CN105023196A - Analysis method and device for charging transaction data of charging stations - Google Patents
Analysis method and device for charging transaction data of charging stations Download PDFInfo
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Abstract
The invention discloses an analysis method and device for charging transaction data of charging stations. The method comprises that basic information, which at least includes the number of the charging station, information of charging piles, position information and the type of the station, of each charging station is obtained; transaction data which is generated by the charging station in charging transaction is collected; the transaction data is pre-processed to obtain pre-processing data; and an analysis model is established according to the basic information and the pre-processing data, and operation parameters, which at least comprise the utilization rate of the charging station, the monthly averaged charging load parameter, the charging time parameter of the charging station and the manner of turnover, of the charging station are determined via the analysis model. Tedious operations in traditional table handling is avoided, the working efficiency is improved, the error rate is reduced, and the technical problems that manual analysis on the transaction data of the charging station causes low analysis efficiency and high error rate are solved.
Description
Technical field
The present invention relates to electric automobile field, in particular to a kind of analytical approach and device of charging station charging transaction data.
Background technology
Along with the development of ev industry, the continuous construction of electric automobile relevant information system, the continuous increase of the charging transaction data amount of electric automobile, how effective centralized stores analyzes these data, formation data centralization manages, the unified application model shared of resource, and the potential value of therefrom mining data are needed for current current events.
At present, Beijing's charging electric vehicle transaction record is from about nearly 500W bar in 2010 so far, and transaction record is stored in trading record sheet.Now, if during for analyzing the information such as charging time of concentration, charge capacity increment situation, charging electric degree situation of change, just need the data in transaction record form are carried out to segmentation as requested and gathered, therefrom excavate potential value, diversified analysis is carried out to data.
In existing technology, the computing formula that manually can only arrange needs also inputs pending data one by one and calculates, and then calculation result data is inserted in electrical form to manually.Because the data volume of calculative data object is usually all very huge, this carries out repetitive operation loaded down with trivial details in a large number with regard to needing by manual, not only wastes time and efforts, also reduces work efficiency, and error rate can be caused very high.
For above-mentioned problem, at present effective solution is not yet proposed.
Summary of the invention
Embodiments provide analytical approach and the device of a kind of charging station charging transaction data, by manual, the transaction data of charging station is analyzed at least to solve, cause the technical matters that analysis efficiency is low, error rate is high.
According to an aspect of the embodiment of the present invention, provide the analytical approach of a kind of charging station charging transaction data, comprise: the Back ground Information obtaining each charging station, wherein, Back ground Information at least comprises: charging station numbering, charging pile information, positional information, type of site; Gather the transaction data that charging station generates in charging transaction; Pre-service is carried out to transaction data, obtains preprocessed data; Set up analytical model according to Back ground Information and preprocessed data, by the operational parameter of analytical model determination charging station, wherein, operational parameter at least comprises: charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume.
Further, pre-service is carried out to transaction data, obtains preprocessed data, comprising: by carrying out data type conversion to transaction data, generate the pending data of target data type; By carrying out abnormality processing to there are abnormal data in pending data, generate preprocessed data.
Further, gather the transaction data that charging station generates in charging transaction, comprising: according to the charging pile information of charging station, obtain the charging pile numbering of charging pile in charging station; According to charging pile numbering, gather the sub-transaction data of each charging pile; Sub-transaction data is gathered, generates the transaction data of charging station.
Further, set up analytical model according to Back ground Information and preprocessed data, by the operational parameter of analytical model determination charging station, comprising: according to Back ground Information, the corresponding relation between the sub-transaction data determining charging station and charging pile; Analyzed by antithetical phrase transaction data, determine the charging pile utilization rate of charging pile, the monthly load parameter of charging pile, charging pile duration of charging; According to the charging pile utilization rate of charging pile, the monthly load parameter of charging pile and charging pile duration of charging, determine the operational parameter of charging station.
Further, setting up analytical model according to Back ground Information and preprocessed data, after the operational parameter by analytical model determination charging station, method also comprises: according to operational parameter, divides business rank to charging station.
According to the another aspect of the embodiment of the present invention, additionally providing the analytical equipment of a kind of charging station charging transaction data, comprising: acquisition module, for obtaining the Back ground Information of each charging station, wherein, Back ground Information at least comprises: charging station numbering, charging pile information, positional information, type of site; Acquisition module, for gathering the transaction data that charging station generates in charging transaction; Pretreatment module, for carrying out pre-service to transaction data, obtains preprocessed data; Analysis module, for setting up analytical model according to Back ground Information and preprocessed data, by the operational parameter of analytical model determination charging station, wherein, operational parameter at least comprises: charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume.
Further, pretreatment module comprises: the first sub-generation module, for by carrying out data type conversion to transaction data, generates the pending data of target data type; Second sub-generation module, for by carrying out abnormality processing to there are abnormal data in pending data, generates preprocessed data.
Further, acquisition module comprises: sub-acquisition module, for the charging pile information according to charging station, obtains the charging pile numbering of charging pile in charging station; Sub-acquisition module, for according to charging pile numbering, gathers the sub-transaction data of each charging pile; Sub-generation module, for being gathered by sub-transaction data, generates the transaction data of charging station.
Further, analysis module comprises: sub-determination module, for according to Back ground Information, and the corresponding relation between the sub-transaction data determining charging station and charging pile; Sub-analysis module, for being analyzed by antithetical phrase transaction data, determines the charging pile utilization rate of charging pile, the monthly load parameter of charging pile, charging pile duration of charging; Sub-processing module, for the charging pile utilization rate according to charging pile, the monthly load parameter of charging pile and charging pile duration of charging, determines the operational parameter of charging station.
Further, device also comprises: diversity module, for according to operational parameter, divides business rank to charging station.
In embodiments of the present invention, by obtaining the Back ground Information of each charging station, wherein, Back ground Information at least comprises: charging station numbering, charging pile information, positional information, type of site; Gather the transaction data that charging station generates in charging transaction; Pre-service is carried out to transaction data, obtains preprocessed data; Analytical model is set up according to Back ground Information and preprocessed data, by the operational parameter of analytical model determination charging station, wherein, operational parameter at least comprises: the mode of charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume, avoid the troublesome operation of traditional list processing (LISP), reach and increase work efficiency and reduce the object of error rate.And then solve by analyzing the transaction data of charging station by hand, cause the technical matters that analysis efficiency is low, error rate is high.
Accompanying drawing explanation
Accompanying drawing described herein is used to provide a further understanding of the present invention, and form a application's part, schematic description and description of the present invention, for explaining the present invention, does not form inappropriate limitation of the present invention.In the accompanying drawings:
Fig. 1 is the process flow diagram of the analytical approach according to the charging station of embodiment of the present invention charging transaction data;
Fig. 2 is the hardware configuration frame diagram of analytical approach of the charging station charging transaction data for realizing the embodiment of the present invention;
Fig. 3 is the frame diagram of distributed system of the analytical approach of charging station charging transaction data for realizing the embodiment of the present invention; And
Fig. 4 is the structured flowchart of the analytical equipment according to the charging station of embodiment of the present invention charging transaction data.
Embodiment
The present invention program is understood better in order to make those skilled in the art person, below in conjunction with the accompanying drawing in the embodiment of the present invention, technical scheme in the embodiment of the present invention is clearly and completely described, obviously, described embodiment is only the embodiment of a part of the present invention, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, should belong to the scope of protection of the invention.
It should be noted that, term " first ", " second " etc. in instructions of the present invention and claims and above-mentioned accompanying drawing are for distinguishing similar object, and need not be used for describing specific order or precedence.Should be appreciated that the data used like this can be exchanged in the appropriate case, so as embodiments of the invention described herein can with except here diagram or describe those except order implement.In addition, term " comprises " and " having " and their any distortion, intention is to cover not exclusive comprising, such as, contain those steps or unit that the process of series of steps or unit, method, system, product or equipment is not necessarily limited to clearly list, but can comprise clearly do not list or for intrinsic other step of these processes, method, product or equipment or unit.
According to the embodiment of the present invention, provide the analytical approach embodiment of a kind of charging station charging transaction data, it should be noted that, can perform in the computer system of such as one group of computer executable instructions in the step shown in the process flow diagram of accompanying drawing, and, although show logical order in flow charts, in some cases, can be different from the step shown or described by order execution herein.
Fig. 1 is the process flow diagram provided according to the analytical approach of the charging station charging transaction data of the embodiment of the present invention, and as shown in Figure 1, the method comprises the steps:
Step S21, obtains the Back ground Information of each charging station, and wherein, Back ground Information at least comprises: charging station numbering, charging pile information, positional information, type of site.
Concrete, first step S21 obtains charging station numbering, charging pile information, positional information and the type of site of each charging station.Wherein, charging pile information can comprise charging pile numbering and the charging pile quantity of the installation in charging station; Type of site is for recording the main application of charging station; Positional information is for marking the position residing for charging station.
Step S23, gathers the transaction data that charging station generates in charging transaction.
Concrete, step S23 transaction data comprises structural data and unstructured data, and wherein, structural data is that charging station is when carrying out charging transaction, to conclude the business corresponding data with charging, at least comprise: charge capacity, duration of charging, the charging amount of money etc. for one group that produces.Unstructured data be charging station produce in daily operation conclude the business there is no the data of corresponding relation with charging, at least comprise charging daily record, charge the files such as video.
Step S25, carries out pre-service to transaction data, obtains preprocessed data.
Concrete, step S25, by carrying out pre-service to the transaction data collected, obtains for further and process data.Wherein, pre-service can, by due to reasons such as charging pile clock transition, abnormal charging, stealings, cause abnormal data to remove from transaction data.
Step S27, analytical model is set up according to Back ground Information and preprocessed data, by the operational parameter of analytical model determination charging station, wherein, operational parameter at least comprises: charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume.
Concrete, step S27, according to the relation between the preprocessed data of Back ground Information and transaction data, is set up analytical model, can be determined the operational parameter of each charging station by analytical model.Wherein, analysis condition can be set to analytical model, obtain corresponding analysis result by analytical model.
Wherein, by above-mentioned steps S21 to step S27, respectively the Transaction Information of each charging station charging transaction is gathered, and by screening transaction data, after the pre-service such as arrangement, by the analytical model that preprocessed data and basic data are set up, analyze the operational parameter obtaining each charging station.
It should be noted that, the operational parameter of analytical model determination charging station is utilized to be a kind of optional embodiment of this programme, this programme also can utilize the analytical model set up by Back ground Information and preprocessed data, by inputting different conditions to it, obtain the operational parameter except charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume, do not repeat herein.
The present embodiment sets up analytical model by utilizing Back ground Information and transaction data, by the method for the operational parameter of analytical model determination charging station, solve and by manual, the transaction data of charging station is analyzed, cause the technical matters that analysis efficiency is low, error rate is high.Avoid the troublesome operation of traditional list processing (LISP), reach and increase work efficiency and reduce the object of error rate.
As a kind of optional embodiment, for analyzing the operational parameter determined, with the form of tables of data, graphical interfaces can be generated in chronological order, also according to the positional information of each charging station, map can show according to the color corresponding with charging station operational parameter.
As a kind of optional embodiment, step S25 carries out pre-service to transaction data, and obtaining preprocessed data can comprise:
Step S251, by carrying out data type conversion to transaction data, generates the pending data of target data type.
Step S253, by carrying out abnormality processing to there are abnormal data in pending data, generates preprocessed data.
By above-mentioned steps S251 to step S253, first changed by the data type of the transaction data collected, obtain the pending data of uniform data type.Then abnormality processing is carried out to pending data, removes data invalid, abnormal in pending data, generate for set up analytical model with process data.By abnormality processing, validity and the accuracy of transaction data can be guaranteed.
As a kind of optional embodiment, pretreated processing mode can also at least comprise: the checking of processing empty value, data correctness and the process of field integrality, pass through processing empty value, the methods such as data correctness checking and field integrality, the transaction data collected is processed, thus further guarantees validity and the accuracy of transaction data.
As a kind of optional embodiment, the transaction data that step S23 gathers charging station generation in charging transaction can comprise:
Step S231, according to the charging pile information of charging station, obtains the charging pile numbering of charging pile in charging station.
Step S233, according to charging pile numbering, gathers the sub-transaction data of each charging pile.
Step S235, gathers sub-transaction data, generates the transaction data of charging station.
Concrete, in each charging station, have at least a charging pile to charge for electric automobile.If when the charging pile quantity in charging station is greater than two, need to obtain respectively the sub-transaction data of each charging pile in charging station.Then to gathering from transaction data, adjusting, the transaction data of charging station entirety is obtained.
As a kind of optional embodiment, step S27 sets up analytical model according to Back ground Information and preprocessed data, is comprised by the operational parameter of analytical model determination charging station:
Step S271, according to Back ground Information, the corresponding relation between the sub-transaction data determining charging station and charging pile.
Step S273, according to sub-transaction data, determines the charging pile utilization rate of charging pile, the monthly load parameter of charging pile, charging pile duration of charging.
Step S275, according to the charging pile utilization rate of charging pile, the monthly load parameter of charging pile and charging pile duration of charging, determines the operational parameter of charging station.
Concrete, above-mentioned steps S271, to step S273, first, carries out analytic operation by the sub-transaction data produced each charging pile in charging station, determines the sub-operational parameter of each charging pile.By being gathered by sub-operational parameter corresponding with sub-charging pile in charging station, finally determine the operational parameter of charging station.
Wherein, the standard weighing economic benefit is the output that unit drops into, and under normal circumstances, the utilization rate of electrically-charging equipment is higher, and its economic benefit is more remarkable.
Make N
stakerepresent the utilization rate of charging pile, then its computing formula is:
N
stake=T
stake/ TALL*100%
Wherein:
T
stakefor within certain time period, this charging pile charging duration; TALL is the duration within certain time period.
Illustrate: the charging pile utilization rate at statistics certain station interior some day, suppose that there are charging pile 1 and charging pile 2 in this station, utilization rate can be calculated respectively by above formula.TALL be 1 day * 24 hours (in hour), T
stakefor in this sky, this charging pile charging duration, namely this day every bar transaction record every bar end time of this charging pile subtracts the conjunction (in hour) of start time:
If calculate the overall availability of this charging pile, available following formula:
Wherein:
t
stake mfor the conjunction of all piles charging duration in this station; TALL is the duration within certain time period; M is the charging pile number at this station; The monthly load Analysis of charging station.
In order to understand the service condition of charging pile in each district charging station further, we are in units of the moon, calculate monthly load.
Make P
stakerepresent charging pile single charge load, then its computing formula is:
P
stake=Q
stake/ (T
terminate-T
start).
Wherein:
Q
stakefor charging pile charge capacity; T
terminatefor charging end time when charging pile charges; T
startfor charging start time when charging pile charges.
If calculate the monthly load of charging station, available following formula:
Wherein:
p
stake mfor the conjunction of charging piles all in this station this month load.
TALL is total number of days of this month.
Duration of charging is analyzed:
By the statistical study to the electric automobile user duration of charging, understand the charging Behavior law of electric automobile user, the layout in conjunction with each region charging station distributes, and proposes recommendation on improvement.
We were divided into 24 nodes by 1 day 24 hours by integral point, and charging start time when at every turn being charged by electric automobile user and end time are added up.
This total charging times when making Cn represent n point in timing statistics section, then its computing formula is:
Wherein:
T
mfor whether m article record of charging during n point is in charged state, if charge, count 1, otherwise be designated as 0.
If calculate the duration of charging of charging station, available following formula:
Wherein:
C
nmfor the charging times of m charging pile during n point.
As a kind of optional embodiment, analyze according to Back ground Information in step S27 to preprocessed data, after determining the operational parameter of charging station, method also comprises:
Step S28, according to operational parameter, divides business rank to charging station.
By step S28, can directly react the index of charging station operational parameter according to the sales volume, charging station utilization rate etc. that charging station is monthly to charging station classification, thus tentatively can be understood the operational parameter of charging station intuitively by business rank.
As a kind of optional embodiment, the transaction data carrying out charging transaction generation at charging station for electric automobile is studied, by improving available data processing mode, be convenient to Information Statistics such as charging time of concentration, charge capacity increment situation, charging electric degree situations of change, for the guidance of subsequent charge Facilities Construction, electric automobile operation model study, user behavioural habits analysis etc. of charging provides a kind of new analytical approach.As shown in Figure 2, can utilize that Hadoop distributed system frame system stores transaction data, pre-service.Hadoop frame system, its core technology comprises HDFS (distributed file system), Hbase (distributed data base), Tool for Data Warehouse Hive, MapReduce (processing procedure) etc.
Wherein, charging electric vehicle transaction data (i.e. charging station charging transaction data) process is divided into four processes, is divided into data storage, Data Integration, data processing, data analysis.
Data store: the Hbase in Hadoop is that one distributed, towards the PostgreSQL database arranged, it is different from general relational database, are databases being suitable for unstructured data and storing.The unstructured datas such as the charging daily record of the such as charging station produced in therefore electric automobile can being concluded the business and charging video are stored in Hbase lane database, because this database stores based on row, be conducive to compressing efficiently database and reducing data scale, be therefore conducive to electric automobile transaction data and store.
Data Integration: because dispersion is compared and data type disunity in electric automobile transaction data source, this just needs to integrate data.Electric automobile transaction data integration technology utilizes a Tool for Data Warehouse HIVE based on Hadoop, builds a data warehouse, the data of Hbase database are all stored to this data warehouse.This data warehouse storage mode is that structurized data file is mapped as a database table, and provides SQL-like language, realizes complete SQL query function.SQL statement can be converted to MapReduce (distributed programmed pattern) task run, the statistical study of very applicable data warehouse.
Data processing: due to situations such as charging pile clock transition, abnormal charging, stealings, cause electric automobile transaction data to there are abnormal conditions, before data mining analysis is carried out to electric automobile transaction data, data processing need be carried out, guarantee data accuracy.
We use the MapReduce Synchronous data dispose framework of Hadoop, as shown in Figure 3, MapReduce is a kind of distributed programmed pattern of simplification, complex distributions formula processing procedure is decomposed into Map (mapping) and Reduce (reduction) process, improves concurrent processing ability.Program is allowed automatically to be distributed to concurrence performance on a super large cluster be made up of common machines.Its enough batch processing analyser fast meets business demand and business report, data analysis, behavioural analysis, as click-stream analysis etc.
MapReduce is devoted to the problem solving large-scale data process, at the beginning of design, therefore just considers the principle of locality of data, utilize principle of locality whole problem to be divided and rule.MapReduce cluster is made up of ordinary PC, for without shared framework.Before treatment, data set is distributed to each node.During process, each node reads the local data processing (map) stored nearby, distribute again (to reduce node) after data after process being carried out merge (combine), sequence (shuffleand sort), avoid the transmission of mass data, improve treatment effeciency.Another benefit without shared framework coordinates to copy (replication) strategy, and cluster can have good fault-tolerance, and the normal work of down machine to cluster of a part of node can not impact.Be one and use easy software frame, the application program write out based on it can operate on the large-scale cluster that is made up of thousands of business machines, and with the data set of TB rank in a kind of reliably fault-tolerant mode parallel processing.
Data analysis:
1, electric automobile user charging row is custom analysis
At present, Beijing's electric automobile intelligence is filled and is changed electric service network operation management system and accumulated a large amount of electric automobile user and to charge transaction data, pass through the present invention, realize data analyses such as electric automobile charging station utilization rate, the monthly load of charging station, charging station duration of charging, continue tracing observation electric automobile user charging feature situation, explore electric automobile user charging row for custom, research electric automobile user charges consumption mode, thus carry out charging electric vehicle business better, improve efficiency and the quality of user's service.
2, electric automobile charging pile ruuning situation is analyzed
At present, Beijing's electric automobile intelligence is filled and is changed the registered charging pile assets of electric service network operation management system 1913, and has carried out Data Collection to the transaction record of these charging piles.Statistical study is carried out by day-to-day operation data such as the charge capacity to charging pile, the charging electricity charge, charging duration, charging electricity price, clearing marks, calculate charging pile charge power, the electricity electricity charge calculate the analysis result such as accuracy rate, electric meter address accuracy rate, continue follow-up study electric automobile charging pile charge operation state, metering and billing situation.Beijing's electric automobile charging station asset data and transaction data has mainly been used in this programme.Asset data refers to the essential information data of charging station and charging pile, mainly comprises the assets informations such as charging station numbering, charging station title, charging pile device numbering, concentrator numbering, electric meter address numbering.Transaction data refers to the related data that electric automobile user produces when charging, and mainly comprises the information such as charging pile device numbering, electric meter address numbering, charge capacity, the charging electricity charge, charging start time, charging end time.When carrying out data analysis to charging electric vehicle transaction data, transaction data need be associated with asset data, the charging row each time of electric automobile being as the criterion really navigates to charging pile and charging station.Need the consistance detecting asset data and transaction data in real time before this, thus grasp the ruuning situation of electric automobile charging pile better, provide theoretical foundation for planning electrically-charging equipment construction further and carrying out charging business.
3, electric automobile charging station ruuning situation is analyzed
At present, the charging station that the charging station that Beijing builds mainly can be divided into taxi charging station, sanitation cart charging station and open towards masses.By the statistical study of the data such as the charge capacity to charging pile in each charging station station, the charging electricity charge, charging duration, duration of charging, charge frequency, detect the information such as each charging station charging pile utilization factor, idle charging pile number, explore towards the ruuning situation of the charging station of dissimilar user, for the charging service planned electrically-charging equipment construction better and carry out characterization is provided fundamental basis in the future.
Additionally provide the analytical equipment of a kind of charging station charging transaction data in the present embodiment, this device is used for realizing above-described embodiment and optional embodiment, has carried out repeating no more of explanation.As used below, term " module " can realize the software of predetermined function and/or the combination of hardware.Although the device described by following examples preferably realizes with software, hardware, or the realization of the combination of software and hardware also may and conceived.
Fig. 4 is the structured flowchart of the analytical equipment according to the charging station of embodiment of the present invention charging transaction data, and as shown in Figure 4, this device comprises: acquisition module 31, acquisition module 33, pretreatment module 35 and analysis module 37.
Wherein, acquisition module 31, for obtaining the Back ground Information of each charging station, wherein, Back ground Information at least comprises: charging station numbering, charging pile information, positional information, type of site; Acquisition module 33, for gathering the transaction data that charging station generates in charging transaction; Pretreatment module 35, for carrying out pre-service to transaction data, obtains preprocessed data; Analysis module 37, for setting up analytical model according to Back ground Information and preprocessed data, by the operational parameter of analytical model determination charging station, wherein, operational parameter at least comprises: charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume.
Wherein, by above-mentioned acquisition module 31, acquisition module 33, pretreatment module 35 and analysis module 37, respectively the Transaction Information of each charging station charging transaction is gathered, and by screening transaction data, after the pre-service such as arrangement, by the analytical model that preprocessed data and basic data are set up, analyze the operational parameter obtaining each charging station.
It should be noted that, the operational parameter of analytical model determination charging station is utilized to be a kind of optional embodiment of this programme, this programme also can utilize the analytical model set up by Back ground Information and preprocessed data, by inputting different conditions to it, obtain the operational parameter except charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume, do not repeat herein.
The present embodiment sets up analytical model by utilizing Back ground Information and transaction data, by the method for the operational parameter of analytical model determination charging station, solve and by manual, the transaction data of charging station is analyzed, cause the technical matters that analysis efficiency is low, error rate is high.Avoid the troublesome operation of traditional list processing (LISP), reach and increase work efficiency and reduce the object of error rate.
As a kind of optional embodiment, pretreatment module 35 comprises: the first sub-generation module 351 and the second sub-generation module 353.
Wherein, the first sub-generation module 351, for by carrying out data type conversion to transaction data, generates the pending data of target data type; Second sub-generation module 353, for by carrying out abnormality processing to there are abnormal data in pending data, generates preprocessed data.
By above-mentioned first sub-generation module 351 and the second sub-generation module 353, first changed by the data type of the transaction data collected, obtain the pending data of uniform data type.Then abnormality processing is carried out to pending data, removes data invalid, abnormal in pending data, generate for set up analytical model with process data.By abnormality processing, validity and the accuracy of transaction data can be guaranteed.
As a kind of optional embodiment, acquisition module 33 comprises: sub-acquisition module 331, sub-acquisition module 333 and sub-generation module 335.
Wherein, sub-acquisition module 331, for the charging pile information according to charging station, obtains the charging pile numbering of charging pile in charging station; Sub-acquisition module 333, for according to charging pile numbering, gathers the sub-transaction data of each charging pile; Sub-generation module 335, for being gathered by sub-transaction data, generates the transaction data of charging station.
Concrete, in each charging station, have at least a charging pile to charge for electric automobile.If when the charging pile quantity in charging station is greater than two, need to obtain respectively the sub-transaction data of each charging pile in charging station.Then to gathering from transaction data, adjusting, the transaction data of charging station entirety is obtained.
As a kind of optional embodiment, described analysis module 37 comprises: sub-determination module 371, sub-analysis module 373 and sub-processing module 375.
Wherein, sub-determination module 371, for according to described Back ground Information, the corresponding relation between the described sub-transaction data determining described charging station and described charging pile; Sub-analysis module 373, for by analyzing described sub-transaction data, determines the charging pile utilization rate of described charging pile, the monthly load parameter of charging pile, charging pile duration of charging; Sub-processing module 375, for the charging pile utilization rate according to described charging pile, the monthly load parameter of described charging pile and described charging pile duration of charging, determines the described operational parameter of described charging station.
Concrete, above-mentioned sub-determination module 371, sub-analysis module 373 and sub-processing module 375, first, carry out analytic operation by the sub-transaction data produced each charging pile in charging station, determine the sub-operational parameter of each charging pile.By being gathered by sub-operational parameter corresponding with sub-charging pile in charging station, finally determine the operational parameter of charging station.
The invention described above embodiment sequence number, just to describing, does not represent the quality of embodiment.
In the above embodiment of the present invention, the description of each embodiment is all emphasized particularly on different fields, in certain embodiment, there is no the part described in detail, can see the associated description of other embodiments.
In several embodiments that the application provides, should be understood that, disclosed technology contents, the mode by other realizes.Wherein, device embodiment described above is only schematic, the such as division of described unit, can be that a kind of logic function divides, actual can have other dividing mode when realizing, such as multiple unit or assembly can in conjunction with or another system can be integrated into, or some features can be ignored, or do not perform.Another point, shown or discussed coupling each other or direct-coupling or communication connection can be by some interfaces, and the indirect coupling of unit or module or communication connection can be electrical or other form.
The described unit illustrated as separating component or can may not be and physically separates, and the parts as unit display can be or may not be physical location, namely can be positioned at a place, or also can be distributed on multiple unit.Some or all of unit wherein can be selected according to the actual needs to realize the object of the present embodiment scheme.
In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, also can be that the independent physics of unit exists, also can two or more unit in a unit integrated.Above-mentioned integrated unit both can adopt the form of hardware to realize, and the form of SFU software functional unit also can be adopted to realize.
If described integrated unit using the form of SFU software functional unit realize and as independently production marketing or use time, can be stored in a computer read/write memory medium.Based on such understanding, the part that technical scheme of the present invention contributes to prior art in essence in other words or all or part of of this technical scheme can embody with the form of software product, this computer software product is stored in a storage medium, comprises all or part of step of some instructions in order to make a computer equipment (can be personal computer, server or the network equipment etc.) perform method described in each embodiment of the present invention.And aforesaid storage medium comprises: USB flash disk, ROM (read-only memory) (ROM, Read-OnlyMemory), random access memory (RAM, Random Access Memory), portable hard drive, magnetic disc or CD etc. various can be program code stored medium.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (10)
1. an analytical approach for charging station charging transaction data, is characterized in that, comprising:
Obtain the Back ground Information of each charging station, wherein, described Back ground Information at least comprises: charging station numbering, charging pile information, positional information, type of site;
Gather the transaction data that described charging station generates in charging transaction;
Pre-service is carried out to described transaction data, obtains preprocessed data;
Analytical model is set up according to described Back ground Information and described preprocessed data, the operational parameter of described charging station is determined by described analytical model, wherein, described operational parameter at least comprises: charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume.
2. method according to claim 1, is characterized in that, carries out pre-service, obtains preprocessed data, comprising described transaction data:
By carrying out data type conversion to described transaction data, generate the pending data of target data type;
By carrying out abnormality processing to there are abnormal data in described pending data, generate described preprocessed data.
3. method according to claim 1, is characterized in that, gathers the transaction data that described charging station generates in charging transaction, comprising:
According to the described charging pile information of described charging station, obtain the charging pile numbering of charging pile in described charging station;
Number according to described charging pile, gather the sub-transaction data of each charging pile;
Described sub-transaction data is gathered, generates the described transaction data of described charging station.
4. method according to claim 3, is characterized in that, sets up analytical model, determined the operational parameter of described charging station, comprising by described analytical model according to described Back ground Information and described preprocessed data:
According to described Back ground Information, the corresponding relation between the described sub-transaction data determining described charging station and described charging pile;
By analyzing described sub-transaction data, determine the charging pile utilization rate of described charging pile, the monthly load parameter of charging pile, charging pile duration of charging;
According to the charging pile utilization rate of described charging pile, the monthly load parameter of described charging pile and described charging pile duration of charging, determine the described operational parameter of described charging station.
5. method as claimed in any of claims 1 to 4, is characterized in that, setting up analytical model according to described Back ground Information and described preprocessed data, determined the operational parameter of described charging station by described analytical model after, described method also comprises:
According to described operational parameter, business rank is divided to described charging station.
6. an analytical equipment for charging station charging transaction data, is characterized in that, comprising:
Acquisition module, for obtaining the Back ground Information of each charging station, wherein, described Back ground Information at least comprises: charging station numbering, charging pile information, positional information, type of site;
Acquisition module, for gathering the transaction data that described charging station generates in charging transaction;
Pretreatment module, for carrying out pre-service to described transaction data, obtains preprocessed data;
Analysis module, for setting up analytical model according to described Back ground Information and described preprocessed data, the operational parameter of described charging station is determined by described analytical model, wherein, described operational parameter at least comprises: charging station utilization rate, the monthly load parameter that charges, charging station duration of charging parameter, the sales volume.
7. device according to claim 6, is characterized in that, described pretreatment module comprises:
First sub-generation module, for by carrying out data type conversion to described transaction data, generates the pending data of target data type;
Second sub-generation module, for by carrying out abnormality processing to there are abnormal data in described pending data, generates described preprocessed data.
8. device according to claim 6, is characterized in that, described acquisition module comprises:
Sub-acquisition module, for the described charging pile information according to described charging station, obtains the charging pile numbering of charging pile in described charging station;
Sub-acquisition module, for numbering according to described charging pile, gathers the sub-transaction data of each charging pile;
Sub-generation module, for being gathered by described sub-transaction data, generates the described transaction data of described charging station.
9. device according to claim 8, is characterized in that, described analysis module comprises:
Sub-determination module, for according to described Back ground Information, the corresponding relation between the described sub-transaction data determining described charging station and described charging pile;
Sub-analysis module, for by analyzing described sub-transaction data, determines the charging pile utilization rate of described charging pile, the monthly load parameter of charging pile, charging pile duration of charging;
Sub-processing module, for the charging pile utilization rate according to described charging pile, the monthly load parameter of described charging pile and described charging pile duration of charging, determines the described operational parameter of described charging station.
10. according to the device in claim 6 to 9 described in any one, it is characterized in that, described device also comprises:
Diversity module, for according to described operational parameter, divides business rank to described charging station.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105654622A (en) * | 2016-03-04 | 2016-06-08 | 深圳市速满跑科技有限公司 | Charging pile management method and system |
| CN106920132A (en) * | 2015-12-28 | 2017-07-04 | 航天信息股份有限公司 | A kind of super tax control management method of business based on Hadoop platform and system |
| CN107025236A (en) * | 2016-02-01 | 2017-08-08 | 阿里巴巴集团控股有限公司 | Data processing method and data system for settling account between system for settling account |
| CN107220781A (en) * | 2017-06-26 | 2017-09-29 | 北京中电普华信息技术有限公司 | A kind of electrically-charging equipment utilization rate appraisal procedure and device |
| CN108306068A (en) * | 2017-01-11 | 2018-07-20 | 宁波轩悦行电动汽车服务有限公司 | A kind of charging pile reservation charging method |
| CN108304944A (en) * | 2017-01-11 | 2018-07-20 | 宁波轩悦行电动汽车服务有限公司 | A kind of electric automobile charging pile reservation management method |
| CN109118136A (en) * | 2018-07-28 | 2019-01-01 | 江苏苏宁物流有限公司 | A kind of relay station method and system for planning for logistics unmanned plane |
| CN109146689A (en) * | 2018-08-30 | 2019-01-04 | 国网北京市电力公司 | A kind of electric car data assessment method and device |
| CN109684315A (en) * | 2018-12-19 | 2019-04-26 | 国网北京市电力公司 | Data processing method and device for electric car |
| CN109783265A (en) * | 2018-12-30 | 2019-05-21 | 国网北京市电力公司 | Abnormal traffic data processing method and device |
| CN110163463A (en) * | 2018-12-19 | 2019-08-23 | 国网北京市电力公司 | Electric car electrically-charging equipment data processing method and device |
| CN110263783A (en) * | 2019-05-27 | 2019-09-20 | 华东师范大学 | Multiple features charging addressing analysis of Influential Factors method and system based on deep learning |
| CN110867880A (en) * | 2019-11-28 | 2020-03-06 | 国网福建省电力有限公司厦门供电公司 | Orderly charging control system for electric ship |
| CN111047388A (en) * | 2019-10-31 | 2020-04-21 | 深圳猛犸电动科技有限公司 | Splicing method and device of charging data and server |
| CN111340549A (en) * | 2020-02-27 | 2020-06-26 | 南京师范大学 | Electric vehicle stage electricity fee bilateral transaction method based on big data and block chain |
| CN112200471A (en) * | 2020-10-15 | 2021-01-08 | 国网北京市电力公司 | Charging pile quantity configuration method and device and charging pile distribution system |
| CN112418585A (en) * | 2019-08-23 | 2021-02-26 | 北京京东乾石科技有限公司 | Method and device for calculating effective charging time of charging pile, storage medium and equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101995864A (en) * | 2010-10-15 | 2011-03-30 | 云南电力试验研究院(集团)有限公司 | Monitoring system and method for distributed charging piles |
| CN102855293A (en) * | 2012-08-10 | 2013-01-02 | 广东电网公司电力科学研究院 | Mass data processing method of electric vehicle and charging/battery swap facility system |
| CN103529340A (en) * | 2013-10-31 | 2014-01-22 | 重庆大学 | Online monitoring, analysis and evaluation system of electric vehicle charging station |
| CN104281129A (en) * | 2014-09-19 | 2015-01-14 | 安徽旗翔科技发展有限公司 | Intelligent charge-discharge Internet-of-Things cloud comprehensive integration system of electric automobile |
-
2015
- 2015-07-02 CN CN201510383323.8A patent/CN105023196A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101995864A (en) * | 2010-10-15 | 2011-03-30 | 云南电力试验研究院(集团)有限公司 | Monitoring system and method for distributed charging piles |
| CN102855293A (en) * | 2012-08-10 | 2013-01-02 | 广东电网公司电力科学研究院 | Mass data processing method of electric vehicle and charging/battery swap facility system |
| CN103529340A (en) * | 2013-10-31 | 2014-01-22 | 重庆大学 | Online monitoring, analysis and evaluation system of electric vehicle charging station |
| CN104281129A (en) * | 2014-09-19 | 2015-01-14 | 安徽旗翔科技发展有限公司 | Intelligent charge-discharge Internet-of-Things cloud comprehensive integration system of electric automobile |
Non-Patent Citations (3)
| Title |
|---|
| 潘鸣宇等: "电动汽车智能服务平台的设计与实现", 《电力信息与通信技术》 * |
| 谢印成 等: "《企业信息化概论》", 31 January 2013, 徐州：中国矿业大学出版社 * |
| 贺鹏等: "基于高级量测体系的用电信息数据分类与采集", 《山西电力》 * |
Cited By (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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