CN111915436B

CN111915436B - Digital asset assessment method, device, equipment and storage medium

Info

Publication number: CN111915436B
Application number: CN202010777243.1A
Authority: CN
Inventors: 聂二保; 朱江
Original assignee: Beijing Kingsoft Cloud Network Technology Co Ltd
Current assignee: Beijing Kingsoft Cloud Network Technology Co Ltd
Priority date: 2020-08-05
Filing date: 2020-08-05
Publication date: 2024-02-23
Anticipated expiration: 2040-08-05
Also published as: CN111915436A

Abstract

The embodiment of the application discloses a digital asset assessment method, a device, equipment and a storage medium, wherein the method comprises the following steps: after each transaction aiming at the target digital asset is finished, acquiring transaction prediction information given by an original holder of the transaction aiming at the target digital asset as a transaction prediction information user corresponding to the transaction, and writing the transaction prediction information corresponding to the transaction into a alliance chain; verifying transaction prediction information corresponding to the last transaction of the current transaction to obtain a prediction judgment result according to the transaction result of the target digital asset in the current transaction, taking the prediction judgment result as the prediction judgment result corresponding to the last transaction, and writing the prediction judgment result corresponding to the last transaction into a alliance chain; and evaluating the target digital asset according to the transaction prediction information and the prediction judgment result corresponding to each transaction of the target digital asset in the alliance chain. The scheme can ensure that the evaluation result of the digital asset has higher reference value and guidance for traders.

Description

Digital asset assessment method, device, equipment and storage medium

Technical Field

The present disclosure relates to the field of internet technologies, and in particular, to a digital asset assessment method, apparatus, device, and storage medium.

Background

The digital transformation of the original assets is realized, and the digital assets are traded by virtue of a system circulation constructed based on a alliance chain, so that the digital assets become a common trading mode in a modern digital financial scene. In the digital asset transaction process, the digital asset is evaluated, and a digital asset evaluation result is provided for a transactor, so that the transactor can be helped to know the value and risk of the digital asset.

Currently, the digital asset is mainly relied on a transaction platform and a third-party professional institution, and the digital asset is evaluated based on the information of a digital asset holder, the digital asset transaction information, the information of the digital asset itself and the like through an existing model, so that a digital asset evaluation result is determined.

However, the above implementation suffers from the following drawbacks: first, the digital asset assessment framework is not complete enough, whether it is a trading platform or a third party professional organization, the digital asset is assessed by standing on the perspective of bystanders, and it is difficult to know and grasp factors such as market performance, market risk, trading profit opportunity and the like of the digital asset deeply, so that the assessment result of the digital asset is often not deep enough, and the reference value is not high for traders. Secondly, the transaction platform and the third party professional institutions often rely on generated transaction data when evaluating the digital assets, and lack of transaction expected information for markets, so that the digital asset evaluation results are not strong in guidance.

Disclosure of Invention

The embodiment of the application provides a digital asset evaluation method, device, equipment and storage medium, which can evaluate digital assets better and ensure that the evaluation result of the digital assets has higher reference value and guidance for traders.

In view of this, a first aspect of the present application provides a digital asset assessment method, the method comprising:

after each transaction aiming at a target digital asset is finished, acquiring transaction prediction information given by an original holder of the transaction aiming at the target digital asset as transaction prediction information corresponding to the transaction, wherein the original holder of the transaction is a user holding the target digital asset before the transaction; writing the transaction prediction information corresponding to the current transaction into a alliance chain;

verifying transaction prediction information corresponding to the last transaction of the current transaction according to the transaction result of the target digital asset in the current transaction to obtain a prediction judgment result of the transaction prediction information corresponding to the last transaction as the prediction judgment result corresponding to the last transaction; writing the prediction judgment result corresponding to the last transaction into the alliance chain;

And evaluating the target digital asset according to the corresponding transaction prediction information and the prediction judgment result of each transaction aiming at the target digital asset in the alliance chain.

Optionally, the obtaining transaction prediction information given by the original holder of the present transaction for the target digital asset includes:

acquiring transaction prediction information given by an original holder of the current transaction aiming at the target digital asset through an intelligent contract;

writing the transaction prediction information corresponding to the current transaction into a alliance chain, wherein the writing comprises the following steps:

encrypting the intelligent contract by using a private key, and writing the encrypted intelligent contract into the alliance chain.

Optionally, the transaction prediction information includes any one of the following: the target digital asset price increases, the target digital asset price decreases, and the target digital asset price levels;

verifying the transaction prediction information corresponding to the last transaction of the current transaction according to the transaction result of the target digital asset in the current transaction to obtain a prediction judgment result of the transaction prediction information corresponding to the last transaction, wherein the method comprises the following steps:

acquiring the transaction price of the target digital asset in the current transaction as a first transaction price; acquiring the transaction price of the target digital asset in the last transaction as a second transaction price;

When the transaction prediction information corresponding to the last transaction is the target digital asset price rising, if the first transaction price is higher than the second transaction price, determining that the prediction judgment result of the transaction prediction information corresponding to the last transaction is correct; if the first transaction price is not higher than the second transaction price, determining that the prediction judgment result of the transaction prediction information corresponding to the last transaction is wrong;

when the transaction prediction information corresponding to the last transaction is the target digital asset price drop, if the first transaction price is lower than the second transaction price, determining that the prediction judgment result of the transaction prediction information corresponding to the last transaction is correct; if the first transaction price is not lower than the second transaction price, determining that the prediction judgment result of the transaction prediction information corresponding to the last transaction is wrong;

when the transaction prediction information corresponding to the last transaction is the target digital asset price, if the first transaction price is equal to the second transaction price, determining that the prediction judgment result of the transaction prediction information corresponding to the last transaction is correct; if the first transaction price is not equal to the second transaction price, determining that the prediction judgment result of the transaction prediction information corresponding to the last transaction is wrong.

Optionally, after verifying the transaction prediction information corresponding to the last transaction of the current transaction according to the transaction result of the target digital asset in the current transaction to obtain the prediction judgment result of the transaction prediction information corresponding to the last transaction, the method further includes:

and when the prediction judgment result corresponding to the last transaction indicates that the transaction prediction information corresponding to the last transaction is correct, giving a rewarding asset to the original holder of the last transaction according to the transaction price of the target digital asset in the current transaction and/or the transaction price of the target digital asset in the last transaction.

and giving a bonus asset to the original holder of the last transaction according to the transaction price of the target digital asset in the current transaction and/or the transaction price of the target digital asset in the last transaction, including:

When the transaction prediction information corresponding to the last transaction is that the price of the target digital asset rises, determining the rewarded asset according to a first rewarding proportion and a difference value between the first transaction price and the second transaction price;

when the transaction prediction information corresponding to the last transaction is that the price of the target digital asset drops, determining the rewarded asset according to a second rewarding proportion and a difference value between the second transaction price and the first transaction price;

and when the transaction prediction information corresponding to the last transaction is the target digital asset price, determining the bonus asset according to a third bonus proportion and the first transaction price or the second transaction price.

Optionally, the first prize scale, the second prize scale and the third prize scale are set by the original holder of the last transaction or are fixedly configured by a transaction platform.

Optionally, the bonus asset is paid by the original holder in the current transaction to the original holder of the last transaction.

Optionally, before the obtaining the transaction prediction information given by the original holder of the present transaction for the target digital asset, the method further includes:

Determining a time difference value between the current time and the time when the current transaction is ended;

and judging whether the time difference value exceeds a predicted time threshold, and if so, not allowing the original holder of the transaction to give transaction prediction information for the target digital asset.

Optionally, the evaluating the target digital asset according to the transaction prediction information and the prediction judgment result corresponding to each transaction of the target digital asset in the coalition chain includes:

generating a transaction forecast information sequence for the target digital asset according to the transaction forecast information corresponding to each transaction for the target digital asset in the alliance chain;

generating a prediction judgment result sequence aiming at the target digital asset according to the prediction judgment results corresponding to each transaction aiming at the target digital asset in the alliance chain;

and evaluating the target digital asset according to the transaction prediction information sequence and the prediction judgment result sequence.

A second aspect of the present application provides a digital asset assessment device, the device comprising:

the prediction information storage module is used for acquiring transaction prediction information given by an original holder of a current transaction for the target digital asset after each transaction for the target digital asset is finished, wherein the original holder of the current transaction is a user holding the target digital asset before the current transaction; writing the transaction prediction information corresponding to the current transaction into a alliance chain;

The judgment result storage module is used for verifying the transaction prediction information corresponding to the last transaction of the current transaction according to the transaction result of the target digital asset in the current transaction to obtain the prediction judgment result of the transaction prediction information corresponding to the last transaction as the prediction judgment result corresponding to the last transaction; writing the prediction judgment result corresponding to the last transaction into the alliance chain;

and the evaluation module is used for evaluating the target digital asset according to the corresponding transaction prediction information and the prediction judgment result of each transaction aiming at the target digital asset in the alliance chain.

Optionally, the prediction information storage module is specifically configured to:

the judging result storage module is specifically configured to:

Optionally, the apparatus further includes:

and the rewarding module is used for giving out rewarding assets to the original holder of the last transaction according to the transaction price of the target digital asset in the current transaction and/or the transaction price of the target digital asset in the last transaction when the forecast judgment result corresponding to the last transaction indicates that the transaction forecast information corresponding to the last transaction is correct.

the reward module is specifically configured to:

Optionally, the apparatus further includes:

a predictive time control module for determining a time difference between a current time and a time when the current transaction is ended; and judging whether the time difference value exceeds a predicted time threshold, and if so, not allowing the original holder of the transaction to give transaction prediction information for the target digital asset.

Optionally, the evaluation module is specifically configured to:

A third aspect of the present application provides an apparatus comprising: a processor and a memory:

the memory is used for storing a computer program and transmitting the computer program to the processor;

the processor is configured to execute the digital asset assessment method according to the first aspect according to the computer program.

A fourth aspect of the present application provides a computer readable storage medium for storing a computer program for performing the digital asset assessment method of the first aspect described above.

A fifth aspect of the present application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the digital asset assessment method of the first aspect described above.

From the above technical solutions, the embodiments of the present application have the following advantages:

The embodiment of the application provides a digital asset assessment method, in the process of assessing digital assets, transaction prediction information given by an original holder of the digital assets for the digital assets and a prediction judgment result corresponding to the transaction prediction information are innovatively introduced, and as the original holder of the digital assets generally understands more deeply about the variation condition of the digital assets in the market, the given transaction prediction information has higher reference significance for assessing the digital assets; in addition, the prediction judgment result corresponding to the transaction prediction information is introduced into the evaluation of the digital asset, so that a transactor can be helped to know the digital asset by combining the transaction prediction information and the corresponding prediction judgment result, and the evaluation result of the digital asset is ensured to provide higher reference value and guiding value for the transactor.

Drawings

FIG. 1 is a schematic diagram of an overall framework for digital asset transactions provided by embodiments of the present application;

FIG. 2 is a schematic diagram of a transaction preparation layer provided in an embodiment of the present application;

FIG. 3 is a flow chart of a digital asset assessment method according to an embodiment of the present application;

FIG. 4 is a schematic diagram of an implementation process of a digital asset assessment method according to an embodiment of the present application;

FIG. 5 is a schematic diagram of a digital asset assessment device according to an embodiment of the present application;

fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application;

fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present application.

Detailed Description

In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.

The terms "first," "second," "third," "fourth" and the like in the description and in the claims of this application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In the related art, a transaction platform and a third-party professional institution adopt a specific model to evaluate the digital asset according to the historical transaction data of the digital asset, and the reference value and the guiding value of the digital asset evaluation result obtained in this way are generally not high. The reason is that on one hand, the trading platform and the third party professional institutions are not deeply aware of the market performance, market risk, trading profit opportunity and other factors of the digital asset, so that the obtained digital asset assessment result has low reference value; on the other hand, when the trading platform and the third-party professional institution evaluate the digital asset, the historical trading data of the digital asset is based on, and the trading forecast information of the market is lacking, so that the guiding value of the obtained digital asset evaluation result is not high.

Aiming at the problems of the related art, the embodiment of the application provides a digital asset assessment method, wherein in the process of assessing digital assets, transaction prediction information of a transactor on the digital assets and a prediction judgment result corresponding to the transaction prediction information are introduced, so that the obtained digital asset assessment result can have higher reference value and guiding value.

Specifically, in the digital asset assessment method provided in the embodiment of the present application, after each transaction for a target digital asset is completed, transaction prediction information given by an original holder of the current transaction (a user who holds the target digital asset before the current transaction) for the target digital asset is obtained as transaction prediction information corresponding to the current transaction, and the transaction prediction information corresponding to the current transaction is written into a coalition chain; in addition, according to the transaction result of the target digital asset in the current transaction, verifying the transaction prediction information corresponding to the last transaction of the current transaction, determining the prediction judgment result of the transaction prediction information corresponding to the last transaction as the prediction judgment result corresponding to the last transaction, and writing the prediction judgment result corresponding to the last transaction into the alliance chain; further, the target digital asset may be evaluated based on the respective transaction prediction information and prediction decisions for each transaction of the target digital asset in the coalition chain.

In the process of evaluating the digital asset, the method creatively introduces the transaction prediction information given by the original holder of the digital asset for the digital asset and the prediction judgment result corresponding to the transaction prediction information. The original holder of the digital asset generally understands the change condition of the digital asset in the market more deeply, so that the given transaction prediction information has higher reference significance for the evaluation of the digital asset, and the transaction prediction information corresponding to each transaction is introduced into the digital asset evaluation frame, so that the integrity of the digital asset evaluation frame can be continuously improved. The method and the device have the advantages that the prediction judgment result corresponding to the transaction prediction information is introduced into the evaluation of the digital asset, so that a transactor can be helped to combine the transaction prediction information and the corresponding prediction judgment result to evaluate the digital asset. In this way, the assessment results of the digital assets are guaranteed to provide higher reference and guiding values for the trader.

It should be appreciated that the subject of execution of the digital asset assessment method described above may be a device, such as a terminal device or server, having federated chain access rights. The terminal equipment can be a smart phone, a computer, a tablet personal computer, a personal digital assistant and the like; the server can be an application server or a Web server, and can be an independent server or a cluster server in actual deployment.

In order to facilitate understanding of the digital asset assessment method provided by the embodiments of the present application, the following description is given of an overall framework for digital asset transactions.

Referring to fig. 1, fig. 1 is a schematic diagram of an overall framework of a digital asset transaction according to an embodiment of the present application. As shown in fig. 1, the whole framework of the digital asset transaction is divided into two layers, namely a transaction preparation layer and an asset transaction layer, and in practical application, the implementation of the transaction preparation layer and the asset transaction layer is based on a alliance chain system.

The transaction preparation layer is used for providing an implementation basis for the transaction of the digital asset, and comprises a digital asset creation stage, a digital asset registration stage, a digital asset pricing stage and a digital asset publishing stage.

To facilitate an understanding of the specific implementation of the transaction preparation layer, an exemplary description will be given below of the specific implementation of the transaction preparation layer, taking the accounts payable as the original asset, in conjunction with the schematic of the transaction preparation process shown in fig. 2. As shown in fig. 2, enterprises a and B may form accounts receivable C in a transaction form of product sales and purchase based on a real trade background, and the enterprise a may initiate a policy service for the accounts receivable C; after the money C enters a digital asset transaction environment, for example, after entering a certain transaction platform P, transaction preparation is completed through a plurality of links such as data submission, joint validation, cross validation, asset registration, asset release and the like, namely, the work of the transaction preparation layer in FIG. 1 is completed; in the trading platform P, after the money C is converted into the digital asset D through the series of standardized processes, a coupon can be issued on the public market of the trading platform P, so as to represent that the digital asset D can enter a subsequent asset trading link.

The asset transaction layer supports multiple transactions on the released digital asset, and the digital asset evaluation method provided by the embodiment of the application is applied to the asset transaction layer. In the process of multiple transactions of the digital asset, the method for evaluating the digital asset provided by the embodiment of the application can obtain the transaction prediction information given by the user holding the digital asset for the digital asset before the transaction as the transaction prediction information corresponding to the transaction and write the transaction prediction information into the alliance chain after each transaction for the digital asset is finished; meanwhile, according to the transaction result of the digital asset in the current transaction, the transaction prediction information corresponding to the last transaction is verified to obtain the prediction judgment result of the transaction prediction information corresponding to the last transaction, and the prediction judgment result is used as the prediction judgment result corresponding to the current transaction and is also written into the alliance chain; furthermore, transaction prediction information and prediction judgment results corresponding to each transaction for the digital asset recorded in the alliance chain can be provided for the transactor, so that transaction reference information for the digital asset is provided for the transactor, and the transactor is helped to evaluate the digital asset better.

The digital asset assessment method provided by the present application is described below by way of example.

Referring to fig. 3, fig. 3 is a flow chart of a digital asset assessment method according to an embodiment of the present application. For convenience of description, the following embodiments will be described taking a server as an execution subject. As shown in fig. 3, the digital asset assessment method includes:

step 301: after each transaction aiming at a target digital asset is finished, acquiring transaction prediction information given by an original holder of the transaction aiming at the target digital asset as transaction prediction information corresponding to the transaction, wherein the original holder of the transaction is a user holding the target digital asset before the transaction; and writing the transaction prediction information corresponding to the current transaction into a alliance chain.

The user holding the target digital asset sells the target digital asset to other users, and the transfer of the target digital asset is completed, i.e., the user can be regarded as ending one transaction for the target digital asset. In this transaction, the user who originally holds the target digital asset (i.e., the seller of the target digital asset) is considered the original holder of the transaction in this application.

After one transaction aiming at the target digital asset is finished, the transaction platform can prompt the original holder of the transaction to give transaction prediction information aiming at the target digital asset, namely prompt the original holder of the transaction to predict the transaction result of the target digital asset in the next transaction. Accordingly, the original holder of the present transaction can give the transaction forecast information for the target digital asset based on his own knowledge of the change of the target digital asset in the market.

In specific implementation, the server can acquire the transaction prediction information given by the original holder of the transaction through the intelligent contract. The transaction platform can set several transaction prediction options in advance through the intelligent contract, such as UP target digital asset price, down target digital asset price DOWM and flat target digital asset price HOLD, when the original holder of the transaction predicts the transaction result of the target digital asset in the next transaction, one option which accords with the expected option can be selected from the transaction prediction options provided by the intelligent contract as the transaction prediction information given by the intelligent contract.

Of course, in some cases, the transaction prediction option may not be given to the original holder of the present transaction, but the transaction prediction information may be directly given by the original holder of the present transaction, which is not limited in this application.

After the server obtains the transaction forecast information given by the original holder of the current transaction, the transaction forecast information is used as the transaction forecast information corresponding to the current transaction, and the transaction forecast information is written into the alliance chain. Specifically, after the server obtains the transaction prediction information given by the original holder of the transaction through the intelligent contract, the private key can be utilized to encrypt the intelligent contract, so that the encrypted intelligent contract is written into the alliance chain, and the information is broadcasted to the whole network.

In some embodiments, in order to encourage the original holder of the present transaction to give a prediction for the price change direction of the target digital asset, and at the same time guarantee the accuracy of the prediction, the method provided by the embodiment of the application further provides a rewarding mechanism. That is, the original holder of the present transaction is allowed to set a reward ratio through the intelligent contract, such as 5% of the overall transaction price of the target digital asset, 10% of the transaction spread of the target digital asset, etc., if the transaction prediction information given by the original holder of the present transaction is verified to be correct according to the next transaction result of the target digital asset, the transaction platform can give the reward asset to the original holder of the present transaction according to the reward ratio.

It should be noted that, considering the problem of compliance of the financial asset transaction, an upper limit is usually set on the above-mentioned bonus proportion, such as 10% of the total transaction price of the target digital asset, 20% of the transaction spread of the target digital asset, and so on. Of course, in practical application, the reward ratio may also be set by the first holder of the target digital asset, the former holder of the last transaction may be rewarded by the subsequent transaction according to the reward ratio, and the transaction platform may also set a fixed reward ratio, which is not limited in this application.

In some embodiments, the transaction platform and the first holder of the target digital asset may also set a prediction time threshold, and the original holder of the present transaction is required to give the transaction prediction information within the prediction time threshold, so as to ensure that the transaction prediction information can be obtained in time. Specifically, after one transaction for the target digital asset is finished, the server can continuously determine a time difference between the current time and the time when the transaction is finished before the transaction prediction information given by the original holder of the transaction is obtained, and judge whether the time difference exceeds a predicted time threshold, if so, the original holder of the transaction is not allowed to give the transaction prediction information for the target digital asset.

For example, assuming that the predicted time threshold is one hour, the server allows the original holder of the current transaction to give the transaction prediction information within one hour after the end of the current transaction, beyond which the server will not receive the transaction prediction information given by the original holder of the current transaction.

In practical application, after the bearer of the transaction completes the setting of parameters such as transaction prediction information, rewarding proportion and the like through an intelligent contract, the server can generate quintuple < target digital asset encryption information, an original bearer account, transaction prediction information, rewarding proportion and forecast time threshold >, wherein the target digital asset encryption information refers to information obtained by encrypting information related to the transaction of the target digital asset; further, a plaintext abstract is generated based on the five-tuple, namely, the type of the target digital asset, the rewarding proportion and the predicted time threshold, and after the server signs the private key, the message is broadcasted to the whole network.

Step 302: after each transaction of a target digital asset is finished, verifying transaction prediction information corresponding to the last transaction of the current transaction according to the transaction result of the target digital asset in the current transaction to obtain a prediction judgment result of the transaction prediction information corresponding to the last transaction, wherein the prediction judgment result is used as the prediction judgment result corresponding to the last transaction; and writing the prediction judgment result corresponding to the last transaction into the alliance chain.

After one transaction for the target digital asset is finished, the server can verify the transaction prediction information corresponding to the last transaction according to the transaction result of the target digital asset in the current transaction, namely verify the transaction prediction information given by the original holder of the last transaction, so as to obtain a prediction judgment result of the transaction prediction information corresponding to the last transaction, and the prediction judgment result can represent whether the transaction prediction information corresponding to the last transaction is correct or not. And correspondingly writing the prediction judgment result serving as the prediction judgment result corresponding to the last transaction into the alliance chain.

When the method is concretely implemented, after determining that one transaction for the target digital asset is finished, the server can start an intelligent contract, and judge the transaction prediction information corresponding to the last transaction according to the transaction result of the target digital asset in the current transaction so as to determine the prediction judgment result of the transaction prediction information corresponding to the last transaction.

In some embodiments, the transaction prediction information may include any of the following: target digital asset price UP, target digital asset price DOWN, and target digital asset price HOLD. When the server verifies the transaction prediction information corresponding to the last transaction, the transaction price of the target digital asset in the current transaction can be obtained as a first transaction price, and the transaction price of the target digital asset in the last transaction is obtained as a second transaction price.

Under the condition that the transaction prediction information corresponding to the last transaction is the target digital asset price rising UP, if the first transaction price is higher than the second transaction price, the prediction judgment result of the transaction prediction information corresponding to the last transaction can be determined to be correct; otherwise, if the first transaction price is not higher than the second transaction price, it may be determined that the prediction and judgment result of the transaction prediction information corresponding to the last transaction is wrong.

Under the condition that the transaction prediction information corresponding to the last transaction is the target digital asset price DOWN, if the first transaction price is lower than the second transaction price, the prediction judgment result of the transaction prediction information corresponding to the last transaction can be determined to be correct; otherwise, if the first transaction price is not lower than the second transaction price, it may be determined that the prediction and judgment result of the transaction prediction information corresponding to the last transaction is wrong.

Under the condition that the transaction prediction information corresponding to the last transaction is the target digital asset price level HOLD, if the first transaction price is equal to the second transaction price, the prediction judgment result of the transaction prediction information corresponding to the last transaction can be determined to be correct; otherwise, if the first transaction price is not equal to the second transaction price, it may be determined that the prediction and judgment result of the transaction prediction information corresponding to the last transaction is wrong.

As introduced above, to encourage the primary holder of each transaction to give a forecast for the price change direction of the target digital asset, while ensuring the accuracy of the forecast, the method provided by embodiments of the present application further support giving rewards to the correct primary holder for the forecast. Specifically, when the prediction judgment result corresponding to the last transaction indicates that the transaction prediction information corresponding to the last transaction is correct, the server may provide the rewarded asset for the original holder of the last transaction according to the transaction price of the target digital asset in the current transaction and/or the transaction price of the target digital asset in the last transaction.

In one possible implementation, when the transaction prediction information corresponding to the previous transaction is the target digital asset price UP, and the prediction judgment result of the transaction prediction information indicates that the transaction prediction information is correct, the server may determine, according to the first reward proportion and the difference between the first transaction price and the second transaction price, a reward asset that needs to be given to the original holder of the previous transaction. I.e., calculating the product of the first bonus proportion and the difference between the first transaction price and the second transaction price as a bonus asset.

When the transaction prediction information corresponding to the last transaction is the target digital asset price drop DOWN, and the prediction judgment result of the transaction prediction information indicates that the transaction prediction information is correct, the server can determine the bonus asset required to be given for the original holder of the last transaction according to the second bonus proportion and the difference value between the second transaction price and the first transaction price. I.e., calculating the product of the second bonus proportion and the difference between the second transaction price and the first transaction price as a bonus asset.

When the transaction prediction information corresponding to the last transaction is the target digital asset price level HOLD, and the prediction judgment result of the transaction prediction information indicates that the transaction prediction information is correct, the server can determine the bonus asset required to be given for the original holder of the last transaction according to the third bonus proportion and the first transaction price or the second transaction price. I.e., calculating the product of the third bonus proportion and the first transaction price as a bonus asset or calculating the product of the third bonus proportion and the second transaction price as a bonus asset.

In practical applications, the first prize scale, the second prize scale, and the third prize scale may be the same or different, and specific numerical values of the first prize scale, the second prize scale, and the third prize scale are not limited in this application. Furthermore, as described above, the first, second, and third rates may be set by the original holder of the last transaction or the first holder of the target digital asset via smart contracts, or may be fixedly configured by the transaction platform.

It should be noted that, the implementation manner of calculating the bonus asset is merely an example, and in practical application, the trading platform may also provide for calculating the bonus asset according to the bonus proportion or the trading price of the target digital asset in the current trade, or provide for calculating the bonus asset according to the bonus proportion or the trading price of the target digital asset in the last trade, or may directly provide a fixed asset as the bonus asset, which is not limited in any way herein.

It should be understood that if the prediction judgment result corresponding to the previous transaction indicates that the transaction prediction information corresponding to the previous transaction is wrong, the bonus asset is not required to be given for the original holder of the previous transaction.

In practical application, the bonus assets can be paid by the original holder in the current transaction to the original holder who makes the last transaction. That is, if it is determined through the above-described operation that the transaction prediction information corresponding to the previous transaction is correct, the original holder of the present transaction needs to pay the bonus asset to the original holder of the previous transaction. Of course, the above-mentioned bonus asset may also be paid by the first holder of the target digital asset, for example, the first holder of the target digital asset may specify the bonus asset in advance, and if it is determined that the transaction prediction information corresponding to the last transaction is correct, part or all of the bonus asset may be paid to the original holder of the last transaction.

Alternatively, the rewards results described above may also be written by the server to the federation chain.

Step 303: and evaluating the target digital asset according to the corresponding transaction prediction information and the prediction judgment result of each transaction aiming at the target digital asset in the alliance chain.

The server obtains transaction prediction information given by the original holder of the current transaction through the intelligent contract and judges the transaction prediction information given by the original holder of the last transaction through the intelligent contract to obtain a corresponding prediction judgment result when each transaction for the target digital asset is finished through the steps 301 and 302, so that the respective corresponding transaction prediction information and prediction judgment result for each transaction for the target digital asset are obtained. For potential traders of the target digital asset, the server can evaluate the target digital asset accordingly according to the corresponding trade prediction information and the prediction judgment result of each trade of the target digital asset recorded in the alliance chain.

In some embodiments, the server may generate a sequence of transaction forecast information for the target digital asset based on the transaction forecast information recorded in the coalition chain for each transaction for the target digital asset; generating a predicted judgment result sequence aiming at the target digital asset according to the predicted judgment results corresponding to each transaction aiming at the target digital asset and recorded in the alliance chain; further, the target digital asset is evaluated based on the transaction forecast information sequence and the forecast determination sequence.

In one possible implementation, the server may directly provide the transaction prediction information sequence and the prediction judgment result sequence corresponding to the target digital asset for the transactor focusing on the target digital asset, so that the transactor measures the value of the target digital asset according to the transaction prediction information sequence and the prediction judgment result sequence corresponding to the target digital asset.

In another possible implementation, the server may use a specific asset assessment model to assess the target digital asset according to the transaction prediction information sequence and the prediction judgment result sequence corresponding to the target digital asset, generate a corresponding digital asset assessment result, and provide the digital asset assessment result corresponding to the target digital asset to a transactor focusing on the target digital asset.

Of course, in practical applications, the server may also evaluate the target digital asset according to the transaction prediction information sequence and the prediction result sequence corresponding to the target digital asset in other manners, and the specific implementation manner of evaluating the target digital asset by the server is not limited in this application.

In the digital asset evaluation method provided by the embodiment of the application, in the process of evaluating the digital asset, the transaction prediction information given by the original holder of the digital asset for the digital asset and the prediction judgment result corresponding to the transaction prediction information are innovatively introduced. The original holder of the digital asset generally understands the change condition of the digital asset in the market more deeply, so that the given transaction prediction information has higher reference significance for the evaluation of the digital asset, and the transaction prediction information corresponding to each transaction is introduced into the digital asset evaluation frame, so that the integrity of the digital asset evaluation frame can be continuously improved. The method and the device have the advantages that the prediction judgment result corresponding to the transaction prediction information is introduced into the evaluation of the digital asset, so that a transactor can be helped to combine the transaction prediction information and the corresponding prediction judgment result to evaluate the digital asset. In this way, the assessment results of the digital assets are guaranteed to provide higher reference and guiding values for the trader.

To facilitate further understanding of the digital asset assessment method provided by embodiments of the present application, an implementation of the digital asset assessment method is described below with reference to fig. 4.

After the target digital asset D enters the transaction link, a seller of the target digital asset D in each transaction may be defined as a front hand (i.e., the original holder of the current transaction in the above text), a buyer of the target digital asset D in each transaction is defined as a back hand, the back hand of the previous transaction is actually the front hand of the current transaction, and after each transaction of the target digital asset D is finished, the front hand of the current transaction predicts the transaction of the target digital asset D.

In the implementation shown in FIG. 4, the transaction for the target digital asset D is denoted by e, n denotes the number of transactions, and the multiple transactions constitute a transaction sequence consisting of e1, e2, … …, en; the trader of the target digital asset D is denoted by s, and multiple trades form a series of traders consisting of s1, s2, … …, sn; the transaction forecast information given by the transactor is denoted by f, f1 by the transactor s1, f2 by the transactor s2, and so on, the multiple transactions constituting a sequence of transaction forecast information consisting of f1, f2, … …, fn-1.

As shown in fig. 4, the first transaction for the target digital asset D corresponds to e1, s1 being the first transactor of the target digital asset, which may be the original holder of the target digital asset D, or the user who purchased the target digital asset D from the original holder after it entered the transaction platform. s1 hands over the target digital asset D to s2 through e1, completing the process of e1, wherein in the process, s1 is the front hand, s2 is the back hand, and the transaction price of the target digital asset D in the transaction is recorded as p1.

The front hand gives transaction prediction information to the target digital asset D after the transaction is finished, the transaction prediction information can be set through an intelligent contract, and the method can set UP target digital asset price UP, target digital asset price DOWN and target digital asset level HOLD, and after the front hand gives the transaction prediction information through the intelligent contract, the transaction prediction information is written into a message which needs to be broadcast in a alliance chain.

To encourage the lead to give predictions for the price change direction of the target digital asset D and to ensure the accuracy level of the predictions, the present application may set a rewards mechanism that allows the lead to set the rewards ratio r via an intelligent contract.

In e2, s2 transfers the target digital asset D to s 3. And e2, after the completion of the e2, obtaining the transaction price p2 of the target digital asset in the transaction, and enabling the server to start an intelligent contract to judge the transaction prediction information given by s 1.

Specifically, if p2 > p1, then consider that the price of the target digital asset D is rising, and a bonus asset b= (p 2-p 1) r can be calculated; if p2 < p1, considering that the price of the target digital asset D drops, calculating a bonus asset B= (p 1-p 2) r; if p2=p1, then the price of the target digital asset D is considered flat, and the bonus asset b=p1×r can be calculated.

If the transaction prediction information given by s1 is determined to be correct according to the relation between p2 and p1, an intelligent contract is started, the rewarding asset B is paid for s1 by s2, and the rewarding asset B is paid through the intelligent contract in a TOKEN mode in a alliance chain. If the transaction forecast information given by s1 is determined to be incorrect based on the relationship between p2 and p1, then the bonus asset B need not be paid for s 1.

And repeating the process, giving transaction prediction information by the front hand of each transaction, judging whether the transaction prediction information is correct according to the transaction result of the next transaction by the intelligent contract, and paying the bonus asset for the front hand by the rear hand under the condition that the transaction prediction information is correct. In this way, a transaction prediction information sequence, a prediction judgment result sequence and a bonus asset sequence corresponding to n transactions are formed, and reference information is provided for other traders to evaluate the target digital asset D. Further, the transaction prediction information sequence, the prediction judgment result sequence and the bonus asset sequence can be written into the ledger book according to a transaction summarization mechanism corresponding to the alliance chain, so as to generate blocks and link the blocks to the main chain.

The embodiment of the application also provides a digital asset assessment device. Referring to fig. 5, fig. 5 is a schematic structural diagram of a digital asset assessment device according to an embodiment of the present application, as shown in fig. 5, the device includes:

the forecast information storage module 501 is configured to obtain, after each transaction for a target digital asset is completed, transaction forecast information given by an original holder of the current transaction for the target digital asset, where the original holder of the current transaction is a user who holds the target digital asset before the current transaction, as transaction forecast information corresponding to the current transaction; writing the transaction prediction information corresponding to the current transaction into a alliance chain;

the judgment result storage module 502 is configured to verify, according to a transaction result of the target digital asset in the current transaction, transaction prediction information corresponding to a last transaction of the current transaction, to obtain a prediction judgment result of the transaction prediction information corresponding to the last transaction, as a prediction judgment result corresponding to the last transaction; writing the prediction judgment result corresponding to the last transaction into the alliance chain;

and the evaluation module 503 is configured to evaluate the target digital asset according to the transaction prediction information and the prediction judgment result corresponding to each transaction of the target digital asset in the coalition chain.

Optionally, the prediction information storage module 501 is specifically configured to:

the judgment result storage module 502 is specifically configured to:

Optionally, the apparatus further includes:

the reward module is specifically configured to:

Optionally, the apparatus further includes:

Optionally, the evaluation module 503 is specifically configured to:

In the digital asset assessment device provided by the embodiment of the application, in the process of assessing the digital asset, the transaction prediction information given by the original holder of the digital asset for the digital asset and the prediction judgment result corresponding to the transaction prediction information are innovatively introduced. The original holder of the digital asset generally understands the change condition of the digital asset in the market more deeply, so that the given transaction prediction information has higher reference significance for the evaluation of the digital asset, and the transaction prediction information corresponding to each transaction is introduced into the digital asset evaluation frame, so that the integrity of the digital asset evaluation frame can be continuously improved. The method and the device have the advantages that the prediction judgment result corresponding to the transaction prediction information is introduced into the evaluation of the digital asset, so that a transactor can be helped to combine the transaction prediction information and the corresponding prediction judgment result to evaluate the digital asset. In this way, the assessment results of the digital assets are guaranteed to provide higher reference and guiding values for the trader.

The embodiment of the application also provides equipment for evaluating the digital assets, which can be particularly a server and terminal equipment, and the server and the terminal equipment provided by the embodiment of the application are described below from the aspect of hardware materialization.

Referring to fig. 6, fig. 6 is a schematic structural diagram of a server 600 according to an embodiment of the present application. The server 600 may vary considerably in configuration or performance and may include one or more central processing units (central processing units, CPU) 622 (e.g., one or more processors) and memory 632, one or more storage media 630 (e.g., one or more mass storage devices) storing applications 642 or data 644. Wherein memory 632 and storage medium 630 may be transitory or persistent storage. The program stored on the storage medium 630 may include one or more modules (not shown), each of which may include a series of instruction operations on a server. Still further, the central processor 622 may be configured to communicate with a storage medium 630 and execute a series of instruction operations in the storage medium 630 on the server 600.

The server 600 may also include one or more power supplies 626, one or more wired or wireless network interfaces 650, one or more input/output interfaces 658, and/or one or more operating systems 641, such as Windows ServerTM, mac OS XTM, unixTM, linuxTM, freeBSDTM, and the like.

The steps performed by the server in the above embodiments may be based on the server structure shown in fig. 6.

Wherein, CPU 622 is configured to perform the following steps:

Optionally, CPU 622 may also be used to perform the steps of any one implementation of the digital asset assessment method provided by embodiments of the present application.

Referring to fig. 7, fig. 7 is a schematic structural diagram of a terminal device according to an embodiment of the present application. For convenience of explanation, only those portions relevant to the embodiments of the present application are shown, and specific technical details are not disclosed, refer to the method portions of the embodiments of the present application. The terminal can be any terminal equipment including a computer, a tablet personal computer, a personal digital assistant (English full name: personal Digital Assistant, english abbreviation: PDA) and the like, taking the terminal as an example of the computer:

fig. 7 is a block diagram showing a part of the structure of a computer related to a terminal provided in an embodiment of the present application. Referring to fig. 7, a computer includes: radio Frequency (RF) circuit 710, memory 720, input unit 730, display unit 740, sensor 750, audio circuit 760, wireless fidelity (wireless fidelity, wiFi) module 770, processor 780, and power supply 790. Those skilled in the art will appreciate that the computer architecture shown in fig. 7 is not limiting and that more or fewer components than shown may be included, or that certain components may be combined, or that different arrangements of components may be provided.

The memory 720 may be used to store software programs and modules, and the processor 780 performs various functional applications and data processing of the computer by running the software programs and modules stored in the memory 720. The memory 720 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to the use of the computer (such as audio data, phonebooks, etc.), and the like. In addition, memory 720 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The processor 780 is a control center of the computer, connects various parts of the entire computer using various interfaces and lines, and performs various functions of the computer and processes data by running or executing software programs and/or modules stored in the memory 720 and calling data stored in the memory 720, thereby performing overall monitoring of the computer. Optionally, the processor 780 may include one or more processing units; preferably, the processor 780 may integrate an application processor that primarily processes operating systems, user interfaces, applications, etc., with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 780.

In the embodiment of the present application, the processor 780 included in the terminal further has the following functions:

Optionally, the processor 780 is further configured to perform the steps of any one of the implementations of digital asset assessment provided by the embodiments of the present application.

The embodiments of the present application also provide a computer readable storage medium storing a computer program for executing any one of the digital asset assessment methods described in the foregoing embodiments.

Embodiments of the present application also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform any one of the implementations of a digital asset assessment method described in the foregoing respective embodiments.

It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.

In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.

The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, including several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc. various media for storing computer program.

The above embodiments are merely for illustrating the technical solution of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims

1. A digital asset assessment method, the method comprising:

after each transaction aiming at a target digital asset is finished, transaction prediction information given by an original holder of the transaction aiming at the target digital asset is obtained and used as transaction prediction information corresponding to the transaction, the original holder of the transaction is a user holding the target digital asset before the transaction, and the transaction prediction information comprises any one of the following: the target digital asset price increases, the target digital asset price decreases, and the target digital asset price levels; writing the transaction prediction information corresponding to the current transaction into a alliance chain;

evaluating the target digital asset according to the corresponding transaction prediction information and the prediction judgment result of each transaction aiming at the target digital asset in the alliance chain;

the evaluating the target digital asset according to the transaction prediction information and the prediction judgment result corresponding to each transaction of the target digital asset in the alliance chain includes:

2. The method of claim 1, wherein the obtaining transaction forecast information given by the original holder of the present transaction for the target digital asset comprises:

3. The method according to claim 1, wherein verifying the transaction prediction information corresponding to the last transaction of the current transaction according to the transaction result of the target digital asset in the current transaction, to obtain the prediction judgment result of the transaction prediction information corresponding to the last transaction, includes:

4. The method according to claim 1, wherein after verifying the transaction prediction information corresponding to the last transaction of the current transaction according to the transaction result of the target digital asset in the current transaction, to obtain the prediction judgment result of the transaction prediction information corresponding to the last transaction, the method further comprises:

5. The method of claim 4, wherein the offering the bonus asset to the original holder of the last transaction based on the transaction price of the target digital asset in the current transaction and/or the transaction price of the target digital asset in the last transaction comprises:

6. The method of claim 5, wherein the first prize scale, the second prize scale, and the third prize scale are set by an original holder of the last transaction or are fixedly configured by a transaction platform.

7. The method of claim 4, wherein the bonus asset is paid by an original holder in the current transaction to an original holder of the last transaction.

8. The method of claim 1, wherein prior to the obtaining transaction forecast information given by the original holder of the present transaction for the target digital asset, the method further comprises:

9. A digital asset assessment device, the device comprising:

the prediction information storage module is used for acquiring transaction prediction information given by an original holder of a current transaction for the target digital asset after each transaction of the target digital asset is finished, wherein the transaction prediction information is used as transaction prediction information corresponding to the current transaction, the original holder of the current transaction is a user holding the target digital asset before the current transaction, and the transaction prediction information comprises any one of the following: the target digital asset price increases, the target digital asset price decreases, and the target digital asset price levels; writing the transaction prediction information corresponding to the current transaction into a alliance chain;

the evaluation module is used for evaluating the target digital asset according to the corresponding transaction prediction information and the prediction judgment result of each transaction aiming at the target digital asset in the alliance chain;

the evaluation module is specifically used for:

10. The apparatus of claim 9, wherein the prediction information storage module is specifically configured to:

11. The apparatus of claim 9, wherein the determination result storage module is specifically configured to:

12. The apparatus of claim 9, wherein the apparatus further comprises:

13. The apparatus of claim 12, wherein the reward module is specifically configured to:

14. The apparatus of claim 13, wherein the first prize scale, the second prize scale, and the third prize scale are set by an original holder of the last transaction or are fixedly configured by a transaction platform.

15. The apparatus of claim 12, wherein the bonus asset is paid by an original holder in the current transaction to an original holder of the last transaction.

16. The apparatus of claim 9, wherein the apparatus further comprises:

17. An apparatus, the apparatus comprising: a processor and a memory:

the processor is configured to perform the digital asset assessment method of any of claims 1 to 8 in accordance with the computer program.

18. A computer readable storage medium for storing a computer program for executing the digital asset assessment method according to any one of claims 1 to 8.