Data value estimation method and system based on information quantity

Abstract

The present invention discloses a data value estimation method and system based on information quantity, which relates to the field of data processing technology, including: normalizing the data to be estimated to obtain normalized data containing multiple attributes; determining the information quantity of each attribute based on the value of each attribute of the normalized data; and calculating the estimated value of the data to be estimated based on the information quantity of each attribute. The present invention alleviates the technical problem that the prior art cannot reflect the comprehensive value of data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for estimating data value based on information quantity. Background Art

[0002] With the rise of the digital economy, data as an asset has received great attention from various organizations. Since data is an asset, it can be traded in the market, and transactions inevitably involve the value of data. Since data content is very rich, the value of various data varies greatly. Therefore, a method for evaluating the value of data is needed to promote the development of the digital economy.

[0003] When evaluating the value of data, we usually need to consider the cost of acquiring, processing, and storing data, as well as factors such as the scarcity of data and the security of data (such as privacy and confidentiality). Currently, most methods for estimating the value of data are mainly based on the number of times a piece of data is queried and modified, as well as the level of users who use the data. This type of method of determining data value based on traffic is easily manipulated and cannot well reflect the comprehensive value of the data. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for estimating data value based on information quantity in order to solve at least one of the above technical problems.

[0005] In a first aspect, an embodiment of the present invention provides a method for estimating data value based on information quantity, comprising: normalizing the data to be estimated to obtain normalized data containing multiple attributes; determining the information quantity of each attribute based on the value of each attribute of the normalized data; and calculating the estimated value of the data to be estimated based on the information quantity of each attribute.

[0006] Furthermore, the value of the attribute includes numerical values ​​and non-numerical values; based on the value of each attribute of the normalized data, the amount of information of each attribute is determined, including: when the value of the attribute is a numerical value, the amount of information of each attribute is determined based on the distribution of the value of the attribute in a preset division interval; when the value of the attribute is a non-numerical value, the amount of information of each attribute is determined based on the distribution of the value of the attribute in a preset division category.

[0007] Furthermore, when the value of the attribute is a numerical value, the amount of information of each attribute is determined based on the distribution of the preset partition intervals in which the value of the attribute is located, including: determining multiple preset partition intervals based on preset partition rules or statistical methods; determining the amount of information corresponding to each preset partition interval based on historical statistical information of the data to be estimated; determining the amount of information of each attribute based on the preset partition interval in which the value of each attribute is located.

[0008] Furthermore, based on a statistical method, a plurality of preset division intervals are determined, including: calculating an average value and a variance based on historical statistical information of the data to be estimated; and determining a plurality of preset division intervals based on the average value and the variance.

[0009] Further, based on the historical statistical information of the data to be estimated, determining the amount of information corresponding to each preset partition includes:

[0010] S i =-p i ×log2p i

[0011] p i =h i / h

[0012] In the formula, S i is the amount of information corresponding to the i-th preset partition interval, h i is the amount of historical data within the i-th preset partition interval, and h is the total amount of historical data.

[0013] Furthermore, the estimated value includes: the sum of the amount of information of each attribute, or the product of the amount of information of each attribute.

[0014] In the second aspect, an embodiment of the present invention also provides a data value estimation system based on information quantity, comprising: a standardization module, a determination module and an estimation module; wherein the standardization module is used to normalize the data to be estimated to obtain normalized data containing multiple attributes; the determination module is used to determine the information quantity of each attribute based on the value of each attribute of the normalized data; and the estimation module is used to calculate the estimated value of the data to be estimated based on the information quantity of each attribute.

[0015] Furthermore, the value of the attribute includes numerical values ​​and non-numerical values; the determination module is also used to: when the value of the attribute is a numerical value, determine the amount of information of each attribute based on the distribution of the value of the attribute in the preset division interval; when the value of the attribute is a non-numerical value, determine the amount of information of each attribute based on the distribution of the value of the attribute in the preset division category.

[0016] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method provided in the embodiment of the present invention is implemented.

[0018] The present invention provides a data value estimation method and system based on information volume, which takes into account the amount of information corresponding to each attribute. It can overcome the technical problem that methods of estimating data based on data flow, etc. cannot well reflect the comprehensive value of data. It can well reflect the comprehensive value of data and play an important role in the digital economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 A flowchart of a method for estimating data value based on information volume provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of a data value estimation system based on information quantity provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Embodiment 1

[0024] Figure 1 FIG. 1 is a flow chart of a method for estimating data value based on information volume according to an embodiment of the present invention. Figure 1 As shown, the method specifically comprises the following steps:

[0025] Step S102, normalizing the data to be estimated to obtain normalized data containing multiple attributes.

[0026] Step S104, based on the value of each attribute of the normalized data, determine the amount of information of each attribute.

[0027] Step S106, calculating the estimated value of the data to be estimated based on the information amount of each attribute.

[0028] Specifically, in step S102, the estimated data is normalized so that a piece of data D i Contains multiple required attributes A ij , i = 1, 2, ..., m, j = 1, 2, ..., n. Optionally, the attributes include time, place, subject, tool used by the subject, object, action taken by the subject on the object, quantity, etc.

[0029] For example, in the field of Internet of Things, the structure of a piece of data is: (time, location, sensor, object, object characteristics, sensor value), which means: at a certain time and place, a certain sensor sensed a certain characteristic of a certain object and obtained a certain sensed value. For example, in the field of finance, the structure of a piece of data is: (time, depositor, branch, operation, amount), which means: at a certain time, a depositor deposited / withdrawn / loaned a certain amount of money at a certain branch.

[0030] Specifically, the value of the attribute includes a numerical value and a non-numerical value. Step S104 also includes the following steps:

[0031] Step S1041: when the value of the attribute is a numerical value, the amount of information of each attribute is determined based on the distribution of the attribute value in the preset partition interval. Specifically:

[0032] Determine a plurality of preset division intervals based on preset division rules or statistical methods;

[0033] Based on the historical statistical information of the data to be estimated, determine the amount of information corresponding to each preset division interval;

[0034] Based on the preset partition interval where the value of each attribute lies, the amount of information of each attribute is determined.

[0035] In an optional implementation provided by an embodiment of the present invention, multiple preset division intervals are determined based on preset division rules, including: according to actual conditions, multiple preset division intervals are determined uniformly or non-uniformly. For example, for numerical attributes such as time, 24 hours a day can be divided into intervals every 2 hours, 1 hour or 30 minutes, and 12, 24, and 48 intervals are obtained respectively. For example, for numerical attributes such as sensor quantity and amount, the intervals can be divided uniformly or non-uniformly according to the historical maximum and minimum values.

[0036] In another optional implementation provided by the embodiment of the present invention, multiple preset division intervals are determined based on a statistical method, including:

[0037] Calculate the mean and variance based on the historical statistical information of the data to be estimated;

[0038] Based on the mean value and the variance, a plurality of preset division intervals are determined.

[0039] For example, based on the historical statistical information of the data to be estimated, the mean value μ and the variance σ are calculated and divided into multiple preset division intervals, such as less than μ-3σ, μ-3σ to μ-2σ, μ-2σ to μ-σ, μ-σ to μ+σ, μ+σ to μ+2σ, μ+2σ to μ+3σ, and greater than μ+3σ.

[0040] Step S1042: when the value of the attribute is non-numeric, the amount of information of each attribute is determined based on the distribution of the preset classification categories in which the value of the attribute belongs.

[0041] For example, for sensing locations, they can be divided into types such as urban indoor, urban outdoor, suburban indoor, and suburban outdoor, reflecting the cost of obtaining data; for sensors, they can be divided into types such as identification sensors, physical quantity sensors, chemical quantity sensors, biomass sensors, and position sensors, reflecting the cost of obtaining data; for depositors, they can be divided into types such as ordinary employees, managers, and owners, reflecting the sensitivity of the data.

[0042] Optionally, the amount of information corresponding to each preset division interval may be directly assigned according to historical statistical information, or the amount of information corresponding to each preset division interval may be determined based on the following calculation formula:

[0043] S i =-p i ×log2p i

[0044] p i =h i / h

[0045] In the formula, S i is the amount of information corresponding to the i-th preset partition interval, h i is the amount of historical data in the i-th preset partition interval, h is the total amount of historical data, h=h1+h2+…+h k , k is the number of preset partition intervals.

[0046] In an optional implementation provided by an embodiment of the present invention, the estimated value includes: the sum of the amount of information of each attribute, or the product of the amount of information of each attribute.

[0047] For example, a piece of data D i The attributes that can be included are A ij , i=1,2,…,m; j=1,2,…,n. A ij The amount of information is S j , then data D i The estimated value of V i=S1+S2+…+S n , or V i =S1×S2×…×S n .

[0048] From the above description, it can be seen that the embodiment of the present invention provides a data value estimation method based on information volume, which takes into account the scarcity of data as well as the cost and security of data acquisition in the calculation of the amount of data attribute information. It can overcome the method of estimating data value based on data flow, etc., can reflect the comprehensive value of data, and can play an important role in the digital economy.

[0049] Embodiment 2

[0050] Figure 2 FIG. 1 is a schematic diagram of a data value estimation system based on information quantity provided according to an embodiment of the present invention. Figure 2 As shown, the system includes: a specification module 10 , a determination module 20 and an estimation module 30 .

[0051] Specifically, the standardization module 10 is used to perform standardization processing on the data to be estimated to obtain standardized data containing multiple attributes.

[0052] The determination module 20 is used to determine the information amount of each attribute based on the value of each attribute of the normalized data.

[0053] The estimation module 30 is used to calculate the estimated value of the data to be estimated based on the information amount of each attribute.

[0054] Specifically, the value of the attribute includes a numerical value and a non-numerical value; the determination module 20 is further used to:

[0055] When the value of an attribute is a numerical value, the amount of information of each attribute is determined based on the distribution of the preset partition intervals in which the attribute value is located; specifically, multiple preset partition intervals are determined based on preset partition rules or statistical methods; the amount of information corresponding to each preset partition interval is determined based on historical statistical information of the data to be estimated; the amount of information of each attribute is determined based on the preset partition interval in which the value of each attribute is located.

[0056] When the value of an attribute is non-numeric, the amount of information for each attribute is determined based on the distribution of the attribute value in the preset classification category.

[0057] The present invention further provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the embodiment of the present invention when executing the computer program.

[0058] The present invention also provides a computer-readable storage medium, which stores computer instructions. When the computer instructions are executed by a processor, the method provided in the embodiment of the present invention is implemented.

[0059] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0060] 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 technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. A data value estimation method based on information quantity, characterized in that: include: Normalize the data to be estimated to obtain normalized data containing multiple attributes; Determining the amount of information of each attribute based on the value of each attribute of the normalized data; Based on the information amount of each attribute, the estimated value of the data to be estimated is calculated.

2. The method according to claim 1, characterized in that: The value of the attribute includes numerical value and non-numerical value; Determining the amount of information of each attribute based on the value of each attribute of the normalized data includes: When the value of the attribute is a numerical value, the amount of information of each attribute is determined based on the distribution of the value of the attribute in the preset partition interval; When the value of the attribute is non-numeric, the amount of information of each attribute is determined based on the distribution of the value of the attribute in the preset classification category.

3. The method according to claim 2, characterized in that: When the value of the attribute is a numerical value, the amount of information of each attribute is determined based on the distribution of the value of the attribute in the preset partition interval, including: Determine a plurality of preset division intervals based on preset division rules or statistical methods; Based on the historical statistical information of the data to be estimated, determining the amount of information corresponding to each preset division interval; Based on the preset partition interval where the value of each attribute lies, the amount of information of each attribute is determined.

4. The method according to claim 3, characterized in that: Based on statistical methods, multiple preset division intervals are determined, including: Calculate the mean and variance based on the historical statistical information of the data to be estimated; Based on the mean value and the variance, a plurality of preset division intervals are determined.

5. The method according to claim 3, characterized in that: Determining the amount of information corresponding to each preset partition interval based on the historical statistical information of the data to be estimated includes: S i =-p i ×log2p i p i =h i / h In the formula, S i is the amount of information corresponding to the i-th preset partition interval, h i is the amount of historical data within the i-th preset partition interval, and h is the total amount of historical data.

6. The method according to claim 1, characterized in that: The estimated value includes: the sum of the amount of information of each attribute, or the product of the amount of information of each attribute.

7. A data value estimation system based on information quantity, characterized in that: include: specification module, determination module and estimation module; among them, The standardization module is used to perform standardization processing on the data to be estimated to obtain standardized data containing multiple attributes; The determination module is used to determine the information amount of each attribute based on the value of each attribute of the normalized data; The estimation module is used to calculate the estimated value of the data to be estimated based on the information amount of each attribute.

8. The system according to claim 7, characterized in that: The value of the attribute includes a numerical value and a non-numerical value; the determination module is further used to: When the value of the attribute is a numerical value, the amount of information of each attribute is determined based on the distribution of the value of the attribute in the preset partition interval; When the value of the attribute is non-numeric, the amount of information of each attribute is determined based on the distribution of the preset classification categories in which the value of the attribute belongs.

9. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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