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
- Publication number
- CN105023196A CN105023196A CN201510383323.8A CN201510383323A CN105023196A CN 105023196 A CN105023196 A CN 105023196A CN 201510383323 A CN201510383323 A CN 201510383323A CN 105023196 A CN105023196 A CN 105023196A
- Authority
- CN
- China
- Prior art keywords
- charging
- data
- charging station
- transaction data
- station
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 72
- 238000000034 method Methods 0.000 claims abstract description 30
- 238000007781 pre-processing Methods 0.000 claims abstract description 30
- 230000007306 turnover Effects 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims description 36
- 230000002159 abnormal effect Effects 0.000 claims description 12
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 description 7
- 230000006399 behavior Effects 0.000 description 5
- 238000007405 data analysis Methods 0.000 description 5
- 230000005611 electricity Effects 0.000 description 5
- 230000008901 benefit Effects 0.000 description 4
- 238000010276 construction Methods 0.000 description 4
- 238000013500 data storage Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 238000007619 statistical method Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000013439 planning Methods 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 238000012216 screening Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000003252 repetitive effect Effects 0.000 description 1
- 230000010076 replication Effects 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
技术领域technical field
本发明涉及电动汽车领域，具体而言，涉及一种充电站充电交易数据的分析方法及装置。The invention relates to the field of electric vehicles, in particular to a method and device for analyzing charging transaction data of a charging station.
背景技术Background technique
随着电动汽车产业的不断发展，电动汽车相关信息系统的不断建设，电动汽车的充电交易数据量的不断增加，如何有效的集中存储分析这些数据，形成数据集中管理，资源统一共享的应用模式，并从中挖掘数据的潜在价值，是目前时事所需。With the continuous development of the electric vehicle industry, the continuous construction of electric vehicle-related information systems, and the continuous increase in the amount of charging transaction data for electric vehicles, how to effectively store and analyze these data in a centralized manner to form an application model of centralized data management and unified resource sharing, And mining the potential value of data from it is what is needed in current affairs.
目前，北京市电动汽车充电交易记录从2010年至今大约有500W条左右，交易记录存储在交易记录表中。此时，若欲分析充电集中时间、充电电量增量情况、充电电度变化情况等信息时，便需要对交易记录表格中的数据按照要求进行分割与汇总，从中挖掘潜在价值，对数据进行多样化的分析。At present, there are about 5 million records of electric vehicle charging transactions in Beijing from 2010 to the present, and the transaction records are stored in the transaction record table. At this time, if you want to analyze information such as charging concentration time, charging power increments, charging power changes, etc., you need to divide and summarize the data in the transaction record form according to requirements, tap potential value from them, and diversify the data. analyzed.
现有的技术中，只能手动设置需要的计算公式并逐一输入待处理的数据进行计算，然后再以手工方式将计算结果数据填入电子表格中。由于需要计算的数据对象的数据量通常都很庞大，这就需要通过手工进行大量繁琐的重复性操作，不仅浪费了时间和精力，还降低了工作效率，而且会导致出错率很高。In the existing technology, it is only possible to manually set the required calculation formulas and input the data to be processed one by one for calculation, and then manually fill the calculation result data into the electronic form. Since the amount of data objects to be calculated is usually huge, a large number of tedious and repetitive operations need to be performed manually, which not only wastes time and energy, but also reduces work efficiency and leads to a high error rate.
针对上述的问题，目前尚未提出有效的解决方案。For the above problems, no effective solution has been proposed yet.
发明内容Contents of the invention
本发明实施例提供了一种充电站充电交易数据的分析方法及装置，以至少解决通过手工对充电站的交易数据进行分析，导致分析效率低、出错率高的技术问题。Embodiments of the present invention provide a method and device for analyzing charging transaction data of a charging station, so as to at least solve the technical problems of low analysis efficiency and high error rate caused by manually analyzing the transaction data of a charging station.
根据本发明实施例的一个方面，提供了一种充电站充电交易数据的分析方法，包括：获取各个充电站的基础信息，其中，基础信息至少包括：充电站编号、充电桩信息、位置信息、站点类型；采集充电站在充电交易中生成的交易数据；对交易数据进行预处理，得到预处理数据；根据基础信息和预处理数据建立分析模型，通过分析模型确定充电站的运营参数，其中，运营参数至少包括：充电站使用率、充电月均负荷参数、充电站充电时间参数、营业额。According to an aspect of an embodiment of the present invention, a method for analyzing charging transaction data of a charging station is provided, including: acquiring basic information of each charging station, wherein the basic information at least includes: charging station number, charging pile information, location information, Site type; collect transaction data generated by charging stations in charging transactions; preprocess transaction data to obtain preprocessed data; establish an analysis model based on basic information and preprocessed data, and determine the operating parameters of the charging station through the analysis model, among which, The operating parameters at least include: charging station utilization rate, charging monthly average load parameters, charging station charging time parameters, and turnover.
进一步地，对交易数据进行预处理，得到预处理数据，包括：通过对交易数据进行数据类型转换，生成目标数据类型的待处理数据；通过对待处理数据中存在异常的数据进行异常处理，生成预处理数据。Further, preprocessing the transaction data to obtain the preprocessing data includes: generating data to be processed of the target data type by performing data type conversion on the transaction data; Data processing.
进一步地，采集充电站在充电交易中生成的交易数据，包括：根据充电站的充电桩信息，获取充电站中充电桩的充电桩编号；根据充电桩编号，采集各个充电桩的子交易数据；将子交易数据汇总，生成充电站的交易数据。Further, collecting the transaction data generated by the charging station in the charging transaction includes: obtaining the charging pile number of the charging pile in the charging station according to the charging pile information of the charging station; collecting the sub-transaction data of each charging pile according to the charging pile number; Summarize the sub-transaction data to generate the transaction data of the charging station.
进一步地，根据基础信息和预处理数据建立分析模型，通过分析模型确定充电站的运营参数，包括：根据基础信息，确定充电站与充电桩的子交易数据间的对应关系；通过对子交易数据进行分析，确定充电桩的充电桩使用率、充电桩月均负荷参数、充电桩充电时间；根据充电桩的充电桩使用率、充电桩月均负荷参数和充电桩充电时间，确定充电站的运营参数。Further, an analysis model is established based on the basic information and pre-processing data, and the operating parameters of the charging station are determined through the analysis model, including: determining the corresponding relationship between the charging station and the sub-transaction data of the charging pile according to the basic information; Carry out analysis to determine the charging pile utilization rate of charging piles, the monthly average load parameters of charging piles, and the charging time of charging piles; determine the operation of charging stations according to the charging pile utilization rate of charging piles, the monthly average load parameters of charging piles, and the charging time of charging piles parameter.
进一步地，在根据基础信息和预处理数据建立分析模型，通过分析模型确定充电站的运营参数之后，方法还包括：根据运营参数，对充电站划分营业级别。Further, after the analysis model is established according to the basic information and the pre-processing data, and the operating parameters of the charging station are determined through the analysis model, the method further includes: classifying the charging stations into business levels according to the operating parameters.
根据本发明实施例的另一方面，还提供了一种充电站充电交易数据的分析装置，包括：获取模块，用于获取各个充电站的基础信息，其中，基础信息至少包括：充电站编号、充电桩信息、位置信息、站点类型；采集模块，用于采集充电站在充电交易中生成的交易数据；预处理模块，用于对交易数据进行预处理，得到预处理数据；分析模块，用于根据基础信息和预处理数据建立分析模型，通过分析模型确定充电站的运营参数，其中，运营参数至少包括：充电站使用率、充电月均负荷参数、充电站充电时间参数、营业额。According to another aspect of the embodiments of the present invention, there is also provided an analysis device for charging transaction data of a charging station, including: an acquisition module, configured to acquire basic information of each charging station, wherein the basic information at least includes: charging station number, Charging pile information, location information, site type; acquisition module, used to collect transaction data generated by charging stations in charging transactions; preprocessing module, used to preprocess transaction data to obtain preprocessed data; analysis module, used for Establish an analysis model based on the basic information and preprocessed data, and determine the operating parameters of the charging station through the analysis model. The operating parameters at least include: charging station utilization rate, charging monthly average load parameters, charging station charging time parameters, and turnover.
进一步的，预处理模块包括：第一子生成模块，用于通过对交易数据进行数据类型转换，生成目标数据类型的待处理数据；第二子生成模块，用于通过对待处理数据中存在异常的数据进行异常处理，生成预处理数据。Further, the preprocessing module includes: a first sub-generation module, which is used to generate data to be processed of the target data type by performing data type conversion on the transaction data; The data is exception-handled to generate pre-processed data.
进一步的，采集模块包括：子获取模块，用于根据充电站的充电桩信息，获取充电站中充电桩的充电桩编号；子采集模块，用于根据充电桩编号，采集各个充电桩的子交易数据；子生成模块，用于将子交易数据汇总，生成充电站的交易数据。Further, the acquisition module includes: a sub-acquisition module, which is used to obtain the charging pile number of the charging pile in the charging station according to the charging pile information of the charging station; a sub-acquisition module, which is used to collect the sub-transactions of each charging pile according to the charging pile number Data; a sub-generation module, used to summarize the sub-transaction data to generate transaction data of the charging station.
进一步的，分析模块包括：子确定模块，用于根据基础信息，确定充电站与充电桩的子交易数据间的对应关系；子分析模块，用于通过对子交易数据进行分析，确定充电桩的充电桩使用率、充电桩月均负荷参数、充电桩充电时间；子处理模块，用于根据充电桩的充电桩使用率、充电桩月均负荷参数和充电桩充电时间，确定充电站的运营参数。Further, the analysis module includes: a sub-determination module, which is used to determine the corresponding relationship between the charging station and the sub-transaction data of the charging pile according to the basic information; Charging pile usage rate, charging pile monthly average load parameters, charging pile charging time; sub-processing module, used to determine charging station operating parameters according to charging pile usage rate of charging piles, charging pile monthly average load parameters and charging pile charging time .
进一步的，装置还包括：分级模块，用于根据运营参数，对充电站划分营业级别。Further, the device further includes: a grading module, which is used to classify the charging stations into business levels according to the operation parameters.
在本发明实施例中，通过获取各个充电站的基础信息，其中，基础信息至少包括：充电站编号、充电桩信息、位置信息、站点类型；采集充电站在充电交易中生成的交易数据；对交易数据进行预处理，得到预处理数据；根据基础信息和预处理数据建立分析模型，通过分析模型确定充电站的运营参数，其中，运营参数至少包括：充电站使用率、充电月均负荷参数、充电站充电时间参数、营业额的方式，避免传统表处理的繁琐操作，达到了提高工作效率并降低出错率的目的。进而解决了通过手工对充电站的交易数据进行分析，导致分析效率低、出错率高的技术问题。In the embodiment of the present invention, by acquiring the basic information of each charging station, the basic information at least includes: charging station number, charging pile information, location information, and station type; collecting transaction data generated by charging stations in charging transactions; The transaction data is pre-processed to obtain the pre-processed data; an analysis model is established based on the basic information and pre-processed data, and the operating parameters of the charging station are determined through the analysis model. The method of charging time parameters and turnover of the charging station avoids the cumbersome operations of traditional meter processing, and achieves the purpose of improving work efficiency and reducing error rates. Furthermore, the technical problem of low analysis efficiency and high error rate caused by manually analyzing the transaction data of the charging station is solved.
附图说明Description of drawings
此处所说明的附图用来提供对本发明的进一步理解，构成本申请的一部分，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。在附图中：The accompanying drawings described here are used to provide a further understanding of the present invention and constitute a part of the application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations to the present invention. In the attached picture:
图1是根据本发明实施例的充电站充电交易数据的分析方法的流程图；FIG. 1 is a flowchart of a method for analyzing charging transaction data of a charging station according to an embodiment of the present invention;
图2是用于实现本发明实施例的充电站充电交易数据的分析方法的硬件结构框架图；Fig. 2 is a hardware structural framework diagram for implementing the method for analyzing charging transaction data of a charging station according to an embodiment of the present invention;
图3是用于实现本发明实施例的充电站充电交易数据的分析方法的分布式系统的框架图；以及Fig. 3 is a framework diagram of a distributed system for implementing a method for analyzing charging transaction data of a charging station according to an embodiment of the present invention; and
图4是根据本发明实施例的充电站充电交易数据的分析装置的结构框图。Fig. 4 is a structural block diagram of a device for analyzing charging transaction data of a charging station according to an embodiment of the present invention.
具体实施方式Detailed ways
为了使本技术领域的人员更好地理解本发明方案，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分的实施例，而不是全部的实施例。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本发明保护的范围。In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only It is an embodiment of a part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.
需要说明的是，本发明的说明书和权利要求书及上述附图中的术语“第一”、“第二”等是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本发明的实施例能够以除了在这里图示或描述的那些以外的顺序实施。此外，术语“包括”和“具有”以及他们的任何变形，意图在于覆盖不排他的包含，例如，包含了一系列步骤或单元的过程、方法、系统、产品或设备不必限于清楚地列出的那些步骤或单元，而是可包括没有清楚地列出的或对于这些过程、方法、产品或设备固有的其它步骤或单元。It should be noted that the terms "first" and "second" in the description and claims of the present invention and the above drawings are used to distinguish similar objects, but not necessarily used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein can be practiced in sequences other than those illustrated or described herein. Furthermore, the terms "comprising" and "having", as well as any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product or device comprising a sequence of steps or elements is not necessarily limited to the expressly listed Those steps or elements may instead include other steps or elements not explicitly listed or inherent to the process, method, product or apparatus.
根据本发明实施例，提供了一种充电站充电交易数据的分析方法实施例，需要说明的是，在附图的流程图示出的步骤可以在诸如一组计算机可执行指令的计算机系统中执行，并且，虽然在流程图中示出了逻辑顺序，但是在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤。According to an embodiment of the present invention, an embodiment of a method for analyzing charging transaction data of a charging station is provided. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions , and, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.
图1是根据本发明实施例的充电站充电交易数据的分析方法提供的流程图，如图1所示，该方法包括如下步骤：Fig. 1 is a flow chart provided by a method for analyzing charging transaction data of a charging station according to an embodiment of the present invention. As shown in Fig. 1 , the method includes the following steps:
步骤S21，获取各个充电站的基础信息，其中，基础信息至少包括：充电站编号、充电桩信息、位置信息、站点类型。Step S21, acquiring basic information of each charging station, wherein the basic information at least includes: charging station number, charging pile information, location information, and station type.
具体的，步骤S21首先获取各个充电站的充电站编号、充电桩信息、位置信息和站点类型。其中，充电桩信息可以包括充电站内的安装的充电桩编号以及充电桩数量；站点类型用于记录充电站的主要用途；位置信息用于标记充电站所处的位置。Specifically, step S21 first acquires the charging station number, charging pile information, location information and station type of each charging station. Wherein, the charging pile information may include the number of the charging pile installed in the charging station and the number of charging piles; the station type is used to record the main purpose of the charging station; the location information is used to mark the location of the charging station.
步骤S23，采集充电站在充电交易中生成的交易数据。Step S23, collecting the transaction data generated by the charging station in the charging transaction.
具体的，步骤S23交易数据中包括结构化数据和非结构化数据，其中，结构化数据是充电站在进行充电交易时，产生的一组与充电交易对应的数据，至少包括：充电电量、充电时间、充电金额等。非结构化数据是充电站在日常运营中产生的与充电交易没有对应关系的数据，至少包括充电日志、充电视频等文件。Specifically, the transaction data in step S23 includes structured data and unstructured data, wherein the structured data is a set of data corresponding to the charging transaction generated by the charging station when the charging transaction is performed, at least including: charging power, charging time, charging amount, etc. Unstructured data is the data generated during the daily operation of charging stations that has no corresponding relationship with charging transactions, including at least charging logs, charging videos and other files.
步骤S25，对交易数据进行预处理，得到预处理数据。Step S25, preprocessing the transaction data to obtain preprocessed data.
具体的，步骤S25通过对采集到的交易数据进行预处理，得到用于进一步的与处理数据。其中，预处理可以将由于充电桩时钟跳变、异常充电、窃电等原因，造成异常数据从交易数据中去除。Specifically, step S25 obtains data for further processing by preprocessing the collected transaction data. Among them, preprocessing can remove abnormal data from transaction data due to charging pile clock jumps, abnormal charging, power theft and other reasons.
步骤S27，根据基础信息和预处理数据建立分析模型，通过分析模型确定充电站的运营参数，其中，运营参数至少包括：充电站使用率、充电月均负荷参数、充电站充电时间参数、营业额。Step S27, establish an analysis model based on the basic information and pre-processing data, and determine the operating parameters of the charging station through the analysis model, wherein the operating parameters at least include: charging station utilization rate, charging monthly average load parameters, charging station charging time parameters, turnover .
具体的，步骤S27根据基础信息和交易数据的预处理数据间的关系，建立分析模型，通过分析模型可以确定各个充电站的运营参数。其中，可以对分析模型设置分析条件，通过分析模型得到相应的分析结果。Specifically, in step S27, an analysis model is established according to the relationship between the basic information and the pre-processed data of the transaction data, and the operation parameters of each charging station can be determined through the analysis model. Among them, analysis conditions can be set for the analysis model, and corresponding analysis results can be obtained through the analysis model.
其中，通过上述步骤S21至步骤S27，分别对各个充电站充电交易的交易信息进行采集，并通过对交易数据进行筛选、整理等预处理后，通过预处理数据和基础数据建立的分析模型，分析得到每个充电站的运营参数。Among them, through the above steps S21 to S27, the transaction information of the charging transaction of each charging station is collected respectively, and after the preprocessing of the transaction data such as screening and sorting, the analysis model established by the preprocessing data and the basic data is analyzed. Get the operating parameters of each charging station.
需要说明的是，利用分析模型确定充电站的运营参数只是本方案的一种可选的实施例，本方案也可以利用通过基础信息和预处理数据建立的分析模型，通过向其输入不同的条件，得到除充电站使用率、充电月均负荷参数、充电站充电时间参数、营业额之外的运营参数，此处不做赘述。It should be noted that using the analytical model to determine the operating parameters of the charging station is only an optional embodiment of this scheme, and this scheme can also use the analytical model established through basic information and pre-processed data, by inputting different conditions , to obtain operating parameters other than charging station utilization rate, charging monthly average load parameters, charging station charging time parameters, and turnover, which will not be described here.
本实施例通过利用基础信息和交易数据建立分析模型，通过分析模型确定充电站的运营参数的方法，解决通过手工对充电站的交易数据进行分析，导致分析效率低、出错率高的技术问题。避免传统表处理的繁琐操作，达到了提高工作效率并降低出错率的目的。This embodiment solves the technical problems of low analysis efficiency and high error rate caused by manually analyzing the transaction data of the charging station by using the basic information and transaction data to establish an analysis model and determining the operating parameters of the charging station through the analysis model. It avoids the cumbersome operations of traditional table processing, and achieves the purpose of improving work efficiency and reducing error rates.
作为一种可选的实施例，对于分析确定的运营参数，可以以数据表的形式，按时间顺序生成图形界面，也可以根据各个充电站的位置信息，在地图上按照与充电站运营参数对应的颜色进行显示。As an optional embodiment, for the operating parameters determined by analysis, a graphical interface can be generated in chronological order in the form of a data table, or it can be displayed on the map according to the corresponding charging station operating parameters according to the location information of each charging station. color to display.
作为一种可选的实施例，步骤S25对交易数据进行预处理，得到预处理数据可以包括：As an optional embodiment, step S25 preprocesses the transaction data, and obtaining the preprocessed data may include:
步骤S251，通过对交易数据进行数据类型转换，生成目标数据类型的待处理数据。In step S251, data to be processed of the target data type is generated by performing data type conversion on the transaction data.
步骤S253，通过对待处理数据中存在异常的数据进行异常处理，生成预处理数据。Step S253, generating pre-processed data by performing exception processing on the abnormal data among the data to be processed.
通过上述步骤S251至步骤S253，首先通过采集到的交易数据的数据类型进行转换，得到统一数据类型的待处理数据。然后对待处理数据进行异常处理，去除待处理数据中无效、异常的数据，生成用于建立分析模型的与处理数据。通过异常处理，可以确保交易数据的有效性和准确性。Through the above steps S251 to S253, the data types of the collected transaction data are firstly converted to obtain data to be processed of a unified data type. Then, abnormal processing is performed on the data to be processed, invalid and abnormal data in the data to be processed are removed, and processing data for establishing an analysis model is generated. Through exception handling, the validity and accuracy of transaction data can be ensured.
作为一种可选的实施例，预处理的处理方式还可以至少包括：空值处理、数据正确性验证和字段完整性处理，通过空值处理，数据正确性验证和字段完整性等方法，对采集到的交易数据进行处理，从而进一步的确保交易数据的有效性和准确性。As an optional embodiment, the preprocessing processing method may also include at least: null value processing, data correctness verification and field integrity processing, through methods such as null value processing, data correctness verification and field integrity, the The collected transaction data is processed to further ensure the validity and accuracy of the transaction data.
作为一种可选的实施例，步骤S23采集充电站在充电交易中生成的交易数据可以包括：As an optional embodiment, the collection of transaction data generated by the charging station in the charging transaction in step S23 may include:
步骤S231，根据充电站的充电桩信息，获取充电站中充电桩的充电桩编号。Step S231, according to the charging pile information of the charging station, the charging pile number of the charging pile in the charging station is obtained.
步骤S233，根据充电桩编号，采集各个充电桩的子交易数据。Step S233, collect the sub-transaction data of each charging pile according to the charging pile number.
步骤S235，将子交易数据汇总，生成充电站的交易数据。In step S235, the sub-transaction data are aggregated to generate transaction data of the charging station.
具体的，在每个充电站中，至少有一个充电桩供电动汽车进行充电。如果充电站中的充电桩数量大于两个时，需要分别获取充电站中每个充电桩的子交易数据。然后对自交易数据进行汇总、核算，得到充电站整体的交易数据。Specifically, in each charging station, there is at least one charging pile for electric vehicles to charge. If the number of charging piles in the charging station is greater than two, the sub-transaction data of each charging pile in the charging station needs to be obtained separately. Then aggregate and calculate the self-transaction data to obtain the overall transaction data of the charging station.
作为一种可选的实施例，步骤S27根据基础信息和预处理数据建立分析模型，通过分析模型确定充电站的运营参数包括：As an optional embodiment, step S27 establishes an analysis model based on the basic information and preprocessed data, and determining the operating parameters of the charging station through the analysis model includes:
步骤S271，根据基础信息，确定充电站与充电桩的子交易数据间的对应关系。Step S271, according to the basic information, determine the corresponding relationship between the sub-transaction data of the charging station and the charging pile.
步骤S273，根据子交易数据，确定充电桩的充电桩使用率、充电桩月均负荷参数、充电桩充电时间。Step S273, according to the sub-transaction data, determine the charging pile usage rate of the charging pile, the monthly average load parameter of the charging pile, and the charging time of the charging pile.
步骤S275，根据充电桩的充电桩使用率、充电桩月均负荷参数和充电桩充电时间，确定充电站的运营参数。Step S275: Determine the operating parameters of the charging station according to the charging pile usage rate of the charging pile, the monthly average load parameter of the charging pile, and the charging time of the charging pile.
具体的，上述步骤S271至步骤S273，首先，通过对充电站中的各个充电桩产生的子交易数据进行分析运算，确定各个充电桩的子运营参数。通过将充电站内与子充电桩对应的子运营参数进行汇总，最后确定充电站的运营参数。Specifically, in the above step S271 to step S273, firstly, the sub-operation parameters of each charging pile are determined by analyzing and calculating the sub-transaction data generated by each charging pile in the charging station. By summarizing the sub-operating parameters corresponding to the sub-charging piles in the charging station, the operating parameters of the charging station are finally determined.
其中，衡量经济效益的标准是单位投入的产出，通常情况下，充电设施的使用率越高，其经济效益越显著。Among them, the standard for measuring economic benefits is the output per unit of input. Generally, the higher the utilization rate of charging facilities, the more significant the economic benefits.
令N桩表示充电桩的使用率，则其计算公式为：Let N piles represent the utilization rate of charging piles, then the calculation formula is:
N桩＝T桩/TALL*100％N stake = T stake / TALL * 100%
其中：in:
T桩为在某个时间段内，该充电桩充电时长；TALL为在某个时间段内的时长。T pile is the charging time of the charging pile within a certain period of time; TALL is the duration of charging within a certain period of time.
举例说明：统计某一天内某站的充电桩使用率，假设该站有充电桩1和充电桩2，通过上式可分别计算使用率。TALL为1天*24小时(以小时计)，T桩为该天内，该充电桩充电时长，即该充电桩该天每条交易记录每条结束时间减开始时间的合(以小时计)：For example: To calculate the usage rate of charging piles at a certain station in a certain day, assuming that the station has charging piles 1 and 2, the usage rates can be calculated separately through the above formula. TALL is 1 day * 24 hours (in hours), and T pile is the charging time of the charging pile in the day, that is, the sum of the end time minus the start time of each transaction record of the charging pile in the day (in hours):
若计算该充电桩的总使用率，可用下式：To calculate the total utilization rate of the charging pile, the following formula can be used:
其中：in:
T桩m为该站内所有桩充电时长的合；TALL为在某个时间段内的时长；m为该站的充电桩数；充电站月均负荷分析。 T pile m is the sum of the charging time of all piles in the station; TALL is the duration in a certain period of time; m is the number of charging piles in the station; monthly average load analysis of the charging station.
为了进一步了解各区充电站内充电桩的使用情况，我们以月为单位，计算月均负荷。In order to further understand the usage of charging piles in charging stations in each district, we calculate the monthly average load on a monthly basis.
令P桩表示充电桩单次充电负荷，则其计算公式为：Let the P pile represent the single charging load of the charging pile, and its calculation formula is:
P桩＝Q桩/(T结束-T开始)。P stake = Q stake / (T end - T start ).
其中：in:
Q桩为充电桩充电电量；T结束为充电桩充电时的充电结束时间；T开始为充电桩充电时的充电开始时间。Q pile is the charging power of the charging pile; T end is the charging end time when the charging pile is charging; T start is the charging start time when the charging pile is charging.
若计算充电站月均负荷，可用下式：To calculate the monthly average load of the charging station, the following formula can be used:
其中：in:
P桩m为该站内所有充电桩该月负荷的合。 P pile m is the sum of the monthly loads of all charging piles in the station.
TALL为该月的总天数。TALL is the total number of days in the month.
充电时间分析：Charging time analysis:
通过对电动汽车用户充电时间的统计分析，了解电动汽车用户的充电行为规律，结合各区域充电站的布局分布，提出改进建议。Through the statistical analysis of the charging time of electric vehicle users, the charging behavior of electric vehicle users is understood, combined with the layout and distribution of charging stations in various regions, suggestions for improvement are put forward.
我们将1天24小时按整点分为24个节点，通过电动汽车用户每次充电时的充电开始时间与结束时间进行统计。We divide 24 hours a day into 24 nodes according to the whole point, and make statistics based on the charging start time and end time of each charging of electric vehicle users.
令Cn表示统计时间段内n点时该桩的总充电次数，则其计算公式为：Let Cn represent the total charging times of the pile at point n in the statistical time period, then its calculation formula is:
其中：in:
Tm为n点时第m个条充电记录是否处于充电状态，若正在充电则计为1，否则记为0。T m is whether the mth charging record is in the charging state at n points, if it is charging, it is counted as 1, otherwise it is recorded as 0.
若计算充电站的充电时间，可用下式：To calculate the charging time of the charging station, the following formula can be used:
其中：in:
Cnm为n点时第m个充电桩的充电次数。C nm is the charging times of the mth charging pile at n points.
作为一种可选的实施例，在步骤S27根据基础信息对预处理数据进行分析，确定充电站的运营参数之后，方法还包括：As an optional embodiment, after analyzing the preprocessed data according to the basic information in step S27 and determining the operating parameters of the charging station, the method further includes:
步骤S28，根据运营参数，对充电站划分营业级别。Step S28, according to the operating parameters, classify the charging stations into business levels.
通过步骤S28，可以按照充电站月度的营业额、充电站使用率等直接反应充电站运营参数的指标对充电站分级，从而可以直观的通过营业级别初步了解充电站的运营参数。Through step S28, the charging stations can be graded according to the indicators that directly reflect the operating parameters of the charging station, such as the monthly turnover of the charging station, the utilization rate of the charging station, etc., so that the operating parameters of the charging station can be intuitively known through the business level.
作为一种可选的实施例，针对电动汽车在充电站进行充电交易产生的交易数据进行研究，通过改善现有数据处理方式，便于对充电集中时间、充电电量增量情况、充电电度变化情况等信息统计，为后续充电设施建设指导、电动汽车运营模式研究、用户充电行为习惯分析等提供一种新的分析方法。如图2所示，可以利用Hadoop分布式系统框架体系对交易数据进行存储、预处理。Hadoop框架体系，其核心技术包括HDFS(分布式文件系统)、Hbase(分布式数据库)、数据仓库工具Hive、MapReduce(处理过程)等。As an optional embodiment, research is conducted on the transaction data generated by charging transactions of electric vehicles at charging stations. By improving the existing data processing methods, it is convenient to analyze the charging concentration time, charging power increments, and charging power changes. It provides a new analysis method for the follow-up charging facility construction guidance, electric vehicle operation mode research, and user charging behavior habit analysis. As shown in Figure 2, the Hadoop distributed system framework can be used to store and preprocess transaction data. Hadoop framework system, its core technologies include HDFS (distributed file system), Hbase (distributed database), data warehouse tool Hive, MapReduce (processing), etc.
其中，电动汽车充电交易数据(即充电站充电交易数据)处理分为四个环节，分为数据存储、数据整合、数据处理、数据分析。Among them, the processing of electric vehicle charging transaction data (that is, charging station charging transaction data) is divided into four links, which are divided into data storage, data integration, data processing, and data analysis.
数据存储：Hadoop中的Hbase是一个分布式的、面向列的开源数据库，它不同于一般的关系数据库，是一个适合于非结构化数据存储的数据库。因此可将电动汽车交易中产生的例如充电站的充电日志和充电视频等非结构化数据存储于Hbase数据库里，由于该数据库是基于列存储，有利于对数据库进行高效的压缩而减少数据规模，因此有利于电动汽车交易数据存储。Data storage: Hbase in Hadoop is a distributed, column-oriented open source database, which is different from general relational databases and is a database suitable for unstructured data storage. Therefore, unstructured data such as charging logs and charging videos of charging stations generated in electric vehicle transactions can be stored in the Hbase database. Since the database is based on column storage, it is beneficial to efficiently compress the database and reduce the data size. Therefore, it is beneficial for electric vehicle transaction data storage.
数据整合：由于电动汽车交易数据来源比较分散且数据类型不统一，这就需要对数据进行整合。电动汽车交易数据整合技术利用基于Hadoop的一个数据仓库工具HIVE，搭建一个数据仓库，将Hbase数据库的数据全部存储至该数据仓库。该数据仓库存储方式是将结构化的数据文件映射为一张数据库表，并提供类SQL语言，实现完整的SQL查询功能。可以将SQL语句转换为MapReduce(分布式编程模式)任务运行，十分适合数据仓库的统计分析。Data integration: Since the sources of electric vehicle transaction data are scattered and the data types are not uniform, it is necessary to integrate the data. The electric vehicle transaction data integration technology uses HIVE, a data warehouse tool based on Hadoop, to build a data warehouse and store all the data in the Hbase database in the data warehouse. The storage method of the data warehouse is to map structured data files into a database table, and provide SQL-like language to realize complete SQL query functions. SQL statements can be converted into MapReduce (distributed programming mode) tasks to run, which is very suitable for statistical analysis of data warehouses.
数据处理：由于充电桩时钟跳变、异常充电、窃电等情况，造成电动汽车交易数据存在异常情况，在对电动汽车交易数据进行数据挖掘分析前，需进行数据处理，确保数据准确性。Data processing: Due to clock jumps of charging piles, abnormal charging, power theft, etc., there are abnormalities in the transaction data of electric vehicles. Before data mining and analysis of transaction data of electric vehicles, data processing is required to ensure the accuracy of the data.
我们使用Hadoop的MapReduce并行数据处理框架，如图3所示，MapReduce是一种简化的分布式编程模式，将复杂分布式处理过程分解为Map(映射)和Reduce(归约)过程，提高并发处理能力。让程序自动分布到一个由普通机器组成的超大集群上并发执行。它足够快速的批处理分析仪满足业务需求和业务报告、数据分析、行为分析，如点击流分析等We use Hadoop's MapReduce parallel data processing framework, as shown in Figure 3, MapReduce is a simplified distributed programming model that decomposes complex distributed processing into Map (mapping) and Reduce (reduction) processes to improve concurrent processing ability. Let the program be automatically distributed to a super-large cluster composed of ordinary machines for concurrent execution. It is fast enough batch analyzer to meet business needs and business reporting, data analysis, behavioral analysis such as clickstream analysis, etc.
MapReduce致力于解决大规模数据处理的问题，因此在设计之初就考虑了数据的局部性原理，利用局部性原理将整个问题分而治之。MapReduce集群由普通PC机构成，为无共享式架构。在处理之前，将数据集分布至各个节点。处理时，每个节点就近读取本地存储的数据处理(map)，将处理后的数据进行合并(combine)、排序(shuffleand sort)后再分发(至reduce节点)，避免了大量数据的传输，提高了处理效率。无共享式架构的另一个好处是配合复制(replication)策略，集群可以具有良好的容错性，一部分节点的down机对集群的正常工作不会造成影响。是一个使用简易的软件框架，基于它写出来的应用程序能够运行在由上千个商用机器组成的大型集群上，并以一种可靠容错的方式并行处理上TB级别的数据集。MapReduce is committed to solving the problem of large-scale data processing, so the principle of data locality is considered at the beginning of the design, and the whole problem is divided and conquered by using the principle of locality. The MapReduce cluster is composed of ordinary PCs and has a shared-nothing architecture. Before processing, the dataset is distributed across nodes. During processing, each node reads the locally stored data processing (map) nearby, combines (combine), sorts (shuffle and sort) the processed data, and then distributes (to the reduce node), avoiding the transmission of a large amount of data. Improved processing efficiency. Another advantage of the shared-nothing architecture is that with the replication strategy, the cluster can have good fault tolerance, and the down machine of some nodes will not affect the normal operation of the cluster. It is an easy-to-use software framework. Applications written based on it can run on large clusters consisting of thousands of commercial machines, and process TB-level data sets in parallel in a reliable and fault-tolerant manner.
数据分析：data analysis:
1、电动汽车用户充电行为习惯分析1. Analysis of charging behavior habits of electric vehicle users
目前，北京市电动汽车智能充换电服务网络运营管理系统已积累大量电动汽车用户充电交易数据，通过本发明，实现对电动汽车充电站使用率、充电站月均负荷、充电站充电时间等数据分析，持续跟踪观察电动汽车用户充电特征状况，探索电动汽车用户充电行为习惯，研究电动汽车用户充电消费模式，从而更好地开展电动汽车充电业务，提高用户服务的效率和质量。At present, the operation and management system of Beijing's electric vehicle intelligent charging and swapping service network has accumulated a large amount of charging transaction data of electric vehicle users. Through the present invention, data such as the utilization rate of electric vehicle charging stations, the average monthly load of charging stations, and the charging time of charging stations are realized. Analysis, continuous tracking and observation of the charging characteristics of electric vehicle users, exploration of charging behavior and habits of electric vehicle users, and research on charging consumption patterns of electric vehicle users, so as to better develop electric vehicle charging business and improve the efficiency and quality of user services.
2、电动汽车充电桩运行情况分析2. Analysis of the operation of electric vehicle charging piles
目前，北京市电动汽车智能充换电服务网络运营管理系统已登记充电桩资产1913台，并对这些充电桩的交易记录进行了数据收集。通过对充电桩的充电电量、充电电费、充电时长、充电电价、结算标识等日常运行数据进行统计分析，计算充电桩充电功率、电量电费计算准确率、电表地址准确率等分析结果，持续跟踪研究电动汽车充电桩充电运行状态、计量计费情况。本方案中主要用到了北京市电动汽车充电站资产数据与交易数据。资产数据是指充电站及充电桩的基本信息数据，主要包括充电站编号、充电站名称、充电桩设备编号、集中器编号、电表地址编号等资产信息。交易数据是指电动汽车用户在进行充电时所产生的相关数据，主要包括充电桩设备编号、电表地址编号、充电电量、充电电费、充电开始时间、充电结束时间等信息。在对电动汽车充电交易数据进行数据分析时，需将交易数据与资产数据进行关联，将电动汽车的每一次充电行为准确定位到充电桩与充电站。在此之前需实时检测资产数据与交易数据的一致性，从而更好地掌握电动汽车充电桩的运行情况，为进一步规划充电设施建设及开展充电业务提供理论依据。At present, the Beijing Electric Vehicle Intelligent Charging and Swapping Service Network Operation Management System has registered 1,913 charging pile assets, and has collected data on the transaction records of these charging piles. Through the statistical analysis of daily operation data such as charging quantity, charging electricity fee, charging time, charging electricity price, and settlement logo of charging piles, the analysis results such as charging power of charging piles, accuracy rate of electricity charge calculation, and accuracy rate of electric meter address are calculated, and continuous follow-up research is carried out. Charging operation status, metering and billing of electric vehicle charging piles. This program mainly uses the asset data and transaction data of electric vehicle charging stations in Beijing. Asset data refers to the basic information data of charging stations and charging piles, mainly including asset information such as charging station numbers, charging station names, charging pile equipment numbers, concentrator numbers, and meter address numbers. Transaction data refers to the relevant data generated by electric vehicle users when charging, mainly including charging pile equipment number, meter address number, charging quantity, charging electricity fee, charging start time, charging end time and other information. When performing data analysis on electric vehicle charging transaction data, it is necessary to associate transaction data with asset data, and accurately locate each charging behavior of electric vehicles to charging piles and charging stations. Prior to this, it is necessary to detect the consistency of asset data and transaction data in real time, so as to better grasp the operation of electric vehicle charging piles, and provide a theoretical basis for further planning the construction of charging facilities and developing charging services.
3、电动汽车充电站运行情况分析3. Analysis of the operation of electric vehicle charging stations
目前，北京市已建成的充电站主要可分为出租车充电站、环卫车充电站及面向大众开放的充电站。通过对各充电站站内充电桩的充电电量、充电电费、充电时长、充电时间、充电频率等数据的统计分析，检测各充电站充电桩利用率、空闲充电桩数等信息，探索面向不同类型用户的充电站的运行情况，为日后更好地规划充电设施建设及开展特性化的充电服务提供理论基础。At present, the charging stations that have been built in Beijing can be mainly divided into taxi charging stations, sanitation vehicle charging stations and charging stations open to the public. Through the statistical analysis of the charging quantity, charging electricity fee, charging time, charging time, charging frequency and other data of the charging piles in each charging station, the information such as the utilization rate of the charging piles and the number of idle charging piles in each charging station are detected, and the exploration is aimed at different types of users. The operation of the charging station provides a theoretical basis for better planning the construction of charging facilities and developing personalized charging services in the future.
在本实施例中还提供了一种充电站充电交易数据的分析装置，该装置用于实现上述实施例及可选的实施方式，已经进行过说明的不再赘述。如以下所使用的，术语“模块”可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的装置较佳地以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能并被构想的。In this embodiment, a device for analyzing charging transaction data of a charging station is also provided. The device is used to implement the above embodiments and optional implementation modes, and those that have already been described will not be repeated. As used below, the term "module" may be a combination of software and/or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.
图4是根据本发明实施例的充电站充电交易数据的分析装置的结构框图，如图4所示，该装置包括：获取模块31、采集模块33、预处理模块35和分析模块37。FIG. 4 is a structural block diagram of a device for analyzing charging transaction data of a charging station according to an embodiment of the present invention. As shown in FIG. 4 , the device includes: an acquisition module 31 , a collection module 33 , a preprocessing module 35 and an analysis module 37 .
其中，获取模块31，用于获取各个充电站的基础信息，其中，基础信息至少包括：充电站编号、充电桩信息、位置信息、站点类型；采集模块33，用于采集充电站在充电交易中生成的交易数据；预处理模块35，用于对交易数据进行预处理，得到预处理数据；分析模块37，用于根据基础信息和预处理数据建立分析模型，通过分析模型确定充电站的运营参数，其中，运营参数至少包括：充电站使用率、充电月均负荷参数、充电站充电时间参数、营业额。Among them, the acquisition module 31 is used to acquire the basic information of each charging station, wherein the basic information at least includes: charging station number, charging pile information, location information, and station type; the acquisition module 33 is used to collect charging stations in charging transactions The generated transaction data; the preprocessing module 35 is used to preprocess the transaction data to obtain preprocessed data; the analysis module 37 is used to establish an analysis model based on the basic information and preprocessed data, and determine the operating parameters of the charging station through the analysis model , wherein the operating parameters at least include: charging station utilization rate, charging monthly average load parameters, charging station charging time parameters, and turnover.
其中，通过上述获取模块31、采集模块33、预处理模块35和分析模块37，分别对各个充电站充电交易的交易信息进行采集，并通过对交易数据进行筛选、整理等预处理后，通过预处理数据和基础数据建立的分析模型，分析得到每个充电站的运营参数。Among them, through the above acquisition module 31, acquisition module 33, preprocessing module 35 and analysis module 37, the transaction information of each charging station charging transaction is collected respectively, and after preprocessing such as screening and sorting the transaction data, through preprocessing The analysis model established by processing data and basic data is analyzed to obtain the operating parameters of each charging station.
需要说明的是，利用分析模型确定充电站的运营参数只是本方案的一种可选的实施例，本方案也可以利用通过基础信息和预处理数据建立的分析模型，通过向其输入不同的条件，得到除充电站使用率、充电月均负荷参数、充电站充电时间参数、营业额之外的运营参数，此处不做赘述。It should be noted that using the analytical model to determine the operating parameters of the charging station is only an optional embodiment of this scheme, and this scheme can also use the analytical model established through basic information and pre-processed data, by inputting different conditions , to obtain operating parameters other than charging station utilization rate, charging monthly average load parameters, charging station charging time parameters, and turnover, which will not be described here.
本实施例通过利用基础信息和交易数据建立分析模型，通过分析模型确定充电站的运营参数的方法，解决通过手工对充电站的交易数据进行分析，导致分析效率低、出错率高的技术问题。避免传统表处理的繁琐操作，达到了提高工作效率并降低出错率的目的。This embodiment solves the technical problems of low analysis efficiency and high error rate caused by manually analyzing the transaction data of the charging station by using the basic information and transaction data to establish an analysis model and determining the operating parameters of the charging station through the analysis model. It avoids the cumbersome operations of traditional table processing, and achieves the purpose of improving work efficiency and reducing error rates.
作为一种可选的实施例，预处理模块35包括：第一子生成模块351和第二子生成模块353。As an optional embodiment, the preprocessing module 35 includes: a first subgenerating module 351 and a second subgenerating module 353 .
其中，第一子生成模块351，用于通过对交易数据进行数据类型转换，生成目标数据类型的待处理数据；第二子生成模块353，用于通过对待处理数据中存在异常的数据进行异常处理，生成预处理数据。Among them, the first sub-generating module 351 is used to generate data to be processed of the target data type by performing data type conversion on the transaction data; the second sub-generating module 353 is used to perform exception processing on abnormal data in the data to be processed , to generate preprocessed data.
通过上述第一子生成模块351和第二子生成模块353，首先通过采集到的交易数据的数据类型进行转换，得到统一数据类型的待处理数据。然后对待处理数据进行异常处理，去除待处理数据中无效、异常的数据，生成用于建立分析模型的与处理数据。通过异常处理，可以确保交易数据的有效性和准确性。Through the above-mentioned first sub-generating module 351 and second sub-generating module 353, the data type of the collected transaction data is firstly converted to obtain the unprocessed data of a unified data type. Then, abnormal processing is performed on the data to be processed, invalid and abnormal data in the data to be processed are removed, and processing data for establishing an analysis model is generated. Through exception handling, the validity and accuracy of transaction data can be ensured.
作为一种可选的实施例，采集模块33包括：子获取模块331、子采集模块333和子生成模块335。As an optional embodiment, the collection module 33 includes: a sub-acquisition module 331 , a sub-collection module 333 and a sub-generation module 335 .
其中，子获取模块331，用于根据充电站的充电桩信息，获取充电站中充电桩的充电桩编号；子采集模块333，用于根据充电桩编号，采集各个充电桩的子交易数据；子生成模块335，用于将子交易数据汇总，生成充电站的交易数据。Among them, the sub-acquisition module 331 is used to obtain the charging pile number of the charging pile in the charging station according to the charging pile information of the charging station; the sub-acquisition module 333 is used to collect the sub-transaction data of each charging pile according to the charging pile number; The generating module 335 is configured to summarize the sub-transaction data to generate transaction data of the charging station.
具体的，在每个充电站中，至少有一个充电桩供电动汽车进行充电。如果充电站中的充电桩数量大于两个时，需要分别获取充电站中每个充电桩的子交易数据。然后对自交易数据进行汇总、核算，得到充电站整体的交易数据。Specifically, in each charging station, there is at least one charging pile for electric vehicles to charge. If the number of charging piles in the charging station is greater than two, the sub-transaction data of each charging pile in the charging station needs to be obtained separately. Then aggregate and calculate the self-transaction data to obtain the overall transaction data of the charging station.
作为一种可选的实施例，所述分析模块37包括：子确定模块371、子分析模块373和子处理模块375。As an optional embodiment, the analyzing module 37 includes: a sub-determining module 371 , a sub-analyzing module 373 and a sub-processing module 375 .
其中，子确定模块371，用于根据所述基础信息，确定所述充电站与所述充电桩的所述子交易数据间的对应关系；子分析模块373，用于通过对所述子交易数据进行分析，确定所述充电桩的充电桩使用率、充电桩月均负荷参数、充电桩充电时间；子处理模块375，用于根据所述充电桩的充电桩使用率、所述充电桩月均负荷参数和所述充电桩充电时间，确定所述充电站的所述运营参数。Among them, the sub-determination module 371 is used to determine the corresponding relationship between the charging station and the sub-transaction data of the charging pile according to the basic information; the sub-analysis module 373 is used to analyze the sub-transaction data Perform analysis to determine the charging pile usage rate of the charging pile, the monthly average load parameters of the charging pile, and the charging time of the charging pile; the sub-processing module 375 is used to determine the charging pile usage rate of the charging pile, the monthly average The load parameter and the charging time of the charging pile determine the operation parameter of the charging station.
具体的，上述子确定模块371、子分析模块373和子处理模块375，首先，通过对充电站中的各个充电桩产生的子交易数据进行分析运算，确定各个充电桩的子运营参数。通过将充电站内与子充电桩对应的子运营参数进行汇总，最后确定充电站的运营参数。Specifically, the sub-determining module 371, the sub-analyzing module 373 and the sub-processing module 375 first determine the sub-operation parameters of each charging pile by analyzing and calculating the sub-transaction data generated by each charging pile in the charging station. By summarizing the sub-operating parameters corresponding to the sub-charging piles in the charging station, the operating parameters of the charging station are finally determined.
上述本发明实施例序号仅仅为了描述，不代表实施例的优劣。The serial numbers of the above embodiments of the present invention are for description only, and do not represent the advantages and disadvantages of the embodiments.
在本发明的上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。In the above-mentioned embodiments of the present invention, the descriptions of each embodiment have their own emphases, and for parts not described in detail in a certain embodiment, reference may be made to relevant descriptions of other embodiments.
在本申请所提供的几个实施例中，应该理解到，所揭露的技术内容，可通过其它的方式实现。其中，以上所描述的装置实施例仅仅是示意性的，例如所述单元的划分，可以为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，单元或模块的间接耦合或通信连接，可以是电性或其它的形式。In the several embodiments provided in this application, it should be understood that the disclosed technical content can be realized in other ways. Wherein, the device embodiments described above are only illustrative. For example, the division of the units may be a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or may be Integrate into another system, or some features may be ignored, or not implemented. In another point, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of units or modules may be in electrical or other forms.
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
另外，在本发明各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可为个人计算机、服务器或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、只读存储器(ROM，Read-OnlyMemory)、随机存取存储器(RAM，Random Access Memory)、移动硬盘、磁碟或者光盘等各种可以存储程序代码的介质。If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the essence of the technical solution of the present invention or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include: various media that can store program codes such as U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk.
以上所述仅是本发明的优选实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, it should be pointed out that for those skilled in the art, without departing from the principle of the present invention, some improvements and modifications can also be made, and these improvements and modifications are also It should be regarded as the protection scope of the present invention.
Claims (10)
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510383323.8A CN105023196A (en) | 2015-07-02 | 2015-07-02 | Analysis method and device for charging transaction data of charging stations |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510383323.8A CN105023196A (en) | 2015-07-02 | 2015-07-02 | Analysis method and device for charging transaction data of charging stations |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105023196A true CN105023196A (en) | 2015-11-04 |
Family
ID=54413144
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510383323.8A Pending CN105023196A (en) | 2015-07-02 | 2015-07-02 | Analysis method and device for charging transaction data of charging stations |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105023196A (en) |
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 |
| 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 |
| 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 |
| 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 | An orderly charging control system for electric boats |
| 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 | Bilateral transaction method of electric vehicle electricity charges based on big data and blockchain |
| CN112200471A (en) * | 2020-10-15 | 2021-01-08 | 国网北京市电力公司 | Quantity configuration method, device and charging pile distribution system of charging piles |
| 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 |
|---|---|---|---|---|
| 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 |
| CN107025236B (en) * | 2016-02-01 | 2020-04-07 | 阿里巴巴集团控股有限公司 | Data processing method between clearing systems and data clearing system |
| CN105654622B (en) * | 2016-03-04 | 2019-10-15 | 深圳市速满跑科技有限公司 | Charging pile management method and system |
| 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 |
| 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 |
| 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 |
| 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 |
| 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 |
| CN109118136B (en) * | 2018-07-28 | 2022-03-29 | 江苏苏宁物流有限公司 | Relay station planning method and system for logistics unmanned aerial vehicle |
| 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 |
| 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 |
| 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 |
| CN109783265B (en) * | 2018-12-30 | 2022-08-12 | 国网北京市电力公司 | Abnormal business 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 |
| 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 |
| CN112418585B (en) * | 2019-08-23 | 2024-04-19 | 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 |
| 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 |
| 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 | An orderly charging control system for electric boats |
| CN111340549A (en) * | 2020-02-27 | 2020-06-26 | 南京师范大学 | Bilateral transaction method of electric vehicle electricity charges based on big data and blockchain |
| CN112200471A (en) * | 2020-10-15 | 2021-01-08 | 国网北京市电力公司 | Quantity configuration method, device and charging pile distribution system of charging piles |
| CN112200471B (en) * | 2020-10-15 | 2023-11-21 | 国网北京市电力公司 | Charging pile quantity configuration method and device and charging pile distribution system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105023196A (en) | Analysis method and device for charging transaction data of charging stations | |
| CN107145586B (en) | A method and device for label production based on power marketing data | |
| CN109916413B (en) | Mesh-based road matching method, system, device and storage medium | |
| WO2016054908A1 (en) | Internet of things big data platform-based intelligent user profiling method and apparatus | |
| US20190149344A1 (en) | Intelligent search system for service cost and method thereof | |
| CN111680855A (en) | A method and system for automatic detection and early warning of project whole process risk | |
| CN106600103A (en) | Statistic data model building method facing programs, plans, and decisions | |
| CN105005570A (en) | Method and apparatus for mining massive intelligent power consumption data based on cloud computing | |
| CN113064866A (en) | A power business data integration system | |
| CN110297847A (en) | A kind of intelligent information retrieval method based on big data principle | |
| CN115423429A (en) | Multimode integrated distribution network operation system based on image and sound information | |
| CN113642299A (en) | One-key generation method based on power grid statistical form | |
| CN104820939A (en) | Analysis method and device based on charging transaction data | |
| CN104809246A (en) | Method and device for processing charging data | |
| CN111382149A (en) | Financial data analysis system and method | |
| CN113590607A (en) | Electric power marketing report realization method and system based on report factor | |
| Richard et al. | EVStationSIM: An end-to-end platform to identify and interpret similar clustering patterns of EV charging stations across multiple time slices | |
| CN113254517A (en) | Service providing method based on internet big data | |
| CN112381583A (en) | Power consumption calculation method and device based on distributed memory calculation technology | |
| CN106126727A (en) | A kind of big data processing method of commending system | |
| CN115130811A (en) | Method and device for establishing power user portrait and electronic equipment | |
| CN109522934A (en) | A kind of power consumer clustering method based on clustering algorithm | |
| CN111209329A (en) | Dynamic analysis method, device and system based on full-service volume price change path | |
| CN116089431A (en) | Data processing method, device, electronic device and storage medium of data warehouse | |
| CN113515515B (en) | Method, device, equipment and medium for fusing customer data and power grid equipment data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20151104 |