WO2020119017A1 - System and method for achieving data asset sensing and pricing functions in big data background - Google Patents

System and method for achieving data asset sensing and pricing functions in big data background Download PDF

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WO2020119017A1
WO2020119017A1 PCT/CN2019/086393 CN2019086393W WO2020119017A1 WO 2020119017 A1 WO2020119017 A1 WO 2020119017A1 CN 2019086393 W CN2019086393 W CN 2019086393W WO 2020119017 A1 WO2020119017 A1 WO 2020119017A1
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data
value
module
pricing
data value
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PCT/CN2019/086393
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French (fr)
Chinese (zh)
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魏明
李书超
张鹏伟
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普元信息技术股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0283—Price estimation or determination

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  • the invention relates to the field of computer software, in particular to the field of data pricing in the context of big data, and specifically refers to a system and method for realizing data asset sensing and pricing functions in the context of big data.
  • the current public big data asset value pricing method is to establish a hierarchical structure model and integrate different pricing strategies to fully consider.
  • the impact of various factors on the value of data assets, using the AHP analysis method to obtain the weight of each pricing strategy, thereby obtaining the evaluation value of data assets, solves the problem that the existing technology cannot reasonably quantitatively evaluate the value of data assets, Data assets are reasonably priced and enter the circulation and exchange links, thereby guiding the flow of data and providing a reliable method for data exchange.
  • the hierarchical model includes three levels from top to bottom, the first level is the target level, the second level is the criterion level, and the third level is the plan level;
  • the target layer is the evaluation value of the evaluated data assets
  • the criteria layer is data integrity, data update frequency, data structure degree, data transaction frequency, data volume, data openness, and data search frequency;
  • the plan layer is the data asset price priced by the cost method, the data asset price priced by the liposuction method, and the data asset price priced by the auction method.
  • the existing technology has realized the evaluation of the value of big data assets within a certain range by establishing a data hierarchy model and using different pricing strategies.
  • any one or several pricing schemes cannot accurately approximate the true value of big data.
  • Data is different from information. Before data is used, there is no specific meaning, so data Value is difficult to assess. The value of data depends on the needs of different subjects. Big data will only generate value in front of those who need it. That is to say, the value of big data is generated after it is used. Therefore, the evaluation of data value by existing technologies is established Before the data is used, pricing the buy-out data is not objective.
  • the existing technology divides the principles followed by the evaluation of big data in the criterion layer, but compared with the basic characteristics of large, diverse, and high speed of big data, these evaluation principles are very limited, so they have strong limitation.
  • the purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art, and to provide a system and method for realizing data asset sensing and pricing functions in the context of big data that meets the needs of big data, has objectivity, and has general adaptability.
  • the main feature of the system that realizes data asset sensing and pricing functions in the context of big data is that the system includes:
  • Data initialization module used to initialize the data that needs to be priced, and give the unique identification in the entire data pricing system to the data to be priced;
  • the data value element and range configuration module connected with the data initialization module, is used to abstract the data value element according to different data attributes, and obtain the segment values of different elements by segmenting different elements and defining the attribute value range ;
  • the data value perception module is connected to the data value element and range configuration module, and is used for real-time perception of data usage and data value element information and pushing it to the data value evaluation module;
  • the data value evaluation module is connected to the data value perception module and is used to calculate and evaluate the value of the data in real time according to the data usage and value element information.
  • the unique identifier in the data initialization module is a unique ID that senses the configuration of data value elements and data usage.
  • the ID is a combination of 18 digits and letters.
  • the data initialization module further includes initializing the extended information of the data according to actual needs.
  • the extended information of the data includes data content, data name and data unique identification.
  • the data value element and range configuration module senses data usage and data value element information in real time through the data interface.
  • the data value elements and the data value elements in the range configuration module include data quality, data scarcity, data density level, data timeliness, difficulty in generating data type data, and difficulty in collection, exchange, transmission and storage.
  • the definition of data by the data value element and range configuration module includes the definition of data value dimension, data value element type and number of elements.
  • the number of segments of the data value element and range configuration module is not greater than 10.
  • the data value sensing module data ID and the set interval time sense the usage of the data.
  • the usage of the data includes usage times and duration.
  • the monetary value of the data calculated according to the data usage and value element information is specifically:
  • DataValue C ⁇ f (data value element information, data usage);
  • C is the currency value conversion factor
  • f() is the comprehensive calculation function
  • the value of the data is the amount of money.
  • the pricing price of the calculated data in the data value evaluation module is specifically:
  • r is the profit rate.
  • the method for realizing data asset sensing and pricing processing in the context of big data based on the above system has the main feature that the method includes the following steps:
  • the data initialization module initializes the data that needs to be priced, and gives the unique identification in the entire data pricing system to the data to be priced;
  • the data value element and range configuration module abstracts data value elements based on different data attributes, and obtains segment values of different elements by segmenting different elements and defining attribute value ranges;
  • the data value perception module described in real time senses the data usage and data value element information, and pushes it to the data value evaluation module;
  • the data value evaluation module calculates the value of the evaluation data in real time based on the data usage and value element information.
  • the system and method for realizing data asset sensing and pricing functions in the context of big data of the present invention are adopted, which realizes flexible configuration and segmented management of data value elements, and at the same time transfers element information through data interfaces, thereby realizing the unlimitedness of the underlying elements Expansion, and these elements are often closely related to the data producer, management, and storage. Therefore, the present invention fully considers the data value related party information, and has strong adaptability. At the same time, the present invention considers the difference between data and information and The sparseness of data value does not set the initial value of the data, but dynamically calculates the value of the data in combination with the data usage. Therefore, as the data is continuously used, the value calculation of the data will become more and more accurate, thus solving the large In the context of data, it is difficult to price data assets, and the pricing is unreasonable and inaccurate.
  • FIG. 1 is a structural diagram of a system for realizing data asset sensing and pricing functions in the context of big data of the present invention.
  • FIG. 2 is a structural diagram of a data value perception module of a system that implements data asset perception and pricing functions in the context of big data of the present invention.
  • system for realizing data asset sensing and pricing functions in the context of the big data of the present invention, wherein the system includes
  • Data initialization module used to initialize the data that needs to be priced, and give the unique identification in the entire data pricing system to the data to be priced;
  • the data value element and range configuration module connected with the data initialization module, is used to abstract the data value element according to different data attributes, and obtain the segment values of different elements by segmenting different elements and defining the attribute value range ;
  • the data value perception module is connected to the data value element and range configuration module, and is used for real-time perception of data usage and data value element information and pushing it to the data value evaluation module;
  • the data value evaluation module is connected to the data value perception module and is used to calculate and evaluate the value of the data in real time according to the data usage and value element information.
  • the unique identifier in the data initialization module is a unique ID that senses the configuration of data value elements and data usage.
  • the ID is a combination of 18 digits and letters.
  • the data initialization module further includes initializing the extended information of the data according to actual needs.
  • the data extension information includes data content, data name, and data unique identification.
  • the data value element and range configuration module perceives data usage and data value element information in real time through a data interface.
  • the data value elements and data value elements in the range configuration module include data quality, data scarcity, data density, data timeliness, data type data generation difficulty, and collection, exchange and transmission Difficulty of storage.
  • the definition of data by the data value element and range configuration module includes the definition of data value dimension, data value element type and number of elements.
  • the number of segments of the data value element and range configuration module is not greater than 10.
  • the data value sensing module data ID and the set interval time sense the usage of data.
  • the usage of the data includes the number of uses and the duration of use.
  • the data value calculation module in the data value evaluation module calculates the monetary value of the data based on the data usage and value element information, specifically:
  • DataValue C ⁇ f (data value element information, data usage);
  • C is the currency value conversion factor
  • f() is the comprehensive calculation function
  • the value of the data is the amount of money.
  • the pricing price of the calculated data in the data value evaluation module is specifically:
  • r is the profit rate.
  • the method for realizing data asset sensing and pricing processing in the context of big data based on the above system in the present invention includes the following steps:
  • the data initialization module initializes the data that needs to be priced, and gives the unique identification in the entire data pricing system to the data to be priced;
  • the data value element and range configuration module abstracts data value elements based on different data attributes, and obtains segment values of different elements by segmenting different elements and defining attribute value ranges;
  • the data value perception module described in real time senses data usage and data value element information and pushes it to the data value evaluation module;
  • the data value evaluation module calculates the value of the evaluation data in real time based on the data usage and value element information.
  • the present invention Track data usage in real time through data value perception, evaluate data value after the data is used, and calculate data value in real time by integrating data value elements, data attributes, and data usage, thereby realizing dynamic pricing of data and realizing one-time use Value pricing. With the continuous use of data, the value of data will become more and more accurate. It solves the shortcomings that the current value of data cannot be close to the actual value of data, and provides a comprehensive and objective evaluation of data value in a big data environment. 3. Pricing method with universal adaptability.
  • the invention provides a method and system for data asset perception and pricing in the context of big data, including a data initialization module, a data value element and range configuration module, a data value perception module and a data value evaluation module.
  • the data initialization module is mainly to initialize the data that needs to be priced, so that the data has a unique identifier in the entire value evaluation system.
  • This module can not only identify the unique information of the data, but also define the extended information of the data; data value elements And range configuration module to achieve multi-dimensional management of data value elements and value ranges.
  • the data value perception module senses the data usage and data value element information in real time through the data interface, and pushes the data usage and value element information to the data value evaluation module after the data is used; the data value evaluation module After receiving the data usage and value element information sent by the perception module, the value of the data is evaluated in real time.
  • FIG. 1 The structure of the present invention is shown in FIG. 1.
  • data initialization module data value element and range configuration module
  • data value perception module data value evaluation module
  • the data initialization module implements the function of initializing data that needs to be priced.
  • Data initialization is mainly to give the data to be priced a unique identifier in the entire data pricing system, and provide a unique ID that is aware of the configuration of data value elements and data usage.
  • the data initialization module can also initialize the extended information of the data according to actual needs, as shown in the following table:
  • ID is a unique identification of data, defined as a combination of 18 digits and letters, used as a unique distinction between different data.
  • the data value element and range configuration module mainly implements the configurable service of the data value element and range, and can abstract the data value element according to different data attributes, such as data quality, data scarcity, data density, data timeliness, data type data The difficulty of generation, the difficulty of collection, exchange, transmission and storage, etc., and segment it according to the data value element, and finally obtain its segment value data.
  • the module provides a fixed data interface, and the underlying data value elements can be infinitely expanded according to the data.
  • the module can push new elements to the data value awareness module on the existing data interface, thereby achieving unlimited expansion of the underlying elements.
  • the adaptability of the present invention is improved.
  • data value dimension ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
  • Element name Element meaning Number of segments Element1 A Sub1 Element2 A Sub2 ... ... ...
  • the number of segments is less than or equal to 10.
  • the range is set according to different element information.
  • the data value perception module realizes the perception of data usage and the acquisition of data value element information.
  • the data value perception module perceives the usage of data (number of uses, duration of use) through the data ID and the set interval. After obtaining the data usage, the complete information of the data value element (element and its segment) is obtained through the data ID Value), and push the complete information of data value elements and data usage to the data value evaluation module through the data interface. as shown in picture 2.
  • the data value evaluation module After the data value evaluation module receives the data usage and value element information, it calculates the monetary value of the data.
  • the calculation formula is:
  • C is the currency value conversion factor
  • f() is the comprehensive calculation function
  • the value of the data is the amount of money.
  • the data value perception module senses data usage and obtains data value element information at a set time, the value of data will change with the use of data and changes in data value elements, thus comprehensively considering the use of data Parties, data producers, data managers and other parties have an impact on data value factors.
  • the following uses commodity retail data as an example to illustrate the application of the present invention in data asset pricing.
  • the commodity retail data includes the following data content:
  • Store data store name, company name, address, product conformity evaluation, shipment speed evaluation, service attitude evaluation, seller name, store opening time, store opening time, business scope, monthly sales, main categories, provinces, cities;
  • Commodity data commodity name, brand, number of collections, inventory, monthly sales, commodity country, province, city, original price, discount price, mobile terminal price, transaction price, total product evaluation, first category, second category, Category III;
  • Commodity attribute data commodity name, original price, transaction price, price range, brand, first-level category, second-level category, third-level category, place of production, season of listing year, sales channel, size, color classification, pattern, Style, applicable objects, occasions, seasons, styles, popular elements, production techniques, closing methods, functions;
  • the data value element and scope configuration module the data is analyzed according to the content of commodity retail data, and the value dimension of commodity retail data is divided into: data itself attributes, data cost attributes, and data application attributes.
  • the types of data value elements included in the data itself attributes are: data quality (number of elements 6), basic data information (number of elements 2) and data types (number of elements 3); the types of data value elements included in the data cost attribute are: data cost (Number of elements 5); The types of data value elements included in the data application attributes are: data characteristics (number of elements 3).
  • the data retail data value dimension, data value element type and data quantity are as follows:
  • the following describes the segmentation method of commodity retail data elements by the data basic information element type in the attribute dimension of the data itself.
  • the segmentation methods of other data value element types are similar and not listed one by one.
  • the segmentation of elements can be combined with expert opinions, industry experience, difficulty in obtaining elements, and scarcity of elements.
  • ⁇ Data value element type basic data information
  • the data value perception module obtains the commodity retail data ID: K8Gy9FwiWkRmaOYSCG, and sets the interval to 5 minutes at the same time, then the data value perception module obtains the usage of the data through the commodity retail data ID: K8Gy9FwiWkRmaOYSCG every 5 minutes. The number of uses in 5 minutes is 10,000, and the duration is 200 seconds. After obtaining the data usage, through the commodity retail data ID: K8Gy9FwiWkRmaOYSCG, obtain the retail data elements and element segment values, and push the complete information of the data value elements and data usage to the data value evaluation module through the data interface.
  • the system and method for realizing data asset sensing and pricing functions in the context of this big data are adopted to realize flexible configuration and segmented management of data value elements, and at the same time to transmit element information through data interfaces, thereby achieving unlimited expansion of the underlying elements, These elements are often closely related to the data producer, management, and storage. Therefore, the present invention fully considers the data value related party information and has strong adaptability. At the same time, the present invention considers the difference between data and information and data value The characteristics of sparseness do not set the initial value of the data, but dynamically calculate the value of the data in conjunction with the data usage. Therefore, as the data is continuously used, the value of the data will be more and more accurate, thus solving the background of big data The following is the problem of difficult pricing of data assets and unreasonable and inaccurate pricing.

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Abstract

A system for achieving data asset sensing and pricing functions in a big data background, and a method for achieving, on the basis of the system, data asset sensing and pricing in a big data background. The system comprises: a data initialization module, a data value element and range configuration module, a data value sensing module, and a data value evaluation module. The method comprises: giving a unique identification in the whole data pricing system to data to be priced; abstracting a data value element according to different data attributes, segmenting different elements, and defining the attribute value range; sensing the use situation of data and data value element information in real time; and calculating, according to the use situation of data and the value element information, the evaluated data value in real time. According to the system and the method therefor, flexibly configuration and segmentation management of data value elements are achieved, the unlimited expansion of bottom elements is achieved, and the data value is calculated more accurately, thereby solving the problem of being difficult to price data assets, and being unreasonable and inaccurate to price in the big data background.

Description

大数据背景下实现数据资产感知及定价功能的***及其方法System and method for realizing data asset perception and pricing function under big data background
相关申请的交叉引用Cross-reference of related applications
本申请主张2018年12月13日提交的申请号为201811525208.X的中国发明专利申请的优先权，其内容通过引用的方式并入本申请中。This application claims the priority of the Chinese invention patent application with the application number 201811525208.X filed on December 13, 2018, and its content is incorporated into this application by reference.
技术领域Technical field
本发明涉及计算机软件领域，尤其涉及大数据背景下的数据定价领域，具体是指一种大数据背景下实现数据资产感知及定价功能的***及其方法。The invention relates to the field of computer software, in particular to the field of data pricing in the context of big data, and specifically refers to a system and method for realizing data asset sensing and pricing functions in the context of big data.
背景技术Background technique
2015年6月份国务院办公厅51号文发布了《关于运用大数据加强对市场主体服务和监管的若干意见》，大数据的价值再次成为关注的焦点。特别是近年来，随着大数据技术在世界各地特别是在中国的长足发展，大数据本身也逐渐成为一种商品，为了促进数据开放和利用，应当制定合理的数据价格政策，但是大数据本身具有数据类型多样、数据量巨大、数据交换频率高、数据价值不确定等特征，加上大数据价值的双向不确定性，使得大数据买卖双方对于大数据的价值都无法做出合理的评估，更重要的是，数据可以反复使用的特性，当其应用于不同领域时，产生的价值往往会超出其预期价值，因此虽然现阶段国内大数据交换已经初具规模，但是大数据的定价一直还是一个难点，无法形成统一的认识。In June 2015, the State Council General Office No. 51 issued "Several Opinions on Using Big Data to Strengthen Services and Supervision of Market Subjects", and the value of big data has once again become the focus of attention. Especially in recent years, with the rapid development of big data technology in all parts of the world, especially in China, big data itself has gradually become a commodity. In order to promote data opening and utilization, reasonable data price policies should be formulated, but big data itself With the characteristics of diverse data types, huge data volume, high frequency of data exchange, and uncertain data value, coupled with the two-way uncertainty of the value of big data, big data buyers and sellers cannot make a reasonable assessment of the value of big data. More importantly, the data can be used repeatedly. When it is used in different fields, the value generated will often exceed its expected value. Therefore, although the domestic big data exchange has begun to take shape at this stage, the pricing of big data has always been One difficulty is that a unified understanding cannot be formed.
由于大数据及其价值的稀缺性和不确定性，大数据定价一直是研究的热点和难点，目前公开的大数据资产价值定价方法是通过合理建立层次结构模型，并综合不同定价策略，充分考虑各因素对数据资产价值的影响，利用AHP分析法，求得各定价策略的权重，从而求得数据资产的评估值，解决了现有技术中不能合理定量对数据资产价值进行评估的问题，为数据资产合理定价并进入流通和交换环节，从而引导数据流动、进行数据交换提供了可靠的方法。Due to the scarcity and uncertainty of big data and its value, big data pricing has always been a hotspot and difficulty in research. The current public big data asset value pricing method is to establish a hierarchical structure model and integrate different pricing strategies to fully consider. The impact of various factors on the value of data assets, using the AHP analysis method to obtain the weight of each pricing strategy, thereby obtaining the evaluation value of data assets, solves the problem that the existing technology cannot reasonably quantitatively evaluate the value of data assets, Data assets are reasonably priced and enter the circulation and exchange links, thereby guiding the flow of data and providing a reliable method for data exchange.
具体包含了三个步骤：It contains three steps:
1、建立层次结构模型。1. Establish a hierarchical structure model.
层次模型自上而下包括三个层次，第一层为目标层，第二层为准则层，第三层为方案层；The hierarchical model includes three levels from top to bottom, the first level is the target level, the second level is the criterion level, and the third level is the plan level;
目标层为被评估数据资产的评估值；The target layer is the evaluation value of the evaluated data assets;
准则层为数据完整性、数据更新频率、数据结构化程度、数据交易频率、数据量、数据公开性、数据被搜索频率；The criteria layer is data integrity, data update frequency, data structure degree, data transaction frequency, data volume, data openness, and data search frequency;
方案层为以成本法定价所得的数据资产价格、以吸脂法定价所得的数据资产价格、以拍卖法定价所得的数据资产价格。The plan layer is the data asset price priced by the cost method, the data asset price priced by the liposuction method, and the data asset price priced by the auction method.
2、通过AHP层次分析法，求得方案层权重的权重。2. Through the AHP analytic method, the weights of the scheme layer weights are obtained.
3、求得数据资产的评估值。3. Obtain the evaluation value of the data asset.
现有技术通过建立数据层次结构模型，采用不同的定价策略，实现了一定范围内的大数据资产价值的评估。但是由于大数据定价的双向不确定性，采用任何一种或者几种定价方案都无法准确的接近大数据的真实价值，数据不同于信息，数据未被使用之前，无任何特定意义存在，因此数据的价值很难评估。数据的价值取决于不同主体的需求，大数据只有在需要的人面前才会产生价值，也就是说，大数据的价值产生于其被使用之后，因此现有的技术对于数据价值的评估是建立在数据使用之前，为买断式的数据定价，并不具有客观性。The existing technology has realized the evaluation of the value of big data assets within a certain range by establishing a data hierarchy model and using different pricing strategies. However, due to the two-way uncertainty of big data pricing, any one or several pricing schemes cannot accurately approximate the true value of big data. Data is different from information. Before data is used, there is no specific meaning, so data Value is difficult to assess. The value of data depends on the needs of different subjects. Big data will only generate value in front of those who need it. That is to say, the value of big data is generated after it is used. Therefore, the evaluation of data value by existing technologies is established Before the data is used, pricing the buy-out data is not objective.
另外现有技术在准则层中对大数据的评估遵循的原则进行了划分，但是相对于大数据大量、多样、高速的基本特征而言，这些评估的原则是十分有限的，所以具有较强的局限性。In addition, the existing technology divides the principles followed by the evaluation of big data in the criterion layer, but compared with the basic characteristics of large, diverse, and high speed of big data, these evaluation principles are very limited, so they have strong limitation.
发明内容Summary of the invention
本发明的目的是克服了上述现有技术的缺点，提供了一种满足大数据需求、具有客观性、具有普遍适应力的大数据背景下实现数据资产感知及定价功能的***及其方法。The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art, and to provide a system and method for realizing data asset sensing and pricing functions in the context of big data that meets the needs of big data, has objectivity, and has general adaptability.
为了实现上述目的，本发明的大数据背景下实现数据资产感知及定价功能的***及其方法如下：In order to achieve the above objectives, the system and method for realizing data asset sensing and pricing functions in the context of big data of the present invention are as follows:
该大数据背景下实现数据资产感知及定价功能的***，其主要特点是，所述的***包括：The main feature of the system that realizes data asset sensing and pricing functions in the context of big data is that the system includes:
数据初始化模块，用于对需要进行定价的数据进行初始化，并将整个数据定价体系中唯一的标识赋予待定价数据；Data initialization module, used to initialize the data that needs to be priced, and give the unique identification in the entire data pricing system to the data to be priced;
数据价值元素及范围配置模块，与所述的数据初始化模块相连接，用于根据不同的数据属性抽象出数据价值元素，通过对不同元素分段及属性值范围定义，从而得到不同元素的段值；The data value element and range configuration module, connected with the data initialization module, is used to abstract the data value element according to different data attributes, and obtain the segment values of different elements by segmenting different elements and defining the attribute value range ;
数据价值感知模块，与所述的数据价值元素及范围配置模块相连接，用于实时感知数据的使用情况及数据价值元素信息，并将其推送至数据价值评价模块；The data value perception module is connected to the data value element and range configuration module, and is used for real-time perception of data usage and data value element information and pushing it to the data value evaluation module;
数据价值评价模块，与所述的数据价值感知模块相连接，用于根据数据使用情况及价值元素信息实时计算评价数据价值。The data value evaluation module is connected to the data value perception module and is used to calculate and evaluate the value of the data in real time according to the data usage and value element information.
较佳地，所述的数据初始化模块中的唯一的标识为对数据价值元素配置及数据使用情况 进行感知的唯一ID。Preferably, the unique identifier in the data initialization module is a unique ID that senses the configuration of data value elements and data usage.
较佳地，所述的ID为18位数字和字母组合。Preferably, the ID is a combination of 18 digits and letters.
较佳地，所述的数据初始化模块还包括根据实际需求对数据的扩展信息进行初始化。Preferably, the data initialization module further includes initializing the extended information of the data according to actual needs.
较佳地，所述的数据的扩展信息包括数据内容、数据名称和数据唯一标识。Preferably, the extended information of the data includes data content, data name and data unique identification.
较佳地，所述的数据价值元素及范围配置模块通过数据接口实时感知数据的使用情况及数据价值元素信息。Preferably, the data value element and range configuration module senses data usage and data value element information in real time through the data interface.
较佳地，所述的数据价值元素及范围配置模块中的数据价值元素包括数据质量、数据稀缺性、数据密级、数据时效性、数据类型数据的产生难易程度和采集交换传输存储难以程度。Preferably, the data value elements and the data value elements in the range configuration module include data quality, data scarcity, data density level, data timeliness, difficulty in generating data type data, and difficulty in collection, exchange, transmission and storage.
较佳地，所述的数据价值元素及范围配置模块对数据的定义包括数据价值维度、数据价值元素类型和元素个数的定义。Preferably, the definition of data by the data value element and range configuration module includes the definition of data value dimension, data value element type and number of elements.
较佳地，所述的数据价值元素及范围配置模块的分段数不大于10。Preferably, the number of segments of the data value element and range configuration module is not greater than 10.
较佳地，所述的数据价值感知模块数据ID及设置的间隔时间感知数据的使用情况。Preferably, the data value sensing module data ID and the set interval time sense the usage of the data.
较佳地，所述的数据的使用情况包括使用次数和使用时长。Preferably, the usage of the data includes usage times and duration.
较佳地，所述的数据价值评价模块中的根据数据使用情况及价值元素信息计算数据的货币价值DataValue，具体为：Preferably, in the data value evaluation module, the monetary value of the data calculated according to the data usage and value element information is specifically:
根据以下公式计算数据的货币价值DataValue：Calculate the data value DataValue according to the following formula:
DataValue＝C×f(数据价值元素信息、数据使用情况)；DataValue=C×f (data value element information, data usage);
其中，C为货币价值转换系数，f()为综合计算函数，数据的价值为货币数量。Among them, C is the currency value conversion factor, f() is the comprehensive calculation function, and the value of the data is the amount of money.
较佳地，所述的数据价值评价模块中的计算数据的定价Price，具体为：Preferably, the pricing price of the calculated data in the data value evaluation module is specifically:
根据以下公式计算数据的定价Price：Calculate the pricing price of the data according to the following formula:
Price＝DataValue(1+r)；Price=DataValue(1+r);
其中，r为利润率。Among them, r is the profit rate.
该基于上述的***实现大数据背景下数据资产感知及定价处理的方法，其主要特点是，所述的方法包括以下步骤：The method for realizing data asset sensing and pricing processing in the context of big data based on the above system has the main feature that the method includes the following steps:
(1)所述的数据初始化模块对需要进行定价的数据进行初始化，并将整个数据定价体系中唯一的标识赋予待定价数据；(1) The data initialization module initializes the data that needs to be priced, and gives the unique identification in the entire data pricing system to the data to be priced;
(2)所述的数据价值元素及范围配置模块根据不同的数据属性抽象出数据价值元素，通过对不同元素分段及属性值范围定义，得到不同元素的段值；(2) The data value element and range configuration module abstracts data value elements based on different data attributes, and obtains segment values of different elements by segmenting different elements and defining attribute value ranges;
(3)所述的数据价值感知模块实时感知数据的使用情况及数据价值元素信息，并将其推 送至数据价值评价模块；(3) The data value perception module described in real time senses the data usage and data value element information, and pushes it to the data value evaluation module;
(4)所述的数据价值评价模块根据数据使用情况及价值元素信息实时计算评价数据价值。(4) The data value evaluation module calculates the value of the evaluation data in real time based on the data usage and value element information.
采用了本发明的大数据背景下实现数据资产感知及定价功能的***及其方法，实现了数据价值元素的灵活配置和分段管理，同时通过数据接口传递元素信息，从而实现了底层元素的无限扩展，而这些元素往往与数据的生产方、管理方、存储方密切相关，因此本发明充分考虑了数据价值相关方信息，具有很强的适应性，同时本发明考虑了数据与信息的区别及数据价值稀疏性的特点，并不对数据作初始价值设置，而是结合数据使用情况，动态计算数据价值，因此随着数据的不断被使用，数据的价值计算将越来越准确，从而解决了大数据背景下数据资产定价难及定价不合理不准确的问题。The system and method for realizing data asset sensing and pricing functions in the context of big data of the present invention are adopted, which realizes flexible configuration and segmented management of data value elements, and at the same time transfers element information through data interfaces, thereby realizing the unlimitedness of the underlying elements Expansion, and these elements are often closely related to the data producer, management, and storage. Therefore, the present invention fully considers the data value related party information, and has strong adaptability. At the same time, the present invention considers the difference between data and information and The sparseness of data value does not set the initial value of the data, but dynamically calculates the value of the data in combination with the data usage. Therefore, as the data is continuously used, the value calculation of the data will become more and more accurate, thus solving the large In the context of data, it is difficult to price data assets, and the pricing is unreasonable and inaccurate.
附图说明BRIEF DESCRIPTION
图1为本发明的大数据背景下实现数据资产感知及定价功能的***的构成图。FIG. 1 is a structural diagram of a system for realizing data asset sensing and pricing functions in the context of big data of the present invention.
图2为本发明的大数据背景下实现数据资产感知及定价功能的***的数据价值感知模块的结构图。2 is a structural diagram of a data value perception module of a system that implements data asset perception and pricing functions in the context of big data of the present invention.
具体实施方式detailed description
为了能够更清楚地描述本发明的技术内容，下面结合具体实施例来进行进一步的描述。In order to be able to describe the technical content of the present invention more clearly, further description will be given below in conjunction with specific embodiments.
本发明的该大数据背景下实现数据资产感知及定价功能的***，其中，所述的***包括The system for realizing data asset sensing and pricing functions in the context of the big data of the present invention, wherein the system includes
数据初始化模块，用于对需要进行定价的数据进行初始化，并将整个数据定价体系中唯一的标识赋予待定价数据；Data initialization module, used to initialize the data that needs to be priced, and give the unique identification in the entire data pricing system to the data to be priced;
数据价值元素及范围配置模块，与所述的数据初始化模块相连接，用于根据不同的数据属性抽象出数据价值元素，通过对不同元素分段及属性值范围定义，从而得到不同元素的段值；The data value element and range configuration module, connected with the data initialization module, is used to abstract the data value element according to different data attributes, and obtain the segment values of different elements by segmenting different elements and defining the attribute value range ;
数据价值感知模块，与所述的数据价值元素及范围配置模块相连接，用于实时感知数据的使用情况及数据价值元素信息，并将其推送至数据价值评价模块；The data value perception module is connected to the data value element and range configuration module, and is used for real-time perception of data usage and data value element information and pushing it to the data value evaluation module;
数据价值评价模块，与所述的数据价值感知模块相连接，用于根据数据使用情况及价值元素信息实时计算评价数据价值。The data value evaluation module is connected to the data value perception module and is used to calculate and evaluate the value of the data in real time according to the data usage and value element information.
作为本发明的优选实施方式，所述的数据初始化模块中的唯一的标识为对数据价值元素 配置及数据使用情况进行感知的唯一ID。As a preferred embodiment of the present invention, the unique identifier in the data initialization module is a unique ID that senses the configuration of data value elements and data usage.
作为本发明的优选实施方式，所述的ID为18位数字和字母组合。As a preferred embodiment of the present invention, the ID is a combination of 18 digits and letters.
作为本发明的优选实施方式，所述的数据初始化模块还包括根据实际需求对数据的扩展信息进行初始化。As a preferred embodiment of the present invention, the data initialization module further includes initializing the extended information of the data according to actual needs.
作为本发明的优选实施方式，所述的数据的扩展信息包括数据内容、数据名称和数据唯一标识。As a preferred embodiment of the present invention, the data extension information includes data content, data name, and data unique identification.
作为本发明的优选实施方式，所述的数据价值元素及范围配置模块通过数据接口实时感知数据的使用情况及数据价值元素信息。As a preferred embodiment of the present invention, the data value element and range configuration module perceives data usage and data value element information in real time through a data interface.
作为本发明的优选实施方式，所述的数据价值元素及范围配置模块中的数据价值元素包括数据质量、数据稀缺性、数据密级、数据时效性、数据类型数据的产生难易程度和采集交换传输存储难以程度。As a preferred embodiment of the present invention, the data value elements and data value elements in the range configuration module include data quality, data scarcity, data density, data timeliness, data type data generation difficulty, and collection, exchange and transmission Difficulty of storage.
作为本发明的优选实施方式，所述的数据价值元素及范围配置模块对数据的定义包括数据价值维度、数据价值元素类型和元素个数的定义。As a preferred embodiment of the present invention, the definition of data by the data value element and range configuration module includes the definition of data value dimension, data value element type and number of elements.
作为本发明的优选实施方式，所述的数据价值元素及范围配置模块的分段数不大于10。As a preferred embodiment of the present invention, the number of segments of the data value element and range configuration module is not greater than 10.
作为本发明的优选实施方式，所述的数据价值感知模块数据ID及设置的间隔时间感知数据的使用情况。As a preferred embodiment of the present invention, the data value sensing module data ID and the set interval time sense the usage of data.
作为本发明的优选实施方式，所述的数据的使用情况包括使用次数和使用时长。As a preferred embodiment of the present invention, the usage of the data includes the number of uses and the duration of use.
作为本发明的优选实施方式，所述的数据价值评价模块中的根据数据使用情况及价值元素信息计算数据的货币价值DataValue，具体为：As a preferred embodiment of the present invention, the data value calculation module in the data value evaluation module calculates the monetary value of the data based on the data usage and value element information, specifically:
根据以下公式计算数据的货币价值DataValue：Calculate the data value DataValue according to the following formula:
DataValue＝C×f(数据价值元素信息、数据使用情况)；DataValue=C×f (data value element information, data usage);
其中，C为货币价值转换系数，f()为综合计算函数，数据的价值为货币数量。Among them, C is the currency value conversion factor, f() is the comprehensive calculation function, and the value of the data is the amount of money.
作为本发明的优选实施方式，所述的数据价值评价模块中的计算数据的定价Price，具体为：As a preferred embodiment of the present invention, the pricing price of the calculated data in the data value evaluation module is specifically:
根据以下公式计算数据的定价Price：Calculate the pricing price of the data according to the following formula:
Price＝DataValue(1+r)；Price=DataValue(1+r);
其中，r为利润率。Among them, r is the profit rate.
本发明中的该基于上述的***实现大数据背景下数据资产感知及定价处理的方法，其中包括以下步骤：The method for realizing data asset sensing and pricing processing in the context of big data based on the above system in the present invention includes the following steps:
(1)所述的数据初始化模块对需要进行定价的数据进行初始化，并将整个数据定价体系中唯一的标识赋予待定价数据；(1) The data initialization module initializes the data that needs to be priced, and gives the unique identification in the entire data pricing system to the data to be priced;
(2)所述的数据价值元素及范围配置模块根据不同的数据属性抽象出数据价值元素，通过对不同元素分段及属性值范围定义，得到不同元素的段值；(2) The data value element and range configuration module abstracts data value elements based on different data attributes, and obtains segment values of different elements by segmenting different elements and defining attribute value ranges;
(3)所述的数据价值感知模块实时感知数据的使用情况及数据价值元素信息，并将其推送至数据价值评价模块；(3) The data value perception module described in real time senses data usage and data value element information and pushes it to the data value evaluation module;
(4)所述的数据价值评价模块根据数据使用情况及价值元素信息实时计算评价数据价值。(4) The data value evaluation module calculates the value of the evaluation data in real time based on the data usage and value element information.
本发明的具体实施方式中，通过对大数据价值元素及价值范围多维度的管理，实现了在不同场景下数据价值相关元素的配置和调整，提升了大数据定价方法的适用性，同时本发明通过数据价值感知实时跟踪数据使用情况，在数据被使用后进行数据价值评价，综合数据价值元素、数据属性及数据使用情况实时计算出数据价值，从而实现数据的动态定价，实现为一次使用形成的价值定价，随着数据的不断被使用，数据的价值将会越来越准确，解决了目前数据价值评估中无法接近数据实际价值的弊端，为大数据环境下数据价值的评估提供了全面、客观、具有普遍适应力的定价方法。In the specific implementation of the present invention, through the multi-dimensional management of big data value elements and value ranges, the configuration and adjustment of data value related elements in different scenarios are realized, and the applicability of the big data pricing method is improved. At the same time, the present invention Track data usage in real time through data value perception, evaluate data value after the data is used, and calculate data value in real time by integrating data value elements, data attributes, and data usage, thereby realizing dynamic pricing of data and realizing one-time use Value pricing. With the continuous use of data, the value of data will become more and more accurate. It solves the shortcomings that the current value of data cannot be close to the actual value of data, and provides a comprehensive and objective evaluation of data value in a big data environment. 3. Pricing method with universal adaptability.
本发明提供一种大数据背景下数据资产感知及定价的方法及***，包含数据初始化模块、数据价值元素及范围配置模块、数据价值感知模块及数据价值评价模块。The invention provides a method and system for data asset perception and pricing in the context of big data, including a data initialization module, a data value element and range configuration module, a data value perception module and a data value evaluation module.
数据初始化模块主要是对需要进行定价的数据进行初始化，使数据在整个价值评价体系中拥有唯一的标识，该模块不仅可以标识数据的唯一信息，还可以对数据的扩展信息进行定义；数据价值元素及范围配置模块实现对数据价值元素及价值范围的多维度的管理，通过对不同元素分段及属性值范围定义，从而得到不同元素的段值，最终实现底层元素的无限扩展，可用于各种类型的大数据定价；数据价值感知模块通过数据接口实时感知数据的使用情况及数据价值元素信息，在数据被使用后将该数据使用情况及价值元素信息推送至数据价值评价模块；数据价值评价模块在收到感知模块发送的数据使用情况及价值元素信息后实时评价出数据价值。The data initialization module is mainly to initialize the data that needs to be priced, so that the data has a unique identifier in the entire value evaluation system. This module can not only identify the unique information of the data, but also define the extended information of the data; data value elements And range configuration module to achieve multi-dimensional management of data value elements and value ranges. By segmenting different elements and defining the range of attribute values, the segment values of different elements can be obtained, and finally the unlimited expansion of the underlying elements can be achieved, which can be used for various Types of big data pricing; the data value perception module senses the data usage and data value element information in real time through the data interface, and pushes the data usage and value element information to the data value evaluation module after the data is used; the data value evaluation module After receiving the data usage and value element information sent by the perception module, the value of the data is evaluated in real time.
本发明构成如图1所示。The structure of the present invention is shown in FIG. 1.
以下将详细描述数据初始化模块、数据价值元素及范围配置模块、数据价值感知模块及数据价值评价模块的构成及原理。The structure and principles of the data initialization module, data value element and range configuration module, data value perception module, and data value evaluation module will be described in detail below.
一、数据初始化模块1. Data initialization module
数据初始化模块实现了需要定价的数据的初始化功能。数据初始化主要是给待定价数据赋予整个数据定价体系中唯一的标识，提供对数据价值元素配置及数据使用情况感知的唯一ID。数据初始化模块除了对数据的唯一标识进行初始化，还可以根据实际需求对数据的扩展信息进行初始化，如下表所示：The data initialization module implements the function of initializing data that needs to be priced. Data initialization is mainly to give the data to be priced a unique identifier in the entire data pricing system, and provide a unique ID that is aware of the configuration of data value elements and data usage. In addition to initializing the unique identification of the data, the data initialization module can also initialize the extended information of the data according to actual needs, as shown in the following table:
数据内容Data content 数据名称Data name 数据唯一标识Unique data identification
Data1Data1 Name1Name1 ID1ID1
Data2Data2 Name2Name2 ID2ID2
……... ……... ……...
ID为数据的唯一标识，定义为18位数字和字母组合，作为唯一区分不同数据使用。ID is a unique identification of data, defined as a combination of 18 digits and letters, used as a unique distinction between different data.
二、数据价值元素及范围配置模块2. Data value elements and scope configuration module
数据价值元素及范围配置模块主要实现了数据价值元素及范围的可配置服务，可以根据不同的数据属性抽象出数据价值元素，如数据质量、数据稀缺性、数据密级、数据时效性、数据类型数据的产生难易程度、采集交换传输存储难以程度等，并根据数据价值元素对其进行分段，最终获取其段值数据。The data value element and range configuration module mainly implements the configurable service of the data value element and range, and can abstract the data value element according to different data attributes, such as data quality, data scarcity, data density, data timeliness, data type data The difficulty of generation, the difficulty of collection, exchange, transmission and storage, etc., and segment it according to the data value element, and finally obtain its segment value data.
该模块提供固定的数据接口，底层数据价值元素可以根据数据情况实现无限扩展，该模块可在现有的数据接口上将新的元素推送到数据价值感知模块，从而实现了底层元素的无限扩展，提升了本发明的适应性。The module provides a fixed data interface, and the underlying data value elements can be infinitely expanded according to the data. The module can push new elements to the data value awareness module on the existing data interface, thereby achieving unlimited expansion of the underlying elements. The adaptability of the present invention is improved.
根据不同的数据可进行数据价值维度、数据价值元素类型及元素个数的定义，如下表：According to different data, the definition of data value dimension, data value element type and number of elements can be defined as follows:
Figure PCTCN2019086393-appb-000001
Figure PCTCN2019086393-appb-000001
数据接口定义：Data interface definition:
Figure PCTCN2019086393-appb-000002
Figure PCTCN2019086393-appb-000002
综合考虑不同维度，不同类型的数据价值元素信息，可结合专家意见、行业经验等对元 素进行分段，最多可分为十段，分段序号01-10表示元素数值从小到大排列顺序，如下表所示：Comprehensive consideration of different dimensions and different types of data value element information can be combined with expert opinions, industry experience, etc. to segment the elements, which can be divided into up to ten segments. The segment numbers 01-10 indicate the order of element values from small to large, as follows The table shows:
●数据价值维度：Dimension1●Data value dimension: Dimension1
●数据价值元素类型：Type1●Data value element type: Type1
元素名Element name 元素含义Element meaning 分段数Number of segments
Element1Element1  A Sub1Sub1
Element2Element2  A Sub2Sub2
……... ……... ……...
其中分段数小于等于10。The number of segments is less than or equal to 10.
根据不同的价值元素信息可以获取其分段明细信息，如下表：According to the different value element information, you can obtain the detailed information of its segmentation, as shown in the following table:
Figure PCTCN2019086393-appb-000003
Figure PCTCN2019086393-appb-000003
其中范围根据不同的元素信息而设置。The range is set according to different element information.
三、数据价值感知模块3. Data value perception module
数据价值感知模块实现对数据使用情况的感知和数据价值元素信息的获取。The data value perception module realizes the perception of data usage and the acquisition of data value element information.
首先数据价值感知模块通过数据ID及设置的间隔时间感知数据的使用情况(使用次数、使用时长)，在获取到数据使用情况后，通过数据ID获取该数据价值元素的完整信息(元素及其段值)，并通过数据接口将数据价值元素完整信息及数据使用情况推送至数据价值评价模块。如图2所示。First, the data value perception module perceives the usage of data (number of uses, duration of use) through the data ID and the set interval. After obtaining the data usage, the complete information of the data value element (element and its segment) is obtained through the data ID Value), and push the complete information of data value elements and data usage to the data value evaluation module through the data interface. as shown in picture 2.
四、数据价值评价模块4. Data value evaluation module
数据价值评价模块在接收到该数据使用情况及价值元素信息，计算出该数据的货币价值，计算公式为：After the data value evaluation module receives the data usage and value element information, it calculates the monetary value of the data. The calculation formula is:
DataValue＝C×f(数据价值元素信息、数据使用情况)DataValue=C×f (data value element information, data usage)
其中C为货币价值转换系数，f()为综合计算函数，数据的价值为货币数量。Where C is the currency value conversion factor, f() is the comprehensive calculation function, and the value of the data is the amount of money.
设置利润率为r，则该数据的定价：Set the profit rate as r, then the pricing of the data:
Price＝DataValue(1+r)。Price=DataValue(1+r).
由于数据价值感知模块是按照设定的时间定时感知数据使用情况及获取数据价值元素信息，因此，数据的价值将随着数据的使用和数据价值元素的变化而变化，从而综合考虑了数据的使用方、数据的生产方、数据的管理方等各方对数据价值因素的影响。Since the data value perception module senses data usage and obtains data value element information at a set time, the value of data will change with the use of data and changes in data value elements, thus comprehensively considering the use of data Parties, data producers, data managers and other parties have an impact on data value factors.
下面以商品零售数据为例说明本发明在数据资产定价中的应用，商品零售数据包含如下数据内容：The following uses commodity retail data as an example to illustrate the application of the present invention in data asset pricing. The commodity retail data includes the following data content:
店铺数据：店铺名称、企业名称、所在地址、商品相符评价、发货速度评价、服务态度评价、卖家名称、开店时间、开店时长、经营范围、月销售额、主营大类、省份、城市；Store data: store name, company name, address, product conformity evaluation, shipment speed evaluation, service attitude evaluation, seller name, store opening time, store opening time, business scope, monthly sales, main categories, provinces, cities;
商品数据：商品名称、品牌、收藏数、库存、月销量、商品国别、省份、城市、原价、折扣价、移动端价格、成交价格、商品总评数、一级类目、二级类目、三级类目；Commodity data: commodity name, brand, number of collections, inventory, monthly sales, commodity country, province, city, original price, discount price, mobile terminal price, transaction price, total product evaluation, first category, second category, Category III;
商品属性数据：商品名称、原始价格、交易价格、价格区间、品牌、一级类目、二级类目、三级类目、生产地、上市年份季节、销售渠道、尺码、颜色分类、图案、风格、适用对象、场合、季节、款式、流行元素、制作工艺、闭合方式、功能；Commodity attribute data: commodity name, original price, transaction price, price range, brand, first-level category, second-level category, third-level category, place of production, season of listing year, sales channel, size, color classification, pattern, Style, applicable objects, occasions, seasons, styles, popular elements, production techniques, closing methods, functions;
共8000万条商品信息数据。A total of 80 million pieces of commodity information data.
整个实施过程分为四个步骤：The entire implementation process is divided into four steps:
一、通过数据初始化模块对商品零售数据进行初始化，使其获取在整个数据定价***中唯一的18位标识。如下表所示：1. Initialize the retail data of the commodity through the data initialization module, so that it can obtain the unique 18-bit identifier in the entire data pricing system. As shown in the following table:
Figure PCTCN2019086393-appb-000004
Figure PCTCN2019086393-appb-000004
二、通过数据价值元素及范围配置模块，根据商品零售数据内容，对该数据进行分析，将商品零售数据价值维度分为：数据自身属性、数据成本属性、数据应用属性。Second, through the data value element and scope configuration module, the data is analyzed according to the content of commodity retail data, and the value dimension of commodity retail data is divided into: data itself attributes, data cost attributes, and data application attributes.
其中数据自身属性包含的数据价值元素类型为：数据质量(元素数量6)、数据基本信息(元素数量2)和数据类型(元素数量3)；数据成本属性包含的数据价值元素类型为：数据成本(元素数量5)；数据应用属性包含的数据价值元素类型为：数据特质(元素数量3)。The types of data value elements included in the data itself attributes are: data quality (number of elements 6), basic data information (number of elements 2) and data types (number of elements 3); the types of data value elements included in the data cost attribute are: data cost (Number of elements 5); The types of data value elements included in the data application attributes are: data characteristics (number of elements 3).
商品零售数据价值维度、数据价值元素类型及数据数量如下表：The data retail data value dimension, data value element type and data quantity are as follows:
Figure PCTCN2019086393-appb-000005
Figure PCTCN2019086393-appb-000005
下面通过数据自身属性维度中的数据基本信息元素类型来说明商品零售数据元素分段方法，其他数据价值元素类型分段方法类似，不一一列举。元素的分段可结合专家意见、行业经验、元素获取难易程度、元素稀缺性等进行。The following describes the segmentation method of commodity retail data elements by the data basic information element type in the attribute dimension of the data itself. The segmentation methods of other data value element types are similar and not listed one by one. The segmentation of elements can be combined with expert opinions, industry experience, difficulty in obtaining elements, and scarcity of elements.
●数据价值维度：数据自身属性●Data value dimension: data itself attributes
●数据价值元素类型：数据基本信息●Data value element type: basic data information
●元素数量2●Number of elements 2
Figure PCTCN2019086393-appb-000006
Figure PCTCN2019086393-appb-000006
最后根据数据主体和数据行业定义其分段明细信息，如下表：Finally, according to the data subject and data industry, define its segmentation details, as shown in the following table:
Figure PCTCN2019086393-appb-000007
Figure PCTCN2019086393-appb-000007
Figure PCTCN2019086393-appb-000008
Figure PCTCN2019086393-appb-000008
三、数据价值感知模块获取商品零售数据ID：K8Gy9FwiWkRmaOYSCG，同时设置间隔时间为5分钟，则数据价值感知模块每隔5分钟通过商品零售数据ID：K8Gy9FwiWkRmaOYSCG，获取该数据的使用情况，该数据在此5分钟内的使用次数为10000，使用时长为200秒。在获取到数据使用情况后，通过商品零售数据ID：K8Gy9FwiWkRmaOYSCG，获取商品零售数据元素及元素段值，并通过数据接口将数据价值元素完整信息及数据使用情况推送至数据价值评价模块。3. The data value perception module obtains the commodity retail data ID: K8Gy9FwiWkRmaOYSCG, and sets the interval to 5 minutes at the same time, then the data value perception module obtains the usage of the data through the commodity retail data ID: K8Gy9FwiWkRmaOYSCG every 5 minutes. The number of uses in 5 minutes is 10,000, and the duration is 200 seconds. After obtaining the data usage, through the commodity retail data ID: K8Gy9FwiWkRmaOYSCG, obtain the retail data elements and element segment values, and push the complete information of the data value elements and data usage to the data value evaluation module through the data interface.
四、数据价值评价模块接收到商品零售数据的使用次数为10000，使用时长为200秒，同时接收到了商品零售数据的元素信息及段值信息，因此通过公式DataValue＝C×f(数据价值元素信息、数据使用情况)可计算出商品零售数据的价值为DataValue。4. The data value evaluation module has received 10,000 times of retail data and 200 seconds of use time, and has received element information and segment value information of the retail data of the commodity, so through the formula DataValue=C×f (data value element information , Data usage) can calculate the value of commodity retail data as DataValue.
设定商品零售数据利润率为40％，则通过公式Price＝DataValue(1+r)，可以计算得出商品零售数据的定价为1.4×DataValue。If the profit rate of the retail data of commodities is set to 40%, the price of the retail data of commodities can be calculated as 1.4×DataValue by the formula Price=DataValue(1+r).
采用了该大数据背景下实现数据资产感知及定价功能的***及其方法，实现了数据价值元素的灵活配置和分段管理，同时通过数据接口传递元素信息，从而实现了底层元素的无限扩展，而这些元素往往与数据的生产方、管理方、存储方密切相关，因此本发明充分考虑了数据价值相关方信息，具有很强的适应性，同时本发明考虑了数据与信息的区别及数据价值稀疏性的特点，并不对数据作初始价值设置，而是结合数据使用情况，动态计算数据价值，因此随着数据的不断被使用，数据的价值计算将越来越准确，从而解决了大数据背景下数据资产定价难及定价不合理不准确的问题。The system and method for realizing data asset sensing and pricing functions in the context of this big data are adopted to realize flexible configuration and segmented management of data value elements, and at the same time to transmit element information through data interfaces, thereby achieving unlimited expansion of the underlying elements, These elements are often closely related to the data producer, management, and storage. Therefore, the present invention fully considers the data value related party information and has strong adaptability. At the same time, the present invention considers the difference between data and information and data value The characteristics of sparseness do not set the initial value of the data, but dynamically calculate the value of the data in conjunction with the data usage. Therefore, as the data is continuously used, the value of the data will be more and more accurate, thus solving the background of big data The following is the problem of difficult pricing of data assets and unreasonable and inaccurate pricing.
在此说明书中，本发明已参照其特定的实施例作了描述。但是，很显然仍可以作出各种修改和变换而不背离本发明的精神和范围。因此，说明书和附图应被认为是说明性的而非限制性的。In this specification, the invention has been described with reference to specific embodiments thereof. However, it is clear that various modifications and changes can still be made without departing from the spirit and scope of the present invention. Therefore, the description and drawings should be regarded as illustrative rather than restrictive.

Claims (14)

  1. 一种大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的***包括：A system for realizing data asset sensing and pricing functions in the context of big data, characterized in that the system includes:
    数据初始化模块，用于对需要进行定价的数据进行初始化，并将整个数据定价体系中唯一的标识赋予待定价数据；Data initialization module, used to initialize the data that needs to be priced, and give the unique identification in the entire data pricing system to the data to be priced;
    数据价值元素及范围配置模块，与所述的数据初始化模块相连接，用于根据不同的数据属性抽象出数据价值元素，通过对不同元素分段及属性值范围定义，从而得到不同元素的段值；The data value element and range configuration module, connected with the data initialization module, is used to abstract the data value element according to different data attributes, and obtain the segment values of different elements by segmenting different elements and defining the attribute value range ;
    数据价值感知模块，与所述的数据价值元素及范围配置模块相连接，用于实时感知数据的使用情况及数据价值元素信息，并将其推送至数据价值评价模块；The data value perception module is connected to the data value element and range configuration module, and is used for real-time perception of data usage and data value element information and pushing it to the data value evaluation module;
    数据价值评价模块，与所述的数据价值感知模块相连接，用于根据数据使用情况及价值元素信息实时计算评价数据价值。The data value evaluation module is connected to the data value perception module and is used to calculate and evaluate the value of the data in real time according to the data usage and value element information.
  2. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据初始化模块中的唯一的标识为对数据价值元素配置及数据使用情况进行感知的唯一ID。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, characterized in that the unique identifier in the data initialization module is the only one that senses the configuration of data value elements and data usage ID.
  3. 根据权利要求2所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的ID为18位数字和字母组合。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 2, wherein the ID is a combination of 18 digits and letters.
  4. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据初始化模块还包括根据实际需求对数据的扩展信息进行初始化。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, wherein the data initialization module further includes initializing the extended information of the data according to actual needs.
  5. 根据权利要求4所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据的扩展信息包括数据内容、数据名称和数据唯一标识。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 4, wherein the extended information of the data includes data content, data name and data unique identification.
  6. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据价值元素及范围配置模块通过数据接口实时感知数据的使用情况及数据价值元素信息。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, wherein the data value element and range configuration module senses data usage and data value element information in real time through a data interface.
  7. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据价值元素及范围配置模块中的数据价值元素包括数据质量、数据稀缺性、数据密级、数据时效性、数据类型数据的产生难易程度和采集交换传输存储难以程度。The system for realizing data asset perception and pricing functions in the context of big data according to claim 1, wherein the data value elements and data value elements in the range configuration module include data quality, data scarcity, and data density , Data timeliness, the difficulty of data type data generation and the difficulty of collection, exchange, transmission and storage.
  8. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据价值元素及范围配置模块对数据的定义包括数据价值维度、数据价值元素类 型和元素个数的定义。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, characterized in that the data value elements and the scope configuration module define data including data value dimensions, data value element types and elements The definition of the number.
  9. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据价值元素及范围配置模块的分段数不大于10。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, wherein the number of segments of the data value element and range configuration module is not greater than 10.
  10. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据价值感知模块数据ID及设置的间隔时间感知数据的使用情况。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, characterized in that the data value sensing module data ID and the set interval time sense data usage.
  11. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据的使用情况包括使用次数和使用时长。The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, characterized in that the usage of the data includes the number of uses and the duration of use.
  12. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据价值评价模块中的根据数据使用情况及价值元素信息计算数据的货币价值DataValue，具体为：The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, wherein the data value evaluation module in the data value evaluation module calculates the monetary value of data based on data usage and value element information, specifically for:
    根据以下公式计算数据的货币价值DataValue：Calculate the data value DataValue according to the following formula:
    DataValue＝C×f(数据价值元素信息、数据使用情况)；DataValue=C×f (data value element information, data usage);
    其中，C为货币价值转换系数，f()为综合计算函数，数据的价值为货币数量。Among them, C is the currency value conversion factor, f() is the comprehensive calculation function, and the value of the data is the amount of money.
  13. 根据权利要求1所述的大数据背景下实现数据资产感知及定价功能的***，其特征在于，所述的数据价值评价模块中的计算数据的定价Price，具体为：The system for realizing data asset sensing and pricing functions in the context of big data according to claim 1, wherein the pricing price of the calculated data in the data value evaluation module is specifically:
    根据以下公式计算数据的定价Price：Calculate the pricing price of the data according to the following formula:
    Price＝DataValue(1+r)；Price=DataValue(1+r);
    其中，r为利润率。Among them, r is the profit rate.
  14. 一种基于权利要求1至13中任一项所述的***实现大数据背景下数据资产感知及定价处理的方法，其特征在于，所述的方法包括以下步骤：A method for realizing data asset sensing and pricing processing in the context of big data based on the system according to any one of claims 1 to 13, wherein the method includes the following steps:
    (1)所述的数据初始化模块对需要进行定价的数据进行初始化，并将整个数据定价体系中唯一的标识赋予待定价数据；(1) The data initialization module initializes the data that needs to be priced, and gives the unique identification in the entire data pricing system to the data to be priced;
    (2)所述的数据价值元素及范围配置模块根据不同的数据属性抽象出数据价值元素，通过对不同元素分段及属性值范围定义，得到不同元素的段值；(2) The data value element and range configuration module abstracts data value elements based on different data attributes, and obtains segment values of different elements by segmenting different elements and defining attribute value ranges;
    (3)所述的数据价值感知模块实时感知数据的使用情况及数据价值元素信息，并将其推送至数据价值评价模块；(3) The data value perception module described in real time senses data usage and data value element information and pushes it to the data value evaluation module;
    (4)所述的数据价值评价模块根据数据使用情况及价值元素信息实时计算评价数据价值。(4) The data value evaluation module calculates the value of the evaluation data in real time based on the data usage and value element information.
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CN107633181A (en) * 2017-09-12 2018-01-26 复旦大学 The data model and its operation system of data-oriented opening and shares
CN109615431A (en) * 2018-12-13 2019-04-12 普元信息技术股份有限公司 The system and method for data assets perception and pricing function are realized under big data background

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