Data element pricing method based on pricing magic cube

Abstract

The invention provides a data element pricing method based on a pricing Rubik's cube. The method specifically comprises the following steps: S1, establishing a data element quality evaluation model; s2, dividing a model based on a data element pricing scene; s3, establishing a data element pricing grade calculation model; and S4, establishing a data element quotation strategy model. According to the method, the problem of non-standardized pricing caused by data element difference is effectively solved, and a standardized pricing model is constructed. Meanwhile, a cost interest bearing method is introduced, and a reasonable solution is provided for income distribution of data element supply and demand parties. In addition, specific pricing dimensions and indexes are provided, and the basis of data element pricing is improved. All links of the pricing method are closely buckled, the data elements can be automatically evaluated, the popularization of the data elements is facilitated, the specific pricing mode of the data elements is defined, and the development of data circulation transaction is further promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of data element pricing, and specifically to a data element pricing method based on a pricing magic cube. The method specifically includes four parts: one is a data element quality assessment model; two is a data element pricing scenario classification model; three is a data element pricing level calculation model; and four is a data element quotation strategy model. Background Art

[0002] At present, my country is setting off a wave of marketization of data elements. As a platform for data trading and circulation, the data element market is of great significance for realizing efficient and rational allocation of data elements and giving full play to the production efficiency of data elements. However, under the current technological background, the data element market faces many challenges in terms of pricing mechanism, which seriously affect the efficient operation of the market and the rational allocation of data resources.

[0003] First, there is a general lack of data element quality assessment process before data elements flow into the market, and there is also a lack of clear data element quality evaluation standards. As a result, a large amount of low- and medium-value data floods the market, which not only reduces the overall transaction level of the data element market, but may also mislead data users. Secondly, the existing pricing strategies often adopt non-standard pricing strategies on a case-by-case basis, which seriously hinders the construction of a national integrated data element market. Furthermore, the existing pricing methods lack specific assessment indicators and standards, and it is still difficult to form a specific quotation plan. In addition, traditional pricing methods often fail to take into account the interests of both supply and demand sides, resulting in unfair distribution of benefits, which is not conducive to the long-term stability of the market.

[0004] In order to address the above problems, the present invention proposes a data element pricing method based on the pricing Rubik's Cube. The present invention constructs a data element quality assessment model to effectively identify and filter medium and low-value data to improve the quality of market data; at the same time, with the help of the data element pricing scenario classification model, differentiated but standardized pricing strategies are formulated according to different demand entities, target uses and application industries to meet the diverse needs of the market. In addition, the present invention also proposes a comprehensive price evaluation system and pricing level calculation model to make the pricing process more transparent and easy to understand, while taking into account the long-term interests of both supply and demand parties, and establishing a fair and sustainable benefit distribution mechanism. Finally, through the data element quotation strategy model, the present invention provides two pricing methods: by item and by data set, thereby improving the flexibility and adaptability of transactions. Summary of the invention

[0005] In order to solve the problems existing in the prior art background, the present invention proposes a data element pricing method based on the pricing Rubik's Cube. On the one hand, the present invention can effectively identify high, medium and low value data sets, thereby ensuring that medium and low value data sets will not flow into the data element market, avoiding adverse effects on the overall data element transaction level. On the other hand, the present invention can also formulate differentiated and standardized pricing strategies for different industries, entities and their purposes of use to meet diverse market demands. In addition, the present invention breaks through the imbalance in the distribution of benefits between the supply and demand sides of the traditional pricing method, and aims to take into account the long-term interests of both the supply and demand sides and achieve a more fair and sustainable benefit distribution mechanism.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A data element pricing method based on a pricing cube comprises the following steps:

[0008] Step S1: Data element quality assessment model:

[0009] This step is mainly aimed at classifying the quality levels of data elements, and by effectively filtering out medium and low-value data elements, the demand for improving the quality of data circulating in the data element market can be met. To this end, the present invention provides a modeling method for a data element quality assessment algorithm, which describes data elements in a standardized manner from multiple dimensions, including common elements such as value assignment factors, time factors, content factors, and authority factors.

[0010] (1) Assignment factors: This dimension mainly examines the amount of information contained in the data elements, including the observed value and effective dimension of the data contained in the data elements.

[0011] (2) Time factor: This dimension mainly examines the timeliness of data elements, including the update frequency of data elements.

[0012] (3) Content factors: This dimension mainly examines the availability of data elements, including the integrity of the data contained in the data elements.

[0013] (4) Authoritative factors: This dimension mainly examines the market value potential and sensitivity of data elements, including the scoring results of experts in related fields.

[0014] Step S2: Data element pricing scenario classification model:

[0015] The main purpose of this step is to distinguish the differentiated scenarios of data element pricing to meet the needs of establishing standardized and differentiated pricing processes. To this end, the present invention proposes a scenario segmentation strategy based on the pricing Rubik's Cube, where the Rubik's Cube covers three key dimensions: demand subject, target use, and application industry. Different pricing scenarios are formed at the intersection of these three dimensions.

[0016] (1) Demand subject: This dimension mainly divides the nature of the demand subject, including enterprises and individuals, scientific research institutions, public organizations, etc.

[0017] (2) Target use: This dimension mainly distinguishes the purpose of each demand subject using data elements, including public welfare, self-empowerment, industry application, etc.

[0018] (3) Application industry: This dimension mainly distinguishes the target application scenarios of each data element, including twelve fields such as industrial manufacturing, modern agriculture, commercial circulation, transportation, financial services, scientific and technological innovation, cultural tourism, medical health, emergency management, meteorological services, urban governance, and green and low-carbon.

[0019] Step S3: Data element pricing level calculation model

[0020] The main purpose of this step is to determine the final price level of the data element, which provides an important basis for the formation of the transaction price. To this end, the present invention proposes a price evaluation system and constructs a pricing level calculation model based on this system.

[0021] (1) Price evaluation system: The present invention proposes a data element price evaluation framework, including 7 types of first-level indicators such as granularity, compliance, multi-dimensionality, activity, scale, correlation, and risk, and contains 26 second-level indicators. Among them, the granularity indicator mainly examines the richness of the information contained in the data element; compliance mainly examines whether the data form contained in the data element meets the specification requirements; multi-dimensionality mainly examines the breadth of the source of the data element; activity mainly examines the timeliness of the data contained in the data element; scale mainly examines the development and use scale of the data element; correlation mainly examines the degree of correlation between the data element and other data elements during the application process; risk mainly examines the risk of leakage of the data element during use and the severity of the corresponding consequences. According to the measurement score of the price evaluation system, the present invention divides the data elements into five different price levels in equal proportion, and divides these levels into specific pricing standards in the price range corresponding to the element quality.

[0022] (2) Cost-interest method: This invention proposes that in a specific scenario where data sets are priced, the demander needs to pay the supplier a proportion of the data element cost according to its corresponding pricing level. In addition, in future operations, the data demander should pay the data supplier a percentage of the profit obtained from the use of data elements according to the pricing level requirements.

[0023] Step S4: Data element quotation strategy model

[0024] The main purpose of this step is to establish a variety of data element quotation methods according to the usage of the demand side to improve the flexibility and adaptability of data transactions. To this end, the present invention proposes a quotation model, which specifically divides the algorithm into two pricing methods: quotation by item and quotation by data set.

[0025] (1) Pricing by item: When the demander has precise requirements for specific data items, the pricing method of data elements is calculated based on each data record, and the transaction price is determined based on the number of items required by the demander.

[0026] (2) Pricing by data set: When the demand side does not have a clear and precise demand for a specific data item, the pricing strategy for data elements adopts a combination of "base price" and "option price". In this pricing strategy, the base price and option price are set differently according to the pricing level. At the same time, the comprehensive pricing is finally determined based on the ratio of the amount of data required by the demand side to the total amount of data.

[0027] Compared with the existing methods, the present invention has the following advantages:

[0028] 1. The present invention evaluates the quality of data elements. This method ensures that medium- and low-value data will not flow into the market, thereby improving the quality of the overall market data and promoting the circulation of higher-value data.

[0029] 2. The present invention can formulate differentiated pricing strategies according to different demand entities, target uses and application industries. This flexibility can meet the diverse needs of the market and establish standardized pricing standards and processes, which is the key prerequisite for realizing a national integrated data element market.

[0030] 3. The present invention proposes a comprehensive price evaluation system covering multiple primary and secondary indicators. This structure helps users combine price evaluation with data characteristics, making the pricing process more transparent and easy to understand.

[0031] 4. The present invention constructs a reasonable profit distribution mechanism, takes into account the long-term interests of both supply and demand parties, avoids the imbalance in profit distribution in traditional pricing methods, and achieves a more fair and sustainable trading environment.

[0032] 5. The present invention allows two pricing methods: by item and by data set, to meet the specific needs of different demanders. The demander can choose the pricing model according to the accuracy of its own needs, which improves the flexibility of the transaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Flowchart for pricing data elements;

[0034] Figure 2 Pricing the Rubik's Cube for data elements. DETAILED DESCRIPTION

[0035] The technical solution of the present invention is further described below in conjunction with the accompanying drawings, but is not limited thereto. Any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention should be included in the protection scope of the present invention.

[0036] The present invention provides a data element pricing method based on the pricing magic cube, such as Figure 1 As shown, the method includes the following steps.

[0037] Step S1: Data element quality assessment model:

[0038] First, the basic dimensions and assessment methods for data element quality assessment include:

[0039] (1) Value assignment factors: This dimension mainly includes data observation and data dimension. The measurement method of data observation is as follows:

[0040]

[0041] Where x is the number of rows in the data table, y is the number of columns in the data table, αi is the data observation of the ith data set, and min(α) and max(α) are defined as the minimum and maximum values ​​in all data sets. When the calculated result A1 is in the interval [0, 0.2), it is scored as 1; in the interval [0.2, 0.4), it is scored as 2; in the interval [0.4-0.6), it is scored as 3; in the interval [0.6-0.8), it is scored as 4; in the interval [0.8-1], it is scored as 5.

[0042] The measurement method of data dimension is as follows:

[0043]

[0044] Where βi is the number of valid columns in the ith data set, and min(β) and max(β) are defined as the minimum and maximum values ​​in all data sets. Valid columns are columns with clear meanings in the data table. When the calculated result A2 is in the interval [0, 0.2), it is scored as 1; in the interval [0.2, 0.4), it is scored as 2; in the interval [0.4-0.6), it is scored as 3; in the interval [0.6-0.8), it is scored as 4; in the interval [0.8-1], it is scored as 5.

[0045] (2) Time factor: This dimension mainly includes the frequency of data update. The calculation result A3 is assigned by the update and maintenance frequency of the data set. An update frequency of 3 years or more is scored as 1 point; an update frequency of 1-3 years is scored as 2 points; an update frequency of annual update is scored as 3 points; an update frequency of quarterly update is scored as 4 points; and an update frequency of monthly update is scored as 5 points.

[0046] (3) Content factor: This dimension is measured using the indicator of data integrity. The calculation method of data integrity is as follows:

[0047] r a =(r1+r2+...r k )

[0048]

[0049] Where λi is the ratio of the number of null values ​​in the ith column to the total number of rows, and min(λ) and max(λ) are defined as the minimum and maximum values ​​in all data sets. When the calculated result A4 is in the interval [0, 0.2), it is scored as 1; in the interval [0.2, 0.4), it is scored as 2; in the interval [0.4-0.6), it is scored as 3; in the interval [0.6-0.8), it is scored as 4; in the interval [0.8-1], it is scored as 5.

[0050] (4) Authority factor: This dimension is subjectively scored by experts based on the sensitivity and market potential of the data set. The same data set should be scored by 5 or more experts, and the average score will be taken after removing the highest and lowest scores. The A5 scoring standard and corresponding scores are as follows: very unimportant is scored as 1 point; unimportant is scored as 2 points; average is scored as 3 points; important is scored as 4 points; very important is scored as 5 points.

[0051] (5) Result calculation: Based on the calculation results of A1-A5 above, the final score of the data set is as follows.

[0052] A=(A1+A2)×0.4+A3×0.2+A4×0.2+A5×0.2

[0053] When the calculation result S is within the range of (3-5], it is a high-quality data set and enters the data pricing scenario. When the calculation result S is within the range of [1-3], it is a medium or low-quality data set. Such data sets will be sent back to the data supplier for data processing, otherwise they will be made available free of charge.

[0054] Step S2: Data element pricing scenario classification model:

[0055] The high-value data sets generated after the first step of screening will further flow into the data element pricing scenario classification model. Data elements will be divided into corresponding pricing scenarios based on the subject type, purpose of use and specific application scenarios of the data demander, thereby establishing a pricing range. Specifically, if the demander's usage requirements are based on a single piece of data, the pricing range will be reflected in the form of a single price range; if the demander's usage requirements are based on a data set, the pricing range will be based on the current cost payment ratio range of the data set and the future interest calculation range. In addition, the relevant item price range, cost payment ratio range and future interest calculation range need to be confirmed in combination with the specific systems of various places.

[0056] Step S3: Data element pricing level calculation model

[0057] The basic dimensions and evaluation methods for calculating the pricing level of data elements include:

[0058] (1) Granularity: This dimension mainly includes the number, type, and precision of data elements. The number of data elements mainly examines the number of data attributes of data elements. The measurement method is as follows:

[0059]

[0060] Where ai is the number of valid columns in each data set. Assuming there are n data sets in total, the set of valid column numbers of each data set is recorded as a = {a1, a2, ..., an}, and min(a) and max(a) are defined as the minimum and maximum values ​​in the set C. When the calculation result is in the interval [0, 0.2), it is scored as 1; in the interval [0.2, 0.4), it is scored as 2; in the interval [0.4-0.6), it is scored as 3; in the interval [0.6-0.8), it is scored as 4; in the interval [0.8-1], it is scored as 5.

[0061] The type of data element mainly examines the data type contained in the data element. If the data element contains only one data type, the score will be recorded as 1 point; if the data element contains multiple data types, the score of this dimension will be recorded as 5 points.

[0062] The accuracy of data elements mainly examines the relative accuracy of the values ​​in the data elements. If the data elements contain values ​​and other data types, the value type is mainly examined. The evaluation criteria for numerical data are: when the data accuracy is ten digits or more, it is recorded as 1 point; when the accuracy is single digits or more, it is recorded as 2 points; when the accuracy is one decimal place, it is recorded as 3 points; when the accuracy is two decimal places, it is recorded as 4 points; when the accuracy is three decimal places or more, it is recorded as 5 points. If the data element does not contain values ​​but contains text and other data types, the value type is mainly examined, and the text type is mainly examined. The evaluation criteria for text data are as follows:

[0063]

[0064] Where bi is the number of text data with characters (excluding punctuation and special characters) greater than 200 in each data set. Assuming there are n data sets in total, the set of text data that meets the standard for each data set is recorded as b = {b1, b2, ..., bn}, and min(b) and max(b) are defined as the minimum and maximum values ​​in the set b. When the calculation result is in the interval [0, 0.2), it is scored as 1 point; in the interval [0.2, 0.4), it is scored as 2 points; in the interval [0.4-0.6), it is scored as 3 points; in the interval [0.6-0.8), it is scored as 4 points; in the interval [0.8-1], it is scored as 5 points. If the data element does not contain numerical and text type data, the dimension score is recorded as 3 points.

[0065] (2) Compliance: This dimension mainly includes the format specifications, coding methods, data standards and naming rules of data elements. The format specifications of data elements mainly examine the proportion of data element data entry specifications that meet the data element format specification requirements in the local data element management regulations. If the compliance rate is less than 50%, it will be scored as 1 point; if the compliance rate is [50%, 70%), it will be scored as 1 point; if the compliance rate is [70%, 90%), it will be scored as 3 points; if the compliance rate is [90%, 100%), it will be scored as 4 points; if it is fully compliant, it will be scored as 5 points.

[0066] The coding method of data elements mainly examines the proportion of data element coding specifications that meet the data element coding method requirements in the local data element management regulations. If the compliance rate is below 50%, it is scored as 1 point; if the compliance rate is [50%, 70%), it is scored as 1 point; if the compliance rate is [70%, 90%), it is scored as 3 points; if the compliance rate is [90%, 100%), it is scored as 4 points; if it is fully compliant, it is scored as 5 points.

[0067] The standards for data elements mainly examine the proportion of data element value specifications that meet the data element value requirements in the local data element management regulations. If the compliance rate is below 50%, it is scored as 1 point; if the compliance rate is [50%, 70%), it is scored as 1 point; if the compliance rate is [70%, 90%), it is scored as 3 points; if the compliance rate is [90%, 100%), it is scored as 4 points; if it is fully compliant, it is scored as 5 points.

[0068] The naming rules for data elements mainly examine the proportion of row and column naming of data elements that meet the data element naming requirements in the local data element management regulations. If the compliance rate is below 50%, it is scored as 1 point; if the compliance rate is [50%, 70%), it is scored as 1 point; if the compliance rate is [70%, 90%), it is scored as 3 points; if the compliance rate is [90%, 100%), it is scored as 4 points; if it is fully compliant, it is scored as 5 points.

[0069] (3) Multi-dimensionality: This dimension mainly examines the number of sources, source methods, coverage, and repetition rate of data elements. The number of sources of data elements reflects the number of source channels of data within the data element. If the data is composed of a single source, it is scored as 1 point; if the data is composed of two sources, it is scored as 2 points; if the data is composed of three sources, it is scored as 3 points; if the data is composed of four sources, it is scored as 4 points; if the data is composed of five or more sources, it is scored as 5 points.

[0070] The source of data elements mainly examines the acquisition and production methods of data elements. If the data in the data element is completely manually counted, it will be scored 1 point; if the data in the data element is completely captured by computer, it will be scored 3 points; if the data in the data element is completely captured by computer, it will be scored 3 points.

[0071] The coverage of the data element mainly examines whether the data in the data element comes from multiple business departments. If the data in the data element comes from a single business department, it is scored 1 point; if the data set comes from multiple business departments, it is scored 5 points.

[0072] The repetition rate of data elements mainly tests the repetition of data attributes contained in data elements and other data elements. The specific evaluation criteria are as follows:

[0073]

[0074] Where ci is the percentage of repeated data attributes in each data set. Assuming there are n data sets in total, the set of repeated data attribute percentages of each data set is recorded as c = {c1, c2, ..., cn}, and min(c) and max(c) are defined as the minimum and maximum values ​​in set c. When the calculation result is in the interval [0, 0.2), it is scored as 5 points; in the interval [0.2, 0.4), it is scored as 4 points; in the interval [0.4-0.6), it is scored as 3 points; in the interval [0.6-0.8), it is scored as 2 points; in the interval [0.8-1], it is scored as 1 point.

[0075] (4) Activity: This dimension mainly examines the update interval, access interval, update difference, number of commonly used data attributes, cumulative access times, and cumulative update times of data elements. Among them, the update interval mainly examines how often the data elements are updated on average. If the data element is updated once every 3 years or more, it is scored as 1 point; if the data element is updated once every 1-3 years, it is scored as 2 points; if the data element is updated once a year, it is scored as 3 points; if the data element is updated once a quarter, it is scored as 4 points; if the data element is updated once a month, it is scored as 5 points. It should be noted that those who do not meet the evaluation standards of the next level will be scored downward.

[0076] The access interval mainly examines how often the data element is accessed on average. If the data element is accessed once a year or more, it is scored as 1 point; if the data element is accessed once a quarter, it is scored as 2 points; if the data element is accessed once a month, it is scored as 3 points; if the data element is accessed once a week, it is scored as 4 points; if the data element is accessed every day, it is scored as 5 points. It should be noted that those who do not meet the evaluation criteria of the next level will be scored downward.

[0077] The update difference mainly examines the proportion of updated data to all data after the latest update of the data element. The specific evaluation criteria are as follows:

[0078]

[0079] In the formula, di is the percentage of updated data in the latest update of each data set to all data. Assuming there are n data sets in total, the set of updated data percentages of each data element is recorded as d = {d1, d2, ..., dn}, and min(d) and max(d) are defined as the minimum and maximum values ​​in the set d. When the calculation result is in the interval [0, 0.2), it is scored as 1 point; in the interval [0.2, 0.4), it is scored as 2 points; in the interval [0.4-0.6), it is scored as 3 points; in the interval [0.6-0.8), it is scored as 4 points; in the interval [0.8-1], it is scored as 5 points.

[0080] The number of commonly used data attributes mainly examines the proportion of a data attribute called more than 50% of all the data attributes in the case of data elements being used. The specific evaluation criteria are as follows:

[0081]

[0082] Where ei is the percentage of data attributes that are called more than 50% of all usage times for each data set. Assuming there are n data sets in total, let the set be e = {e1, e2, …, en}, and define min(e) and max(e) as the minimum and maximum values ​​in the set e. When the calculation result is in the interval [0, 0.2), it is scored as 1 point; in the interval [0.2, 0.4), it is scored as 2 points; in the interval [0.4-0.6), it is scored as 3 points; in the interval [0.6-0.8), it is scored as 4 points; in the interval [0.8-1], it is scored as 5 points.

[0083] The cumulative number of visits mainly examines the number of times a data element has been visited since it went online. The specific evaluation criteria are as follows:

[0084]

[0085] Where fi is the number of times each data set has been accessed since it was launched. Assuming there are n data sets in total, the set of access times for each data element is recorded as f = {f1, f2, ..., fn}, and min(f) and max(f) are defined as the minimum and maximum values ​​in the set f. When the calculation result is in the interval [0, 0.2), it is scored as 1; in the interval [0.2, 0.4), it is scored as 2; in the interval [0.4-0.6), it is scored as 3; in the interval [0.6-0.8), it is scored as 4; in the interval [0.8-1], it is scored as 5.

[0086] The cumulative number of updates mainly examines the number of updates of the data element since it went online. The specific evaluation criteria are as follows:

[0087]

[0088] In the formula, gi is the number of updates of each data set since it was launched. Assuming there are n data sets in total, the set of update times of each data element is recorded as g = {g1, g2, ..., gn}, and min(g) and max(g) are defined as the minimum and maximum values ​​in the set g. When the calculation result is in the interval [0, 0.2), it is scored as 1; in the interval [0.2, 0.4), it is scored as 2; in the interval [0.4-0.6), it is scored as 3; in the interval [0.6-0.8), it is scored as 4; in the interval [0.8-1], it is scored as 5.

[0089] (5) Scale: This dimension mainly examines the number of data items, data size, and usage scope of data elements. The number of data items mainly examines the number of valid rows contained in the data element, where the number of valid rows refers to rows with clear names and specific meanings. The specific evaluation criteria are as follows:

[0090]

[0091] In the formula, hi is the number of valid rows of each data element. Assuming there are n data sets in total, the set of valid rows of each data element is recorded as h = {h1, h2, ..., hn}, and min(h) and max(h) are defined as the minimum and maximum values ​​in the set h. When the calculation result is in the interval [0, 0.2), it is scored as 1 point; in the interval [0.2, 0.4), it is scored as 2 points; in the interval [0.4-0.6), it is scored as 3 points; in the interval [0.6-0.8), it is scored as 4 points; in the interval [0.8-1], it is scored as 5 points.

[0092] Data size mainly examines the storage volume of data elements. The specific evaluation criteria are as follows:

[0093]

[0094] Where ji is the storage volume of each data element. Assuming there are n data sets in total, the set of storage volumes of each data element is recorded as j = {j1, j2, ..., jn}, and min(j) and max(j) are defined as the minimum and maximum values ​​in the set j. When the calculation result is in the interval [0, 0.2), it is scored as 1 point; in the interval [0.2, 0.4), it is scored as 2 points; in the interval [0.4-0.6), it is scored as 3 points; in the interval [0.6-0.8), it is scored as 4 points; in the interval [0.8-1], it is scored as 5 points.

[0095] The scope of data usage mainly examines the number of use fields of the data elements. If the data element is applicable to 1 use field, it is scored as 1 point; if the data element is applicable to 2 use fields, it is scored as 2 points; if the data element is applicable to 3 use fields, it is scored as 3 points; if the data element is applicable to 4 use fields, it is scored as 4 points; if the data element is applicable to 5 or more use fields, it is scored as 5 points.

[0096] (5) Relevance: This dimension mainly examines the number of data element calls, call frequency, and data dependency. The number of data element calls mainly examines the cumulative amount of data elements that have been called. The specific evaluation criteria are as follows:

[0097]

[0098] In the formula, ki is the product of the number of cumulatively called data and the number of data attributes of each data element. Assuming that there are n data sets in total, the set of the amount of called data for each data element is recorded as k = {k1, k2, ..., kn}, and min(k) and max(k) are defined as the minimum and maximum values ​​in the set k. When the calculation result is in the interval [0, 0.2), it is recorded as 1 point; in the interval [0.2, 0.4), it is recorded as 2 points; in the interval [0.4-0.6), it is recorded as 3 points; in the interval [0.6-0.8), it is recorded as 4 points; in the interval [0.8-1], it is recorded as 5 points.

[0099] The call frequency of data elements mainly examines the average frequency of data elements being called. If a data element is called once a year or more, it is scored as 1 point; if a data element is called once a quarter, it is scored as 2 points; if a data element is called once a month, it is scored as 3 points; if a data element is called once a week, it is scored as 4 points; if a data element is called every day, it is scored as 5 points. It should be noted that those who do not meet the evaluation standards of the next level will be scored downward.

[0100] Data dependency mainly examines the number of other data elements that are jointly called with a data element during the calling process. The specific evaluation criteria are as follows:

[0101]

[0102] In the formula, li is the product of the number of cumulatively called data and the number of data attributes of each data element. Assuming that there are n data sets in total, the set of the amount of called data for each data element is recorded as l = {l1, l2, ..., ln}, and min (l) and max (l) are defined as the minimum and maximum values ​​in the set l. When the calculation result is in the interval [0, 0.2), it is recorded as 1 point; in the interval [0.2, 0.4), it is recorded as 2 points; in the interval [0.4-0.6), it is recorded as 3 points; in the interval [0.6-0.8), it is recorded as 4 points; in the interval [0.8-1], it is recorded as 5 points.

[0103] (6) Risk level: This dimension mainly examines the management risk, circulation risk and security risk of data elements. Among them, the management risk of data elements mainly examines the level of application affected by asymmetric management information, poor management, and judgment of affairs. Its assignment needs to be scored by 5 or more experts, and the average of the scoring results after removing the highest and lowest scores is taken as the final score. The assignment standards and corresponding scores are as follows: very influential is recorded as 1 point; influential is recorded as 2 points; general is recorded as 3 points; no influential is recorded as 4 points; no influential is recorded as 5 points.

[0104] The circulation risk of data elements mainly examines the risks of data in the circulation process of open sharing, exchange and transaction. Its assignment needs to be scored by 5 or more experts, and the average of the scoring results minus the highest and lowest scores is taken as the final score. The assignment standards and corresponding scores are as follows: very influential is recorded as 1 point; influential is recorded as 2 points; general is recorded as 3 points; no influential is recorded as 4 points; no influential is recorded as 5 points.

[0105] The security risk of data elements mainly examines the risks of data elements being leaked, tampered, etc. Very significant impact is scored as 1 point; impact is scored as 2 points; moderate impact is scored as 3 points; no impact is scored as 4 points; and no impact at all is scored as 5 points.

[0106] The scores of the above-mentioned secondary indicators are weighted averaged to obtain the scores of the primary indicators. Furthermore, the scores of the primary indicators are weighted averaged again to obtain the final pricing score of the data element. If the final score is in the range of [0,1), the first-tier pricing standard is adopted; if the final score is in the range of [1,2), the second-tier pricing standard is adopted; if the final score is in the range of [2,3), the third-tier pricing standard is adopted; if the final score is in the range of [3,4), the fourth-tier pricing standard is adopted; if the final score is in the range of [4,5], the fifth-tier pricing standard is adopted. The specific pricing standards shall be implemented in accordance with the local data element pricing management methods.

[0107] S4. Data element quotation strategy model

[0108] Based on the pricing level, the model adopts two quotation methods according to the application needs of the data demander: per data set and per item. The quotation by data set is based on the proportion of the data used by the demander to the data element, and the corresponding proportional pricing is carried out according to the cost payment standard of the data pricing level in a specific scenario and the interest calculation standard of future income; while the per item billing is to set the fee for each piece of data under a specific scenario and pricing level.

[0109] It should be noted that the data element pricing system proposed in the present invention needs to clarify the compliance indicators based on the local data element resource management methods and the data element pricing management methods to clarify the specific cost payment and interest calculation ratio under various scenarios and standards. At the same time, the cost part of the data element pricing method proposed in the present invention needs to be calculated by the asset management company, and the specific calculation standards and methods are not included in the present invention.

[0110] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A data element pricing method based on the pricing cube, characterized in that: The method comprises the following steps: Step S1: Data element quality assessment model: The quality assessment model includes value factors, time factors, content factors and authority factors. Step S2: Data element pricing scenario classification model: The data element pricing scenario division is achieved through a three-dimensional pricing cube, which covers three key dimensions: demand subject, target use and application industry. Different pricing scenarios are formed at the intersection of these three dimensions. Step S3: Data element pricing level calculation model: The pricing level calculation model includes a data element pricing system and a cost-interest method. Among them, the data element pricing system includes 7 types of first-level indicators such as granularity, compliance, multi-dimensionality, activity, scale, correlation, and risk, which contain 26 second-level indicators. According to the calculation score of the price evaluation system, the data elements are divided into five different price levels in equal proportion. In addition, the cost-interest method is that in a specific scenario where pricing is based on a data set, the demander needs to pay the supplier a proportion of the data element cost according to its corresponding pricing level. In addition, in future operations, the data demander should pay the data supplier a percentage of the profit required by the pricing level from the income obtained from the use of data elements. Step S4: Data element quotation strategy model The quotation strategies include quotation by data set and quotation by item.

2. The data element quality assessment model according to claim 1 comprises a value factor, a time factor, a content factor and an authority factor. The specific contents of each dimension are as follows: (1) Assignment factors: This dimension mainly examines the amount of information contained in the data elements, including the observed value and effective dimension of the data contained in the data elements. (2) Time factor: This dimension mainly examines the timeliness of data elements, including the update frequency of data elements. (3) Content factors: This dimension mainly examines the availability of data elements, including the integrity of the data contained in the data elements. (4) Authoritative factors: This dimension mainly examines the market value potential and sensitivity of data elements, including the scoring results of experts in related fields.

3. The data factor pricing scenario classification model according to claim 1 covers three key dimensions: demand subject, target use and application industry. The specific contents of each dimension are as follows: (1) Demand subject: This dimension mainly divides the nature of the demand subject, including enterprises and individuals, scientific research institutions, public organizations, etc. (2) Target use: This dimension mainly distinguishes the purpose of each demand subject using data elements, including public welfare, self-empowerment, industry application, etc. (3) Application industry: This dimension mainly distinguishes the target application scenarios of each data element, including twelve fields such as industrial manufacturing, modern agriculture, commercial circulation, transportation, financial services, scientific and technological innovation, cultural tourism, medical health, emergency management, meteorological services, urban governance, and green and low-carbon.

4. The data element pricing level calculation model according to claim 1 includes an evaluation framework of 7 categories of primary indicators and 27 secondary indicators. The specific contents of each dimension are as follows: (1) Granularity: This dimension mainly includes the number, type and precision of data elements. The number of data elements mainly examines the number of data attributes of the data elements; the type of data elements mainly examines the type of data contained in the data elements; the accuracy of data elements mainly examines the relative accuracy of the values ​​in the data elements. (2) Compliance: This dimension mainly includes the format specifications, coding methods, data standards and naming rules of data elements. The format specifications of data elements mainly examine the proportion of data element data entry specifications that meet the requirements of data element format specifications in the local data element management regulations; the coding methods of data elements mainly examine the proportion of data element coding specifications that meet the requirements of data element coding methods in the local data element management regulations; the standards of data elements mainly examine the proportion of data element value specifications that meet the requirements of data element value in the local data element management regulations; the naming rules of data elements mainly examine the proportion of data element row and column naming that meet the requirements of data element naming in the local data element management regulations. (3) Multi-dimensionality: This dimension mainly examines the number of sources, source methods, coverage, and repetition rate of data elements. The number of sources of data elements reflects the number of source channels of data within the data elements; the source methods of data elements mainly examine the acquisition and production methods of data element data; the coverage of data elements mainly examines whether the data of data elements comes from multiple business departments; the repetition rate of data elements mainly examines the repetition of data attributes contained in data elements with other data elements. (4) Activity: This dimension mainly examines the update interval, access interval, update difference, number of commonly used data attributes, cumulative access times, and cumulative update times of data elements. Among them, the update interval mainly examines how often the data element is updated on average; the access interval mainly examines how often the data element is accessed on average; the update difference mainly examines the proportion of updated data to all data after the latest update of the data element; the number of commonly used data attributes mainly examines the proportion of data attributes that are called more than 50% of the total number of times a data attribute is used when the data element is used; the cumulative number of visits mainly examines the number of times the data element has been accessed since it was launched; the cumulative number of updates mainly examines the number of times the data element has been updated since it was launched. (5) Scale: This dimension mainly examines the number of data items, data size, and scope of use of data elements. The number of data items mainly examines the number of valid rows contained in the data element; the data size mainly examines the storage volume of the data element; and the scope of use of the data mainly examines the number of fields in which the data element is used. (6) Correlation: This dimension mainly examines the number of data element calls, call frequency, and data dependency. The number of data element calls mainly examines the cumulative amount of data that the data element is called; the call frequency of the data element mainly examines the average frequency of the data element being called; and the data dependency mainly examines the number of other data elements that are jointly called with the data element during the call process. (7) Risk level: This dimension mainly examines the management risk, circulation risk and security risk of data elements. Among them, the management risk of data elements mainly examines the level of application affected by information asymmetry, poor management, and judgment of affairs; the circulation risk of data elements mainly examines the risks of data in the circulation process of open sharing, exchange and transaction; the security risk of data elements mainly examines the risks of data elements being leaked or tampered with. (8) Pricing level: The scores of the above-mentioned secondary indicators are weighted averaged to obtain the score of the primary indicator. Furthermore, the scores of the primary indicators are weighted averaged again to obtain the final pricing score of the data element. If the final score is in the range of [0,1), the first-tier pricing standard is adopted; if the final score is in the range of [1,2), the second-tier pricing standard is adopted; if the final score is in the range of [2,3), the third-tier pricing standard is adopted; if the final score is in the range of [3,4), the fourth-tier pricing standard is adopted; if the final score is in the range of [4,5), the fifth-tier pricing standard is adopted.

5. The data element quotation strategy model according to claim 1 includes quotation by data set and quotation by item. The quotation by data set is to make corresponding proportional pricing according to the proportion of the data used by the demander to the data element, the cost payment standard of the data pricing level in a specific scenario and the interest calculation standard of future income; while the quotation by item is to set the fee for each piece of data under a specific scenario and pricing level.

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