WO2019100504A1 - 构建基于大数据技术的电力交易指标体系的方法 - Google Patents
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- the invention aims to summarize the data of the national unified power transaction into comprehensive indicators, and then display the core indicators.
- the technology mainly involves technologies related to big data applications and cloud weight evaluation methods.
- the national unified power market trading platform can realize market functions such as power generation rights trading and direct trading of power users, and has the parallel operation capability of multi-variety and multi-cycle transactions, which has important practical significance for improving the level of power trading operations and promoting optimal allocation of power resources.
- the members of the nation's electricity trading market continue to grow, the transaction volume has steadily increased, and a large number of small and medium-sized power users have entered the market. They are eager to obtain information and services on the electricity market from the Internet at any time and anywhere.
- Internet + is a new format of Internet development under Innovation 2.0. It is the evolution of the Internet form driven by Knowledge Society Innovation 2.0 and its new form of economic and social development. Data collection, storage, transmission, processing and other technologies based on power trading systems and corresponding infrastructure. The establishment of a corresponding indicator system for the power transaction data system can effectively reflect the various aspects of the units on the selling side and the overall situation of the electricity trading market.
- an object of the present invention is to provide a method for constructing a power trading index system based on big data technology.
- a method for constructing a power trading index system based on big data technology characterized in that:
- the method includes the following steps:
- the basic data is obtained from the power trading system database or other unstructured database through Sqoop. After the data is cleaned, the data is stored in the HDFS database of the Hadoop cluster to provide support for the next calculation;
- the construction of the indicator system is mainly based on the cloud center of gravity evaluation method to solve the problem of variable conversion, and then the data analysis is carried out through the high concurrent distributed computing capability of Spark in-memory access;
- Indicator display According to the indicators required by the application layer, the corresponding data is taken out from the HDFS, and displayed in the foreground interface through a series of interface display processing or combination of indicator data.
- step 2 further includes the following steps:
- step 2.2 determining the index weights adopts a subjective weighting method or an objective weighting method
- the subjective weighting method refers to a choice made based on empirical judgment, including an analytic hierarchy process and a Delphi method;
- the objective weighting method means that the determination of the weight is not affected by subjective factors, but the weight value is obtained by analyzing the actual data.
- a power trading indicator system based on big data technology constructed according to the method as described above, characterized in that:
- the power transaction indicator system includes a data acquisition layer, a data analysis layer, and an application display layer;
- the data collection layer mainly collects data in various channels, and finally saves it in a distributed database
- the data analysis layer is mainly for constructing the indicator system.
- the Spark is used to summarize the indicator data, and finally stored in the distributed database;
- the application presentation layer is mainly responsible for generating visual interfaces such as reports and charts for indicators built by the data analysis layer.
- the distributed database is HDFS of Hadoop.
- a separate indicator is a comprehensive reflection of the absolute (or under certain circumstances) of a power trading system. Number, relative number or average.
- the constructed indicator system is a comprehensive system composed of a series of interrelated indicators, which can effectively reflect the various aspects of the research object according to the research object.
- Figure 1 is a schematic diagram of establishing an indicator system through big data technology.
- FIG. 2 is a schematic diagram of a power flow Spark calculation process.
- the invention aims to establish a set of key indicators of the power market with multi-dimensional characteristics such as market structure, market behavior and market efficiency by establishing an index system of a power trading system under big data.
- the technical architecture of the present invention is divided into a data acquisition layer, a data analysis layer, and an application presentation layer.
- the data collection layer mainly collects data from various channels and finally stores it in a distributed database (such as Hadoop HDFS).
- the data analysis layer is mainly used to build an indicator system. By configuring the index calculation rules, using Spark to summarize the indicator data, and finally storing it in a distributed database (such as Hadoop HDFS).
- the application presentation layer is mainly responsible for generating visual interfaces such as reports and charts for indicators built by the data analysis layer.
- the architecture allows developers to focus on only one of the layers in the structure, and the structure is more explicit. In the later maintenance, it is easy to replace the original level with a new implementation, greatly reducing maintenance costs and maintenance time.
- the overall architecture is shown in Figure 1.
- the basic data is obtained from the power trading system database or other unstructured databases through Sqoop. After the data is cleaned, the data is stored in the HDFS database of the Hadoop cluster to support the next calculation. .
- the construction of the indicator system is mainly based on the cloud center of gravity evaluation method to solve the problem of variable conversion, and then through the high concurrent distributed computing ability of Spark in-memory access for data analysis.
- the subjective empowerment method refers to the choices made based on empirical judgments, such as the analytic hierarchy process and the Delphi method.
- the objective weighting method means that the determination of the weight is not affected by subjective factors, but the weight value is obtained by analyzing the actual data.
- the invention selects the objective weighting method to determine the weight of each index.
- the number field corresponding to the specified comment set is [0, 1].
- Each comment in the comment set corresponds to a change interval in the number field.
- the weights of the indicators are determined by an objective weighting method, and then the weighted offset of each index is calculated by the cloud center of gravity evaluation method.
- the final comment results of each indicator are stored in the HDFS database.
- the indicators show. According to the indicators required by the application layer, the corresponding data is taken out from the HDFS, and displayed in the foreground interface through a series of interface display processing or combination of indicator data.
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Abstract
一种构建基于大数据技术的电力交易指标体系的方法,涉及电力交易领域。主要步骤为:1、获取数据;2、构建指标体系:指标体系的构建主要基于云重心评价法来解决变量转换问题,再通过Spark内存式访问的高并发分布式计算能力来进行数据分析;3、指标计算:在配置指标之后,通过客观赋权法来确定各指标权重,然后通过云重心评价法来计算出每个指标的加权偏移度;经过Spark计算之后将最终的每个指标的评语结果存入HDFS数据库中;4、指标展现。构建的指标体系是一系列相互联系的指标构成的综合体系,能根据研究的对象,有效的反映出研究对象的各个方面的情况。
Description
本发明旨在将全国统一电力交易的数据归纳出全面指标,继而展示出核心指标。技术主要涉及大数据应用相关的技术以及云重心评价法。
全国统一电力市场交易平台能够实现发电权交易和电力用户直接交易等市场功能,具备多品种多周期交易并行的运营能力,对于提高电力交易运营水平、促进电力资源优化配置具有重要的现实意义。当前,全国的电力交易市场成员不断增长,交易成交量稳步上升,大量中小电力用户进入市场,迫切希望能够随时随地都能方便地从互联网获取对电力市场信息和服务。
“互联网+”是创新2.0下的互联网发展的新业态,是知识社会创新2.0推动下的互联网形态演进及其催生的经济社会发展新形态。基于电力交易系统的数据收集、存储、传输、处理等技术以及相应的基础设施。建立电力交易数据系统的相应指标体系,可以有效的反映出发售电侧单位的各个方面的情况以及对电力交易市场的一个整体情况展示。
发明内容
针对背景技术中的问题,本发明的目的在于提供一种构建基于大数据技术的电力交易指标体系的方法。
为了实现上述目的,本发明提出如下技术方案:
一种构建基于大数据技术的电力交易指标体系的方法,其特征在于:
所述方法包括以下步骤:
1、获取数据:通过Sqoop从电力交易系统数据库或其它非结构化数据库,得到基础数据,在进行数据清洗之后,将数据存放在Hadoop集群的HDFS数据库中,为下一步的计算提供支持;
2、构建指标体系:指标体系的构建主要基于云重心评价法来解决变量转换问题,再通过Spark内存式访问的高并发分布式计算能力来进行数据分析;
3、指标计算:在配置指标之后,通过客观赋权法来确定各指标权重,然后通过云重心评价法来计算出每个指标的加权偏移度;经过Spark计算之后将最终
的每个指标的评语结果存入HDFS数据库中;
4、指标展现:根据应用层所需的指标,从HDFS中取出相应的数据,通过一系列的界面展示处理或者指标数据组合展示在前台界面。
进一步地,步骤2又包括如下步骤:
2.1建立指标体系:
设指标体系为C,C={C1,C2,C3,…,Cm},其中Ci={Ci1,Ci2,Ci3,…,Cin}(i=1,2,…,m),这里Cij表示第i个一级指标中的第j(j=1,2,…,n)个二级指标依次类推,建立多个层次的指标体系;
2.2确定指标权重;
2.3指标评语集的云模型表示:规定评语集所对应的数域为[0,1];评语集中每个评语对应数域内一个变化区间;假设指标评语集V={较差,差,一般,好,较好,很好,极好},设定对应评语区间P={(0,0.15],(0.15,0.3],(0.3,0.45],(0.45,0.6],(0.6,0.75],(0.75,0.9],(0.9,1]},这样就可以把具体的数据转换成评语值。
进一步地,在步骤2.2中,确定指标权重采用主观赋权法或者客观赋权法;
所述主观赋权法指根据经验判断做出的选择,包括层次分析法和德尔菲法;
所述客观赋权法是指权重的确定不受主观因素的影响,而是通过对实际数据的分析来获得权重值。
根据如上所述方法构建的基于大数据技术的电力交易指标体系,其特征在于:
所述电力交易指标体系包括数据采集层、数据分析层以及应用展示层;
所述数据采集层主要对数据进行各种渠道的收集,最后保存在分布式数据库中;
数据分析层主要为构建指标体系,通过配置指标计算规则,运用Spark归纳成指标数据,最后存储在分布式数据库中;
应用展示层主要负责将数据分析层构建的指标生成报表、图表等可视化界面。
进一步地,所述分布式数据库为Hadoop的HDFS。
与现有技术相比,本发明的有益效果为:
单独的指标是综合反映电力交易系统中某一事物(或某一种情况下)的绝对
数、相对数或平均数。构建的指标体系则是一系列相互联系的指标构成的综合体系,它能根据研究的对象,有效的反映出研究对象的各个方面的情况。
图1是通过大数据技术建立指标体系示意图。
图2是电力交易Spark计算流程示意图。
下面结合附图和具体实施方式,对本发明的具体实施方案作详细的阐述。这些具体实施方式仅供叙述而并非用来限定本发明的范围或实施原则,本发明的保护范围仍以权利要求为准,包括在此基础上所作出的显而易见的变化或变动等。
本发明旨在通过建立大数据下的电力交易系统的指标体系,提出具有市场结构、市场行为、市场效率等多维度特点的电力市场关键指标集。
为了达到上述目的,本发明的技术解决方案如下:
一、体系总体介绍
本发明的技术架构分为数据采集层、数据分析层以及应用展示层。数据采集层主要对数据进行各种渠道的收集,最后保存在分布式数据库(如Hadoop的HDFS)中。数据分析层主要为构建指标体系,通过配置指标计算规则,运用Spark等归纳成指标数据,最后存储在分布式数据库(如Hadoop的HDFS)中。应用展示层主要负责将数据分析层构建的指标生成报表、图表等可视化界面。
该架构体系可以使得开发人员只关注整个结构中的其中某一层,结构更加的明确。在后期维护的时候,可以很容易的用新的实现来替换原有层次的实现,极大地降低了维护成本和维护时间。
二、获取数据
整体架构如图1所示,通过Sqoop从电力交易系统数据库或其它非结构化数据库,得到基础数据,在进行数据清洗之后,将数据存放在Hadoop集群的HDFS数据库中,为下一步的计算提供支持。
三、构建指标体系
指标体系的构建主要基于云重心评价法来解决变量转换问题,再通过Spark内存式访问的高并发分布式计算能力来进行数据分析。
(1)建立指标体系。设指标体系为C:
C={C1,C2,C3,…,Cm}其中Ci={Ci1,Ci2,Ci3,…,Cin}(i
=1,2,…,m)这里Cij表示第i个一级指标中的第j(j=1,2,…,n)个二级指标依次类推,建立多个层次的指标体系。
(2)确定指标权重。确定权重的方法有很多种,包括主观赋权法和客观赋权法。其中主观赋权法指根据经验判断做出的选择,如层次分析法、德尔菲法等。客观赋权法是指权重的确定不受主观因素的影响,而是通过对实际数据的分析来获得权重值。本发明选用客观赋权法来确定各指标权重。
(3)指标评语集的云模型表示。规定评语集所对应的数域为[0,1]。评语集中每个评语对应数域内一个变化区间。假设指标评语集V={较差,差,一般,好,较好,很好,极好},设定对应评语区间P={(0,0.15],(0.15,0.3],(0.3,0.45],(0.45,0.6],(0.6,0.75],(0.75,0.9],(0.9,1]}这样就可以把具体的数据转换成评语值。
四、指标计算
如图2所示,在配置指标之后,通过客观赋权法来确定各指标权重,然后通过云重心评价法来计算出每个指标的加权偏移度。经过Spark计算之后将最终的每个指标的评语结果存入HDFS数据库中。
五、指标展现。根据应用层所需的指标,从HDFS中取出相应的数据,通过一系列的界面展示处理或者指标数据组合展示在前台界面。
Claims (5)
- 一种构建基于大数据技术的电力交易指标体系的方法,其特征在于:所述方法包括以下步骤:1、获取数据:通过Sqoop从电力交易系统数据库或其它非结构化数据库,得到基础数据,在进行数据清洗之后,将数据存放在Hadoop集群的HDFS数据库中,为下一步的计算提供支持;2、构建指标体系:指标体系的构建主要基于云重心评价法来解决变量转换问题,再通过Spark内存式访问的高并发分布式计算能力来进行数据分析;3、指标计算:在配置指标之后,通过客观赋权法来确定各指标权重,然后通过云重心评价法来计算出每个指标的加权偏移度;经过Spark计算之后将最终的每个指标的评语结果存入HDFS数据库中;4、指标展现:根据应用层所需的指标,从HDFS中取出相应的数据,通过一系列的界面展示处理或者指标数据组合展示在前台界面。
- 根据权利要求1所述的一种构建基于大数据技术的电力交易指标体系的方法,其特征在于:步骤2又包括如下步骤:2.1建立指标体系:设指标体系为C,C={C1,C2,C3,…,Cm},其中Ci={Ci1,Ci2,Ci3,…,Cin}(i=1,2,…,m),这里Cij表示第i个一级指标中的第j(j=1,2,…,n)个二级指标依次类推,建立多个层次的指标体系;2.2确定指标权重;2.3指标评语集的云模型表示:规定评语集所对应的数域为[0,1];评语集中每个评语对应数域内一个变化区间;假设指标评语集V={较差,差,一般,好,较好,很好,极好},设定对应评语区间P={(0,0.15],(0.15,0.3],(0.3,0.45],(0.45,0.6],(0.6,0.75],(0.75,0.9],(0.9,1]},这样就可以把具体的数据转换成评语值。
- 根据权利要求2所述的一种构建基于大数据技术的电力交易指标体系的方法,其特征在于:在步骤2.2中,确定指标权重采用主观赋权法或者客观赋权法;所述主观赋权法指根据经验判断做出的选择,包括层次分析法和德尔菲法;所述客观赋权法是指权重的确定不受主观因素的影响,而是通过对实际数据的分析来获得权重值。
- 根据权利要求1所述方法构建的基于大数据技术的电力交易指标体系,其特征在于:所述电力交易指标体系包括数据采集层、数据分析层以及应用展示层;所述数据采集层主要对数据进行各种渠道的收集,最后保存在分布式数据库中;数据分析层主要为构建指标体系,通过配置指标计算规则,运用Spark归纳成指标数据,最后存储在分布式数据库中;应用展示层主要负责将数据分析层构建的指标生成报表、图表等可视化界面。
- 根据权利要求4所述的电力交易指标体系,其特征在于:所述分布式数据库为Hadoop的HDFS。
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