CN108132982A - The analysis system and method for train operation monitoring device data based on big data - Google Patents
The analysis system and method for train operation monitoring device data based on big data Download PDFInfo
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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Abstract
The invention discloses a kind of analysis system and method for the train operation monitoring device data based on big data, the log historical data for realizing LKJ devices concentrates storage, data format standardization processing, the LKJ device data of magnanimity can be analyzed.Its technical solution is：Big data platform is established based on the Hadoop ecospheres, realizes centrally stored, the data format standardization processing of the log historical data of LKJ devices, data are ultimately stored in HBase distributed data bases.Map/Reduce is recycled to carry out distributed arithmetic processing to a large amount of associated LKJ operation datas stored in HBase distributed data bases, the analysis of magnanimity LKJ data is completed, is stored in analysis result as intermediate data in HBase.The intermediate result that a part of associated data and analysis are obtained again, is reloaded by ETL in Distributed Data Warehouse Hive, is used so as to be shared with all Data Marts.
Description
Technical field
The present invention relates to data analysis more particularly to based on big data treatment technology to train operation monitoring device data
The data analysis system and method for (LKJ data).
Background technology
LKJ train control on board equipment directly ensures railway operation safety, how to be realized using advanced big data technology and arranges control
Mobile unit big data analysis promotes the reliability of train operation monitoring device, extends product service life, reduces operation maintenance
Cost improves maintenance efficiency and efficiency, reduces equipment failure rate, is current urgent problem to be solved.
Log historical data (offline and online) capacity of system-wide LKJ row control equipment has had reached TB grades at present,
Distant view is up to PB grades, and traditional data storage scheme has been unable to meet the memory requirement of existing data.It is therefore desirable to systems
Property set up LKJ system big data platforms, the acquisition, storage and standardizing standardization of mass data are handled with realizing.
Current LKJ equipment qualities are analyzed, crew manipulates the systems such as analysis and is all based on single LKJ log datas
File or time file are analyzed, and are not yet established for a large amount of log datas text, overhaul of the equipments data, manual analysis number
According to associated big data analysis systems of multi-dimensional datas such as, time-table data, operation diagram data, track datas.
Invention content
A brief summary of one or more aspects is given below to provide to the basic comprehension in terms of these.This general introduction is not
The extensive overview of all aspects contemplated, and be both not intended to identify critical or decisive element in all aspects also non-
Attempt to define the range in terms of any or all.Its unique purpose is to provide the one of one or more aspects in simplified form
A little concepts are with the sequence for more detailed description given later.
It is an object of the invention to solve the above problems, a kind of train operation monitoring device number based on big data is provided
According to analysis system and method, the log historical data for realizing LKJ devices concentrates storage, at data format standardization
Reason, can analyze the LKJ device data of magnanimity.
The technical scheme is that：Present invention is disclosed a kind of train operation monitoring device data based on big data
Analysis system, including：
Platform data AM access module, in the data access to system by train operation monitoring device；
Big data streaming processing module carries out Stream Processing to the data of access, will be after formatting standardization processing
Data according to the device number of engine number or train operation monitoring device in distributed data base into determinant store, wherein
A corresponding list is established to the device number of each engine number or train operation monitoring device；
Distributed statistical analysis module carries out Distributed Calculation processing, to same to the data stored in distributed data base
One vehicle, same engine number train operation monitoring device equipment state or failure added up, according to including time, line
Different dimensions including road or fault type count equipment state or failure, realize equipment state statistical analysis, will unite
The result of meter analysis is stored in Distributed Data Warehouse；
Data-mining module carries out data based on the statistic analysis result in Distributed Data Warehouse and data mining model
It excavates, forms the data results of train operation monitoring device data；
Analysis result output module realizes the displaying and/or inquiry of data results.
One embodiment of the analysis system of the train operation monitoring device data according to the present invention based on big data, platform
The data for the train operation monitoring device that data access module is accessed include train operation monitoring device real-time running data, row
Vehicle running monitor device off-line data, train operation monitoring device related data.
One embodiment of the analysis system of the train operation monitoring device data according to the present invention based on big data, distribution
Formula database is built upon the HBase distributed data bases on distributed file system HDFS.
One embodiment of the analysis system of the train operation monitoring device data according to the present invention based on big data, distribution
Formula data warehouse is built upon the Hive Distributed Data Warehouses on Hadoop Distributed Computing Platforms.
One embodiment of the analysis system of the train operation monitoring device data according to the present invention based on big data, data
Module is excavated first to be out of order sample from the extracting data of train operation monitoring device, obtained based on historical failure analysis data therefore
Hinder the analysis data of sample, the analysis data of fault sample are input in data mining algorithm model and carry out sample training, are obtained
To initialization critical field weights and threshold value, failure extracting rule is recycled to form fault tree database and is input to data mining
In model, data mining model obtains data point based on the data of train operation monitoring device, using fault tree database
Analyse result.
Present invention further teaches a kind of analysis method of the train operation monitoring device data based on big data, including：
It will be in the data access to system of train operation monitoring device；
Stream Processing is carried out to the data of access, by the data after formatting standardization processing according to engine number or
The device number of train operation monitoring device stores in distributed data base into determinant, wherein to each engine number or row
The device number of vehicle running monitor device all establishes a corresponding list；
Distributed Calculation processing is carried out to the data that are stored in distributed data base, to same vehicle, same engine number
The equipment state or failure of train operation monitoring device are added up, according to including time, circuit or fault type not
Equipment state or failure are counted with dimension, realize equipment state statistical analysis, the result of statistical analysis is stored in point
In cloth data warehouse；
Data mining is carried out based on the statistic analysis result in Distributed Data Warehouse and data mining model, forms train
The data results of running monitor device data；
Carry out the displaying and/or inquiry of data results.
One embodiment of the analysis method of the train operation monitoring device data according to the present invention based on big data, access
The data of the train operation monitoring device of system include train operation monitoring device real-time running data, train operation monitoring device
Off-line data, train operation monitoring device related data.
One embodiment of the analysis method of the train operation monitoring device data according to the present invention based on big data, distribution
Formula database is built upon the HBase distributed data bases on distributed file system HDFS.
One embodiment of the analysis method of the train operation monitoring device data according to the present invention based on big data, distribution
Formula data warehouse is built upon the Hive Distributed Data Warehouses on Hadoop Distributed Computing Platforms.
One embodiment of the analysis method of the train operation monitoring device data according to the present invention based on big data, data
The step of excavation, further comprises：It is first out of order sample from the extracting data of train operation monitoring device, based on historical failure
Analysis data obtain the analysis data of fault sample, by the analysis data of fault sample be input in data mining algorithm model into
Row sample training obtains initialization critical field weights and threshold value, and failure extracting rule is recycled to form fault tree database simultaneously
It is input in data mining model, data mining model utilizes fault tree number based on the data of train operation monitoring device
Data results are obtained according to library.
The present invention has following advantageous effect compared with the prior art：The scheme of the invention is based on the big number of the Hadoop ecospheres
According to treatment technology, Hadoop is the distributed storage and concurrent computational system for running general commercial server cluster, is had in cluster
One main controlled node is used for controlling and managing the normal operation of entire cluster, and coordinates and manages each from node completion number in cluster
According to storage and calculating task.The present invention establishes big data platform using Hadoop Distributed Computing Platform technologies, realizes LKJ devices
Log historical data centrally stored, data format standardization processing, data are ultimately stored on HBase distribution numbers
According in library.Map/Reduce is recycled to carry out at distributed arithmetic a large amount of associated LKJ operation datas stored in HBase
Reason is completed the analysis of magnanimity LKJ data, is stored in analysis result as intermediate data in HBase.Again a part of incidence number
According to this and the intermediate result obtained is analyzed, reloaded in Distributed Data Warehouse Hive by ETL, so as to be shared with all numbers
It is used according to fairground.
Description of the drawings
After the detailed description of embodiment of the disclosure is read in conjunction with the following drawings, it better understood when the present invention's
Features described above and advantage.In the accompanying drawings, each component is not necessarily drawn to scale, and with similar correlation properties or feature
Component may have same or similar reference numeral.
Fig. 1 shows an embodiment of the analysis system of the train operation monitoring device data based on big data of the present invention
Schematic diagram.
Fig. 2 shows the schematic diagrams of the train operation monitoring device data analysis of the present invention.
The system that Fig. 3 shows the present invention realizes the schematic diagram of data mining.
Fig. 4 shows an embodiment of the analysis method of the train operation monitoring device data based on big data of the present invention
Flow chart.
Specific embodiment
Below in conjunction with the drawings and specific embodiments, the present invention is described in detail.It is note that below in conjunction with attached drawing and specifically real
The aspects for applying example description is only exemplary, and is understood not to carry out any restrictions to protection scope of the present invention.
Fig. 1 shows an embodiment of the analysis system of the train operation monitoring device data based on big data of the present invention
Principle, Fig. 2 shows the principles of train operation monitoring device data analysis.It refers to Fig. 1 and is aided with shown in Fig. 2, this implementation
The analysis system of example includes：Platform data AM access module, big data streaming processing module, distributed statistical analysis module, data
Excavate module, analysis result output module.
Platform data AM access module is used to (usually deposit the off-line data of the real-time running data of LKJ devices, LKJ devices
Storage is in LKJ log data files), LKJ device related datas are linked into subsequent module.
Big data streaming processing module be with big data treatment technology (spark streaming) to the data of access into
Row Stream Processing, by the data after formatting standardization processing according to engine number or LKJ device numbers in HBase distributions
It is stored in database into determinant, the data of storage include：Type of locomotive, engine number, LKJ device numbers, locomotive attach troops to a unit segment number,
Event, line number, mileage, distance, speed, speed limit, signal, signal serial number, machine occur for LKJ logouts code, LKJ events
Turner condition, pipe pressure, cylinder pressure, pressure, diesel engine speed/current of electric etc..Each engine number or LKJ device numbers are built
A corresponding list is found, and data can infinitely expand.HBase is built upon on distributed file system HDFS
Distributed data base, available for storing mass data.
Distributed statistical analysis module is a large amount of associated to what is stored in HBase distributed data bases using Map/Reduce
LKJ operation datas carry out Distributed Calculation processing, equipment state to the LKJ devices of same vehicle, same engine number or therefore
Barrier is added up, and equipment state or failure are counted according to different dimensions such as time, circuit, fault types, realizes equipment
Statistic is analyzed, and is stored in analysis result as intermediate data in Hbase distributed data bases, while also analysis result
It is stored in Hive Distributed Data Warehouses by ETL, wherein Hive Distributed Data Warehouses are built upon the data on Hadoop
Warehouse.
Data-mining module passes through machine based on the data in Hive Distributed Data Warehouses and Hbase distributed data bases
The skewness of discrete series is analyzed in device study, tentatively summarizes anomaly regularity, then carries out the correlation analysis of multidimensional data,
Diagnosis rule is extracted, data mining model is then established, forms the trend analysis of fault data, it is final to realize that fault pre-alarming carries
Show.
The realization principle of data-mining module is as shown in Figure 3.Fault sample first is extracted from LKJ device operation datas,
The analysis data of fault sample are obtained based on historical failure analysis data, the analysis data of fault sample are input to data mining
Sample training is carried out in algorithm model, obtains initialization critical field weights and threshold value, failure extracting rule is recycled to form event
Barrier tree database is simultaneously input in data mining model.Data mining model utilizes failure based on LKJ device operation datas
Tree database obtains data results and exports.In practical application such as fault pre-alarming, fault location or provide solution party
It is required for using data mining results in case.
Analysis result output module is the displaying and interactive query that LKJ data results are realized in Web page.
Fig. 4 shows an embodiment of the analysis method of the train operation monitoring device data based on big data of the present invention
Flow.Fig. 4 is referred to, here is the detailed description to the implementation steps of the analysis method of the present embodiment.
Step S1：The off-line data of the real-time running data of LKJ devices, LKJ devices (is generally stored inside LKJ operation notes
Record data file in), LKJ devices related data carry out system input.
Step S2：Stream Processing is carried out to the data of access with big data treatment technology (spark streaming), it will
Data after formatting standardization processing are carried out according to engine number or LKJ device numbers in HBase distributed data bases
Column stores.
The data of storage include：Type of locomotive, engine number, LKJ device numbers, locomotive are attached troops to a unit segment number, LKJ logout generations
Code, LKJ events occur event, line number, mileage, distance, speed, speed limit, signal, signal serial number, locomotive operating mode, pipe pressure,
Cylinder pressure, pressure, diesel engine speed/current of electric etc..For each engine number or LKJ device numbers establish one it is right therewith
The list answered, and data can infinitely expand.HBase is built upon the distributed data on distributed file system HDFS
Library, available for storing mass data.
Step S3：:The a large amount of associated LKJ stored in HBase distributed data bases are run using Map/Reduce
Data carry out Distributed Calculation processing, and the equipment state or failure of the LKJ devices of same vehicle, same engine number are tired out
Meter, counts equipment state or failure according to different dimensions such as time, circuit, fault types, realizes equipment state statistics
Analysis, is stored in analysis result as intermediate data in Hbase distributed data bases, while analysis result is also passed through ETL
It is stored in Hive Distributed Data Warehouses, wherein Hive Distributed Data Warehouses are built upon the data warehouse on Hadoop.
Step S4：:Data mining is carried out based on the data in Hive Distributed Data Warehouses and Hbase distributed data bases
Analysis.
The skewness of discrete series is analyzed by machine learning, tentatively summarizes anomaly regularity, then carries out multidimensional number
According to correlation analysis, extract diagnosis rule, then establish data mining model, form the trend analysis of fault data, finally
Realize fault pre-alarming prompting.
The realization principle of data mining is as shown in Figure 3.Fault sample first is extracted from LKJ device operation datas, is based on
Historical failure analysis data obtain the analysis data of fault sample, and the analysis data of fault sample are input to data mining algorithm
Sample training is carried out in model, obtains initialization critical field weights and threshold value, failure extracting rule is recycled to form fault tree
Database is simultaneously input in data mining model.Data mining model utilizes fault tree number based on LKJ device operation datas
Data results are obtained according to library and are exported.In practical application such as fault pre-alarming, fault location or offer solution
It is required for using data mining results.
Step S5：:The displaying and interactive query of LKJ data results are realized in Web page.
The innovation of the present invention essentially consists in：
1) big data computer cluster platform is built based on Hadoop, realizes that the standardization of LKJ log datas is concentrated
Storage；
2) the Distributed Storage side as unit of engine number, LKJ device numbers is established based on HBase distributed data bases
Formula；
3) analyzed based on LKJ equipment state of the HBase distributed data bases progress as unit of engine number, LKJ device numbers,
Fault statistics trend analysis.
4) data mining of LKJ plant failures is established based on Hive Distributed Data Warehouses and Hbase distributed data bases
Model realizes LKJ equipment state fault pre-alarmings.
Although for explanation is simplified to illustrate the above method and is described as a series of actions, it should be understood that and understand,
The order that these methods are not acted is limited, because according to one or more embodiments, some actions can occur in different order
And/or with from it is depicted and described herein or herein it is not shown and describe but it will be appreciated by those skilled in the art that other
Action concomitantly occurs.
Those skilled in the art will further appreciate that, the various illustratives described with reference to the embodiments described herein
Logic plate, module, circuit and algorithm steps can be realized as electronic hardware, computer software or combination of the two.It is clear
Explain to Chu this interchangeability of hardware and software, various illustrative components, frame, module, circuit and step be above with
Its functional form makees generalization description.Such functionality be implemented as hardware or software depend on concrete application and
It is applied to the design constraint of total system.Technical staff can realize each specific application described with different modes
Functionality, but such realization decision should not be interpreted to cause departing from the scope of the present invention.
General place can be used with reference to various illustrative logic plates, module and the circuit that presently disclosed embodiment describes
Reason device, digital signal processor (DSP), application-specific integrated circuit (ASIC), field programmable gate array (FPGA) other are compiled
Journey logical device, discrete door or transistor logic, discrete hardware component or its be designed to carry out function described herein
Any combinations are realized or are performed.General processor can be microprocessor, but in alternative, which can appoint
What conventional processor, controller, microcontroller or state machine.Processor is also implemented as the combination of computing device, example
As the combination of DSP and microprocessor, multi-microprocessor, the one or more microprocessors to cooperate with DSP core or it is any its
His such configuration.
It can be embodied directly in hardware, in by processor with reference to the step of method or algorithm that embodiment disclosed herein describes
It is embodied in the software module of execution or in combination of the two.Software module can reside in RAM memory, flash memory, ROM and deposit
Reservoir, eprom memory, eeprom memory, register, hard disk, removable disk, CD-ROM or known in the art appoint
In the storage medium of what other forms.Exemplary storage medium is coupled to processor so that the processor can be from/to the storage
Medium is read and write-in information.In alternative, storage medium can be integrated into processor.Pocessor and storage media can
It resides in ASIC.ASIC can reside in user terminal.In alternative, pocessor and storage media can be used as discrete sets
Part is resident in the user terminal.
In one or more exemplary embodiments, described function can be in hardware, software, firmware, or any combination thereof
Middle realization.If being embodied as computer program product in software, each function can be used as the instruction of one or more items or generation
Code may be stored on the computer-readable medium or is transmitted by it.Computer-readable medium includes computer storage media and communication
Both media, including any medium that computer program is facilitated to shift from one place to another.Storage medium can be can quilt
Any usable medium that computer accesses.It is non-limiting as example, such computer-readable medium may include RAM, ROM,
EEPROM, CD-ROM or other optical disc storage, disk storage or other magnetic storage apparatus can be used to carrying or store instruction
Or data structure form desirable program code and any other medium that can be accessed by a computer.Any connection is also by by rights
Referred to as computer-readable medium.For example, if software is using coaxial cable, fiber optic cables, twisted-pair feeder, digital subscriber line
(DSL) or the wireless technology of such as infrared, radio and microwave etc is passed from web site, server or other remote sources
It send, then the coaxial cable, fiber optic cables, twisted-pair feeder, DSL or such as infrared, radio and microwave etc is wireless
Technology is just included among the definition of medium.Disk (disk) and dish (disc) as used herein are including compressing dish
(CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blu-ray disc, which disk (disk) are often reproduced in a manner of magnetic
Data, and dish (disc) with laser reproduce data optically.Combinations of the above should also be included in computer-readable medium
In the range of.
Offer is for so that any person skilled in the art all can make or use this public affairs to the previous description of the disclosure
It opens.The various modifications of the disclosure all will be apparent, and as defined herein general for a person skilled in the art
Suitable principle can be applied to spirit or scope of other variants without departing from the disclosure.The disclosure is not intended to be limited as a result,
Due to example described herein and design, but should be awarded and principle disclosed herein and novel features phase one
The widest scope of cause.
Claims (10)
1. a kind of analysis system of the train operation monitoring device data based on big data, which is characterized in that including：
Platform data AM access module, in the data access to system by train operation monitoring device；
Big data streaming processing module carries out Stream Processing, by the number after formatting standardization processing to the data of access
It is stored in distributed data base into determinant according to the device number according to engine number or train operation monitoring device, wherein to every
The device number of one engine number or train operation monitoring device all establishes a corresponding list；
Distributed statistical analysis module carries out Distributed Calculation processing, to same vehicle to the data stored in distributed data base
Type, same engine number train operation monitoring device equipment state or failure added up, according to including the time, circuit or
Different dimensions including fault type count equipment state or failure, equipment state statistical analysis are realized, by statistical
The result of analysis is stored in Distributed Data Warehouse；
Data-mining module carries out data digging based on the statistic analysis result in Distributed Data Warehouse and data mining model
Pick forms the data results of train operation monitoring device data；
Analysis result output module realizes the displaying and/or inquiry of data results.
2. the analysis system of the train operation monitoring device data according to claim 1 based on big data, feature exist
In the data for the train operation monitoring device that platform data AM access module is accessed include train operation monitoring device real time execution
Data, train operation monitoring device off-line data, train operation monitoring device related data.
3. the analysis system of the train operation monitoring device data according to claim 1 based on big data, feature exist
In distributed data base is built upon the HBase distributed data bases on distributed file system HDFS.
4. the analysis system of the train operation monitoring device data according to claim 1 based on big data, feature exist
In Distributed Data Warehouse is built upon the Hive Distributed Data Warehouses on Hadoop Distributed Computing Platforms.
5. the analysis system of the train operation monitoring device data according to claim 1 based on big data, feature exist
It is first out of order sample from the extracting data of train operation monitoring device in, data-mining module, number is analyzed based on historical failure
According to the analysis data of fault sample are obtained, the analysis data of fault sample are input in data mining algorithm model and carry out sample
Training obtains initialization critical field weights and threshold value, and failure extracting rule is recycled to form fault tree database and is input to
In data mining model, data mining model is obtained based on the data of train operation monitoring device using fault tree database
To data results.
6. a kind of analysis method of the train operation monitoring device data based on big data, which is characterized in that including：
It will be in the data access to system of train operation monitoring device；
Stream Processing is carried out to the data of access, by the data after formatting standardization processing according to engine number or train
The device number of running monitor device stores in distributed data base into determinant, wherein being transported to each engine number or train
The device number of row monitoring device all establishes a corresponding list；
Distributed Calculation processing is carried out to the data stored in distributed data base, to the train of same vehicle, same engine number
The equipment state or failure of running monitor device are added up, according to the different dimensional including time, circuit or fault type
Degree counts equipment state or failure, realizes equipment state statistical analysis, the result of statistical analysis is stored in distribution
In data warehouse；
Data mining is carried out based on the statistic analysis result in Distributed Data Warehouse and data mining model, forms train operation
The data results of monitoring device data；
Carry out the displaying and/or inquiry of data results.
7. the analysis method of the train operation monitoring device data according to claim 6 based on big data, feature exist
In the data of the train operation monitoring device of access system include train operation monitoring device real-time running data, train operation
Monitoring device off-line data, train operation monitoring device related data.
8. the analysis method of the train operation monitoring device data according to claim 6 based on big data, feature exist
In distributed data base is built upon the HBase distributed data bases on distributed file system HDFS.
9. the analysis method of the train operation monitoring device data according to claim 6 based on big data, feature exist
In Distributed Data Warehouse is built upon the Hive Distributed Data Warehouses on Hadoop Distributed Computing Platforms.
10. the analysis method of the train operation monitoring device data according to claim 6 based on big data, feature exist
Further comprise in the step of, data mining：It is first out of order sample from the extracting data of train operation monitoring device, based on going through
History accident analysis data obtain the analysis data of fault sample, and the analysis data of fault sample are input to data mining algorithm mould
Sample training is carried out in type, obtains initialization critical field weights and threshold value, failure extracting rule is recycled to form fault tree number
It according to library and is input in data mining model, data mining model utilizes event based on the data of train operation monitoring device
Barrier tree database obtains data results.
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| CN109740772A (en) * | 2019-01-09 | 2019-05-10 | 昆山高新轨道交通智能装备有限公司 | Railroad train Measuring error analysis method based on big data |
| CN110059126A (en) * | 2019-04-25 | 2019-07-26 | 湖南中车时代通信信号有限公司 | Complicated related network analysis method and system based on LKJ exception Value Data |
| CN110309152A (en) * | 2019-06-26 | 2019-10-08 | 广州探迹科技有限公司 | A kind of date storage method and device based on HBase |
| CN111930835A (en) * | 2020-07-16 | 2020-11-13 | 广州运达智能科技有限公司 | Intelligent operation and maintenance big data management system and method for urban rail transit |
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