CN103902838A - TMIS traffic flow determination method and system based on cloud computing - Google Patents
TMIS traffic flow determination method and system based on cloud computing Download PDFInfo
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Abstract
The invention relates to a TMIS traffic flow determination method and system based on cloud computing. The TMIS traffic flow determination method comprises the steps of (1) constructing a distributed storage resource pool on the basis of a cloud platform, collecting and storing monitoring data related to traffic flow calculation through a TMIS, (2) preprocessing the collected monitoring data through a concurrent processing framework, (3) writing a traffic flow calculation algorithm under the concurrent processing framework, utilizing static data for calculating the result of long-term traffic flow calculation, (4) utilizing the static data and dynamic data under the distributed environment for calculating the result of real-time traffic flow calculation corresponding to each station, and (5) providing a traffic flow calculation inquiry service externally through the cloud platform. The TMIS traffic flow determination system comprises a collecting layer, a storage layer, an engine layer, a service layer and a web service layer. According to the TMIS traffic flow determination method and system based on cloud computing, the cloud platform is utilized for obtaining various data required by traffic flow calculation in the TMIS, the concurrent computation is utilized for rapid data processing, and long-term and real-time traffic flow calculation and determination of railway transportation can be achieved.
Description
Technical field
The invention provides a kind of TMIS wagon flow assay method and system based on cloud computing, relate to the technical fields such as railway transport administration information, railway knowledge base, distributed storage, parallel computation, data analysis, machine learning, the estimation of wagon flow problem facing for solving railway transport administration data.
Background technology
TMIS (TMIS, Transportation Management Information System) mainly comprises: the subsystems such as true report, cargo ticket, movement plan, vehicle, marshalling yard, goods station, district station, Railway Sub-administration Dispatch, lorry real-time tracing, locomotive real-time tracing, container real-time tracing, daily transport statistics, cars on hand and estimation of wagon flow, military transportation.Unified management system-wide computer network, the computer equipment of system-wide portion, office, branch office, main station section is unified into an entirety, under gathering, the related data of whole transportation equipments, as daily transportation data, position data etc., has formed complete railway transport administration data.
It is essential condition and the effective means of carrying out wagon flow adjustment that estimation of wagon flow or title wagon flow are measured, only have the trend of the following wagon flow of accurate reckoning and section by situations such as the movement capacities such as, locomotive supply and temporary passenger train increase and decrease, construction arrangements, disasteies, just can have pre-insight to take effective wagon flow adjustment measure.Estimation of wagon flow is divided into again long-term reckoning and calculates in real time, and long-term reckoning is according to the data such as forwarding plan, operation plan, completes the wagon flow of station, divisional station is estimated; It is the real-time estimation of wagon flow for each station, divisional station carrying out according to the existing car situation in current lorry real-time follow-up, station that real-time traffic flow is calculated.
TMIS system can be for realizing in time, estimation of wagon flow/wagon flow is measured accurately, better for transportation organization of production, traffic dispatching command provide decision-making foundation.The overall goal of Railway Bureau's transportation dispatching estimation of wagon flow and adjustment System is take wagon flow organizing as core, take ten eight transport statistics, monthly transport plan static information as basic point, connect the multidate informations such as wagon flow changes, shipping handling tissue take train working plan, boundary oral sex as foundation, rely on computing machine, communication network device, realize Railway Bureau's estimation of wagon flow and adjustment.By real-time processing and the storage administration of the collection to dynamic datas such as train working plan, boundary mouthful wagon flow discrepancy, shippings, transmission, reception, warehouse-in, complete each stage, the daily reckoning of various wagon flow and estimation of wagon flow at a specified future date.But existing wagon flow assay method generally adopts the manual mode of calculating wagon flow, the problem and the shortcoming that exist manpower to waste, real-time is poor, are difficult to the data of processing Real-Time Monitoring.
Summary of the invention
The object of the invention is supplement and optimize for TMIS, utilize cloud platform to obtain the needed various data of estimation of wagon flow in TMIS system, comprise static data and dynamic data, utilize parallel computation to carry out fast processing to data, calculate with the long-term and real-time traffic flow of realizing transportation by railroad.
Cloud computing is a kind of account form based on internet, and in this way, shared software and hardware resources and information can offer computing machine and other equipment by demand.The present invention is based on Intel Virtualization Technology and set up data center, the data centralization that transportation by railroad is produced stores on cloud platform.Undertaken transportation data to carry out unified management and analysis by the parallel computation framework of cloud platform, comprise data storage, set up index, simple data statistics, depth data excavate and the service such as aid decision making.Data analysis processing TMIS being provided in conjunction with cloud platform, can realize wagon flow and measure (or claiming estimation of wagon flow) function, to better meet the plan of each station section of rail transportation system, establishment and the enforcement of scheduling, reduce the risk in transportation, improve transportation safety.
Specifically, the technical solution used in the present invention is as follows:
A TMIS wagon flow assay method based on cloud computing, its step comprises:
1), based on cloud platform building distributed storage resource pool, by the data acquisition interface collection of the TMIS Monitoring Data relevant to estimation of wagon flow, and store in distributed storage resource pool;
2) by parallel processing framework, the Monitoring Data collecting is carried out to pre-service, comprise to static data is carried out pre-service and dynamic data is carried out to pre-service;
3) under parallel processing framework, write estimation of wagon flow algorithm, utilize pretreated static data to calculate the result of long-term estimation of wagon flow;
4) under distributed environment, pretreated static data and dynamic data are calculated, extrapolate the result of the real-time traffic flow reckoning that each website is corresponding;
5) externally provide the inquiry service of estimation of wagon flow result by cloud platform, comprise the interface that long-term estimation of wagon flow and real-time traffic flow are calculated.
Further, distributed storage resource pool described in step 1) comprises distributed file system (HDFS), row formula database (HBase) and relational database cluster, form integratedly, can meet the storage demand of the data of the different required types of estimation of wagon flow.
Further, the Monitoring Data described in step 1) has very strong timing, gives identical timestamp to the data of the synchronization collecting.
Further, described in step 1), cloud platform is connected with TMIS by internal network.The data relevant to estimation of wagon flow that TMIS gathers have that collection point is many, frequency acquisition is high, timing, real-time feature, in the process of data Storage and Processing, require that cloud platform can be stored fast, deal with data, and can be real-time carry out estimation of wagon flow, to meet field demand.For meeting this application requirements, first the Monitoring Data of collection is stored in HBase cluster, HDFS cannot meet a large amount of small documents real-time storage demands, therefore needs to use HBase to carry out real-time storage, then by compile script, regular by the compression storing data in HBase in HDFS.For the needs of subsequent distribution formula processing, different Monitoring Data can be classified, be stored in respectively on different monitoring HBase servers, be convenient to parallel processing.
Further, cloud platform can be based on virtual cluster building described in step 1), and by build virtual resource pond in physical cluster, the difference of shielding bottom hardware is then built cloud computing platform in unified environment.Can better utilize existing resource by virtual plan, avoid the waste of resource.
Further, step 2) described data pre-service, comprise format, half format, nonformatted data are changed, so that platform carries out Storage and Processing to data.
Further, step 2) basic data such as described static packet vinculum road, station, station track equipment; The transportation data such as locomotive, lorry, goods, movement requirement; The data such as annual plan, technical plan, monthly plan, day shift plan, operation plan, cargo hanlding plane; Step 2) described dynamic data mainly comprises the Realtime Statistics to each station section, boundary mouthful, train in transit.
Further, parallel processing framework described in step 3) is based on MapReduce, can realize the parallel computation of cluster by writing MapReduce program, avoid developer that too much energy be wasted in resource distribution, fault-tolerant processing, parallelization etc. are processed above, thereby can be absorbed in the exploitation of service logic.Step 3) can, by the high MapReduce programming language such as Hive, Pig, be carried out shirtsleeve operation to the data of obtaining, as in station car statistics etc.Described MapReduce utilizes distributed memory to realize, because data need to constantly read in, write out in the process of pre-service and reckoning, the medium that original MapReduce uses disk to read as data, can have a strong impact on the speed that data are calculated, do not meet the demand that wagon flow is calculated in real time, read and write the speed that can significantly improve data processing by internal memory.
Further, long-term estimation of wagon flow described in step 3) is the various static datas that provide according to TMIS, then extrapolate according to data such as the existing car situation in station, day shift plan, train in transit, production plans, in certain hour interval, some stations connect car number, at station car number and go out car number.
Further, real-time traffic flow described in step 4) is calculated, except the needed information of long-term estimation of wagon flow, also need to transport dynamic data, comprise the spatial and temporal distributions of train, locomotive, goods etc., cause under emergency case with strain various with plan inconsistent travel position, this just need to according to Real-time Collection to travel position information carry out real-time estimation of wagon flow.
Further, the estimation of wagon flow algorithm that step 4) adopts need to be realized under parallel computation framework, to improve the speed of calculating, because the frequency of data acquisition is higher, and need to carry out estimation of wagon flow for all websites, decomposition mouthful, therefore need a large amount of calculating.When step 4) is calculated for some websites, need to be from HBase acquisition time stamp in the Monitoring Data of certain limit, calculate the ruuning situation of all trains in section sometime, comprise position, speed etc.By the coupling position zone at place, station and the positional information of all vehicles, can extrapolate the wagon flow situation at this station.The estimation of wagon flow that step 4) draws is directly kept on HDFS, need compile script that data are stored in relational database cluster, and in relational database, set up the index strategy with the conventional inquiry such as station, time, so that user can directly carry out query manipulation by database client.Further, step 4) is after carrying out data processing, through calculating after a while, Monitoring Data before stabbing sometime, no longer need to carry out computing, this part data can be processed by compression, and the data in Hbase are converted to an independent large file, is stored in HDFS.
Further, the query interface described in step 5) provides in the mode of webservice, adopts Json or XML form to carry out result feedback.
Realize the TMIS wagon flow based on cloud computing of said method and measure a system, it comprises:
Acquisition layer, the Static and dynamic Monitoring Data relevant to estimation of wagon flow of storing for gathering TMIS, for estimation of wagon flow is for data supporting;
Accumulation layer, for the TMIS Monitoring Data of magnanimity gathering is stored, comprises HDFS distributed file system, HBase row formula database and relational database cluster, realizes the Mass storage to destructuring, semi-structured and structural data;
Engine layers, realizes the processing to TMIS data, comprises that setting up index, data statistics and the degree of depth excavates;
Operation layer, realizes long-term estimation of wagon flow and real-time traffic flow and calculates function;
Web services layer, realizes web services cluster, issues by internet the result that wagon flow is measured.
Further, described engine layers comprises MapReduce engine, Hive engine, Pig engine, Java engine and Oracle engine.
The present invention utilizes cloud computing that transportation by railroad data are stored, manage, analyzed, and the storage by Highly Scalable and computing architecture are measured relevant data to estimation of wagon flow/wagon flow and carried out storage and fast processing, statistical study, and inquiry service interface is provided.Compared with prior art, advantage is as follows:
1) the present invention has realized the Monitoring Data of TMIS collection has been carried out to Storage and Processing, and utilizes cloud platform to realize the long-term reckoning of wagon flow and calculated in real time, for car amount Real-Time Scheduling provides reliable basis, has ensured the security of operation of vehicle.
2) the present invention, by parallel processing framework, can realize the quick calculating to mass data, solves statistics, analysis difficult problem that mass data causes.
3) the present invention, by obtaining the static datas such as forwarding plan, day shift plan, technical plan, cargo hanlding plane, utilizes estimation of wagon flow algorithm, realizes long-term estimation of wagon flow, for establishment, adjustment, the scheduling of plan provide foundation.
4) real time data that the present invention collects by obtaining TMIS, is included in the real time datas such as station car situation, car in transit, in conjunction with various forwarding plans, can extrapolate in some time sections for different stations, divisional station the wagon flow situation at this station.
5) query interface that the present invention externally provides long-term estimation of wagon flow and real-time traffic flow to calculate by the mode of cloud service.The present invention can also be integrated with other transportation by railroad information integrated platform, directly obtains data from transportation by railroad information integrated platform, then by this platform, the result of estimation of wagon flow displayed.
Accompanying drawing explanation
Fig. 1 is cloud computing platform Organization Chart of the present invention.
Fig. 2 is the integrated stand composition that wagon flow of the present invention is measured system.
Fig. 3 is the data flow diagram of estimation of wagon flow.
Fig. 4 is estimation of wagon flow distributed treatment schematic diagram.
Embodiment
Below by concrete enforcement and accompanying drawing, the present invention will be further described.
The present invention is based on cloud computing technology estimation of wagon flow related data is carried out to store and management, as shown in Figure 1, mainly comprise the modules such as distributed file system, row formula database, relational database cluster, parallel processing framework, memory database, complete the functions such as storage, pre-service, calculating and analysis to static data and magnanimity dynamic data.System can also be selected to dispose cluster in virtual machine mode, and virtual machine is easier to the management of cluster and the backup of data.
System is built based on Hadoop and Oracle, by distributed file system HDFS and the parallel processing framework MapReduce of Hadoop, can mass data be carried out effectively storage and be processed efficiently.By building Oracle RAC real-time response cluster, can realize the quick storage of massive structured data and read.Oracle data are mainly the real time datas for accepting TMIS transmission, and transfer in the better Hadoop cluster of extendability according to the regular data by Oracle RAC of the demand of user and system.
Hadoop utilizes distributed file system can realize the storage to mass data, and user, by writing MapReduce program, can realize easily and fast to quick calculating long-term and that real-time traffic flow is calculated.
Fig. 2 is the integrated stand composition of system, comprises acquisition layer, accumulation layer, engine layers, operation layer and web services layer, comprises again respectively different functional units.
Acquisition layer comprises final figure certificate, Freight Invoice, movement plan, making station control data, primary train diagram, daily statistics, locomotive data, lorry data etc. for gathering the data that TMIS stores, these data are carried out the data of Storage and Processing as cloud platform, for estimation of wagon flow is for data supporting.
Accumulation layer for the TMIS data of magnanimity are stored, comprises HDFS distributed file system, HBase row formula database, three parts of Orace Real Application Clusters, realizes the Mass storage to destructuring, semi-structured and structural data.
Engine layers is mainly realized the processing to TMIS data, comprises and sets up index, data statistics, degree of depth excavation etc.For the ease of user's customizable exploitation, system provides multiple exploitation engine, comprises MapReduce engine, Hive, Pig engine, Java engine and Oracle engine.
Operation layer is mainly realized long-term estimation of wagon flow and real-time traffic flow is calculated to function.
Web services layer is used for realizing web services cluster, issues the result of estimation of wagon flow by internet.
Fig. 3 is the master data flow graph of system, and estimation of wagon flow system determines according to the current train part situation collecting of system, reckoning be the vehicle flowrate in a period of time of some stations.Need to count the part situation of current time train, and in stand position, speed, the plan of travelling etc. of car and car in transit.
1) calculate the time of arrival of wagon flow:
Here be provided with m train L(L1, L2..Lm) the identified station S that drives towards, the travel speed of train is V (V1, V2..Vm); Range marker between the current and station S of train is D(D1, D2 ..Dm); The vehicle flowrate of train itself is labeled as Qi.
Train is divided at station and two states on the way, and two states has respectively the computing formula of oneself:
At station: train Li to the distance B at S station is, the distance between this station and S station;
In transit: train Li is that current location arrives the distance between S station to the distance at S station, in the train with real-time navigation capability, can, directly by location, then calculate real-time distance.Need to be according to the speed of train in the time there is no real-time distance, the time leaving from station etc. are because of the current position of calculated column car usually, thereby obtain distance B.
Production plan while adding train operation according to these information, comprises loading-unloading vehicle time, train overhaul time etc., just can calculate train Li and arrive the concrete time that S stands, and specific formula for calculation is as follows:
Time of arrival:
T
di=D
i/V
i+T
li
Wherein T
difor train L
itime of arrival, T
lifor train L
ithe middle activity duration AT STATION;
For website S at T[T1, T2] vehicle flowrate that arrives at a station in time range is, arrive S station time T d in [T1, T2] time range the set of directive train.
2) whereabouts of wagon flow is calculated:
The whereabouts of wagon flow is divided into two classes, and a class is the self-unloading of arriving at a station; One class is that S is just as terminal;
If being divided into n direction, the train that S sets out at station is expressed as P (P1, P2 ..Pn); At certain time period [T1, T2], the computing formula that S stands in train departure flow in certain direction can be expressed as:
Wherein Tic is the train departures time that arrives S station, and the time of going to war can be calculated according to train operation plan and arrival time, production plan etc.Can realize the ability of estimation of wagon flow by above-mentioned steps;
Estimation of wagon flow can be subdivided into loaded vehicle estimation of wagon flow, empty wagons estimation of wagon flow, locomotive estimation of wagon flow etc., and these estimation of wagon flow all can calculate according to above-mentioned formula.
This system need to be carried out estimation of wagon flow to all stations, decomposition station, in order to meet the real-time demand of estimation of wagon flow, adopts parallel processing framework to realize above-mentioned estimation of wagon flow algorithm.Fig. 4 has described the realization of estimation of wagon flow on MapReduce framework, and the Monitoring Data in Hbase is how combination algorithm carries out Distributed Calculation.MapReduce is divided into two stages, is respectively Map stage and Reduce stage.Monitoring Data is stored in HBase cluster, reads concurrently the Static and dynamic Monitoring Data in Ts moment from n platform Hbase server (being Hregion-1～Hregion-n).These data are classified and polymerization by the Map stage, the related data of each train is as an independent processing unit, a total m train (being L1～Lm), the individual pending data cell of corresponding m.The Reduce stage writes the algorithm of estimation of wagon flow and realizes, and parallel carries out independent calculating to train, so just can calculate the positional information of each train of object time Ts.Each website can judge that whether train is at station according to the positional information of train and static data, thereby carries out estimation of wagon flow.In Fig. 4, Tt refers to the reckoning result at moment t.
Above embodiment is only in order to technical scheme of the present invention to be described but not be limited; those of ordinary skill in the art can modify or be equal to replacement technical scheme of the present invention; and not departing from the spirit and scope of the present invention, protection scope of the present invention should be as the criterion with described in claim.
Claims (10)
1. the TMIS wagon flow assay method based on cloud computing, its step comprises:
1), based on cloud platform building distributed storage resource pool, by the data acquisition interface collection of the TMIS Monitoring Data relevant to estimation of wagon flow, and store in distributed storage resource pool;
2) by parallel processing framework, the Monitoring Data collecting is carried out to pre-service, comprise to static data is carried out pre-service and dynamic data is carried out to pre-service;
3) under parallel processing framework, write estimation of wagon flow algorithm, utilize pretreated static data to calculate the result of long-term estimation of wagon flow;
4) under distributed environment, pretreated static data and dynamic data are calculated, extrapolate the result of the real-time traffic flow reckoning that each website is corresponding;
5) externally provide the inquiry service of estimation of wagon flow result by cloud platform, comprise the interface that long-term estimation of wagon flow and real-time traffic flow are calculated.
2. the method for claim 1, is characterized in that: the distributed storage resource pool described in step 1) comprises HDFS distributed file system, HBase row formula database and relational database cluster; By the Monitoring Data of collection being stored in to the real-time storage that realizes large amount of small documents in HBase, and by compile script termly by the compression storing data in HBase in HDFS; Described cloud platform, based on virtual cluster building, by build the difference of virtual resource pond shielding bottom hardware in physical cluster, is then built cloud platform in unified environment.
3. the method for claim 1, is characterized in that: step 2) described pre-service comprises format, partly format, nonformatted data are changed, so that cloud platform carries out Storage and Processing to data; Described static data comprises basic data, transportation data and planning data, and wherein basic data comprises circuit, station, station track device data; Transportation data comprises locomotive, lorry, goods, movement requirement data, and planning data comprises annual plan, technical plan, monthly plan, day shift plan, operation plan, cargo hanlding plane data; Described dynamic data comprises the Realtime Statistics to each station section, boundary mouth, train in transit.
4. the method for claim 1, is characterized in that: described in step 3), parallel processing framework is realized based on MapReduce, realizes the parallel computation of cluster by writing MapReduce program, and described MapReduce utilizes distributed memory to realize.
5. the method for claim 1, it is characterized in that: when step 4) is calculated for some websites, from HBase, acquisition time stamp is in the Monitoring Data of certain limit, calculate the ruuning situation of all trains in section sometime, extrapolate the wagon flow situation at this station by mating the position zone at place, station and the positional information of all vehicles; The estimation of wagon flow result drawing is directly kept on HDFS, by compile script, data are stored in relational database cluster, and in relational database, set up the index strategy of conventional inquiry, so that user can directly carry out query manipulation by database client.
6. the method for claim 1, is characterized in that: the query interface described in step 5) provides in the mode of webservice, adopts Json or XML form to carry out result feedback.
7. the method for claim 1, it is characterized in that, be provided with m train L(L1, L2..Lm) the identified station S that drives towards, the travel speed of train is V (V1, V2..Vm), range marker between the current and station S of train is D(D1, D2 ..Dm), the vehicle flowrate of train itself is labeled as Qi, and the computing formula of carrying out estimation of wagon flow is as follows:
A) be the time of arrival of wagon flow:
T
di=D
i/V
i+T
li，
Wherein T
difor train L
itime of arrival, T
lifor train L
ithe middle activity duration AT STATION;
B) vehicle flowrate at the station of the arrival S in [T1, T2] time range is:
C) standing in train departure flow in certain direction at the interior S of [T1, T2] time range is:
Wherein Tic is the train departures time that arrives S station.
8. realize the TMIS wagon flow based on cloud computing of method described in claim 1 and measure a system, it is characterized in that, comprising:
Acquisition layer, the Static and dynamic Monitoring Data relevant to estimation of wagon flow of storing for gathering TMIS, for estimation of wagon flow is for data supporting;
Accumulation layer, for the TMIS Monitoring Data of magnanimity gathering is stored, comprises HDFS distributed file system, HBase row formula database and relational database cluster, realizes the Mass storage to destructuring, semi-structured and structural data;
Engine layers, realizes the processing to TMIS data, comprises that setting up index, data statistics and the degree of depth excavates;
Operation layer, realizes long-term estimation of wagon flow and real-time traffic flow and calculates function;
Web services layer, realizes web services cluster, issues by internet the result that wagon flow is measured.
9. system as claimed in claim 8, is characterized in that, the data of described acquisition layer collection comprise: final figure certificate, Freight Invoice, movement plan, making station control data, primary train diagram, daily statistics, locomotive data, lorry data.
10. system as claimed in claim 8, is characterized in that: described engine layers comprises MapReduce engine, Hive engine, Pig engine, Java engine and Oracle engine.
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