of the user power utilization data of magnanimity, using being all urgent problem.How by data
Analysis, finds the rule and value wherein contained, provides for the providing of individual service, power grid erections, decision-making etc. scientific
With reference to and foundation, be the key that the business of adapted electric industry gradually develops to intelligent, lean direction.
Intelligent adapted TV university Data application system is built, the method with electricity consumption data processing is advanced optimized, is both theory
The requirement of research achievement verifying, is the indispensable link as docking theoretical research and practice test, can be theoretical research result
Utilization in practical projects and verifying offer means, are the boost motors that theoretical research result carries out achievements conversion.It is intelligence again
Power grid construction demand, more stringent requirements are proposed for fusion and excavation of the deep construction of smart grid to multi-source data, intelligently matches
The construction of electricity consumption big data application system to intelligent adapted electrical domain, matches big data technique extension with big data scientific discovery
Electricity consumption data value realizes that big data technology in the technique extension of intelligent adapted electricity business scope, promotes the intelligence of adapted power grid
Change horizontal and comprehensive benefit.
Due to various with electricity consumption data class, and since from distinct device, caused by outer welding system, architectural difference is big
Problem is facing quantity sharp increase in the prior art, when acquiring the electric power data that frequency substantially enhances, is unable to satisfy to data
Effective integration, the requirement of efficient storage and enhanced scalability
Summary of the invention
Goal of the invention: a kind of intelligent adapted TV university Data application system Data Mart method for building up is provided, can integrate and answer
With big data analysis processing technique, the user power utilization data and other related datas of magnanimity are studied, and by the knot of research
Fruit engineering, further increases adapted TV university data-handling efficiency, builds intelligent adapted TV university Data application system technical solution.
In order to solve the above technical problems, the technical solution adopted by the present invention are as follows:
A kind of intelligence adapted TV university Data application system Data Mart method for building up, by basic data (user power utilization number
According to, grid operation data) it analyzes and excavates by data, Data Mart is built up, is provided for each application module using data；Number
Being based on data processing according to fairground is core, carries out data acquisition, data storage, data cleansing, data analysis, realizes data analysis
Result visualization is shown.
A kind of intelligence adapted TV university Data application system Data Mart method for building up, specifically includes the following steps:
Step 1, data acquire: carrying out data acquisition to source data by interface mode and acquisition storage mode；
Step 2, data store: extracting the unified storage of laggard row format by parsing；
Step 3, data cleansing；Data cleansing is based on linear interpolation or mean value replacement method, to the missing of data, different
Often, it repeats and noise starts the cleaning processing；
Step 4, data are analyzed: being based on big data platform and corresponding machine learning library, analyzed data；
Step 5, data are shown: the structure based on B/S is realized.
More preferably, interface mode and acquisition storage mode specifically include:
For newly building or being able to carry out the outer welding system of transformation, by way of providing data-interface to outer welding system,
By outer welding system when generating data automatic push, HTTP, Web Service and FTP data interface are provided respectively；For cannot
The system being transformed is carried out by acquiring storage mode according to the mode that target provides in real time or by the period to target data
It reads.
More preferably, step 2, specifically includes the following steps:
201) storage organization type selecting and information model are established
Storage assembly includes hive, habase, elastic search and holodesk；
202) SQL structure optimization: being based on service logic, carries out logic simplifying to sql；
The optimization of sql sentence includes logic optimization and structure optimization；
Logic optimization is based on the basis of service logic, carries out logic simplifying to sql；
203) optimiged index
Full-text index is established, supports the fuzzy combined index for receiving rope and multi-field of full text；For hbase table, pass through row
Key will need the attribute that screens all be arranged into line unit, when line unit is not able to satisfy all demands, establish secondary index reality
Existing demand；Holodesk table supports global index, establishes global index on the column of screening to increase retrieval performance.
More preferably, data analysis analyzes data based on big data platform and machine learning library is relied on；
Hive, discover, matlab and spark platform has been respectively adopted in data.
More preferably, data, which are shown, is obtained to analysis as a result, the displaying being patterned, data are shown using B/S's
Structure is realized；Server-side is realized using the three-decker of control layer, operation layer and Data Persistence Layer.
More preferably, in data acquisition, topological data parsing (parsing of CIM data) is based on for grid topology data,
The method combined is parsed using distributed parsing and sax, carries out optimized data collection, specifically includes the following steps:
1) data are sorted
Data file for finishing data prediction sorts, and big file (being greater than 500M) is extracted and is done offline
Parsing；Hdfs is uploaded to, using the customized of hive by the way of uniline compression for small documents (being less than or equal to 500M)
Udtf is parsed, and the result of parsing is merged to obtain final parsing result；
2) big document analysis: sax analysis mode is used, is parsed by reading xml document line by line；
The big file greater than 500M for including in cim file will lead to single inside hive by the way of uniline compression
Row record it is excessive and the case where can not parse, and by the way of using dom to parse, since it is desired that once loading files into memory
In, and will appear the case where memory spilling can not parse.So sax analysis mode can only be used, this method by reading line by line
The mode that xml document is parsed being taken, solving the problems, such as that memory overflows when because parsing big file.
3) small documents parse: the small documents after progress uniline contracting are uploaded into hdfs, database is imported by hive appearance,
Customized udtf function based on hive is parsed.
The invention has the advantages that:
The present invention discloses a kind of intelligent adapted TV university Data application system Data Mart method for building up, is based on all kinds of electric power numbers
According to establishing complete information model, and data mining and analysis are carried out, obtain the application data of each business module, forms data
Fairground；In conjunction with the concurrently technologies such as inquiry, result cache, realize that big data application system is inquired and retrieved to the high speed of data.
Detailed description of the invention
Fig. 1 is the cim data process of analysis of the application intelligence adapted TV university Data application system Data Mart method for building up
Figure；
Fig. 2 is storage organization framework.
Specific embodiment
The invention will be further described with reference to the accompanying drawing and by specific embodiment, and following embodiment is descriptive
, it is not restrictive, this does not limit the scope of protection of the present invention.
A kind of intelligence adapted TV university Data application system Data Mart method for building up, by basic data (user power utilization data
And grid operation data) analyze and excavate by data, Data Mart is built up, is provided for each application module using data.Number
Being based on data processing according to fairground is core, carries out data acquisition, data storage, data cleansing, data analysis, finally realizes data
Analyze the purpose that result visualization is shown.
A kind of intelligence adapted TV university Data application system Data Mart method, specifically includes the following steps:
Step 1, data acquire:
Data acquisition is carried out to source data by interface mode and acquisition storage mode: being changed for newly building or being able to carry out
The outer welding system made, to outer welding system provide data-interface by way of, by outer welding system when generating data automatic push,
In order to adapt to different systems, HTTP, Web Service and FTP data interface are each provided；For what cannot be transformed
System in real time or by the period is read out target data according to the mode that target provides by acquiring storage mode.
It is source data by the data that interface mode obtains, there are the spies such as data volume big, data format is chaotic, shortage of data
Point cannot be directly put in storage, it is necessary to extract the unified storage of laggard row format by parsing.Parsing storage is mainly for difference
Data source write corresponding analysis program, valuable data are extracted, unified format is organized into and is put in storage.
Realize the process of automation from the parsing storage of the acquisition of interface data and source data, data collection cycle be divided into real time,
Day more with week more.
Step 2, data store:
Specifically includes the following steps:
201) storage organization type selecting and information model are established；
Big data platform possesses many technology and component, wherein storage assembly include hive, habase,
Elasticsearch and holodesk, as shown in Figure 2.Every kind of memory technology has its corresponding merits and demerits, such as hive
Table has the characteristics that easy to operate easy-to-use data storage when commonly used in Primary Stage Data analysis, can allow all participation items in this way
Purpose analysis and implementation personnel can easily get data from platform, but because its performance is relatively low, therefore visualize
It is this in the exigent scene of inquiry velocity and being not suitable for.And hbase and elastic search is unstructured deposits
Storage, retrieval performance is higher.For hbase table, there is certain threshold because it is used, therefore only make in specific Visual Scene
With.Holodesk is a kind of data storage realized based on spark, a large amount of result data can be efficiently returned to, for needing
The inquiry scene largely to return the result compares use, while can improve data retrieval by way of adding global index
Speed, but data cannot make an amendment operation, be only suitable for those data write-onces just not in the scene of change.Such as in electric power
During the platform area load factor of map is shown, position and load factor data (total amount of data due to secondary 20,000 area Duo Getai of acquisition
About 1.4M), no matter es table or hbase table are used, query time selects holodesk table at one second or more, successfully will
Query time drops to one second or less.Es table is a kind of building and the storage table of elasticsearch, have can modify, high retrieval
It is the characteristics of performance, very applicable in common query and search, also easily.
202) SQL structure optimization: being based on service logic, carries out logic simplifying to sql；
On the basis of storage organization selection.Also need the optimization that sql level is carried out to the program statement of some complexity.sql
The optimization of sentence mainly includes logic optimization and two kinds of structure optimization.
Logic optimization refers to that during exploitation, due to various reasons, the sql for causing final production to come out is logically
It is with nuisance operation or excessively too fat to move.On the basis of service logic, logic simplifying is carried out to sql, so that sql structure is more clear
Clear, performance is more preferable.Such as in the associated inquiry of multilist, it may be considered that some condition filter functions are write in subquery, this
Sample can make the data volume of cross correlation smaller, facilitate the performance for improving correlation inquiry.SQL statement level optimization and it is traditional
SQL optimization is similar.
203) optimiged index
Full-text index is established, supports the fuzzy combined index for receiving rope and multi-field of full text.For hbase table, pass through row
Key will need the attribute that screens all be arranged into line unit, when line unit is not able to satisfy all demands, establish secondary index reality
Existing demand.Such as in the inquiry according to date garbled data, the date can be arranged in front of line unit, behind splice again
Other information, then according to the setStartRow of scan and setStopRow come setting range lookup, have reached inquire certain day or
The requirement of certain time.Holodesk table supports global index, establishes global index on the column of screening to increase retrieval performance.
Holoesk data are stored on SSD, have good performance support for large batch of return the result.
Step 3, data cleansing；Data cleansing is based on linear interpolation or mean value replacement method, to the missing of data, different
Often, it repeats and noise starts the cleaning processing；
For the data of acquisition storage, there is a large amount of missing or Problem-Errors, bring for the analysis of subsequent data
Many unnecessary troubles.So carrying out necessary data cleansing before data analysis is very important.
Step 4, data are analyzed:
Based on big data platform and corresponding machine learning library, data are analyzed.
Data analysis analyzes data based on big data platform and machine learning library is relied on.Such as according to user day
Freeze electricity and weather data carries out linear regression analysis, obtains user power utilization data by weather, festivals or holidays, temperature, wind speed etc.
Influence coefficient.
Hive, discover, matlab has been respectively adopted in order to achieve the purpose that various analysis scenes in data analysis component
With spark platform or component.In actual implementation process, Technology Selection, model optimization, algorithm are carried out to corresponding algorithm and changed
Precision of analysis and calculated performance are promoted into means.
Step 5, data are shown
Data, which are shown, to be obtained to analysis as a result, the displaying being patterned, has real-time, interactivity and ease for use
Feature.Data are shown to be realized using the structure of B/S.Server-side is using control layer, the three-layered node of operation layer and Data Persistence Layer
Structure is realized, is supported using spring mvc and mybatis frame, and Service control, permission control, safety management and day are realized
Will writing function is successfully rung all interfaces using multichannel inquiry, data buffer storage, SQL statement structure optimization, optimiged index
Control was at one second or less between seasonable.Front end mainly relies on echars, jquery, bootstrap Open Framework, accelerates in conjunction with GPU
The result of analysis is presented in front-end interface by means in a manner of patterned, is provided the user with intuitive analysis result and is shown.
In data acquisition, according to the different and widely different of data source, topological Numbers are based on for grid topology data
According to parsing (parsing of CIM data), the method combined is parsed using distributed parsing and sax, carries out optimized data collection, specifically
The following steps are included:
Grid topology data includes xml document and svg file, includes grid equipment information, device topology and equipment
Co-ordinate position information because file is excessive (about 30,000 or so), and includes a small amount of especially big file mainly with the storage of xml format
(being greater than 500M), thus it is very low using traditional single machine analysis mode efficiency, and the mode for parsing small documents is not suitable for greatly
File, it will usually lead to problems such as memory spilling that can not parse.This method uses what distributed parsing was combined with sax parsing
Method completes the optimized data collection method of a set of procedure.
1) data are sorted
Data file for finishing data prediction sorts, and big file (being greater than 500M) is extracted and is done offline
Parsing.And for small documents (being less than or equal to 500M), by the way of uniline compression, hdfs is uploaded to, using making by oneself for hive
Adopted udtf is parsed, and the result of parsing is merged to obtain final parsing result.
Shown in overall flow Fig. 1 of cim data parsing.Especially big xml document and small documents concurrently executes, and waits two kinds of sides
Formula carries out result merging after the completion of being carried out, and the data of cim, which are tentatively extracted, just to be completed, subsequent to will do it data correlation processing,
Topological structure parsing, extracts the topological structure finally needed.
2) big document analysis: sax analysis mode is used, is parsed by reading xml document line by line；
The big file greater than 500M for including in cim file will lead to this file by the way of uniline compression
Inside hive uniline record it is excessive and the case where can not parse, and by the way of dom parsing, since it is desired that once by file
It is loaded into memory, and will appear the case where memory spilling can not parse.So sax analysis mode, this method can only be used
By way of reading xml document line by line and being parsed, solve the problems, such as that memory overflows when because parsing big file.
Since file is excessive, the result of parsing is also required in asynchronous write-in destination file.The problem of in view of performance, therefore
Using first by data buffer storage to buffer area, then destination file is written in batch data by calling consuming process, is effectively reduced
Frequent operation io saves the problem of data bring performance decline.
3) small documents parse: the small documents after progress uniline contracting are uploaded into hdfs, database is imported by hive appearance,
Customized udtf function based on hive is parsed.
There is about 30,000 small documents for cim file, can be independent since each file is mutually indepedent for small documents
It is parsed, in conjunction with the distributed computing characteristic of big data platform, small documents can be put into big data platform and be distributed
Formula parsing, can effectively improve the efficiency of parsing in this way.The small documents after progress uniline contracting are specially uploaded into hdfs, are passed through
Hive appearance imports database, and the customized udtf function for writing hive is parsed.
The udtf of hive, which is that one kind that hive is provided is customized, realizes that the processing of uniline multiple row is the function of multiple lines and multiple rows, can be with
It is write by java.By way of udtf, uniline xml (contents of namely one complete small documents) is solved
Analysis completes the parsing to small documents in table in the information input to hive extracted.
The analysis mode of big file can effectively parse the xml document of super large, after tested for the text of single 500M or so
Part parses the time at 10 minutes or so.When parsing by the way of concurrently parsing, to one parsing process of each file opening,
It will successfully be increased to the time within 20 minutes.The parsing of small documents takes full advantage of the computing resource of hadoop cluster, in reality
In the production environment on border, 30,000 small documents times are parsed within 10 minutes.
By the Optimal improvements of above step, the cim file that will need originally can just be parsed for several hours is small half
When within just complete from data extract topological structure association parsing storage all processes.
Those skilled in the art can to the present invention be modified or modification design but do not depart from think of of the invention
Think and range.Therefore, if these modifications and changes of the present invention belongs to the claims in the present invention and its equivalent technical scope
Within, then the present invention is also intended to include these modifications and variations.
Claims (7)
1. a kind of intelligence adapted TV university Data application system Data Mart method for building up, it is characterised in that:
Basic data is analyzed and excavated by data, Data Mart is built up, provides for each application module using data；Data
It is core that fairground, which is based on data processing, carries out data acquisition, data storage, data cleansing and data analysis, realizes data analysis
Result visualization is shown.
2. a kind of intelligent adapted TV university Data application system Data Mart method for building up according to claim 1, feature
It is, specifically includes the following steps:
Step 1, data acquire: carrying out data acquisition to source data by interface mode and acquisition storage mode；
Step 2, data store: extracting the unified storage of laggard row format by parsing；
Step 3, data cleansing；Data cleansing is based on linear interpolation or mean value replacement method, to the missing of data, exception, again
Multiple and noise starts the cleaning processing；
Step 4, data are analyzed: being based on big data platform and corresponding machine learning library, analyzed data；
Step 5, data are shown: the structure based on B/S is realized.
3. a kind of intelligent adapted TV university Data application system Data Mart method for building up according to claim 2, feature
It is:
Interface mode and acquisition storage mode specifically include:
For newly building or being able to carry out the outer welding system of transformation, by way of providing data-interface to outer welding system, by outer
Welding system automatic push when generating data, provide HTTP, Web Service and FTP data interface respectively；For not can be carried out
The system of transformation in real time or by the period is read out target data according to the mode that target provides by acquiring storage mode.
4. a kind of intelligent adapted TV university Data application system Data Mart method for building up according to claim 2, feature
It is:
Step 2, specifically includes the following steps:
201) storage organization type selecting and information model are established
Storage assembly includes hive, habase, elastic search and holodesk；
202) SQL structure optimization: being based on service logic, carries out logic simplifying to sql；
The optimization of sql sentence includes logic optimization and structure optimization；
Logic optimization is based on the basis of service logic, carries out logic simplifying to sql；
203) optimiged index
Full-text index is established, supports the fuzzy combined index for receiving rope and multi-field of full text；It will by line unit for hbase table
The attribute for needing to screen all is arranged into line unit, when line unit is not able to satisfy all demands, establishes secondary index to realize and need
It asks；Holodesk table supports global index, establishes global index on the column of screening to increase retrieval performance.
5. a kind of intelligent adapted TV university Data application system Data Mart method for building up according to claim 2, feature
It is:
Data analysis analyzes data based on big data platform and machine learning library is relied on；
Hive, discover, matlab and spark platform has been respectively adopted in data.
6. a kind of intelligent adapted TV university Data application system Data Mart method for building up according to claim 2, feature
It is:
Data, which are shown, to be obtained to analysis as a result, the displaying being patterned, data are shown to be realized using the structure of B/S；Clothes
It is realized using the three-decker of control layer, operation layer and Data Persistence Layer at business end.
7. a kind of intelligent adapted TV university Data application system Data Mart method for building up according to claim 1, feature
It is:
In data acquisition, topological data parsing (parsing of CIM data) is based on for grid topology data, is solved using distribution
Analysis and sax parse the method combined, carry out optimized data collection, specifically includes the following steps:
1) data are sorted:
Data file for finishing data prediction sorts, and big file is extracted and does offline parsing；For small text
Part, using uniline compression by the way of, upload to hdfs, parsed using the customized udtf of hive, by the result of parsing into
Row, which merges, obtains final parsing result；
2) big document analysis: sax analysis mode is used, is parsed by reading xml document line by line；
3) small documents parse: the small documents after progress uniline contracting being uploaded to hdfs, database is imported by hive appearance, is based on
The customized udtf function of hive is parsed.
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