A metadata-based data asset value assessment method

Abstract

The application relates to the technical field of data asset value evaluation, and discloses a data asset value evaluation method based on metadata, which utilizes metadata technology to collect basic information in a data asset value calculation model based on metadata, and then calculates the data asset value through a defined data asset value calculation model based on metadata; since metadata collection is automatically completed by a system, only the process of a data asset production link needs to be clearly defined, and the evaluation can be automatically calculated by the system without manual intervention, so that the value of the data asset is evaluated, and the method can be applied to data asset value evaluation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data asset value evaluation, in particular to a data asset value evaluation method based on metadata. BACKGROUND

[0002] Data assetization is the process of forming data assets by assetizing data resources of an enterprise, and the value evaluation involved in the process is related to the data asset entry table, and the evaluation needs to accurately reflect the value of the data assets. The existing evaluation methods are divided into three categories: cost method, income method and market method, and the specific calculation methods corresponding to each method have not formed a standard at present, and the human-defined way is usually used in various scenes.

[0003] The existing scheme divides the evaluation of data assets into storage and processing costs, storage includes storage of original data, storage of intermediate data and storage of data asset data, and processing is the hardware involved in the production link.

[0004] The main problem of the existing method is that the granularity is relatively coarse, and there is no any technical reference basis, which is easy to cause the problem of inaccurate asset value estimation. SUMMARY

[0005] The present application provides a data asset value evaluation method based on metadata, which can accurately evaluate the value of data assets through a defined data asset value calculation model.

[0006] A data asset value evaluation method based on metadata, comprising the following steps:

[0007] Step 1: The interactive service center component defines the collection work protocol of the metadata of the original data, and constructs the work permission number for the metadata collection component, the metadata service component and the asset value calculation component based on the collection work protocol;

[0008] Step 2: The metadata service component sends the metadata collection task of the original data to the metadata collection component, and the metadata collection component collects the metadata of the original data from the collection server and returns the metadata to the metadata service component;

[0009] Step 3: The metadata service component implements a reassembly operation to construct the metadata of the original data and transmits it to the asset value calculation component;

[0010] Step 4: After receiving the metadata, the asset value calculation component calculates the value of the original data according to the data asset value calculation model.

[0011] Preferably, the metadata collected by the metadata collection component includes:

[0012] Metadata 1: {Total working time of the data collection server in collecting raw data, (Total price of the data collection server / Total working time of the data collection server)};

[0013] Metadata 2: {Storage space size of raw data, (total price of storage server / total storage space of storage server)};

[0014] Metadata 3: {Execution time of the processing server in calculating the raw data, (resource unit price of the processing server in executing the task of calculating the raw data × number of cores used)}.

[0015] Preferably, the metadata obtained by the reassembly operation of the metadata service component is: {total working time of the acquisition server in collecting raw data, (total price of the acquisition server / total working time of the acquisition server), storage space size of the raw data, (total price of the storage server / total storage space of the storage server), execution time of the processing server in calculating the raw data, (resource unit price of the processing server in executing the task of calculating the raw data × number of cores used)}.

[0016] A metadata-based data asset valuation system is provided for executing the aforementioned metadata-based data asset valuation method. The data asset valuation system includes: a metadata acquisition component, a metadata service component, an asset value calculation component, and an interactive service center component; any component in the above data asset valuation system communicates with other components.

[0017] Compared with the prior art, the present invention has the following beneficial technical effects:

[0018] This invention utilizes metadata technology to first collect basic information for calculating the value of data assets based on metadata, and then calculates the value of data assets through the defined data asset value calculation model based on metadata.

[0019] Since metadata collection is completed automatically by the system, it is only necessary to clearly define the process of data asset production, and the valuation can be automatically calculated by the system without human intervention, thus realizing the value assessment of data assets. Moreover, this method can be applied in the valuation of data assets. Attached Figure Description

[0020] Figure 1 This is an architecture for a data asset valuation system based on metadata;

[0021] Figure 2 This describes the workflow of a metadata-based data asset valuation method. Detailed Implementation

[0022] Example 1:

[0023] A data asset valuation system based on metadata, such as Figure 1 As shown, it includes: metadata collection component Cmdc-i, metadata collection component Cmdc-ii, metadata collection component Cmdc-iii, metadata service component Cmds, asset value calculation component Cavc, and interactive service center component Cisc;

[0024] The interactive service center component Cisco defines the metadata collection protocol belonging to the raw data, and constructs work permission numbers for the metadata collection components Cmdc-i, Cmdc-ii, Cmdc-iii, Cmds, and Cavc, based on the collection protocol.

[0025] The asset valuation component Cavc defines a data asset valuation model based on metadata. This data asset valuation model is: Data asset value = Working time cost of the acquisition server + Storage cost of the storage server + Processing cost of the processing server.

[0026] Each component in the aforementioned data asset valuation system communicates with other components.

[0027] Example 2:

[0028] Based on the data asset valuation system in Example 1, a metadata-based data asset valuation method is constructed, such as... Figure 2 As shown, the steps are as follows:

[0029] Step 2-1: The metadata service component Cmds sends a task to the metadata collection component Cmdc-ⅰ to collect the metadata M (Rdi-Cmdc-ⅰ) of the raw data Rdi. The metadata collection component Cmdc-ⅰ collects the metadata M (Rdi-Cmdc-ⅰ) of the raw data Rdi from the collection server (which collects the raw data from the end system that generates the raw data) and returns the metadata M (Rdi-Cmdc-ⅰ) to the metadata service component Cmds.

[0030] Metadata M(Rdi-Cmdc-ⅰ) represents {the total working time of the data acquisition server in collecting raw data Rdi, and the cost per unit working time (total price of the data acquisition server / total working time of the data acquisition server)};

[0031] Step 2-2: The metadata service component Cmds sends a task to the metadata collection component Cmdc-II to collect the metadata M (Rdi-Cmdc-II) of the raw data Rdi. The metadata collection component Cmdc-II collects the metadata M (Rdi-Cmdc-II) of the raw data Rdi from the storage server (which is used to store the raw data collected by the collection server from the end system) and returns the metadata M (Rdi-Cmdc-II) to the metadata service component Cmds.

[0032] Metadata M(Rdi-Cmdc-ⅱ) represents {the storage space size of the original data Rdi, and the unit storage cost (total price of storage server / total storage space of storage server)};

[0033] Steps 2-3: The metadata service component Cmds sends a task to the metadata collection component Cmdc-ⅲ to collect the metadata M (Rdi-Cmdc-ⅲ) of the raw data Rdi. The metadata collection component Cmdc-ⅲ collects the metadata M (Rdi-Cmdc-ⅲ) of the raw data Rdi from the processing server (which performs data processing, statistics, and analysis operations on the raw data stored in the storage server through algorithms) and returns the metadata M (Rdi-Cmdc-ⅲ) to the metadata service component Cmds.

[0034] Metadata M(Rdi-Cmdc-ⅲ) is {the execution time of the processing server to calculate the raw data Rdi, and the unit processing cost (the unit price of resources in the processing server for executing the task of calculating the raw data × the number of cores used)};

[0035] Among them, the processing server uses a variety of algorithms, including analysis methods based on mathematical statistics, complex models, and graphics. Each analysis method uses different types of resources and may use multiple types of resources for calculation at the same time. Therefore, the calculation method in this stage is relatively complex. The items involved in the calculation in this stage are abstracted and a unified calculation formula is defined.

[0036] The data processing phase operates on a task-by-task basis. Costs are calculated by determining the resources used by each task during execution. The following information is obtained by collecting metadata from the processing tasks:

[0037] (1) Task resource type: Obtain the resource type used when executing the task. For the processing stage, there are two types of resources: CPU and GPU. The former is used for general computing, and the latter is used for artificial intelligence model computing. The number of cores and the model are recorded in units of cores.

[0038] (2) Execution time of a single type of resource: Record the time cost of each type of resource in the task. If it is a cluster execution, obtain the execution time of each server participating in the calculation and sum them up as the total execution time of the resource.

[0039] Steps 2-4: The metadata service component Cmds performs a reassembly operation based on metadata M(Rdi-Cmdc-i), metadata M(Rdi-Cmdc-ii), and metadata M(Rdi-Cmdc-iii) to construct metadata M(Rdi) of the original data Rdi, and then transmits metadata M(Rdi) to the asset value calculation component Cavc;

[0040] Metadata M(Rdi) is defined as: {Total working time of the acquisition server in collecting raw data Rdi, cost per unit working time (total price of the acquisition server / total working time of the acquisition server), storage space size of raw data Rdi, unit storage cost (total price of the storage server / total storage space of the storage server), execution time of the processing server in calculating raw data Rdi, and unit processing cost (resource unit price for executing the raw data calculation task in the processing server × number of cores used)};

[0041] Steps 2-5: After receiving the metadata M(Rdi), the asset valuation component Cavc calculates the value of the original data Rdi according to the data asset valuation model;

[0042] The formula for calculating the value of data assets is: S = S1 + S2 + S3;

[0043] S represents the value of data assets;

[0044] S1 represents the working time cost of the data acquisition server. S1 = Total working time of the data acquisition server in collecting raw data Rdi × (Total price of data acquisition server / Total working time of data acquisition server).

[0045] S2 represents the storage cost of the storage server. S2 = storage space size of the original data Rdi × (total price of the storage server / total storage space of the storage server)

[0046] S3 represents the processing cost of the processing server. S3 = Execution time of the processing server to calculate the raw data Rdi × (Resource unit price of the processing server for executing the raw data calculation task × Number of cores used).

[0047] In addition, the value of data assets also includes operation and maintenance costs and management costs. However, the operation and maintenance phase and management phase are calculated based on human resource input costs, which are not within the scope of this invention.

[0048] Example 3:

[0049] The Cisco Interactive Service Center component defines the metadata collection protocol for raw data and implements a specific implementation plan for constructing work permission numbers as follows:

[0050] Define the data acquisition protocol:

[0051] (1) Let p be a large prime number and q be a prime factor of p-1, and satisfy p,q≥2 l Where l is a safety parameter;

[0052] (2) Let g be a group Z of order q. * p Generators of elements in the middle;

[0053] (3) Set H: {0,1} * →Z * q It is a collision-resistant hash function;

[0054] Construct work permission number:

[0055] (1) The working number of the constructed metadata collection component Cmdc-ⅰ is:

[0056] {JID-Ⅰ(Cmdc-ⅰ), JID-Ⅱ(Cmdc-ⅰ)};

[0057] Where JID-Ⅰ(Cmdc-ⅰ)∈Z * p , JID-Ⅱ(Cmdc-ⅰ)=g^[JID-Ⅰ(Cmdc-ⅰ)](mo dp);

[0058] (2) The working number of the constructed metadata collection component Cmdc-II is:

[0059] {JID-Ⅰ(Cmdc-ⅱ), JID-Ⅱ(Cmdc-ⅱ)};

[0060] Where JID-Ⅰ(Cmdc-ⅱ)∈Z * p , JID-Ⅱ(Cmdc-ⅱ)=g^[JID-Ⅰ(Cmdc-ⅱ)](mo dp);

[0061] (3) The working number of the constructed metadata collection component Cmdc-ⅲ is:

[0062] {JID-Ⅰ(Cmdc-ⅲ), JID-Ⅱ(Cmdc-ⅲ)};

[0063] Where JID-Ⅰ(Cmdc-ⅲ)∈Z * p, JID-Ⅱ(Cmdc-ⅲ)=g^[JID-Ⅰ(Cmdc-ⅲ)](mo dp);

[0064] (4) The working number of the constructed metadata service component Cmds is:

[0065] {JID-Ⅰ(Cmds), JID-Ⅱ(Cmds)};

[0066] Where JID-Ⅰ(Cmds)∈Z * p , JID-Ⅱ(Cmds)=g^[JID-Ⅰ(Cmds)](modp);

[0067] (5) The working number of the asset valuation component Cavc constructed is:

[0068] {JID-Ⅰ(Cavc), JID-Ⅱ(Cavc)};

[0069] Where JID-Ⅰ(Cavc)∈Z * p , JID-Ⅱ(Cavc)=g^[JID-Ⅰ(Cavc)](modp);

[0070] (6) The Cisco Interactive Service Center component exposes the following information in the system:

[0071] The working number of the metadata collection component Cmdc-ⅰ is JID-Ⅱ(Cmdc-ⅰ);

[0072] The working number of the metadata collection component Cmdc-II is JID-Ⅱ(Cmdc-ⅱ);

[0073] The working number of the metadata collection component Cmdc-ⅲ is JID-Ⅱ(Cmdc-ⅲ);

[0074] The working number of the metadata service component Cmds is JID-Ⅱ(Cmds);

[0075] The working number for the asset valuation component Cavc is JID-Ⅱ(Cavc).

[0076] Example 4:

[0077] The specific implementation plan for obtaining metadata M(Rdi-Cmdc-ⅰ) is as follows:

[0078] Step 4-1: When the metadata service component Cmds sends the metadata M(Rdi) of the raw data Rdi to the metadata collection component Cmdc-ⅰ, the task T(Rdi-Cmdc-ⅰ)∈{0,1} is completed. *At that time, the metadata service component Cmds performs the following operations:

[0079] (1) Calculate the auxiliary signature information k(Rdi-Cmdc-ⅰ) for task T(Rdi-Cmdc-ⅰ):

[0080] k(Rdi-Cmdc-ⅰ)=[JID-Ⅱ(Cmdc-ⅰ)]^[JID-Ⅰ(Cmds)](modp);

[0081] (2) Calculate the signature σ(Rdi-Cmdc-ⅰ) of the task T(Rdi-Cmdc-ⅰ):

[0082] σ(Rdi-Cmdc-ⅰ)=H([T(Rdi-Cmdc-ⅰ)]∥[k(Rdi-Cmdc-ⅰ)]);

[0083] (3) Send the working command [T(Rdi-Cmdc-ⅰ), σ(Rdi-Cmdc-ⅰ)] of the metadata M(Rdi) belonging to the original data Rdi to the metadata acquisition component Cmdc-ⅰ;

[0084] Step 4-2: When the metadata acquisition component Cmdc-ⅰ receives the working command [T(Rdi-Cmdc-ⅰ), σ(Rdi-Cmdc-ⅰ)] for the metadata M(Rdi) of the raw data Rdi, the metadata acquisition component Cmdc-ⅰ performs the following operations:

[0085] (1) Calculate the auxiliary signature information k'(Rdi-Cmdc-ⅰ) for task T(Rdi-Cmdc-ⅰ):

[0086] k'(Rdi-Cmdc-ⅰ)=[JID-Ⅱ(Cmds))]^[JID-Ⅰ(Cmdc-ⅰ)](modp);

[0087] (2) Calculate the signature σ'(Rdi-Cmdc-ⅰ) of the task T(Rdi-Cmdc-ⅰ):

[0088] σ'(Rdi-Cmdc-ⅰ)=H([T(Rdi-Cmdc-ⅰ)]∥[k'(Rdi-Cmdc-ⅰ)]);

[0089] (3) If the equation σ'(Rdi-Cmdc-ⅰ)=σ(Rdi-Cmdc-ⅰ) is true, then output "true"; otherwise, output "false".

[0090] (4) When “true” is output, start executing the work task T(Rdi-Cmdc-ⅰ) to collect the metadata M(Rdi) of the raw data Rdi;

[0091] Step 4-3: When the metadata collection component Cmdc-ⅰ collects the metadata M(Rdi-Cmdc-ⅰ)∈{0,1} of the raw data Rdi from the collection server... * At that time, the metadata collection component Cmdc-ⅰ performs the following operations:

[0092] (1) Calculate the signature σ(M(Rdi-Cmdc-ⅰ)) of the metadata M(Rdi-Cmdc-ⅰ):

[0093] σ(M(Rdi-Cmdc-ⅰ))=H([M(Rdi-Cmdc-ⅰ)]∥[k'(Rdi-Cmdc-ⅰ)]);

[0094] (2) Return the work results [M(Rdi-Cmdc-ⅰ), σ(M(Rdi-Cmdc-ⅰ))] to the metadata service component Cmds;

[0095] Step 4-4: When the metadata service component Cmds receives the working result [M(Rdi-Cmdc-ⅰ), σ(M(Rdi-Cmdc-ⅰ))] of the original data Rdi, the metadata service component Cmds performs the following operations:

[0096] (1) Calculate the signature σ'(M(Rdi-Cmdc-ⅰ)) of the metadata M(Rdi-Cmdc-ⅰ):

[0097] σ'(M(Rdi-Cmdc-ⅰ))=H([M(Rdi-Cmdc-ⅰ)]∥[k(Rdi-Cmdc-ⅰ)]);

[0098] (2) If the equation σ'(M(Rdi-Cmdc-ⅰ))=σ(M(Rdi-Cmdc-ⅰ)) is true, then output “true”; otherwise, output “false”.

[0099] (3) When “true” is output, the reassembly operation begins.

[0100] Example 5:

[0101] The specific implementation plan for obtaining metadata M(Rdi-Cmdc-ⅱ) is as follows:

[0102] Step 5-1: When the metadata service component Cmds sends the metadata M(Rdi) of the raw data Rdi to the metadata collection component Cmdc-II, the task T(Rdi-Cmdc-II)∈{0,1} is completed. * At that time, the metadata service component Cmds performs the following operations:

[0103] (1) Calculate the auxiliary signature information k(Rdi-Cmdc-ⅱ) for task T(Rdi-Cmdc-ⅱ):

[0104] k(Rdi-Cmdc-ⅱ)=[JID-Ⅱ(Cmdc-ⅱ)]^[JID-Ⅰ(Cmds)](modp);

[0105] (2) Calculate the signature σ(Rdi-Cmdc-ⅱ) of the task T(Rdi-Cmdc-ⅱ):

[0106] σ(Rdi-Cmdc-ⅱ)=H([T(Rdi-Cmdc-ⅱ)]∥[k(Rdi-Cmdc-ⅱ)]);

[0107] (3) Send the working command [T(Rdi-Cmdc-ⅱ), σ(Rdi-Cmdc-ⅱ)] of the metadata M(Rdi) belonging to the original data Rdi to the metadata acquisition component Cmdc-ⅱ;

[0108] Step 5-2: When the metadata acquisition component Cmdc-ⅱ receives the working command [T(Rdi-Cmdc-ⅱ), σ(Rdi-Cmdc-ⅱ)] of the metadata M(Rdi) of the raw data Rdi, the metadata acquisition component Cmdc-ⅱ performs the following operations:

[0109] (1) Calculate the auxiliary signature information k'(Rdi-Cmdc-ⅱ) for task T(Rdi-Cmdc-ⅱ):

[0110] k'(Rdi-Cmdc-ⅱ)=[JID-Ⅱ(Cmds))]^[JID-Ⅰ(Cmdc-ⅱ)](modp);

[0111] (2) Calculate the signature σ'(Rdi-Cmdc-ⅱ) of the task T(Rdi-Cmdc-ⅱ):

[0112] σ'(Rdi-Cmdc-ⅱ)=H([T(Rdi-Cmdc-ⅱ)]∥[k'(Rdi-Cmdc-ⅱ)]);

[0113] (3) If the equation σ'(Rdi-Cmdc-ⅱ)=σ(Rdi-Cmdc-ⅱ) is true, then output "true"; otherwise, output "false".

[0114] (4) When “true” is output, start executing the work task T(Rdi-Cmdc-ⅱ) to collect the metadata M(Rdi) of the raw data Rdi;

[0115] Step 5-3: When the metadata collection component Cmdc-II collects the metadata M(Rdi-Cmdc-II)∈{0,1} of the raw data Rdi from the collection server... * At that time, the metadata collection component Cmdc-II performs the following operations:

[0116] (1) Calculate the signature σ(M(Rdi-Cmdc-ⅱ)) of the metadata M(Rdi-Cmdc-ⅱ):

[0117] σ(M(Rdi-Cmdc-ⅱ))=H([M(Rdi-Cmdc-ⅱ)]∥[k'(Rdi-Cmdc-ⅱ)]);

[0118] (2) Return the work results [M(Rdi-Cmdc-ⅱ), σ(M(Rdi-Cmdc-ⅱ))] to the metadata service component Cmds;

[0119] Step 5-4: When the metadata service component Cmds receives the working result [M(Rdi-Cmdc-ⅱ), σ(M(Rdi-Cmdc-ⅱ))] of the original data Rdi, the metadata service component Cmds performs the following operations:

[0120] (1) Calculate the signature σ'(M(Rdi-Cmdc-ⅱ)) of the metadata M(Rdi-Cmdc-ⅱ):

[0121] σ'(M(Rdi-Cmdc-ⅱ))=H([M(Rdi-Cmdc-ⅱ)]∥[k(Rdi-Cmdc-ⅱ)]);

[0122] (2) If the equation σ'(M(Rdi-Cmdc-ⅱ))=σ(M(Rdi-Cmdc-ⅱ)) is true, then output "true"; otherwise, output "false".

[0123] (3) When “true” is output, the reassembly operation begins.

[0124] Example 6:

[0125] The specific implementation plan for obtaining metadata M(Rdi-Cmdc-ⅲ) is as follows:

[0126] Step 6-1: When the metadata service component Cmds sends the metadata M(Rdi) of the raw data Rdi to the metadata collection component Cmdc-ⅲ, the task T(Rdi-Cmdc-ⅲ)∈{0,1} is completed. * At that time, the metadata service component Cmds performs the following operations:

[0127] (1) Calculate the auxiliary signature information k(Rdi-Cmdc-ⅲ) for task T(Rdi-Cmdc-ⅲ):

[0128] k(Rdi-Cmdc-ⅲ)=[JID-Ⅱ(Cmdc-ⅲ)]^[JID-Ⅰ(Cmds)](modp);

[0129] (2) Calculate the signature σ(Rdi-Cmdc-ⅲ) of the task T(Rdi-Cmdc-ⅲ):

[0130] σ(Rdi-Cmdc-ⅲ)=H([T(Rdi-Cmdc-ⅲ)]∥[k(Rdi-Cmdc-ⅲ)]);

[0131] (3) Send the working command [T(Rdi-Cmdc-ⅲ), σ(Rdi-Cmdc-ⅲ)] of the metadata M(Rdi) belonging to the original data Rdi to the metadata acquisition component Cmdc-ⅲ;

[0132] Step 6-2: When the metadata acquisition component Cmdc-ⅲ receives the working command [T(Rdi-Cmdc-ⅲ), σ(Rdi-Cmdc-ⅲ)] for the metadata M(Rdi) of the raw data Rdi, the metadata acquisition component Cmdc-ⅲ performs the following operations:

[0133] (1) Calculate the auxiliary signature information k'(Rdi-Cmdc-ⅲ) for task T(Rdi-Cmdc-ⅲ):

[0134] k'(Rdi-Cmdc-ⅲ)=[JID-Ⅱ(Cmds))]^[JID-Ⅰ(Cmdc-ⅲ)](modp);

[0135] (2) Calculate the signature σ'(Rdi-Cmdc-ⅲ) of the task T(Rdi-Cmdc-ⅲ):

[0136] σ'(Rdi-Cmdc-ⅲ)=H([T(Rdi-Cmdc-ⅲ)]∥[k'(Rdi-Cmdc-ⅲ)]);

[0137] (3) If the equation σ'(Rdi-Cmdc-ⅲ)=σ(Rdi-Cmdc-ⅲ) is true, then output "true"; otherwise, output "false".

[0138] (4) When “true” is output, start executing the work task T(Rdi-Cmdc-ⅲ) to collect the metadata M(Rdi) of the raw data Rdi;

[0139] Step 6-3: When the metadata collection component Cmdc-ⅲ collects the metadata M(Rdi-Cmdc-ⅲ)∈{0,1} of the original data Rdi from the collection server... * At that time, the metadata collection component Cmdc-iii performs the following operations:

[0140] (1) Calculate the signature σ(M(Rdi-Cmdc-ⅲ)) of the metadata M(Rdi-Cmdc-ⅲ):

[0141] σ(M(Rdi-Cmdc-ⅲ))=H([M(Rdi-Cmdc-ⅲ)]∥[k'(Rdi-Cmdc-ⅲ)]);

[0142] (2) Return the work results [M(Rdi-Cmdc-ⅲ), σ(M(Rdi-Cmdc-ⅲ))] to the metadata service component Cmds;

[0143] Step 6-4: When the metadata service component Cmds receives the working result [M(Rdi-Cmdc-ⅲ), σ(M(Rdi-Cmdc-ⅲ))] of the original data Rdi, the metadata service component Cmds performs the following operations:

[0144] (1) Calculate the signature σ'(M(Rdi-Cmdc-ⅲ)) of the metadata M(Rdi-Cmdc-ⅲ):

[0145] σ'(M(Rdi-Cmdc-ⅲ))=H([M(Rdi-Cmdc-ⅲ)]∥[k(Rdi-Cmdc-ⅲ)]);

[0146] (2) If the equation σ'(M(Rdi-Cmdc-ⅲ))=σ(M(Rdi-Cmdc-ⅲ)) is true, then output "true"; otherwise, output "false".

[0147] (3) When “true” is output, the reassembly operation begins.

[0148] Example 7:

[0149] The specific implementation plan for transferring metadata M(Rdi) from the metadata service component Cmds to the asset value calculation component Cavc is as follows:

[0150] Step 7-2: When the metadata service component Cmds sends metadata M(Rdi)∈{0,1} to the asset value calculation component Cavc * At that time, the metadata service component Cmds performs the following operations:

[0151] (1) Calculate the auxiliary signature information k(M(Rdi)-Cavc) of the metadata M(Rdi):

[0152] k(M(Rdi)-Cavc)=[JID-Ⅱ(Cavc)]^[JID-Ⅰ(Cmds)](modp);

[0153] (2) Calculate the signature σ(M(Rdi)-Cavc) of the metadata M(Rdi):

[0154] σ(M(Rdi)-Cavc)=H([M(Rdi)]∥[k(M(Rdi)-Cavc)]);

[0155] (3) Synchronously send the metadata M(Rdi) containing the signature σ(M(Rdi)-Cavc) to the asset value calculation component Cavc;

[0156] Step 7-1: When the asset valuation component Cavc receives metadata M(Rdi) encapsulated with the signature σ(M(Rdi)-Cavc), the asset valuation component Cavc performs the following operation:

[0157] (1) Calculate the auxiliary signature information k'(M(Rdi)-Cavc) of the metadata M(Rdi):

[0158] k'(M(Rdi)-Cavc)=[JID-Ⅱ(Cmds))]^[JID-Ⅰ(Cavc)](modp);

[0159] (2) Calculate the signature σ'(M(Rdi)-Cavc) of the metadata M(Rdi):

[0160] σ'(M(Rdi)-Cavc)=H([M(Rdi)]∥[k'(M(Rdi)-Cavc)]);

[0161] (3) If the equation σ'(M(Rdi)-Cavc)=σ(M(Rdi)-Cavc) is true, then output "true"; otherwise, output "false".

[0162] (4) When “true” is output, the value of the original data Rdi is calculated.

Claims

1. A method for assessing the value of data assets based on metadata, characterized in that, Includes the following steps: Step 1: The Interaction Service Center component performs the following operations: Let p be a large prime number and q be a prime factor of p-1, satisfying p,q≥2 l , l For safety parameters, g is the group Z of order q. * p The generator of elements in the middle, H: {0,1} * →Z * q It is a collision-resistant hash function; The working numbers for constructing the metadata collection component are {JID-Ⅰ(Cmdc), JID-Ⅱ(Cmdc)}, the metadata service component is {JID-Ⅰ(Cmds), JID-Ⅱ(Cmds)}, and the asset value calculation component is {JID-Ⅰ(Cavc), JID-Ⅱ(Cavc)}. JID-II (Cmdc), JID-II (Cmds), and JID-II (Cavc) will be made public in the system; Step 2: When the metadata service component Cmds sends a task T(Rdi-Cmdc)∈{0,1} to the metadata collection component Cmdc to collect metadata M(Rdi) of the raw data Rdi, * At that time, the metadata service component Cmds performs the following operations: Calculate the auxiliary signature information k(Rdi-Cmdc) for task T(Rdi-Cmdc): k(Rdi-Cmdc)=[JID-Ⅱ(Cmdc)]^[JID-Ⅰ(Cmds)](modp); Calculate the signature σ(Rdi-Cmdc) of the task T(Rdi-Cmdc): σ(Rdi-Cmdc)=H([T(Rdi-Cmdc)]ǁ[k(Rdi-Cmdc)]); Send the work command [T(Rdi-Cmdc), σ(Rdi-Cmdc)] of the metadata M(Rdi) belonging to the original data Rdi to the metadata acquisition component Cmdc; When the metadata acquisition component Cmdc receives the work command [T(Rdi-Cmdc), σ(Rdi-Cmdc)] for acquiring the metadata M(Rdi) of the raw data Rdi, the metadata acquisition component Cmdc performs the following operations: Calculate the auxiliary signature information k'(Rdi-Cmdc) for task T(Rdi-Cmdc): k'(Rdi-Cmdc)=[JID-Ⅱ(Cmds))]^[JID-Ⅰ(Cmdc)](modp); Calculate the signature σ'(Rdi-Cmdc) of the task T(Rdi-Cmdc): σ'(Rdi-Cmdc)=H([T(Rdi-Cmdc)]ǁ[k'(Rdi-Cmdc)]); If the equation σ'(Rdi-Cmdc)=σ(Rdi-Cmdc) is true, then output "true"; otherwise, output "false". When the output is "true", the task T(Rdi-Cmdc) for collecting the metadata M(Rdi) of the raw data Rdi is started. When the metadata collection component Cmdc collects the metadata M(Rdi-Cmdc)∈{0,1} of the raw data Rdi from the collection server, * At that time, the metadata collection component Cmdc performs the following operations: The signature σ(M(Rdi-Cmdc)) of the metadata M(Rdi-Cmdc) is calculated as follows: σ(M(Rdi-Cmdc))=H([M(Rdi-Cmdc)]ǁ[k'(Rdi-Cmdc)]); Return the work results [M(Rdi-Cmdc), σ(M(Rdi-Cmdc))] to the metadata service component Cmds; When the metadata service component Cmds receives the working result [M(Rdi-Cmdc), σ(M(Rdi-Cmdc))] of the original data Rdi, the metadata service component Cmds performs the following operations: Compute the signature σ'(M(Rdi-Cmdc)) of the metadata M(Rdi-Cmdc): σ'(M(Rdi-Cmdc))=H([M(Rdi-Cmdc)]ǁ[k(Rdi-Cmdc)]); If the equation σ'(M(Rdi-Cmdc))=σ(M(Rdi-Cmdc)) is true, then output "true"; otherwise, output "false". When the output is "true", the auxiliary signature information k(M(Rdi)-Cavc) of the metadata M(Rdi) is calculated: k(M(Rdi)-Cavc)=[JID-Ⅱ(Cavc)]^[JID-Ⅰ(Cmds)](modp); Compute the signature σ(M(Rdi)-Cavc) of the metadata M(Rdi): σ(M(Rdi)-Cavc)=H([M(Rdi)]ǁ[k(M(Rdi)-Cavc)]); The metadata M(Rdi) containing the signature σ(M(Rdi)-Cavc) is synchronously sent to the asset value calculation component Cavc; Step 3: When the asset valuation component Cavc receives the metadata M(Rdi) encapsulated with the signature σ(M(Rdi)-Cavc), the asset valuation component Cavc performs the following operations: Compute the auxiliary signature information k'(M(Rdi)-Cavc) of the metadata M(Rdi): k'(M(Rdi)-Cavc)=[JID-Ⅱ(Cmds))]^[JID-Ⅰ(Cavc)](modp); Compute the signature σ'(M(Rdi)-Cavc) of the metadata M(Rdi): σ'(M(Rdi)-Cavc)=H([M(Rdi)]ǁ[k'(M(Rdi)-Cavc)]); If the equation σ'(M(Rdi)-Cavc)=σ(M(Rdi)-Cavc) is true, then output "true"; otherwise, output "false". When the output is "true", the value of the original data Rdi is calculated according to the data asset value calculation model. The data asset value calculation model is: S = S1 + S2 + S3; S represents the value of data assets; S1 represents the working time cost of the data acquisition server. S1 = Total working time of the data acquisition server in collecting raw data × (Total price of data acquisition server / Total working time of data acquisition server) S2 represents the storage cost of the storage server, S2 = storage space size of the original data × (total price of the storage server / total storage space of the storage server); S3 represents the processing cost of the processing server. S3 = Execution time of the processing server in calculating the raw data × (Unit price of resources in the processing server for executing the task of calculating the raw data × Number of cores used).

2. A metadata-based data asset valuation system, used to execute the metadata-based data asset valuation method according to claim 1, characterized in that, The data asset valuation system includes: a metadata acquisition component, a metadata service component, an asset value calculation component, and an interactive service center component; any component in the above data asset valuation system can communicate with other components.

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