CN115099926A

CN115099926A - Credit data analysis method, credit data sharing device and credit data sharing equipment based on block chain

Info

Publication number: CN115099926A
Application number: CN202210665094.9A
Authority: CN
Inventors: 马发燊
Original assignee: Ant Blockchain Technology Shanghai Co Ltd
Current assignee: Ant Blockchain Technology Shanghai Co Ltd
Priority date: 2022-06-13
Filing date: 2022-06-13
Publication date: 2022-09-23

Abstract

The embodiment of the specification discloses a block chain-based credit data analysis method, a block chain-based credit data sharing device and equipment. The scheme can comprise the following steps: after first user credit data of a first organization are obtained and second user credit data of a second organization are obtained from a block chain, the first user credit data and the second user credit data are matched based on a first preset scoring standard, if the matching result shows that the first user credit data and the second user credit data both contain the same user group, a first scoring result used for evaluating the validity of the second user credit data is determined according to the matching result, and the first scoring result is uploaded to the block chain, so that the block chain obtains an analysis result aiming at the second user credit data.

Description

Credit data analysis method, credit data sharing device and credit data sharing equipment based on block chain

Technical Field

The present application relates to the field of blockchain technologies, and in particular, to a method, an apparatus and a device for credit data analysis and sharing based on blockchains.

Background

When a financial institution (a trust institution, a consumption financial company, a bank and the like) performs credit activities, in order to reduce credit risks, risk assessment needs to be performed on a user, and credit risk control needs to be performed according to assessment results. Currently, financial institutions perform credit risk control based on their own user credit data, or based on user credit data collected from some trusted institutions.

Therefore, how to provide more accurate and comprehensive user credit data for financial institutions becomes a technical problem to be solved urgently.

Disclosure of Invention

The block chain-based credit data analysis method, sharing method, device and equipment provided by the embodiment of the specification can provide more accurate and comprehensive user credit data for financial institutions.

In order to solve the above technical problem, the embodiments of the present specification are implemented as follows:

an embodiment of the present specification provides a block chain-based credit data analysis method, including:

acquiring first user credit data of a first organization;

acquiring second user credit data of a second organization through the blockchain;

matching the first user credit data with the second user credit data based on a first preset scoring standard to obtain a matching result;

if the matching result shows that the first user credit data and the second user credit data both contain the same user group, determining a first scoring result aiming at the second organization according to the matching result; the first scoring result is used for evaluating the validity of the second user credit data;

uploading the first scoring result to the blockchain.

An embodiment of the present specification provides a method for sharing credit data based on a block chain, including:

acquiring the integral score of a first mechanism on a block chain; the integral score of the first institution is determined according to each scoring result of the first institution; each scoring result of the first organization is determined by using a block chain-based credit data analysis method described in the embodiment of the specification;

acquiring integral scores of other mechanisms except the first mechanism on the block chain;

determining an access sequence of user credit data aiming at each organization on the block chain according to the overall score of the first organization and the overall scores of other organizations on the block chain except the first organization; and the access sequence is used for accessing the user credit data of each mechanism on the block chain based on the access sequence when the user side equipment has paid access to the user credit data of each mechanism on the block chain.

An embodiment of the present specification provides a credit data analysis apparatus based on a block chain, including:

the first acquisition module is used for acquiring first user credit data of a first organization;

the second acquisition module is used for acquiring second user credit data of a second organization through the block chain;

the data matching module is used for matching the first user credit data with the second user credit data based on a first preset scoring standard to obtain a matching result;

the first scoring module is used for determining a first scoring result aiming at the second organization according to the matching result if the matching result shows that the first user credit data and the second user credit data both contain the same user group; the first scoring result is used for evaluating the validity of the second user credit data;

and the data uploading module is used for uploading the first scoring result to the block chain.

An embodiment of the present specification provides a credit data sharing apparatus based on a block chain, including:

the first acquisition module is used for acquiring the integral score of a first mechanism on the block chain; the overall score of the first institution is determined according to each scoring result of the first institution; each scoring result of the first organization is determined by using a block chain-based credit data analysis method described in the embodiment of the specification;

the second acquisition module is used for acquiring the overall scores of other mechanisms except the first mechanism on the block chain;

a first determining module, configured to determine, according to the overall score of the first mechanism and the overall scores of the other mechanisms except the first mechanism in the block chain, an access sequence of user credit data for each mechanism in the block chain, where the access sequence is used to access the user credit data of each mechanism in the block chain based on the access sequence when a user-side device has paid access to the user credit data of each mechanism in the block chain.

An embodiment of the present specification provides a credit data analysis device based on a block chain, including:

at least one processor; and (c) a second step of,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to:

obtaining first user credit data for a first institution;

acquiring second user credit data of a second organization through the block chain;

uploading the first scoring result to the blockchain.

An embodiment of the present specification provides a credit data sharing device based on a block chain, including:

at least one processor; and (c) a second step of,

acquiring the integral score of a first mechanism on a block chain; the overall score of the first institution is determined according to each scoring result of the first institution; each scoring result of the first organization is determined by adopting a block chain-based credit data analysis method described in the embodiment of the specification;

and determining an access sequence of the user credit data of each organization on the block chain according to the overall score of the first organization and the overall scores of other organizations on the block chain except the first organization, wherein the access sequence is used for accessing the user credit data of each organization on the block chain based on the access sequence when the user-side equipment has paid access to the user credit data of each organization on the block chain.

At least one embodiment provided in the present specification can achieve the following advantageous effects:

after first user credit data of a first organization are acquired and second user credit data of a second organization are acquired from a block chain, the first user credit data and the second user credit data are matched based on a first preset scoring standard, if a matching result shows that the first user credit data and the second user credit data both contain the same user group, a first scoring result used for evaluating the validity of the user credit data of the second organization is determined according to the matching result, and the first scoring result is uploaded to the block chain. Based on the above, by analyzing the validity of the user credit data of each mechanism on the block chain, when the user needs to access the user credit data of each mechanism on the block chain, the user credit data with high validity can be provided for the user according to the analysis result, so that the application can provide more accurate user credit data for the user; and because more mechanisms can be added to the block chain, the user credit data provided by the block chain can be more comprehensive, and further more comprehensive user credit data can be provided for the user.

Drawings

In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the description below are only some embodiments described in the present application, and for those skilled in the art, other drawings may be obtained according to these drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario of a block chain-based credit data analysis method and a block chain-based credit data sharing method in an embodiment of the present specification;

fig. 2 is a flowchart illustrating a block chain-based credit data analysis method according to an embodiment of the present disclosure;

fig. 3 is a flowchart illustrating a block chain-based credit data sharing method according to an embodiment of the present disclosure;

FIG. 4 is a schematic diagram illustrating a mechanism and a blockchain for file transfer according to an embodiment of the present disclosure;

FIG. 5 is a schematic swimlane flow chart of a block chain-based credit data analysis method and a sharing method corresponding to FIGS. 2 and 3 according to an embodiment of the present disclosure;

fig. 6 is a schematic structural diagram of a block chain-based credit data analysis apparatus corresponding to fig. 2 provided in an embodiment of the present specification;

fig. 7 is a schematic structural diagram of a block chain-based credit data sharing apparatus corresponding to fig. 3 according to an embodiment of the present disclosure;

fig. 8 is a schematic structural diagram of a block chain-based credit data analysis device corresponding to fig. 2 provided in an embodiment of the present specification;

fig. 9 is a schematic structural diagram of a block chain-based credit data sharing device corresponding to fig. 3 according to an embodiment of the present disclosure.

Detailed Description

To make the objects, technical solutions and advantages of one or more embodiments of the present disclosure more apparent, the technical solutions of one or more embodiments of the present disclosure will be clearly and completely described below with reference to specific embodiments of the present disclosure and corresponding drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of one or more embodiments of the present specification.

The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.

Credit refers to a form of value movement conditioned on reimbursement and payment. Typically involving a user making credit activities such as deposits and loans at a financial institution. Credit is an important form for the socialist nation to mobilize and allocate funds in a paid manner, and is a powerful lever for developing economy. When a financial institution (a trust institution, a consumption financial company, a bank and the like) performs credit activities, in order to reduce credit risks, risk assessment needs to be performed on a user, the user with higher credit risk degree (hereinafter referred to as a high-risk user) needs to be discovered in time, and then risk control is performed on the high-risk user in time, so that the credit risks of the financial institution are reduced, and benefits of the financial institution and the user are maintained.

In the prior art, a financial institution generally maintains a user credit data, and the financial institution can perform risk assessment on a user according to the user credit data maintained by the financial institution. However, this solution provides the financial institution with user credit data that is less comprehensive and less accurate.

In order to solve the defects in the prior art, the scheme provides the following embodiments:

fig. 1 is a schematic view of an application scenario of a block chain-based credit data analysis method and a block chain-based credit data sharing method in an embodiment of the present specification.

As shown in fig. 1, the organization of the blockchain 140 may be a company that maintains user credit data, for example, the organization may include a financial institution that maintains user credit data, a third-party data company, and the like, and the embodiment of the present specification does not limit the organization of the blockchain 140. It should be noted that the organization has been approved by other organizations of the blockchain 140 before joining the blockchain 140. The device of the organization accessing the blockchain 140 becomes a node of the blockchain 140, also called the node of the organization, and the node can perform data interaction with the blockchain according to the data interaction operation of the organization. The nodes of the blockchain may be computer devices such as servers of the organization.

For a node of any mechanism on the block chain, the node may locally deploy a client of the block chain, so that the node can perform data interaction with the block chain 140 through the client according to data interaction operation of the mechanism, where the client may specifically be a computer program. And after the client corresponding to the node acquires the user credit data maintained by the mechanism corresponding to the node, uploading the user credit data to the block chain. And the client can acquire user credit data of other mechanisms from the blockchain, the data acquired by the client from the blockchain 140 is encrypted data, the client does not expose the encrypted data to the outside, the mechanisms cannot acquire the encrypted data, and the client deletes the encrypted data after using the encrypted data, so that the data on the blockchain 140 cannot be leaked.

In this embodiment of the present specification, the organizations on the block chain 140 may share the user credit data with each other through the block chain 140, so that the organizations can obtain more comprehensive user credit data, and the block chain 140 may analyze the validity of the user credit data uploaded to the block chain 140 by each organization, so that the block chain can provide the user credit data with higher validity to the user according to the analysis result, which is described in detail below:

first, the organizations may share each other's user credit data through blockchain 140. Specifically, as shown in fig. 1, the second client 110 is deployed locally at a node of a second organization, the second organization may be any one of the organizations on the blockchain 140, and the second client 110 may upload user credit data maintained by the second organization to the blockchain 140, so that a plurality of organizations of the blockchain 140 may upload user credit data of the second organization to the blockchain 140, so that the blockchain 140 stores the user credit data of the plurality of organizations.

And, as shown in fig. 1, the third client 120 is deployed locally at a node of a third organization, the third organization may be any organization on the blockchain 140, and the third client 120 may send a credit data access request to the blockchain 140, where the credit data access request includes at least user data to be matched. After the block chain 140 acquires the credit data access request, the user data to be matched in the credit data access request may be respectively matched with the user credit data of each organization except the third organization on the block chain 140 to obtain a matching result, and the matching result is fed back to the third client, where the matching result is used to determine the credit condition of the user to be matched corresponding to the user data to be matched, and the credit condition of the user to be matched is used to determine the credit risk degree of the user to be matched. In this way, since a plurality of organizations of the block chain can access the user credit data on the block chain 140, the purpose that the plurality of organizations of the block chain share the user credit data with each other through the block chain 140 is achieved.

Secondly, any mechanism of the block chain can analyze the validity of the user credit data uploaded to the block chain 140 by other mechanisms on the block chain 140 through the node of the mechanism. As shown in fig. 1, the first client 130 is deployed in a node local of a first organization, the first organization may be any organization on the block chain 140, the first user credit data is user credit data maintained by the first organization, the second user credit data is user credit data uploaded to the block chain 140 by a second organization, after the first client 130 acquires the first user credit data and the second user credit data, the first client 130 may match the first user credit data and the second user credit data based on a first preset scoring criterion, if the matching result indicates that the first user credit data and the second user credit data both include the same user group, a first scoring result for the second organization is determined according to the matching result, and the first scoring result is uploaded to the block chain 140, and the first scoring result is used to evaluate validity of the second user credit data, and thus, the block chain 140 can determine the validity of the user credit data of each organization according to the scoring result of the user credit data of each organization, so that when the user needs to access the user credit data of each organization on the block chain, the user credit data with high validity can be provided for the user according to the analysis result, and further more accurate user credit data can be provided for the user.

It should be noted that the block chain 140 in this embodiment may be a federation chain.

Next, a block chain-based credit data analysis method provided in an embodiment of the present disclosure is described in detail with reference to the accompanying drawings:

fig. 2 is a flowchart illustrating a block chain-based credit data analysis method according to an embodiment of the present disclosure. From a program perspective, the execution subject of the flow may be a node on the blockchain or a client deployed in the node on the blockchain. As shown in fig. 2, the process may include the following steps:

step 202: first user credit data for a first institution is obtained.

In an embodiment of the present specification, the first user credit data may be user credit data maintained by the first organization, and the user credit data may be used to determine a credit type of the first user group, wherein the users may be classified from the credit type perspective, and the users may be classified into distrusted users and credited users, for example, when the user does not perform bad actions such as loan expectation, loan arrearance, criminal crime, and the like, the user may be determined as a credited user, and when the user performs bad actions such as loan expectation, loan arrearance, criminal crime, and the like, the user may be determined as a distrusted user.

In practical application, when a user is identified as a distrusted user, the credit risk degree of the user can be determined to be higher, namely the user is a high-risk user; when the user is deemed to be a trusted user, it may be determined that the user has a low financing risk level, thus enabling the financial institution to credit risk control the user based on the user credit data.

It is understood that the first user credit data specifically includes credit data of each user in the first user group, in a specific example, the first user credit data includes identity information data of each user in the first user group, the identity information data of the user may be data capable of confirming the identity of the user, such as an identity number or a business license registration number of the user, when the first user credit data is composed of the identity information data of each user in the first user group, the credit types of each user in the first user group are the same, for example, the first user credit data is list data of trusted users, and then each user targeted by the list data is a trusted user.

In another specific example, the first user credit data includes identity information data and credit types for each user in the first group of users. In this case, in order to make it more convenient to determine the credit type of each user in the first user group according to the first user credit data, the users in the first user group may be grouped based on the credit type, the credit types of each group of users are the same, and the data of each group of users may form a user name list data.

In another specific example, the first user credit data includes identity information data, credit type, and credit data details of each user in the first group of users, wherein the credit data details may be credit records generated when the user performs credit activities, such as loan records generated when the user performs loan activities.

In practical applications, the first client may locally obtain, from a node of the first organization according to the data obtaining operation of the first organization, the first user credit data maintained by the first organization, or may receive the first user credit data sent by another device of the first organization, which is not limited herein in this embodiment of the present disclosure.

Step 204: second user credit data for the second organization is obtained via the blockchain.

In this embodiment of the present specification, the second user credit data may be user credit data uploaded to the blockchain by the second organization, and the user credit data may be used to determine a credit type of the second user group, where the credit type of the second user group is defined as the same as the credit type of the first user group, and the explanation of the credit type of the first user group may be specifically referred to, which is not described herein again.

It will be appreciated that the second user credit data specifically includes data for each user in the second group of users. In a specific example, the second user credit data may be user name form data composed of identity information data of users in the second user group, and the credit types of the users in the second user group are the same, then the identity type of a user may be determined by determining whether the identity data of the user appears in the user list. The identity information data of each user in the second user group is defined the same as the identity information data of each user in the first user group, and the explanation of the identity information data of each user in the first user group may be referred to specifically, which is not described herein again.

In another specific example, the second user credit data includes identity information data and credit types of users in the second user group, and the credit types of the users in the second user group may be different, in order to make it more convenient to determine the credit types of the users in the second user group according to the second user credit data, the users in the second user group may be grouped based on the credit types, the credit types of each group of users are the same, and the data of each group of users may form a user name list data, so that the first client may determine the credit type of the user only by confirming the grouping of the users in the second user group.

It should be noted that, the first mechanism may perform validity analysis on the user credit data uploaded to the block chain by multiple mechanisms through the node of the first mechanism at the same time.

In practical application, the node of the first organization acquires the credit data of the second user of the second organization from the blockchain through the first client. According to the foregoing, in order to further prevent the second user credit data from being leaked, in this embodiment, after the second client acquires the second user credit data from the blockchain, the second user credit data may be stored in an encrypted preset database, and after the validity analysis on the second user credit data is completed, the second user credit data is deleted in the encrypted preset database.

Step 206: and matching the first user credit data with the second user credit data based on a first preset scoring standard to obtain a matching result.

Step 208, if the matching result indicates that the first user credit data and the second user credit data both contain the same user group, determining a first scoring result for the second organization according to the matching result; the first scoring result is used for evaluating the validity of the second user credit data.

In the embodiment of the present specification, when the user credit data uploaded to the block chain by the mechanism is false data, the result determined according to the user credit data about the user credit type may also be inaccurate, and at this time, the user credit data may be considered to be invalid; on the contrary, when the user credit data uploaded to the block chain by the mechanism is real data, the result about the user credit type determined according to the user credit data is also accurate, and at this time, the user credit data can be considered to be valid.

Second, users in the user group for which user credit data of different organizations are intended may be duplicated, for example, the same user has performed credit activities with different organizations on the blockchain, and the user credit data of the different organizations may include data for the user. Therefore, when any mechanism on the block chain analyzes the validity of the user credit data uploaded to the block chain by other mechanisms on the block chain, the validity of the credit data of a specific user in the user credit data of other mechanisms can be analyzed according to the user credit data maintained by the mechanism, wherein the specific user refers to the same user group for which the user credit data of any mechanism and the user credit data of other mechanisms are both aimed.

In practical application, in the process that an organization corresponding to first user credit data performs validity analysis on second user credit data, when the first user credit data and the second user credit data are both directed at the same user group, for any user in the same user group, if the credit types of the user represented by the first user credit data and the second user credit data are different, determining that the data in the second user credit data directed at the user is false data, and at this time, determining a penalty score directed at a second organization; conversely, if the credit types of the users represented by the first user credit data and the second user credit data are the same, the data for the user in the second user credit data is determined to be real data, and at this time, the reward score for the second institution may be determined. Finally, a first scoring result for the second organization is determined from the analysis of the user credit data for the second organization.

Step 208: uploading the first scoring result to the blockchain.

In this embodiment of the specification, it can be known from the above that the first scoring result is specifically determined according to part of or all of the second user credit data, and if the score indicated by the first scoring result is higher, it indicates that the more real user credit data in the second user credit data is, the higher the validity of the second user credit data is, so that the first scoring result is uploaded to the block chain, so that the block chain can determine the validity of the second user credit data according to the first scoring result. And the second mechanism can be any mechanism on the block chain, so that the block chain can acquire the scoring result of each mechanism on the block chain, and the block chain can provide user credit data with higher effectiveness to the user according to the scoring result of each mechanism.

In addition, in order to prevent the first mechanism from maliciously evaluating the second user credit data, the first client corresponding to the first mechanism can upload data influencing the score of the second user credit data to the blockchain after completing the validity analysis of the second user credit data. For example, the second user credit data shows that zhang san is a distrusted user, but the first user data shows that zhang san is a trusted user, at this time, the first client determines that data about zhang san in the second user credit data is false data, and determines a penalty score for the second user credit data, so that the data about zhang san of the first organization is data influencing the score of the second user credit data, and the first client is used for uploading the data about zhang san of the first organization to a block chain, so that related personnel can conveniently judge whether the first organization maliciously evaluates the second user credit data according to the data influencing the score of the second user credit data.

The method in fig. 2 includes acquiring first user credit data maintained by a first organization, acquiring second user credit data of a second organization through a block chain, matching the first user credit data with the second user credit data based on a first preset scoring standard, determining a first scoring result for evaluating validity of the user credit data of the second organization according to the matching result if the matching result indicates that the first user credit data and the second user credit data both include the same user group, and uploading the first scoring result to the block chain. Based on the above, by analyzing the validity of the user credit data of each mechanism on the block chain, when the user needs to access the user credit data of each mechanism on the block chain, the user credit data with high validity can be provided for the user according to the analysis result, and further more accurate user credit data can be provided for the user by the method and the system; and because more mechanisms can be added to the block chain, the user credit data provided by the block chain can be more comprehensive, and further more comprehensive user credit data can be provided for the user.

Based on the method in fig. 2, some specific embodiments of the method are also provided in the examples of the present specification, which are described below.

In this specification embodiment, the first user credit data may include credit data of a first preset credit type user, the second user credit data may include credit data of a second preset credit type user, and the first preset credit type and the second preset credit type may be different, so that when the first user credit data and the second user credit data are both for the same first user group, since data about the first user group in the first user credit data is real data, data about the first user group in the second user credit data is false data. In practical application, the financial institution generally performs credit risk prevention and control according to the blacklist data, so that in order to make the embodiment of the present specification better meet practical requirements and achieve the purpose of judging whether the second user credit data is real data, the first user credit data may include credit data of a user who has successfully been granted credit; the second user credit data may comprise blacklist data.

Based on this, the first preset scoring criteria may include a first scoring criteria. Step 206 may specifically include:

and matching the credit data of the successfully trusted user with the blacklist data to obtain a first matching result.

Step 208 may specifically include:

and if the first matching result shows that the credit data of the successfully credited user and the blacklist data both contain the same first user group, determining a penalty score for the second organization according to the first matching result based on the first scoring standard.

In the embodiment of the present specification, it may be understood that, in theory, each user in a user group to which credit data of a user who has successfully been granted is a user who has successfully been granted, each user in a user group to which blacklist data is directed is a user who loses credit, and a user may be only one user of the user who has successfully been granted and the user who loses credit, and it is impossible for the user to be both a user who has successfully been granted and a user who loses credit. Therefore, when the credit data of the successfully trusted users of the first organization and the blacklist data of the second organization both contain the same first user group, the data of the blacklist data of the second organization about the first user group can be determined to be false data, and the penalty score for the second organization can be determined based on the false data.

In an actual application process, the credit data of the successfully trusted user may be list data of the successfully trusted user, and in a process of matching the credit data of the successfully trusted user with the blacklist data, specifically, it may be determined whether the credit data of the successfully trusted user and the blacklist data contain the same user identity data, and if the credit data of the successfully trusted user and the blacklist data contain the same user identity data, a user group to which the same user identity data is directed is a first user group. The user identity data is data that can be used to confirm the identity of the user, for example, when the user is an individual user, the identity data may be the identity card number of the user; when the user is a company, the identity data may be a license registration number of the company.

In this specification, the first user credit data may include credit data of a first user of a predetermined credit type, the second user credit data may include credit data of a second user of a predetermined credit type, and the first predetermined credit type and the second predetermined credit type may be the same, so that when the first user credit data and the second user credit data are both for the same first user group, since data about the first user group in the first user credit data is real data, data about the first user group in the second user credit data is also real data. In practical application, a financial institution generally performs credit risk prevention and control according to blacklist data, so that in order to make the embodiment of the present specification better meet practical requirements and achieve the purpose of judging whether the credit data of the second user is real data, the credit data of the first user may include the credit data of the lost credit user; the second user credit data may comprise blacklist data.

Based on this, the first preset scoring criterion comprises a second scoring criterion; step 206 may specifically include:

and matching the credit data of the lost user with the blacklist data to obtain a second matching result.

Step 208 may specifically include:

and if the second matching result shows that the credit data of the distrusted user and the blacklist data both contain the same second user group, determining the reward score aiming at the second organization according to the second matching result based on the second scoring standard.

In the embodiment of the present specification, the execution process of determining the reward score for the second institution based on the second matching result based on the second scoring criterion is similar to the execution process of determining the penalty score for the second institution based on the first scoring criterion and the first matching result, and the difference between the two is that when the credit data and the blacklist data of the untrusted user both include the same second user group, the data about the second user group in the blacklist data of the second institution is determined to be the real data, and the reward score for the second institution is determined based on the real data. Therefore, for the "determining the reward score for the second institution based on the second scoring criterion according to the second matching result" implementation process, refer to the above "determining the penalty score for the second institution based on the first scoring criterion according to the first matching result", and will not be described in detail herein.

In the embodiment of the present specification, as can be seen from the foregoing, both the credit data of the successfully granted user of the first organization and the credit data of the unsuccessfully granted user of the first organization can be used to evaluate the validity of the blacklist data of the second organization, and therefore, if the credit data of the successfully granted user of the first organization and the credit data of the unsuccessfully granted user of the first organization are used to evaluate the validity of the blacklist data of the second organization at the same time, the utilization rate of the credit data of the user of the first organization can be improved, and the credit data of the second user can be analyzed more comprehensively.

Based on this, the first preset scoring standard comprises a first scoring standard and a second scoring standard; the first user credit data comprises credit data of a user who successfully grants credit and credit data of a user who loses credit; the second user credit data comprises blacklist data; step 206 may specifically include:

and matching the credit data of the successfully trusted user with the blacklist data to obtain a first matching result, and matching the credit data of the untrusted user with the blacklist data to obtain a second matching result.

Step 208 may specifically include:

and if the first matching result shows that the credit data of the user who has successfully granted the credit and the blacklist data both contain the same first user group, determining a penalty score for the second institution according to the first matching result based on the first scoring standard, and if the second matching result shows that the credit data of the user who has lost the credit and the blacklist data both contain the same second user group, determining a reward score for the second institution according to the second matching result based on the second scoring standard.

And determining the first scoring result according to the penalty value and the reward value.

In the embodiment of the present specification, the term "match the credit data of the successfully trusted user with the blacklist data" is used to obtain a first matching result. And if the first matching result shows that the credit data of the successfully trusted user and the blacklist data both contain the same first user group, determining a penalty score aiming at the second organization according to the first matching result based on the first scoring standard. And matching the credit data of the distrusted user with the blacklist data to obtain a second matching result. If the second matching result indicates that the credit data of the distrusted user and the blacklist data both include the same second user group, an execution process of determining the reward score for the second organization according to the second matching result based on the second scoring criterion is the same as the foregoing embodiment, which may be referred to the foregoing embodiment specifically, and details are not repeated here.

It should be noted that, the step "matches the credit data of the successfully trusted user with the blacklist data to obtain a first matching result. And if the first matching result shows that the credit data of the successfully credited user and the blacklist data both comprise the same first user group, determining a penalty score aiming at the second organization according to the first matching result based on the first scoring standard, and matching the credit data of the untrusted user and the blacklist data to obtain a second matching result. If the second matching result indicates that both the credit data of the untrusted user and the blacklist data include the same second user group, the execution order of determining the reward score for the second organization according to the second matching result based on the second scoring criterion is not limited in this application, but this embodiment only lists an execution order related to the two steps, and a person skilled in the art may determine the execution order of the two steps according to actual needs, for example, first determine the reward score for the second organization, and then determine the penalty score for the second organization.

Secondly, the first scoring result may be a final score determined according to the bonus score and the penalty score, for example, the bonus score is 10 points, the penalty score is-5 points, and the first scoring result is 5 points. Secondly, the first scoring result may also be a data set composed of the reward score and the penalty score, for example, the reward score is 10 points, and the penalty score is-5 points, then the first scoring result is a data set composed of 10 points and-5 points, after the block chain obtains the first scoring result, the block chain performs a summation operation on each numerical value in the first scoring result and the current overall score of the second organization to obtain a final overall score for the second organization, in connection with the foregoing example, it is assumed that the current overall score of the second organization is 50 points, and after the block chain obtains the first scoring result, the block chain performs a summation operation on each numerical value in the first scoring result and the current overall score of the second organization to obtain a final overall score for the second organization which is 55 points.

In the embodiment of the specification, the first user credit data comprises credit data of a user who has successfully granted credit and credit data of a user who loses credit, and compared with the first user credit data, the first user credit data only comprises the credit data of the user who has successfully granted credit or the credit data of the user who loses credit, so that the first user credit data is richer, further, the authenticity of more data in the second user credit data can be judged at an opportunity through the first user credit data, and the analysis result of the second user credit data is more comprehensive and accurate.

In the embodiment of the present specification, considering that the same user may have credit activities with multiple institutions, for example, the same user may have loans at multiple institutions, but the user has default at only some of the institutions, in this case, it is defined that the user has default at a first institution and has no default at a second institution, after the first institution uploads credit data about the user to the blockchain, and the second institution performs validity analysis on the user credit data uploaded to the blockchain by the first institution, it may be determined that data about the user in the user credit data uploaded to the blockchain by the first institution is false data and deducts a point for the first institution, however, the data about the user of the first institution and the second institution are both true data, so that when the second institution does not maliciously evaluate the user credit data of the first institution, and in order to improve the accuracy of the scoring result of the first organization, reduce the influence of the condition that the same user carries out credit activities with a plurality of organizations but only defaults occur at part of the organizations on the scoring result of the part of the organizations, determining the penalty score aiming at the part of the organizations by adopting a step deduction mode according to the determined false data of the part of the organizations.

Based on this, based on step 208, the determining a penalty score for the second institution according to the first matching result based on the first scoring criterion may specifically include:

and determining the number of users contained in the first user group according to the first matching result.

And determining a deduction coefficient aiming at the second mechanism according to the number of the users.

And determining a penalty score for the second institution according to the deduction coefficient and the number of users.

In this embodiment of the present specification, a preset step deduction standard may be preset, where the preset step deduction standard is used to divide the number of users included in the first user group into different stages, and each stage is bound with one deduction coefficient, the different stages are bound with different deduction coefficients, and the higher the level of the stage is, the higher the deduction coefficient bound in the stage is. Among them, regarding the determination of the level of the stage, it can be interpreted as: in a specific example, the number of users included in the first user group is divided into three stages, the first stage is a stage in which the number of users included in the first user group is greater than or equal to 1 and less than or equal to 5 (the first stage is represented as [1,5], and the stages are represented in the same format in the following description), the second stage is [6,10], and the third stage is [11,15], so that the third stage is defined to have a higher rank than the second stage, and the second stage has a higher rank than the first stage.

Preferably, the length of each segment of the preset step deduction standard may be the same, and the deduction coefficients bound to each segment may form an arithmetic progression, in a specific example, the preset step deduction standard may be set as: dividing the number of users included in a first user group into N stages, wherein the first stage is a stage in which the number of users included in the first user group is greater than or equal to 1 and less than or equal to 10 (the first stage is represented as [1,10], and the stages are represented in the same format), and the score coefficient bound in the first stage is 0.1, that is, when the number of users of the first user group falls into the first stage, the score coefficient for a first institution is determined to be 0.1; similarly, the second stage is [11,20], and the binding deduction coefficient of the second stage is 0.2; the third stage is [21,30], the binding deduction coefficient of the third stage is 0.3, and the analogy is repeated, and finally the preset step deduction standard is obtained. The value of N may be determined by a designer of the preset tiered deduction standard according to the number of users included in the second user credit data and the length of the interval of each stage of the preset tiered deduction standard, for example, the second user credit data is composed of credit data of 10 users, that is, the second user credit data includes 30 users, and the length of the interval of each stage of the preset tiered deduction standard is 10, so that the value of N may be determined to be 3, and the number of users included in the first user group is divided into [1,10], [11,20] and [20,30 ].

Preferably, the length of the interval of each stage of the preset step deduction standard can be different, and the interval is set by a designer of the preset step deduction standard according to actual needs. In a specific example, the second user credit data includes 40 users, and the predetermined default tiered deduction criteria may be set as: dividing the number of users of a first user group into 3 stages, wherein the first stage is [1, 8 ], and defining the deduction coefficient bound in the first stage to be 0.1; the second stage is [8, 20) and the coefficient of the score for the second stage binding is defined to be 0.3, the third stage is [20, 40], and the coefficient of the score for the third stage binding is defined to be 0.5.

In practical application, after the number of users included in the first user group is determined according to the first matching result, which stage the number of users falls into a preset step deduction standard is determined, and a deduction coefficient for the second mechanism is determined according to the determination result, for example, if the number of users falls into the first stage of the preset step deduction standard and the deduction coefficient bound in the first stage is 0.1, the deduction coefficient for the second mechanism is determined to be 0.1. And finally, multiplying the number of the users by the deduction coefficient to obtain a penalty score for the second mechanism. Based on this, because a step deduction mode is adopted, when the same user carries out credit activities with a plurality of organizations, but only partial organizations have defaults, and the partial organizations upload the credit data of the user to the block chain, the punishment score determined for the partial organizations based on the credit data of the user is also very low, and the influence of the credit data of the user on the scoring results of the partial organizations is reduced. And if a certain mechanism uploads a large amount of false user credit data to the block chain, the more false user credit data in the step deduction mode, the higher the deduction coefficient of the mechanism uploading the false user credit data is, the higher the determined penalty score of the mechanism is, and the accuracy of the analysis result of the second user credit data is improved.

In this embodiment of the present specification, when the plurality of organizations except the second organization on the blockchain all analyze the second user credit data of the second organization, and when the second organization uploads the user credit data to the blockchain through the second client for multiple times, the organizations except the second organization on the blockchain may analyze the user credit data uploaded to the blockchain by the second client each time, based on which, the number of scoring results of the second organization may be multiple, and each scoring result is used to evaluate the validity of the second user credit data. Therefore, the overall score of the second organization can be determined according to the scoring results of the second organization, and when a new scoring result of the second organization is obtained, the overall score of the second organization is updated according to the new scoring result, so that the block chain can conveniently and quickly determine the validity of the user credit data of the second organization according to the overall score of the second organization.

Based on this, step 208: after uploading the scoring result to the blockchain, the method of the embodiment of the present specification further includes:

updating the integral score of the second organization according to the first scoring result; the overall score is a score determined according to each scoring result of the second institution.

In this embodiment of the present specification, the blockchain stores an overall score of the second mechanism, the overall score is determined according to each scoring result of the second mechanism, and after the blockchain obtains the scoring result of the second mechanism, the obtained scoring result of the second mechanism and the overall score of the second mechanism stored in the blockchain may be summed, and the operation result is used as a final overall score of the second mechanism.

It should be noted that, if the second organization is an organization that has not undergone credit data analysis, for example, the second organization is an organization that newly joins the block chain, before the second organization does not upload the user credit data to the block chain, the organizations other than the second organization on the block chain do not score the user credit data of the second organization, at this time, the initial overall score of the second organization may be set to 0. And it is understood that the scoring result of the second institution may specifically be a positive number or a negative number, and when the scoring result of the second institution is a positive number, it indicates that real user credit data exists in the second user credit data; and when the scoring result of the second mechanism is negative, the second mechanism indicates that false user credit data exists in the second user credit data.

Additionally, to encourage organizations to upload actual user credit data to the blockchain, organizations may be kicked out of the blockchain when their overall score falls below a predetermined overall score threshold. Specifically, in an example, after the block chain finishes updating the overall score of the specific organization (the specific organization is any organization on the block chain), it may be determined whether the overall score of the specific organization is smaller than a preset overall score threshold, and if it is determined that the overall score of the specific organization is smaller than the preset overall score threshold, the specific organization is kicked out of the block chain. In another example, the blockchain may be timed to determine whether the overall score of each mechanism on the blockchain is smaller than a preset overall score threshold, and kick out the blockchain the mechanism with the overall score smaller than the preset overall score threshold according to the determination result.

In an embodiment of the present specification, an application scenario of the above credit data analysis method based on a blockchain may be that when an organization newly joins the blockchain, other organizations on the blockchain analyze user credit data uploaded to the blockchain by the newly joining organization.

Based on this, the second mechanism may be a mechanism that newly joins the blockchain, and the second user credit data may be user credit data that the second mechanism initially uploaded to the blockchain. After the second mechanism uploads the second user credit data to the block chain, the block chain acquires the second user credit data, and can send an analysis instruction to nodes corresponding to mechanisms except the second mechanism on the block chain, so that the nodes corresponding to the mechanisms except the second mechanism on the block chain analyze the second user credit data. It should be noted that, when acquiring the second user credit data, the blockchain may send an analysis instruction to nodes of at least one entity except the second entity on the blockchain, for example, send an analysis instruction to nodes of all entities except the second entity on the blockchain, each entity that acquires the analysis instruction through the relevant node serves as the first entity in the embodiment of the present specification, and the node of the first entity analyzes the second user credit data by using the method in the embodiment of the present specification.

In an embodiment of the present specification, an application scenario of the above credit data analysis method based on a blockchain may be that when an organization newly joins the blockchain, the newly joining organization analyzes user credit data uploaded to the blockchain by other organizations on the blockchain.

In this regard, the first mechanism may be a newly joined mechanism, the first user credit data may be user credit data stored locally at a node of the first mechanism, and the second user credit data may be user credit data uploaded to the blockchain by the second mechanism.

In practical applications, after the first mechanism joins the block chain, the first mechanism may analyze the user credit data uploaded to the block chain by at least one mechanism other than the first mechanism on the block chain, for example, analyze the user credit data uploaded to the block chain by all mechanisms other than the first mechanism on the block chain, and refer to the above-mentioned embodiment for the analysis method of the second user credit data, which is not described herein again.

In this embodiment, after the mechanism on the blockchain initially uploads the user credit data to the blockchain, the mechanism may also modify the user credit data uploaded to the blockchain, for example, add new user credit data to the user credit data, and at this time, it needs to analyze the new user credit data. Therefore, the application scenario of the credit data analysis method based on the blockchain may also be that when an organization on the blockchain uploads new user credit data to the blockchain, other organizations on the blockchain analyze the new user credit data.

Based on this, the second user credit data may be the second organization's new user credit data on the blockchain; and the newly added user credit data of the second mechanism on the block chain is the user credit data of the second mechanism on the block chain which is not used for scoring the second mechanism.

In practical applications, at least one entity other than the second entity in the block chain may analyze the second user credit data, for example, all entities other than the first entity in the block chain may analyze the second user credit data, and in this case, it is defined that each entity analyzing the second user credit data is the first entity. For the analysis method of the second user credit data, refer to the above embodiments, and are not described herein again.

In this embodiment of the present specification, when the number of organizations on the blockchain is large, if each organization uploads new user credit data to the blockchain each time, the new user credit data is analyzed, so that the blockchain needs to perform credit data analysis frequently, and the blockchain needs to consume a large amount of network resources.

Based on this, when the second user credit data is the new user credit data of the second organization on the block chain, the analysis method may be executed according to a preset execution frequency. For example, the user credit data uploaded to the blockchain by each mechanism on the blockchain in the week may be counted once by the blockchain, and for any mechanism on the blockchain, the blockchain sends a scoring instruction to a node of at least one mechanism other than the mechanism, so that the node receiving the scoring instruction analyzes the user credit data uploaded to the blockchain by the mechanism in the week, and the method for analyzing the second user credit data refers to the above embodiments and is not described herein again.

In this embodiment, after the mechanism on the blockchain analyzes the credit data of the users of other mechanisms on the blockchain, the credit data of the users of the mechanism may be changed, for example, the credit data of the users of the mechanism is added with the credit data of the users, and the added credit data of the users can also be used for analyzing the credit data of the users of other mechanisms on the blockchain. Therefore, the application scenario of the credit data analysis method based on the blockchain may also be a scenario in which after the mechanism on the blockchain analyzes the user credit data of other mechanisms on the blockchain, the user credit data of the mechanism is added with the user credit data, and the user credit data of other mechanisms on the blockchain is analyzed according to the added user credit data.

Based on this, the first user credit data comprises new user credit data of the first institution; and the newly added user credit data of the first institution is the data of the first institution which is not used for scoring the second institution.

In a specific example, after analyzing the second user credit data, the client of the first mechanism acquires new user credit data a, at this time, the user credit data a is the first user credit data, and the client of the first mechanism may analyze the second user credit data again according to the user credit data a. For the analysis method of the second user credit data, refer to the above embodiments, and are not described herein again.

In this specification, in the case where, when any institution on the blockchain except for the institution corresponding to the second institution performs credit risk control using the second user credit data, if the control result indicates that, after performing the credit risk control using the second user credit data, the user reject ratio of the arbitrary institution is reduced as compared to when the credit risk control is not performed using the second user credit data, it may be stated that at least part of the user credit data in the second user credit data is authentic. It can be seen that the second user credit data can be analyzed according to the use effect of the second user credit data.

Based on this, the method of this specification embodiment still includes:

acquiring first wind control result data of a third mechanism; the first wind control result data is wind control result data in a period of time under the condition that credit risk control is not carried out by using the second user credit data; the third institution is an institution that uses the second user credit data for credit risk control.

Obtaining second wind control result data of the third organization for the second user credit data; the second wind control result data is wind control result data in the case of performing credit risk control using the second user credit data.

Determining a first user reject rate of the third organization according to the first wind control result data; the first user reject ratio is a ratio of the number of users who have not paid according to requirements in successful lending users to the number of users who have successfully borrowed within a period of time under the condition that credit risk control is not carried out by using the second user credit data.

Determining a second user reject rate of the third mechanism according to the second wind control result data; and the second user reject ratio is the ratio of the number of the users who have successfully borrowed and are not paid according to the requirements in the users who have successfully borrowed and are under the condition of using the credit data of the second user to carry out credit risk control.

And judging whether the reject ratio of the first user is greater than the reject ratio of the second user.

And if the first user reject ratio is larger than the second user reject ratio, determining a second grading result aiming at the second mechanism according to the first wind control result data and the second wind control result data based on a second preset grading standard.

In the embodiments of the present specification, any organization on the block chain may perform credit risk control only according to the user credit data of an organization on the block chain except for the any organization, and taking a third organization as an example, the third organization may perform credit risk control only according to the second user credit data, which enables the third organization to analyze the second user credit data according to the credit risk control effect.

In the practical application process, the duration of credit risk control performed by using the second user credit data by the third organization is assumed to be one week, in order to avoid influence of other factors on the analysis result of the second user credit data, the duration corresponding to the acquired wind control result data under the condition that credit risk control is not performed by using the second user credit data is also one week, that is, the first wind control result data is wind control result data in any week in the time period that credit risk control is performed by using the second user credit data. And the first and second wind control result data may each include the number of successful lended users and the number of unsolicited payment users among the successful lended users, or the first and second wind control result data may each include a user reject rate determined according to the number of successful lended users and the number of unsolicited payment users among the successful lended users. If the first user reject rate is greater than the second user reject rate, it indicates that the user reject rate is reduced after risk prevention and control is performed by using the second user credit data.

In this embodiment of the specification, after the institution corresponding to the third institution performs credit risk control by using the second user credit data, if the decrease range of the user reject ratio of the third institution is larger, it indicates that the validity of the second user credit data is higher, compared with the case where the third institution does not perform credit risk control by using the second user credit data, and therefore, in order to improve the accuracy of the analysis result of the second user credit data, the second user credit data may be analyzed according to the decrease range of the user reject ratio.

Based on this, the steps: determining a second scoring result for the second organization according to the first wind control result data and the second wind control result data based on a second preset scoring criterion, which may specifically include:

and dividing the reject ratio of the second user by the reject ratio of the first user to obtain a ratio.

A second reward score for a second institution is determined from the ratio.

In this embodiment of the specification, each ratio may correspond to one award value, and the smaller the ratio is, the higher the award value corresponding to the ratio is, in a specific example, it may be set that when the ratio is greater than 0 and less than or equal to 0.1, the award value corresponding to the ratio is 10 points, when the ratio is greater than 0.1 and less than or equal to 0.2, the award value corresponding to the ratio is 9 points, when the ratio is greater than or equal to 0.2 and less than or equal to 0.3, the award value corresponding to the ratio is 8 points, and so on, when the ratio is greater than 0.5 and less than or equal to 0.6, the award value corresponding to the ratio is 5 points; . . . When the ratio is greater than 0.9 and less than or equal to 1, the reward score corresponding to the ratio is 1. Assuming that the first user reject rate is 5% and the second user reject rate is 3%, the ratio is 0.6, and the second incentive score for the second institution may be determined to be 5 points according to the correspondence between the ratio and the incentive score.

In this embodiment, in order to ensure real-time performance and comprehensiveness of the user credit data on the blockchain, it is necessary for each entity of the blockchain to update the user credit data uploaded to the blockchain in time, and in order to encourage each entity of the blockchain to update the user credit data uploaded to the blockchain in time, a reward standard for encouraging each entity of the blockchain to update the user credit data uploaded to the blockchain in time may be set.

Based on this, the method of this specification embodiment still includes:

and aiming at the user credit data on the validated block chain, a mechanism for uploading the user credit data on the validated block chain to the block chain for the first time carries out score rewarding.

In this embodiment of the present specification, it is assumed that an organization a and an organization B on a block chain upload credit data of zhang san to the block chain in sequence, and the credit data of zhang san is verified as valid user credit data, because the organization a uploads the credit data of zhang san to the block chain first, the organization a is awarded with a score, and a specific award score may be set by a person skilled in the art according to an actual situation, for example, the award score is set to 1.

In practical application, when a client of a mechanism other than the mechanism a on the blockchain analyzes user credit data uploaded to the blockchain by the mechanism a, valid user credit data in the user credit data of the mechanism a is determined, and the valid user credit data is uploaded to the blockchain. After the block chain acquires the effective user credit data, determining an organization which uploads the effective user credit data to the block chain for the first time, and awarding scores to the organization. Continuing with the case of zhang san, the client of the organization other than the organization a on the blockchain may determine that the user credit data of the organization a about zhang is the real user credit data, and upload the user credit data about zhang to the blockchain.

Secondly, when the organizations except the organization A on the block chain use the user credit data uploaded to the block chain by the organization A to carry out credit risk control, the effective user credit data in the user credit data uploaded to the block chain by the organization A is determined according to the use result, and the effective user credit data is uploaded to the block chain. Taking zhang san as an example, and assuming that the C agency uses the user credit data uploaded to the block chain by the a agency to perform credit risk control, the user credit data uploaded to the block chain by the a agency is blacklist data, the C agency learns that zhang san is a distrusted user through the user credit data of the a agency, but still credits zhang san, and, in the later period, zhang san is violated at the C agency, so that the C agency can determine that zhang san is really a distrusted user, the credit data of the a agency about zhang is valid user credit data, and upload the credit data of zhang san to the block chain.

In the embodiment of the specification, by aiming at the credit data of the user on the validated block chain, the mechanism for uploading the credit data of the user on the validated block chain to the block chain for the first time is subjected to scoring reward, so that each mechanism of the block chain is encouraged to update the credit data of the user uploaded to the block chain in time, and the real-time performance of the credit data of the user on the block chain is improved.

Based on a general inventive concept, the embodiments of the present specification further provide a credit data sharing method based on a block chain. Next, a block chain-based credit data sharing method provided in an embodiment of the present disclosure will be described in detail with reference to the accompanying drawings:

fig. 3 is a flowchart illustrating a method for sharing credit data based on a block chain according to an embodiment of the present disclosure. From a program perspective, the execution subject of the flow may be a node on the blockchain, or an application of the blockchain deployed in the node on the blockchain. As shown in fig. 3, the process may include the following steps:

step 302: acquiring the integral score of a first mechanism on a block chain; the overall score of the first institution is determined according to each scoring result of the first institution; each scoring result of the first organization is determined by using a block chain-based credit data analysis method in the embodiment of the specification.

Step 304: and acquiring the overall scores of other mechanisms except the first mechanism on the block chain.

Step 306: and determining an access sequence of the user credit data of each organization on the block chain according to the overall score of the first organization and the overall scores of other organizations on the block chain except the first organization, wherein the access sequence is used for accessing the user credit data of each organization on the block chain based on the access sequence when the user-side equipment has paid access to the user credit data of each organization on the block chain.

In the embodiment of the present specification, as is apparent from the above description, since the overall score of an organization indicates the validity of the user credit data of the organization, when the user-side device accesses the user credit data on the block chain, the user credit data can be provided to the user-side device according to the validity of the user credit data of each organization on the block chain, and the access order of the user credit data for each organization can be determined according to the overall score of each organization.

In practical applications, the user-side device may be a node on a block chain. The specific process of the user side device accessing the user credit data on the block chain may be: the user side equipment sends user data to be matched to the block chain, requests the block chain to judge the credit type of a user contained in the user data to be matched, and after receiving the user data to be matched, the block chain matches the user data to be matched with the user credit data of each organization in sequence based on the access sequence of the user credit data of each organization determined by the block chain until the user credit data on the block chain of the user data to be matched or the user data to be matched and the user credit data of each organization are matched, and the matching process is ended.

In a specific example, the a organization on the blockchain sends the identity data of lie four to the blockchain, after receiving the identity data of lie four, the blockchain matches the identity data of lie four with the user credit data of each node on the blockchain except the a organization in sequence based on the determined access sequence of the user credit data of each organization, and when the identity data of lie four matches the user credit data of the D organization, the user credit data of the D organization also includes the identity data of lie four (i.e. the identity data of lie four hits the user credit data of the D organization), at this time, the blockchain may end the matching process and feed back the matching result to the a organization, where the matching result may be a matching result used for indicating that lie four is a distrusted user.

In another specific example, the a organization on the blockchain sends the identity data of lie four to the blockchain, after receiving the identity data of lie four, the blockchain matches the identity data of lie four with the user credit data of each organization on the blockchain in turn based on the determined access sequence of the user credit data of each organization, and after matching the identity data of lie four with the user credit data of each organization in turn, the identity data of lie four does not hit the user credit data of any organization, at this time, the blockchain bundles the matching process, and feeds back the matching result to the a organization, where the matching result may be a matching result indicating that lie four is a trusted user.

In addition, the credit data of the user on the blockchain can also support the mechanism access of the non-joining blockchain, and in this case, the user-side device can be a device of the mechanism of the non-joining blockchain. It should be noted that, before accessing the user credit data on the blockchain, the mechanism that does not join the blockchain needs to obtain authorization authentication, and a specific authorization authentication manner may be determined by those skilled in the art according to actual needs.

In the embodiment of the present specification, as can be seen from the foregoing, the higher the overall score of a mechanism is, the higher the validity of the user credit data of the mechanism is, so that when the user-side device accesses the user credit data on the block chain, the user credit data with higher validity can be preferentially provided to the user-side device, and the user-side device can be made to preferentially access the user credit data with higher overall score.

Based on this, step 306 may specifically include:

and based on a sorting rule of the overall scores from top to bottom, sorting the mechanisms on the block chain according to the overall scores of the mechanisms on the block chain to obtain an access sequence of the user credit data aiming at the mechanisms on the block chain.

In the embodiment of the present specification, for example, in the block chain, the overall score of the organization a is 50 points, the overall score of the organization B is 51 points, and the overall score of the organization C is 45 points, so that the access order of the user credit data for the three organizations is: mechanism B, mechanism A and mechanism C.

In this embodiment of the present specification, when the user-side device accesses the user credit data on the blockchain, the blockchain may enable the user-side device to sequentially access the user credit data of each organization on the blockchain based on the determined access order of the user credit data of each organization; and, in order to guarantee the benefit of the organizations of each organization on the blockchain, the user side device can pay for accessing the user credit data on the blockchain.

Based on this, after step 306, the method for sharing credit data based on a block chain according to the embodiment of the present specification may further include:

acquiring a credit data access request sent by a first client corresponding to a second mechanism; the credit data access request comprises user data to be matched and a payment result; and the payment result is a payment result generated according to the fee value determined according to the user data to be matched.

And determining an access result aiming at the first mechanism according to the user data to be matched and the user credit data of each mechanism on the block chain based on the access sequence.

And feeding back the access result to the first client.

In the embodiment of the present specification, the second mechanism may be a mechanism on the blockchain, or may be a mechanism that is not added to the blockchain. The block chain determines the access fee for the second organization according to the user data to be matched of the second organization when the second organization accesses the user credit data on the block chain, sends a payment instruction about the access fee to the client corresponding to the second organization, and the second organization can access the user credit data on the block chain after the second organization completes payment for the access fee. In one specific example, the access cost criteria for blockchain settings are: and matching the credit data charge of one user by 10 yuan, and the credit data access request sent to the blockchain by the first client corresponding to the second organization comprises the credit data of two users, so that the access fee for the second organization is 20 yuan.

Secondly, when an organization corresponding to a second organization accesses the user credit data on the blockchain, in order to reduce the workload of data matching of the blockchain, improve the matching efficiency of the blockchain, and make the overall score of the organization higher, the higher the profit for the organization is, when the user credit data on the blockchain is hit by the user data to be matched of the second organization, the access process of the second organization is finished by the blockchain, and the access result is fed back to the first client corresponding to the second organization.

For the access fee paid by the second institution, the access fee may be distributed to the institutions according to their contribution, for example, in a block chain, according to a ranking rule that scores are from high to low, an access sequence determined based on the overall scores of the institutions is: the system comprises a mechanism A, a mechanism B, a mechanism C, a mechanism D, a mechanism E and a mechanism F, wherein the mechanism A, the mechanism B and the mechanism C are sequentially accessed by a second mechanism in the process of accessing user credit data on the block chain, and the mechanism D, the mechanism E and the mechanism F on the block chain are not accessed by the second mechanism, so that the mechanism A, the mechanism B and the mechanism C all contribute to the access process of the second mechanism, the mechanism D, the mechanism E and the mechanism F do not contribute to the access process of the second mechanism, and at the moment, the access cost of the second mechanism is averagely distributed to the mechanism A, the mechanism B and the mechanism C. Therefore, the higher the integral score of the mechanism is, the larger the income of the mechanism is, and the process of matching the user data to be matched of the second mechanism with the user credit data of the D mechanism, the E mechanism and the F mechanism is reduced for the block chain, so that the workload of data matching of the block chain is reduced, and the matching efficiency of the block chain is improved.

In this embodiment of the present specification, when the credit data of the user to be matched at the second institution hits the credit data of the user on the blockchain, the institution corresponding to the second institution may perform credit activities with the user to which the credit data of the user to be matched is directed, and determine the credit type of the user according to the fulfillment result of the contract of the user on the credit result at a later stage, at this time, the second institution may determine whether the credit data of the user hit by the second institution is the credit data of the real user according to the determined credit type of the user, and score the credit data of the user hit by the second institution.

Based on the above, the access result contains the identification of a specific mechanism; the specific mechanism is a mechanism in which the user credit data accessed by the first mechanism of the specific mechanism and the user data to be matched both contain the same user group. The method comprises the following steps: after the feeding back the access result to the first client, the method for sharing credit data based on a block chain according to the embodiment of the present specification may further include:

obtaining a scoring result which is sent by the first client and aims at the specific mechanism; the scoring results for the particular organization are used to evaluate the validity of the user credit data for the particular organization.

In a specific example, the user credit data on the blockchain is blacklist data, the user data to be matched of the second organization is identity data of wang five, and the identity data of wang five hits the user credit data of the D organization on the blockchain, at this time, the access result includes the name of the D organization and the user credit data information of the D organization on the blockchain about the identity data hit of wang five. After acquiring the access result, the institution corresponding to the second institution still credits the fifth card, and subsequently, if the fifth card has a default at the institution corresponding to the second institution, the institution corresponding to the second institution can determine that the user credit data of the D institution about the fifth card is the real user credit data according to the default condition of the fifth card and determine the reward score aiming at the D institution; if wang five does not default at the mechanism corresponding to the second mechanism, the mechanism corresponding to the second mechanism can determine that the user credit data of the mechanism D about wang five is false user credit data according to the conservation condition of wang five, and determine a penalty score for the mechanism D.

In an embodiment of the present description, a mechanism of a blockchain may upload user credit data to the blockchain, and after the mechanism of the blockchain uploads the user credit data to the blockchain, the blockchain may notify other mechanisms of the blockchain to analyze the user credit data uploaded to the blockchain by the mechanism in order to determine validity of the user credit data of the mechanism.

Based on this, the method for sharing credit data based on a block chain according to the embodiment of the present specification may further include:

and acquiring user credit data uploaded to the block chain by a third mechanism on the block chain.

Sending a scoring instruction to a second client corresponding to a fourth mechanism on the block chain, so that the second client determines a scoring result for the third mechanism according to user credit data uploaded to the block chain by the third mechanism by using the block chain-based credit data analysis method in the above embodiment of the present specification; and the scoring result of the third mechanism is used for evaluating the validity of the user credit data uploaded to the block chain by the third mechanism.

In this embodiment of the present specification, the fourth mechanism is a mechanism on the blockchain except the third mechanism, and after the third mechanism uploads the user credit data to the blockchain, at least one mechanism on the blockchain except the third mechanism may analyze the user credit data of the third mechanism, and as can be seen, the number of the fourth mechanism is at least one. In a specific example, the third mechanism is a mechanism newly added to the blockchain, and after the third mechanism uploads user credit data to the blockchain for the first time, the blockchain acquires the user credit data of the third mechanism and sends a scoring instruction to the client terminals of all mechanisms except the third mechanism on the blockchain, so that the client terminals of all mechanisms except the third mechanism on the blockchain analyze the user credit data uploaded to the blockchain by the third mechanism.

The block chain-based credit data analysis method according to the embodiment of the present specification may be applied to a first institution-side device, which may be a node of a first institution. Based on step 204, the acquiring the second user credit data of the second organization through the blockchain specifically includes:

acquiring second user credit data which is sent by the block chain and encrypted by adopting a first public key; the first public key is a public key bound to the account identifier of the first organization and used for encrypting data sent by the blockchain to the device of the first organization.

A first private key corresponding to the first public key is determined.

And decrypting the second user credit data encrypted by the first public key by using the first private key to obtain the second user credit data.

Next, the block chain-based credit data analysis method according to the embodiment of the present specification may further include:

encrypting the data of the first organization by adopting a second public key; the second public key is a public key bound with the account identifier of the first organization and used for encrypting the data sent by the first organization to the block chain;

uploading the encrypted data of the first organization to the blockchain;

and after the block link receives the encrypted data of the first mechanism, determining a second private key corresponding to the second public key, and decrypting the encrypted data of the first mechanism by adopting the second private key to obtain the decrypted data of the first mechanism.

In this embodiment of the present description, when an arbitrary organization joins a blockchain, a local device of the organization generates a first public key and a first private key bound to an account identifier of the organization, and sends an application joining request to the blockchain, where the application joining request includes the first public key. After the block chain acquires the application adding request, a second public key and a second private key bound with the account identifier of the organization are generated according to the application adding request, the second public key is fed back to the local equipment of the organization, and the first public key and the second private key are stored in the block chain. Therefore, when the mechanism performs data interaction with the block chain, the public key of the opposite end can be used for encrypting the transmission file, and when the opposite end receives the related file, the private key acquired from the opposite end can be used for decrypting the file, so that the file cannot be leaked in the transmission process.

In a specific example, as shown in fig. 4, taking organization B as an example, when organization B joins the blockchain, the local device of organization B generates a first key pair (b.pri, b.pub) bound to the account identifier of organization B, and the local device further locally stores the private key b.pri and uploads the public key b.pub to the blockchain. After receiving an application join request carrying a public key b.pub and sent by a local device of an organization, the blockchain generates a second key pair (ltob.pri, l.pub) bound with an account identifier of the organization B, stores the public key b.pub and a private key ltob.pri in the blockchain, and distributes the public key l.pub to the local device of the organization B.

When the mechanism B acquires a file from the blockchain, the mechanism B sends a first file acquisition request carrying an account identifier of the mechanism B to the blockchain, when the blockchain receives the file acquisition request, a public key B.pub uploaded by local equipment of the mechanism B is determined according to the account identifier of the mechanism B, the public key B.pub is adopted to encrypt related files, the related files encrypted by the public key B.pub are fed back to the local equipment of the mechanism B, and the local equipment of the mechanism B decrypts the related files encrypted by the public key B.pub through a private key B.pri generated by the local equipment of the mechanism B to obtain the decrypted related files. Similarly, when the mechanism B uploads a file to the block chain, the local device of the mechanism B encrypts the transmission file by using the public key l.pub sent by the block chain, and sends the transmission file encrypted by using the public key l.pub to the block chain, so that the block chain decrypts the transmission file by using the locally stored private key ltob.pri. Therefore, the transmission file is ensured not to be leaked when the mechanism B performs file transmission with the block chain.

In the embodiment of the present specification, a Block chain (Block chain) may be understood as a data chain formed by sequentially storing a plurality of blocks, and a Block header of each Block includes a time stamp of the Block, a hash value of previous Block information, and a hash value of the Block information, so as to implement mutual authentication between the blocks and form a non-tampered Block chain. Each block can be understood as a data block (unit of storing data). The block chain as a decentralized database is a series of data blocks generated by correlating with each other by using a cryptographic method, and each data block contains information of one network transaction, which is used for verifying the validity (anti-counterfeiting) of the information and generating the next block. The block chain is formed by connecting the blocks end to end. If the data in the block needs to be modified, the contents of all blocks after the block need to be modified, and the data backed up by all nodes in the block chain network needs to be modified. Therefore, the blockchain has the characteristic of being difficult to tamper and delete, and the method for maintaining the integrity of the content has reliability after the data is stored in the blockchain.

Fig. 5 is a schematic swimlane flow chart of the block chain-based credit data analysis method and the sharing method corresponding to fig. 2 and 3 according to an embodiment of the present disclosure. As shown in fig. 5, the credit data analysis method and the credit data sharing method based on the blockchain may involve a first client, a second client, a blockchain and other execution subjects.

In fig. 5, the first mechanism and the second mechanism are both mechanisms that join the blockchain, the first mechanism corresponds to the first client, and the third mechanism corresponds to the second client.

In a credit data analysis stage, a first client may locally obtain first user credit data of a first organization from a node of the first organization and obtain second user credit data uploaded to a block chain by a second organization from the block chain, then, the first client matches the first user credit data with the second user credit data based on a first preset scoring standard, if a matching result indicates that the first user credit data and the second user credit data both contain the same user group, a first scoring result for the second organization is determined according to the matching result, and the first scoring result is uploaded to the block chain, and the first scoring result user evaluates validity of the second user credit data. And after the block chain acquires the first scoring result, updating the overall score of the second mechanism according to the first scoring result, wherein the overall score is determined according to each scoring result of the second mechanism.

In the credit data sharing stage, after the block chain acquires the overall score of each mechanism on the block chain, according to a sorting rule that the score is from high to low, the user credit data of each mechanism is sorted according to the overall score of each mechanism, an access sequence of the user credit data of each mechanism is obtained, subsequently, when a second client sends a credit data access request containing the user data to be matched and the payment result to the block chain, the block chain matches the user data to be matched with the user credit data of each mechanism in sequence based on the access sequence after receiving the credit data access request, and obtains an access result, and the access result is fed back to the second client, so that the mechanism corresponding to the second client obtains the access result through the second client.

Based on the same idea, the embodiments of the present specification further provide a device corresponding to the above block chain-based credit data analysis method. Fig. 6 is a schematic structural diagram of a block chain-based credit data analysis apparatus corresponding to fig. 2 according to an embodiment of the present disclosure. As shown in fig. 6, the apparatus may include:

a first obtaining module 602, configured to obtain first user credit data of a first organization.

A second obtaining module 604, configured to obtain second user credit data of the second organization through the blockchain.

And the data matching module 606 is configured to match the first user credit data and the second user credit data based on a first preset scoring criterion, so as to obtain a matching result.

A first scoring module 608, configured to determine, according to the matching result, a first scoring result for the second organization if the matching result indicates that the first user credit data and the second user credit data both include the same user group; the first scoring result is used for evaluating the effectiveness of the second user credit data.

A data uploading module 610, configured to upload the first scoring result to the blockchain.

The examples of this specification also provide some specific embodiments of the apparatus based on the apparatus of fig. 6, which is described below.

In an embodiment of the present specification, the first preset scoring criterion may include a first scoring criterion; the first user credit data may comprise credit data of a successfully credited user; the second user credit data may comprise blacklist data.

The data matching module 606 may be specifically configured to:

The first scoring module 608 may be specifically configured to:

In an embodiment of the present specification, the first preset scoring criteria may include a second scoring criteria; the first user credit data may comprise credit data of a distrusted user; the second user credit data may comprise blacklist data.

The data matching module 606 may be specifically configured to:

The first scoring module 608 may be specifically configured to:

In an embodiment of the present specification, the first preset scoring criteria may include a first scoring criterion and a second scoring criterion; the first user credit data can comprise credit data of a successfully credited user and credit data of an untrusted user; the second user credit data may comprise blacklist data.

The data matching module 606 may be specifically configured to:

The first scoring module 608 may be specifically configured to:

if the first matching result shows that the credit data of the user who has successfully been trusted and the blacklist data both contain the same first user group, determining a penalty score for the second organization according to the first matching result based on the first scoring standard, and if the second matching result shows that the credit data of the user who has lost credit and the blacklist data both contain the same second user group, determining a reward score for the second organization according to the second matching result based on the second scoring standard.

And determining the first scoring result according to the penalty score and the reward score.

The apparatus of this specification embodiment may further include:

the updating module is used for updating the integral score of the second mechanism according to the first scoring result; the overall score is a score determined according to each scoring result of the second institution.

The apparatus of this specification embodiment may further include:

the third acquisition module is used for acquiring first wind control result data of a third mechanism; the first wind control result data is wind control result data in a period of time under the condition that credit risk control is not carried out by using the second user credit data; the third institution is a node for credit risk control using the second user credit data.

A fourth obtaining module, configured to obtain second wind control result data of the third mechanism for the second user credit data; the second wind control result data is wind control result data in the case of performing credit risk control using the second user credit data.

The first determining module is used for determining the reject ratio of a first user of the third organization according to the first wind control result data; the first user reject ratio is a ratio of the number of users who have not paid according to requirements in successful lending users to the number of users who have successfully borrowed within a period of time under the condition that credit risk control is not carried out by using the second user credit data.

The second determining module is used for determining the second user reject ratio of the third mechanism according to the second wind control result data; and the second user reject ratio is the ratio of the number of users who have not yet paid according to requirements in the successful lending users to the number of the successful lending users under the condition of credit risk control by using the second user credit data.

And the judging module is used for judging whether the reject ratio of the first user is greater than the reject ratio of the second user.

And the second scoring module is used for determining a second scoring result aiming at the second mechanism according to the first wind control result data and the second wind control result data based on a second preset scoring standard if the first user reject ratio is greater than the second user reject ratio.

Based on the same idea, the embodiments of the present specification further provide a device corresponding to the above credit data sharing method based on a block chain. Fig. 7 is a schematic structural diagram of a block chain-based credit data sharing apparatus corresponding to fig. 3 according to an embodiment of the present disclosure. As shown in fig. 7, the apparatus may include:

a first obtaining module 702, configured to obtain an overall score of a first mechanism on a blockchain; the overall score of the first institution is determined according to each scoring result of the first institution; each scoring result of the first organization is determined by using a block chain-based credit data analysis method in the embodiment of the specification.

A second obtaining module 704, configured to obtain an overall score of the other organizations on the blockchain except the first organization.

A first determining module 706, configured to determine, according to the overall score of the first mechanism and the overall scores of the other mechanisms except the first mechanism in the block chain, an access order of the user credit data for each mechanism in the block chain, where the access order is used to access the user credit data of each mechanism in the block chain based on the access order when the user-side device has paid access to the user credit data of each mechanism in the block chain.

The apparatus of this specification embodiment may further include:

the third acquisition module is used for acquiring a credit data access request sent by a first client corresponding to the second mechanism; the credit data access request comprises user data to be matched and a payment result; and the payment result is a payment result generated according to the fee value determined according to the user data to be matched.

And the second determining module is used for determining an access result aiming at the first mechanism according to the user data to be matched and the user credit data of each node on the block chain based on the access sequence.

And the feedback module is used for feeding back the access result to the first client.

Based on the same idea, the embodiments of the present specification further provide a device corresponding to the above block chain-based credit data analysis method.

Fig. 8 is a schematic structural diagram of a block chain-based credit data analysis device corresponding to fig. 2 provided in an embodiment of the present specification. As shown in fig. 8, the apparatus 800 may include:

at least one processor 810; and the number of the first and second groups,

a memory 830 communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory 830 stores instructions 820 executable by the at least one processor 810 to enable the at least one processor 810 to:

acquiring first user credit data of a first organization;

uploading the first scoring result to the blockchain.

Based on the same idea, the embodiments of the present specification further provide a device corresponding to the above block chain-based credit data sharing method.

Fig. 9 is a schematic structural diagram of a block chain-based credit data sharing device corresponding to fig. 3 according to an embodiment of the present disclosure. As shown in fig. 9, the apparatus 900 may include:

at least one processor 910; and the number of the first and second groups,

a memory 930 communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,

the memory 930 stores instructions 920 executable by the at least one processor 910 to enable the at least one processor 910 to:

acquiring the integral score of a first mechanism on a block chain; the overall score of the first institution is determined according to each scoring result of the first institution; the results of each scoring by the first institution are determined using the method of any of claims 1-10;

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus shown in fig. 5, since it is substantially similar to the method embodiment, the description is relatively simple, and reference may be made to the partial description of the method embodiment for relevant points.

In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain a corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement of the process flow cannot be realized with hardware physical modules. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is an integrated circuit whose Logic functions are determined by a user programming the Device. A digital character system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate a dedicated integrated circuit chip. Furthermore, nowadays, instead of manually manufacturing an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one kind, but many kinds, such as abel (advanced Expression Language), ahdl (alternate Hardware Description Language), traffic, CUPL (computer universal Programming Language), HDCal (jhddl (Hardware Description Language), Lava, Lola, HDL, PALASM, rhyd (Hardware Description Language), and vhjh-Language, which are currently used in most popular applications. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.

The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller in purely computer readable program code means, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and described separately. Of course, the functionality of the various elements may be implemented in the same software and/or hardware in the practice of the present application.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A credit data analysis method based on a block chain comprises the following steps:

acquiring first user credit data of a first organization;

uploading the first scoring result to the blockchain.

2. The method of claim 1, the first predetermined scoring criteria comprising a first scoring criterion; the first user credit data comprises credit data of a user successfully credited; the second user credit data comprises blacklist data;

the matching the first user credit data and the second user credit data based on the first preset scoring standard to obtain a matching result specifically includes:

matching the credit data of the successfully trusted user with the blacklist data to obtain a first matching result;

if the matching result indicates that the first user credit data and the second user credit data both contain the same user group, determining a first scoring result for the second organization according to the matching result, specifically including:

3. The method of claim 1, the first predetermined scoring criteria comprising a second scoring criteria; the first user credit data comprises credit data of a lost user; the second user credit data comprises blacklist data;

matching the credit data of the lost user with the blacklist data to obtain a second matching result;

and if the second matching result shows that the credit data and the blacklist data of the distrusted user both contain the same second user group, determining the reward score aiming at the second organization according to the second matching result based on the second scoring standard.

4. The method of claim 1, wherein the first predetermined scoring criteria comprises a first scoring criteria and a second scoring criteria; the first user credit data comprises credit data of a user who successfully grants credit and credit data of a user who loses credit; the second user credit data comprises blacklist data;

matching the credit data of the successfully credited user with the blacklist data to obtain a first matching result, and matching the credit data of the untrusted user with the blacklist data to obtain a second matching result;

if the first matching result shows that the credit data of the user who has successfully granted the credit and the blacklist data both contain the same first user group, determining a penalty score for the second organization according to the first matching result based on the first scoring standard, and if the second matching result shows that the credit data of the user who has lost the credit and the blacklist data both contain the same second user group, determining a reward score for the second organization according to the second matching result based on the second scoring standard;

5. The method of claim 2 or 4, wherein determining a penalty score for the second institution based on the first scoring criteria from the first match results comprises:

determining the number of users contained in the first user group according to the first matching result;

determining a deduction coefficient for the second mechanism according to the number of the users;

and determining a penalty score for the second organization according to the deduction coefficient and the number of the users.

6. The method of claim 1, after uploading the scoring results to the blockchain, further comprising:

7. The method of claim 1, further comprising:

acquiring first wind control result data of a third mechanism; the first wind control result data is wind control result data in a period of time under the condition that credit risk control is not carried out by using the second user credit data; the third institution is an institution that uses the second user credit data for credit risk control;

obtaining second wind control result data of the third organization for the second user credit data; the second wind control result data is wind control result data under the condition of credit risk control by using the second user credit data;

determining a first user reject ratio of the third organization according to the first wind control result data; the first user reject ratio is the ratio of the number of the users who have successfully borrowed and lent but not paid according to the requirements in a period of time under the condition that credit risk control is not carried out by using the credit data of the second user;

determining a second user reject rate of the third mechanism according to the second wind control result data; the second user reject ratio is the ratio of the number of the users who have successfully borrowed and are not paid according to the requirements in the users who have successfully borrowed and are under the condition of credit risk control by using the credit data of the second user;

judging whether the reject ratio of the first user is larger than the reject ratio of the second user;

8. The method of claim 1, wherein the second user credit data comprises user credit data initially uploaded to the blockchain by the second organization or newly added user credit data on the blockchain by the second organization; and the newly added user credit data of the second mechanism on the block chain is the user credit data of the second mechanism on the block chain which is not used for scoring the second user credit data.

9. The method of claim 1, wherein the first user credit data comprises added user credit data for the first organization; and the newly added user credit data of the first mechanism is the data of the first mechanism which is not used for scoring the second user credit data.

10. The method of claim 1, further comprising:

11. The method according to claim 1, wherein the obtaining of the second user credit data of the second organization through the blockchain specifically comprises:

acquiring second user credit data which is sent by the block chain and encrypted by a first public key; the first public key is bound with the account identifier of the first organization and is used for encrypting data sent by the block chain to the equipment of the first organization;

determining a first private key corresponding to the first public key;

12. The method of claim 11, further comprising:

uploading the encrypted data of the first organization to the blockchain;

13. A credit data sharing method based on a block chain comprises the following steps:

14. The method of claim 13, wherein determining an order of visit for each node in the blockchain based on the overall score of the first organization and the overall scores of the nodes in the blockchain other than the first organization comprises:

and sequencing the nodes on the block chain according to the integral scores of the nodes on the block chain based on a sequencing rule of the integral scores from top to bottom to obtain an access sequence aiming at the nodes on the block chain.

15. The method of claim 13, after determining an access order for user credit data for institutions on the blockchain based on the overall score of the first institution and the overall scores of institutions on the blockchain other than the first institution, further comprising:

acquiring a credit data access request sent by a second mechanism through a first client; the credit data access request comprises user data to be matched and a payment result; the payment result is a payment result generated according to a fee value determined according to the user data to be matched;

determining an access result aiming at the first mechanism according to the user data to be matched and user credit data of each mechanism on the block chain based on the access sequence;

and feeding back the access result to the first client.

16. The method of claim 13, wherein the access result includes an identification of a particular organization; the specific mechanism is a mechanism in which the user credit data accessed by the first mechanism of the specific mechanism and the user data to be matched both contain the same user group;

after the feeding back the access result to the first client, the method further includes:

17. The method of claim 13, further comprising:

acquiring user credit data uploaded to a block chain by a third mechanism on the block chain;

sending a scoring instruction to a second client corresponding to a fourth organization on the blockchain, so that the second client adopts the method according to any one of claims 1 to 5 to determine a scoring result aiming at the third organization according to the user credit data uploaded to the blockchain by the third organization; and the scoring result of the third mechanism is used for evaluating the validity of the user credit data uploaded to the block chain by the third mechanism.

18. A block-chain-based credit data analysis apparatus, comprising:

the first acquisition module is used for acquiring first user credit data of a first mechanism;

the second acquisition module acquires second user credit data of a second organization through the block chain;

19. The apparatus of claim 18, said first preset scoring criteria comprising a first scoring criterion; the first user credit data comprises credit data of a user who has successfully been granted credit; the second user credit data comprises blacklist data;

the data matching module is specifically configured to:

the first scoring module is specifically configured to:

20. The apparatus of claim 18, said first preset scoring criteria comprising a second scoring criteria; the first user credit data comprises credit data of a user who loses credit; the second user credit data comprises blacklist data;

the data matching module is specifically configured to:

the first scoring module is specifically configured to:

21. The apparatus of claim 18, said first preset scoring criteria comprising a first scoring criterion and a second scoring criterion; the first user credit data comprises credit data of a user successfully credited and credit data of a user losing credit; the second user credit data comprises blacklist data;

the data matching module is specifically configured to:

the first scoring module is specifically configured to:

22. A device for sharing credit data based on a blockchain, comprising:

the first acquisition module is used for acquiring the integral score of a first mechanism on the block chain; the overall score of the first institution is determined according to each scoring result of the first institution; the results of each scoring by the first institution are determined using the method of any of claims 1-10;

a first determining module, configured to determine, according to the overall score of the first organization and the overall scores of the other organizations except the first organization in the block chain, an access sequence of user credit data for each organization in the block chain, where the access sequence is used to access the user credit data of each organization in the block chain based on the access sequence when a user-side device has paid access to the user credit data of each organization in the block chain.

23. The apparatus of claim 22, further comprising:

the third acquisition module is used for acquiring a credit data access request sent by a second mechanism through the first client; the credit data access request comprises user data to be matched and a payment result; the payment result is a payment result generated according to a fee value determined according to the user data to be matched;

a second determining module, configured to determine, based on the access sequence, an access result for the first mechanism according to the user data to be matched and user credit data of each mechanism on the block chain;

24. A block-chain-based credit data analysis device, comprising:

at least one processor; and the number of the first and second groups,

the memory stores instructions executable by the at least one processor to cause the at least one processor to:

acquiring first user credit data of a first organization;

uploading the first scoring result to the blockchain.

25. A device for sharing credit data based on a blockchain, comprising:

at least one processor; and the number of the first and second groups,