CN113988606A - Block chain-based data asset value evaluation method - Google Patents
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Abstract
The invention discloses a data asset value evaluation method based on a block chain, and belongs to the technical field of block chains. The method comprises the steps of comparing newly uploaded data, original data of block nodes and existing data of other block nodes by a data mining method, carrying out block chain authentication on a mark of a mining result to obtain a data asset authentication result, wherein the block chain data authentication is based on authentication of data assets, the data asset authentication is relatively clear, carrying out new evaluation authentication of the data assets by a method of 'full-network notification and partial-node consensus authentication', evaluating new data assets, simultaneously carrying out new evaluation on related assets, improving the reliability of data asset evaluation, and effectively solving the problem that the transaction of a large amount of data assets in a block chain network is limited due to low evaluation reliability.
Description
Technical Field
The invention relates to the technical field of block chains, in particular to a data asset value evaluation method based on a block chain.
Background
A bidding auction and block chain authentication method is commonly adopted for data asset right-confirming evaluation based on a block chain, and the defects that the evaluation of the data asset is lack of comparability, the value of the data asset in a block chain network cannot be accurately evaluated, and the interrelationship between the data assets cannot be completely verified in a traceable way. Meanwhile, the block chain data authority tends to be relatively low in data asset authentication attention from the node authentication perspective, and the block chain tends to be applied to the field where the data asset authentication is clear and high in value, so that the transaction of a large number of data assets in the block chain network is limited.
Disclosure of Invention
In view of the above, the present invention provides a method and an apparatus for determining authority and evaluating data assets based on block chain data mining, and mainly aims to solve the problems in the prior art.
According to one aspect of the invention, a data asset value assessment method based on a block chain is provided, and the method comprises the following steps: the block node issues new upload data; s1: and (3) performing data asset right determination on the newly uploaded data: mining the newly uploaded data and the original data of the block nodes and mining the mutual relation between the newly uploaded data and the data of other block nodes through whole network comparison data mining; according to the data mining result, marking the correlation; performing block chain authentication on the data mining result and the mark to obtain a data asset right confirmation result of the newly uploaded data, wherein the newly uploaded data with right confirmed is called as a new data asset, and feeding back a block chain authentication number to the block node; s2: performing data asset assessment on the new data asset and related data assets: mining the mutual influence of the new data assets, the original data and the other block node data on data asset evaluation by utilizing the mutual relation, and determining related data assets of the new data assets; obtaining a preliminary evaluation result of the new data asset according to an original data asset evaluation result of the related data asset, performing node consensus authentication on the preliminary evaluation result, and correcting the preliminary evaluation result of the new data asset and the original data asset evaluation result of the related data asset according to the authentication result to obtain a final evaluation result of the new data asset and a final evaluation result of the related data in the current round; s3: and (3) data asset evaluation result release: and feeding back the final result of the current evaluation of the new data asset to the block nodes, and feeding back the final result of the current evaluation of the related data to other block chain nodes which issue the related data.
As a further improvement of the present invention, the correlation includes: the new uploaded data has specificity and difference compared with the original data of the block node and the data of the other block nodes; similarity or repeatability of the new uploaded data and the original data of the block nodes and the data of the other block nodes; the new uploaded data and the original data of the block nodes and the data of the other block nodes are in a logical relationship; and the co-occurrence probability of the new uploaded data, the original data of the block nodes and the data of the other block nodes is the co-occurrence probability of the data due to relevance.
As a further improvement of the invention, block chain authentication is performed on the data mining result and the mark by adopting a mode of full network notification and partial node consensus authentication; and a screening system is arranged at part of nodes in the block chain transaction network, screening is carried out according to conditions, an invitation is put forward to the screened nodes, and the invited nodes select whether to participate in authentication according to willingness.
As a further improvement of the present invention, the method for screening authentication nodes by the screening system is: and secondly, the nodes have secondary priority authentication participation rights, and the like until the number of the nodes willing to participate in authentication reaches a certain proportion of the nodes in the whole network.
As a further improvement of the invention, the consensus authentication method of the nodes comprises the following steps: the newly uploaded data with high similarity or repeatability is not considered as a new data asset; the newly uploaded data, for which the specificity and diversity reach a certain level and correctness can be confirmed through a logical relationship, is identified as a new data asset; and when the authentication nodes still can not achieve consensus, the logic relation or the co-occurrence probability is provided for the authentication nodes as a complementary condition of authentication to carry out consensus authentication.
As a further improvement of the present invention, the method for acquiring the related data asset of the new data asset specifically comprises: according to the data mining result and the mark, the interrelation of the new data assets with the original data of the block nodes and the data of other block nodes can be determined, wherein the data assets which have one or more of repeatability, similarity, logic relationship and co-occurrence relationship with the new data assets are related data assets of the new data assets.
As a further improvement of the present invention, if the range value amplitude difference of the preliminary evaluation result of the new data asset is small and meets the requirement of the node consensus certification range, the new data asset is certified by a data asset certification block chain and obtains a data certification number, the preliminary evaluation result of the new data asset is taken as the final evaluation result of the current round, and the original data asset evaluation result of the related data asset is taken as the final evaluation result of the current round; if the range value amplitude difference of the preliminary evaluation result of the new data asset is large and does not meet the requirement of the node consensus authentication range, recalculating the preliminary evaluation method of the new data asset by assigning the availability and the applicability of the related data asset to obtain a corrected evaluation result of the new data asset; and correcting the evaluation result of the related data asset according to the corrected evaluation result of the new data asset.
By the technical scheme, the invention has the following beneficial effects:
(1) and comparing newly uploaded data A of the block nodes, original data B of the block nodes and existing data C of other block nodes by adopting a data mining method, and performing block chain authentication on the marks of the mining results to obtain a data asset right confirmation result, so that the evaluation of the data assets is comparable, and the value of the data assets in a block chain network is accurately evaluated.
(2) The block chain data right determination is based on the authentication of the data assets, the data asset authentication is clear, and the credibility of the evaluation of the data assets is high.
(3) The method of 'full-network notification and partial-node consensus authentication' is adopted to perform new evaluation authentication on the data assets, and new evaluation is performed on the related assets while new data assets are evaluated, so that the evaluation value of the data assets is effectively and timely updated, and the reliability is high.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart illustrating a block chain-based data asset value assessment method according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
It will be understood that the description and claims of the present invention and the method and apparatus of the drawings are referred to one another with respect to the features thereof. Furthermore, the terms "first," "second," and the like in the description and in the claims, and in the drawings, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
The core technical problem to be solved by the invention is that the assessment of the data asset right based on the block chain generally adopts a bidding auction and block chain authentication method, so that the assessment of the data asset lacks comparability, and the value and the interrelation of the data asset cannot be accurately assessed.
Aiming at the technical problem, the invention provides a data asset value evaluation method based on a block chain, which is characterized in that whether a data asset is authenticated and authorized by the block chain is determined by mining data of the data asset; and performing data mining on the relationship between the data assets to perform block chain authentication of data asset evaluation.
In the invention, all data of the block node newly uploaded with data and other block nodes are respectively called new upload data A, original data B of the block node and existing data C of other block nodes. The goal of such classification is to prepare the newly uploaded data a for comparison with other data B, C. The data content structures of the newly uploaded data, the original data of the block nodes and the existing data of other block nodes are the same, and the method comprises the following steps: data category, time, attributes, content, source, domain, precision, volume, and the like.
As shown in fig. 1, the technical scheme adopted by the invention is as follows: firstly, inputting newly uploaded data A of block nodes, original data B of the block nodes and existing data C of other block nodes into a whole network comparison data mining module, comparing the data by adopting a data mining method, carrying out block chain authentication on a mark of a mining result to obtain a data asset right confirmation result, and feeding back a block chain authentication number to the block nodes; and meanwhile, when the newly uploaded data is authenticated as the new data asset by the block chain, recording and storing the newly uploaded data A, the data mining result and the block chain authentication number. And then, when the newly uploaded data A is authenticated as a new data asset by the block chain, converting the data mining result and the mark confirmed by the data asset into an evaluation data mark set. And determining related data assets of the new data assets according to the evaluation data mark set, and tracing back related data nodes to which the related data assets belong. And finally, data mining is carried out on the new data assets, the related data assets and the evaluation data links thereof to obtain a preliminary evaluation result of the new data assets and a new evaluation result of the related data, block chain authentication is carried out on the results, and after the evaluation results are corrected, the evaluation results of the new data assets and the evaluation results of the related data are respectively fed back to the upload data block nodes and the related data nodes.
S1: and performing data asset right determination on the newly uploaded data.
S11: and the correlation between the newly uploaded data A and other data B, C is fully mined through the whole network comparison data mining.
The whole-network comparison data mining is data mining aiming at comparison of new uploaded data A, original data B of block nodes and existing data C of other block nodes, the new uploaded data A and other data B, C are used as two comparison sets for data mining, and various interrelations between the new uploaded data A and other data B, C can be fully mined by adopting large data mining algorithms such as classification, regression analysis, clustering, association rules, characteristics, change and deviation analysis and the like.
The whole network comparison data mining results are the specificity, repeatability, similarity and logical relationship of the newly uploaded data A compared with other data B, C and other interrelations. The specific data mining results include: the specificity and diversity of the newly uploaded data a compared to the other data B, C; similarity or repeatability of the newly uploaded data a with other data B, C; the logical relations of cause, effect, sequence, subordination, inclusion, contradiction and the like between the newly uploaded data A and other data B, C; the co-occurrence probability of the new upload data a with other data B, C, and the correlation between the new upload data a and other data B, C. The co-occurrence probability refers to a co-occurrence probability of data due to correlation.
S12: and marking the excavated correlation.
According to the data mining results, the correlation between the newly uploaded data a and other data B, C is labeled, including specificity and difference labels, similarity or repeatability labels, logical relationship labels, co-occurrence probability labels, and the like. The data specificity can be obtained by data mining through big data mining algorithms such as classification and characteristic, the data repeatability and similarity can be obtained by data mining through big data mining algorithms such as classification and clustering, the data logical relation can be obtained by data mining through big data mining algorithms such as regression analysis, association rule, change and deviation analysis, and other mutual relations can be obtained by big data mining algorithms such as classification, regression analysis, clustering, association rule, characteristic, change and deviation analysis.
S13: and performing data asset block chain authentication on the whole network comparison data mining result and the label.
In the technical solution of this embodiment, the data asset block chain authentication needs to be performed on the comparison data mining result and the tag of the whole network by a method of "whole network notification and partial node consensus authentication".
Specifically, some nodes in the blockchain transaction network are screened by a screening system according to conditions and provide invitations to the screened nodes, and the invited nodes select whether to participate in authentication according to willingness.
The method for screening the authentication nodes by the screening system comprises the following steps: and secondly, the nodes have secondary priority authentication participation rights according to the data interrelation of the historical results of the whole-network comparison data mining until the number of the nodes willing to participate in authentication reaches a certain proportion of the whole-network nodes.
The consensus authentication method of the nodes comprises the following steps: according to the whole network comparison data mining result, the data with high repeatability and similarity with other data B, C are determined not to be regarded as new data assets by the common identification of the authentication nodes; data with data specificity reaching a certain level and correctness proved by logical relation is identified as data assets; when the authentication nodes still can not achieve consensus through the steps, other mutual relations are provided for the authentication nodes as complementary conditions of authentication, and the authentication nodes perform consensus authentication according to specific conditions.
Giving an authentication number through the data asset block chain authentication, performing data asset right confirmation, and feeding back a data asset right confirmation result and the authentication number to the block nodes; otherwise, data that fails data asset blockchain authentication cannot be authenticated as data assets. Meanwhile, when the new uploaded data passes through the data asset block chain authentication, the new uploaded data becomes a new data asset, and the new data asset, the data mining result and the block chain authentication number are subjected to chain recording and stored.
S2: and performing data asset valuation on the new data assets.
The idea of the data asset valuation method based on block chain data mining is as follows: and S1, the mutual influence on the evaluation of the data is mined by utilizing the mutual relationship between the new data asset A and other data B, C mined in the new data asset right determination phase, the related data assets of the new data asset are determined, and the initial evaluation result of the new data asset and the new evaluation result of the related data asset are deduced according to the original data asset evaluation result of the related data asset.
S21: related data assets for the new data asset and original assessment data for the related data assets are determined.
In this embodiment, the first step of the data asset valuation method based on block chain data mining is to convert the data mining results and tags in the data asset validation stage into an evaluation data tag set, so as to prepare for clearing the interrelationship between data assets.
The specific method comprises the following steps:
s211, by taking the new data assets authenticated by the right-confirming blockchain as a reference, comparing the data mining results and the marks according to the whole network, screening out the data mining results and the marks related to the new data assets (data asset confirmation stage), confirming the related data assets of the new data assets according to the mining results and the marks, and linking the new data assets, the data mining results and the marks in the data asset confirmation stage and the related data assets, wherein the linking structure is as follows: new data assets-mining results-related data assets.
S212, according to the data mining results and the tags in the data asset confirmation phase, the interrelationship between the new data asset and the other data B, C can be determined, wherein the data assets having the interrelationship with the new data asset such as repeatability, similarity, logical relationship and co-occurrence relationship are called related data assets of the new data asset. And continuously linking the evaluation data of the related data assets after the data asset related to the new data asset is linked, so as to obtain a new link structure: the link structure constitutes an evaluation data tag set of new data assets-mining results-related data assets-evaluation data of related data assets. The original evaluation data of the related data assets comprise data content, interrelation data with other data and current evaluation result data. Wherein the data content of the related data asset has the same structure as the data content of the new data asset; the correlation data with other data is obtained by the integration of the whole network comparison data mining results of the current new data assets and other data B, C which are historically involved in data asset confirmation; the current evaluation result data includes data evaluation results, value, evaluation time, participating nodes, historical transaction prices, node credits, and the like. The evaluation time is obtained by starting with the most initial data asset evaluation result of the related data, and continuously adding new data assets to the related data and continuously updating the evaluation result by adopting the data asset evaluation method of the embodiment. The screening method of the participating nodes of the current evaluation result is the same as the screening method of the authentication nodes in the whole network comparison data mining result and the whole network comparison data mining mark. Historical trading prices and node credits for current evaluation results are provided by the data asset records.
S22: results of the preliminary evaluation of the new data asset and results of the new evaluation of the related data asset are determined.
In the invention, the new data assets, the related data assets and the evaluation data links thereof are subjected to data mining, so that the initial evaluation result of the new data assets and the new evaluation result of the data assets of the related data can be obtained.
S221: results of the preliminary evaluation of the new data asset.
The specific method comprises the following steps: since the related data assets are used as new uploaded data and the interrelationship between the related data assets and other data is mined, the position and the value of the new data assets in the whole network data asset relational graph can be definitely known through the data mining result of data authority and the combing of new data asset records. The preliminary evaluation result of the new data asset can be inferred through the original evaluation data of the related data. The preliminary evaluation result is obtained by setting an evaluation function of the new data asset through numerical simulation of the evaluation result of the related data asset of the new data asset, and obtaining the preliminary evaluation result of the new data asset according to the correlation between the new data asset and the related data asset.
S222: the final result of the current round of evaluation of the new data asset.
In this embodiment, based on the evaluation result of the data assets of the whole network, the evaluation result of the related data may be corrected to obtain a new evaluation result of the related data. The specific method is to adopt a method of 'full-network notification and partial-node consensus authentication' to carry out new evaluation authentication on data assets. The authentication node screening method is the same as that of the whole network comparison data mining result and the authentication node screening method in the mark.
The consensus authentication method comprises the following steps:
and if the range value amplitude difference of the initial evaluation result of the new data asset is smaller and meets the requirement of the authentication range, the new data asset passes through the data asset evaluation block chain authentication and obtains a data evaluation authentication number, the initial evaluation result of the new data asset is taken as the final evaluation result of the current round, the original evaluation result of the related data is taken as the final evaluation result of the current round, and the set of the initial evaluation result and the final evaluation result of the current round of the data asset is taken as the final evaluation result of the current round of the data asset.
If the range value amplitude difference of the new data asset preliminary evaluation result is larger and does not meet the requirement of the authentication range, the preliminary evaluation result of the new data asset is not authenticated by the data asset evaluation block chain, the preliminary evaluation result of the new data asset needs to be corrected, the requirement of the evaluation function of the corrected new data asset is provided for the authentication node, the numerical simulation of the related data asset evaluation result of the new data asset and the preliminary evaluation result of the new data asset are analyzed, the related data asset and the current evaluation result of the related data asset which cause the excessive range value amplitude difference of the preliminary evaluation result of the new data asset are judged, the availability of the related data asset and the current evaluation result of the related data asset are determined through the consensus of the authentication node, and the applicability of the related data asset in the new data asset evaluation is judged through the consensus of the authentication node according to the mutual relationship between the new data asset and the related data asset, and (4) recalculating the preliminary evaluation method of the new data assets by assigning the availability and the suitability of the related data assets, and obtaining the corrected evaluation result of the new data assets. And taking the evaluation result of the corrected new data asset as the final result of the current evaluation.
S223: the final result of the current round of evaluation of the relevant data assets.
Further, according to the corrected evaluation result of the new data assets and the interrelation of the related data assets and other data assets, the evaluation results of the related data assets are corrected by adopting the same method as the initial evaluation method of the new data assets, and the new evaluation results of the related data assets are obtained. And collecting the new data assets, the new evaluation results of the related data assets and other related data asset evaluation results which do not need to be re-evaluated together and authenticating through a block chain to obtain the final result of the data asset evaluation in the current round.
S3: and 6, issuing the final result of the data asset in the round of evaluation.
And feeding back the final result of the current evaluation of the new data assets to the block nodes for releasing the new uploaded data, and feeding back the final result of the current evaluation of the related data to other block chain nodes for releasing the related data.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
Claims (7)
1. A data asset value assessment method based on a block chain is characterized by comprising the following steps: the block node issues new upload data;
s1: and (3) performing data asset right determination on the newly uploaded data: mining the newly uploaded data and the original data of the block nodes and mining the mutual relation between the newly uploaded data and the data of other block nodes through whole network comparison data mining; according to the data mining result, marking the correlation; performing block chain authentication on the data mining result and the mark to obtain a data asset right confirmation result of the newly uploaded data, wherein the newly uploaded data with right confirmed is called as a new data asset, and feeding back a block chain authentication number to the block node;
s2: performing data asset assessment on the new data asset and related data assets: mining the mutual influence of the new data assets, the original data and the other block node data on data asset evaluation by utilizing the mutual relation, and determining related data assets of the new data assets; obtaining a preliminary evaluation result of the new data asset according to an original data asset evaluation result of the related data asset, performing node consensus authentication on the preliminary evaluation result, and correcting the preliminary evaluation result of the new data asset and the original data asset evaluation result of the related data asset according to the authentication result to obtain a final evaluation result of the new data asset and a final evaluation result of the related data in the current round;
s3: and (3) data asset evaluation result release: and feeding back the final result of the current evaluation of the new data asset to the block nodes, and feeding back the final result of the current evaluation of the related data to other block chain nodes which issue the related data.
2. The blockchain-based data asset worth assessment method according to claim 1, wherein said interrelationships comprise: the new uploaded data has specificity and difference compared with the original data of the block node and the data of the other block nodes; similarity or repeatability of the new uploaded data and the original data of the block nodes and the data of the other block nodes; the new uploaded data and the original data of the block nodes and the data of the other block nodes are in a logical relationship; and the co-occurrence probability of the new uploaded data, the original data of the block nodes and the data of the other block nodes is the co-occurrence probability of the data due to relevance.
3. The method according to claim 2, wherein block chain-based data asset value assessment is performed on the data mining results and the markers by means of full network notification and partial node consensus authentication; and a screening system is arranged at part of nodes in the block chain transaction network, screening is carried out according to conditions, an invitation is put forward to the screened nodes, and the invited nodes select whether to participate in authentication according to willingness.
4. The blockchain-based data asset value assessment method according to claim 3, wherein the method for the screening system to screen the authentication nodes is: and secondly, the nodes have secondary priority authentication participation rights, and the like until the number of the nodes willing to participate in authentication reaches a certain proportion of the nodes in the whole network.
5. The blockchain-based data asset value assessment method according to claim 3, wherein the consensus authentication method of the nodes is as follows: the newly uploaded data with high similarity or repeatability is not considered as a new data asset; the newly uploaded data, for which the specificity and diversity reach a certain level and correctness can be confirmed through a logical relationship, is identified as a new data asset; and when the authentication nodes still can not achieve consensus, the logic relation or the co-occurrence probability is provided for the authentication nodes as a complementary condition of authentication to carry out consensus authentication.
6. The method for assessing the worth of data assets based on a blockchain according to claim 1, wherein the method for obtaining related data assets of the new data assets specifically comprises: according to the data mining result and the mark, the interrelation of the new data assets with the original data of the block nodes and the data of other block nodes can be determined, wherein the data assets which have one or more of repeatability, similarity, logic relationship and co-occurrence relationship with the new data assets are related data assets of the new data assets.
7. The method according to claim 1, wherein if the range value difference of the preliminary evaluation result of the new data asset is small and meets the requirement of the node consensus certification range, the new data asset passes through data asset evaluation blockchain certification and obtains a data evaluation certification number, the preliminary evaluation result of the new data asset is used as a final evaluation result of the current round, and the original data asset evaluation result of the related data asset is used as a final evaluation result of the current round; if the range value amplitude difference of the preliminary evaluation result of the new data asset is large and does not meet the requirement of the node consensus authentication range, recalculating the preliminary evaluation method of the new data asset by assigning the availability and the applicability of the related data asset to obtain a corrected evaluation result of the new data asset; and correcting the evaluation result of the related data asset according to the corrected evaluation result of the new data asset.
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| CN115689596B (en) * | 2022-08-27 | 2023-07-07 | 北京华宜信科技有限公司 | Non-customized data asset valuation method |
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