Credit data analysis method, sharing method, device and equipment based on blockchain

Abstract

The embodiments of this specification disclose a credit data analysis method, sharing method, device and equipment based on blockchain. The scheme may include: after obtaining the credit data of a first user of a first institution and the credit data of a second user of a second institution from the blockchain, matching the credit data of the first user with the credit data of the second user based on a first preset scoring standard, if the matching result indicates that the credit data of the first user and the credit data of the second user both contain the same user group, determining a first scoring result for evaluating the validity of the credit data of the second user according to the matching result, and uploading the first scoring result to the blockchain so that the blockchain obtains the analysis result for the credit data of the second user.

Description

Technical Field

[0001] The present application relates to the field of blockchain technology, and in particular to a credit data analysis method, sharing method, device and equipment based on blockchain. Background Art

[0002] When financial institutions (trust institutions, consumer finance companies, banks, etc.) conduct credit activities, in order to reduce credit risks, they need to conduct risk assessments on users and perform credit risk control based on the assessment results. Currently, financial institutions conduct credit risk control based on their own user credit data or based on user credit data purchased from some trusted institutions.

[0003] Therefore, how to provide financial institutions with more accurate and comprehensive user credit data has become a technical problem that needs to be solved urgently. Summary of the invention

[0004] The embodiments of this specification provide a blockchain-based credit data analysis method, sharing method, device and equipment that can provide financial institutions with more accurate and comprehensive user credit data.

[0005] To solve the above technical problems, the embodiments of this specification are implemented as follows:

[0006] The embodiment of this specification provides a credit data analysis method based on blockchain, including:

[0007] Obtaining credit data of a first user of a first institution;

[0008] Obtain credit data of a second user of a second institution through blockchain;

[0009] Based on a first preset scoring standard, matching the first user credit data with the second user credit data to obtain a matching result;

[0010] If the 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 institution is determined based on the matching result; the first scoring result is used to evaluate the validity of the second user credit data;

[0011] The first scoring result is uploaded to the blockchain.

[0012] The embodiment of this specification provides a credit data sharing method based on blockchain, including:

[0013] Obtaining an overall score of the first institution on the 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 institution is determined using the blockchain-based credit data analysis method described in the embodiment of this specification;

[0014] Obtaining the overall scores of other institutions on the blockchain except the first institution;

[0015] An access order for user credit data of each institution on the blockchain is determined based on the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution; the access order is used to access the user credit data of each institution on the blockchain based on the access order when a user-side device accesses the user credit data of each institution on the blockchain for a fee.

[0016] The embodiment of this specification provides a blockchain-based credit data analysis device, including:

[0017] A first acquisition module, used to acquire credit data of a first user of a first institution;

[0018] A second acquisition module, used to acquire credit data of a second user of a second institution through blockchain;

[0019] A data matching module, configured to match the first user credit data with the second user credit data based on a first preset scoring standard to obtain a matching result;

[0020] a first scoring module, which determines a first scoring result for the second institution according to the 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; the first scoring result is used to evaluate the validity of the second user credit data;

[0021] A data uploading module is used to upload the first scoring result to the blockchain.

[0022] The embodiment of this specification provides a blockchain-based credit data sharing device, including:

[0023] A first acquisition module is used to obtain the overall score of the first institution on the 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 institution is determined using the blockchain-based credit data analysis method described in the embodiment of this specification;

[0024] A second acquisition module, used to obtain the overall scores of other institutions on the blockchain except the first institution;

[0025] The first determination module is used to determine an access order for user credit data of each institution on the blockchain based on the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution, wherein the access order is used to access the user credit data of each institution on the blockchain based on the access order when the user-side device accesses the user credit data of each institution on the blockchain for a fee.

[0026] The embodiment of this specification provides a blockchain-based credit data analysis device, including:

[0027] at least one processor; and,

[0028] a memory communicatively connected to the at least one processor; wherein,

[0029] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0030] Obtaining credit data of a first user of a first institution;

[0031] Obtain credit data of a second user of a second institution through blockchain;

[0032] Based on a first preset scoring standard, matching the first user credit data with the second user credit data to obtain a matching result;

[0033] If the 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 institution is determined based on the matching result; the first scoring result is used to evaluate the validity of the second user credit data;

[0034] The first scoring result is uploaded to the blockchain.

[0035] The embodiment of this specification provides a blockchain-based credit data sharing device, including:

[0036] at least one processor; and,

[0037] a memory communicatively connected to the at least one processor; wherein,

[0038] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0039] Obtaining an overall score of the first institution on the 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 institution is determined by using the blockchain-based credit data analysis method described in the embodiment of this specification;

[0040] Obtaining the overall scores of other institutions on the blockchain except the first institution;

[0041] An access order for user credit data of each institution on the blockchain is determined based on the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution. The access order is used to access the user credit data of each institution on the blockchain based on the access order when a user-side device accesses the user credit data of each institution on the blockchain for a fee.

[0042] At least one embodiment provided in this specification can achieve the following beneficial effects:

[0043] After obtaining the first user credit data of the first institution and the second user credit data of the second institution from the blockchain, the first user credit data and the second user credit data are matched based on the first preset scoring standard. If the 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 evaluating the validity of the user credit data of the second institution is determined according to the matching result, and the first scoring result is uploaded to the blockchain. Based on this, by analyzing the validity of the user credit data of each institution on the blockchain, when the user needs to access the user credit data of each institution on the blockchain, the user credit data with high validity can be provided to the user according to the analysis result, so that the present application can provide the user with more accurate user credit data; and, since more institutions can be added to the blockchain, the user credit data provided by the blockchain will also be more comprehensive, so that the present application can provide the user with more comprehensive user credit data. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 This is a schematic diagram of an application scenario of a credit data analysis method and sharing method based on blockchain in an embodiment of this specification;

[0046] Figure 2 A flowchart of a credit data analysis method based on blockchain provided in an embodiment of this specification;

[0047] Figure 3 A flowchart of a credit data sharing method based on blockchain provided in an embodiment of this specification;

[0048] Figure 4 A schematic diagram of the principle of file transmission between an institution and a blockchain provided in an embodiment of this specification;

[0049] Figure 5 The embodiments of this specification provide corresponding to Figure 2 and Figure 3 A swimlane flow chart of the blockchain-based credit data analysis method and sharing method;

[0050] Figure 6 The embodiments of this specification provide corresponding to Figure 2 A structural diagram of a credit data analysis device based on blockchain;

[0051] Figure 7 The embodiments of this specification provide corresponding to Figure 3 A structural diagram of a credit data sharing device based on blockchain;

[0052] Figure 8 The embodiments of this specification provide corresponding to Figure 2 A schematic diagram of the structure of a credit data analysis device based on blockchain;

[0053] Fig. 9 The embodiments of this specification provide corresponding to Figure 3 A structural diagram of a credit data sharing device based on blockchain. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of one or more embodiments of this specification clearer, the technical solutions of one or more embodiments of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this description, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of one or more embodiments of this specification.

[0055] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0056] Credit refers to a form of value movement with repayment and interest payment as conditions. It usually includes credit activities such as deposits and loans made by users in financial institutions. Credit is an important form of mobilizing and allocating funds in a paid manner in socialist countries, and is a powerful lever for economic development. When financial institutions (trust institutions, consumer finance companies, banks, etc.) conduct credit activities, in order to reduce credit risks, they need to conduct risk assessments on users, so as to promptly identify users with higher credit risks (hereinafter referred to as high-risk users), and then promptly conduct risk control for high-risk users to reduce the credit risks of financial institutions and safeguard the interests of financial institutions and users.

[0057] In the prior art, financial institutions usually maintain a copy of user credit data, and can conduct risk assessment on users based on the user credit data they maintain. In addition, financial institutions can also purchase user credit data from some trusted institutions and conduct risk assessment on users based on the purchased user credit data. However, the user credit data provided to financial institutions by this solution is less comprehensive and accurate.

[0058] In order to solve the defects in the prior art, this solution provides the following embodiments:

[0059] Figure 1 This is a schematic diagram of an application scenario of a blockchain-based credit data analysis method and sharing method in an embodiment of this specification.

[0060] like Figure 1 As shown, the institution of blockchain 140 can be a company that maintains user credit data. For example, the institution can include financial institutions and third-party data companies that maintain user credit data. The embodiments of this specification do not limit the institution of blockchain 140. It should be noted that before joining blockchain 140, the institution has been approved by other institutions of blockchain 140. The device of the institution that accesses blockchain 140 becomes a node of blockchain 140, also known as the node of the institution. The node can interact with the blockchain according to the data interaction operation of the institution. The node of the blockchain can be a computer device such as a server of the institution.

[0061] For any node of an institution on the blockchain, the node can deploy a client of the blockchain locally, so that the node can interact with the blockchain 140 through the client according to the data interaction operation of the institution. The client can be a computer program. After the client corresponding to the node obtains the user credit data maintained by the institution corresponding to the node, it uploads the user credit data to the blockchain. In addition, the client can obtain the user credit data of other institutions from the blockchain, and the data obtained by the client from the blockchain 140 is encrypted data. The client does not expose the encrypted data to the outside, and the institution cannot obtain the encrypted data. When the client has finished using the encrypted data, the client will delete the encrypted data, so that the data on the blockchain 140 will not be leaked.

[0062] In the embodiment of this specification, each institution on the blockchain 140 can share each other's user credit data through the blockchain 140, so that each institution can obtain more comprehensive user credit data, and the blockchain 140 can analyze the validity of the user credit data uploaded to the blockchain 140 by each institution, so that the blockchain can provide users with more effective user credit data based on the analysis results. The following is a detailed description:

[0063] First, each institution can share each other’s user credit data through blockchain 140. Specifically, Figure 1 As shown, the second client 110 is deployed locally on the node of the second institution. The second institution can be any institution on the blockchain 140. The second client 110 can upload the user credit data maintained by the second institution to the blockchain 140. In this way, since multiple institutions of the blockchain 140 can upload their own user credit data to the blockchain 140, the user credit data of multiple institutions are stored on the blockchain 140.

[0064] And, if Figure 1As shown, the third client 120 is deployed locally at the node of the third institution. The third institution can be any institution on the blockchain 140. The third client 120 can send a credit data access request to the blockchain 140, and the credit data access request at least includes the user data to be matched. After obtaining the credit data access request, the blockchain 140 can match the user data to be matched in the credit data access request with the user credit data of each institution on the blockchain 140 except the third institution, obtain a matching result, and feed the matching result back to the third client. The matching result is used to determine the credit status of the user to be matched corresponding to the user data to be matched, and the credit status of the user to be matched is used to determine the credit risk level of the user to be matched. In this way, since multiple institutions of the blockchain can access the user credit data on the blockchain 140, the purpose of multiple institutions of the blockchain sharing each other's user credit data through the blockchain 140 is achieved.

[0065] Secondly, any institution in the blockchain can use the node of the institution to perform validity analysis on the user credit data uploaded to the blockchain 140 by other institutions on the blockchain 140. Figure 1 As shown, the first client 130 is deployed locally on the node of the first institution. The first institution can be any institution on the blockchain 140. The first user credit data is the user credit data maintained by the first institution, and the second user credit data is the user credit data uploaded to the blockchain 140 by the second institution. After the first client 130 obtains the first user credit data and the second user credit data, it can match the first user credit data and the second user credit data based on the first preset scoring standard. If the matching result indicates that the first user credit data and the second user credit data both contain the same user group, then according to the matching result, a first scoring result for the second institution is determined, and the first scoring result is uploaded to the blockchain 140. The first scoring result is used to evaluate the validity of the second user credit data. In this way, the blockchain 140 can determine the validity of the user credit data of each institution according to the scoring results of the user credit data of each institution, so that when the user needs to access the user credit data of each institution on the blockchain, it can provide the user with highly effective user credit data according to the analysis results, thereby enabling the present application to provide the user with more accurate user credit data.

[0066] It should be noted that the blockchain 140 in the embodiments of this specification may be a consortium chain.

[0067] Next, a credit data analysis method based on blockchain provided in the embodiment of the specification will be specifically described in conjunction with the accompanying drawings:

[0068] Figure 2The following is a flowchart of a credit data analysis method based on blockchain provided in the embodiments of this specification. From a program perspective, the execution subject of the process can be a node on the blockchain, or a client deployed in a node on the blockchain. Figure 2 As shown, the process may include the following steps:

[0069] Step 202: Obtain credit data of a first user of a first institution.

[0070] In an embodiment of the present specification, the first user credit data may be user credit data maintained by a first institution, and the user credit data may be used to determine the credit type of a first user group, wherein users are classified from the perspective of credit type, and users may be divided into untrustworthy users and credited users. For example, when a user has not committed any bad behaviors such as loan expectations, non-repayment of loans, and criminal offenses, the user may be identified as a credited user; when a user has committed any bad behaviors such as loan expectations, non-repayment of loans, and criminal offenses, the user may be identified as a untrustworthy user.

[0071] In actual applications, when a user is identified as a dishonest user, it can be determined that the user's credit risk level is high, that is, the user is a high-risk user; when a user is identified as a credit-granted user, it can be determined that the user's financing risk level is low. In this way, financial institutions can control the user's credit risk based on the user's credit data.

[0072] It can be understood that the first user credit data specifically includes the credit data of each user in the first user group. In a specific example, the first user credit data includes the identity information data of each user in the first user group. The user's identity information data can be the user's ID number or business license registration number and other data that can confirm the user's identity. When the first user credit data is composed of the identity information data of each user in the first user group, the credit type of each user in the first user group is the same. For example, the first user credit data is the list data of trusted users, then all users targeted by the list data are trusted users.

[0073] In another specific example, the first user credit data includes identity information data and credit type of each user in the first user group. In this case, in order to make it more convenient to determine the credit type of each user in the first user group based on the first user credit data, the users in the first user group can be grouped based on the credit type, and the credit type of each group of users is the same, and the data of each group of users can form a user list data, so that the first client can determine the credit type of the user by simply confirming the grouping of each user in the first user group.

[0074] 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 user group, wherein the credit data details may be a credit record generated when the user conducts credit activities, for example, a loan record generated when the user conducts a loan activity.

[0075] In actual applications, the first client can obtain the first user credit data maintained by the first institution from the local node of the first institution according to the data acquisition operation of the first institution, or can receive the first user credit data sent by other devices of the first institution. This description embodiment is not limited here.

[0076] Step 204: Obtain credit data of a second user of a second institution through blockchain.

[0077] In the embodiment of this specification, the second user credit data may be user credit data uploaded to the blockchain by the second institution, and the user credit data may be used to determine the credit type of the second user group, wherein the credit type of the second user group is defined the same as the credit type of the first user group. For details, please refer to the explanation of the credit type of the first user group, which will not be elaborated here.

[0078] It is understandable that the second user credit data specifically includes the data of each user in the second user group. In a specific example, the second user credit data can be user list data composed of the identity information data of each user in the second user group, and the credit type of each user in the second user group is the same. Then, by judging whether the identity data of a certain user appears in the user list, the identity type of the user can be judged. Among them, 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. For details, please refer to the explanation of the identity information data of each user in the first user group, which will not be repeated here.

[0079] In another specific example, the second user credit data includes identity information data and credit type of each user in the second user group, and the credit type of each user in the second user group may be different. In order to make it more convenient to determine the credit type of each user in the second user group based on the second user credit data, the users in the second user group can be grouped based on the credit type. The credit type of each group of users is the same, and the data of each group of users can form a user list data. In this way, the first client can determine the credit type of the user by simply confirming the grouping of each user in the second user group.

[0080] It should be noted that the first institution can simultaneously perform validity analysis on user credit data uploaded to the blockchain by multiple institutions through the first institution's nodes.

[0081] In actual application, the node of the first institution obtains the second user credit data of the second institution from the blockchain through the first client. According to the above content, the first institution cannot obtain the content of the second user credit data through the first client. In this embodiment, in order to further prevent the second user credit data from being leaked, the second client can store the second user credit data in an encrypted preset database after obtaining the second user credit data from the blockchain, and delete the second user credit data from the encrypted preset database after completing the validity analysis of the second user credit data.

[0082] Step 206: Based on a first preset scoring standard, the first user credit data and the second user credit data are matched to obtain a matching result.

[0083] 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, a first scoring result for the second institution is determined based on the matching result; the first scoring result is used to evaluate the validity of the second user credit data.

[0084] In the embodiments of the present specification, when the user credit data uploaded to the blockchain by the institution is false data, the result of the user credit type determined based on the user credit data will also be inaccurate. At this time, the user credit data can be determined to be invalid; on the contrary, when the user credit data uploaded to the blockchain by the institution is real data, the result of the user credit type determined based on the user credit data will also be accurate. At this time, the user credit data can be determined to be valid. The embodiments of the present specification analyze the validity of the user credit data by judging the authenticity of each data in the user credit data. The more real data in the user credit data, the higher the validity of the user credit data. Conversely, the less real data in the user credit data, the lower the validity of the user credit data.

[0085] Secondly, there may be duplication of users in the user groups targeted by user credit data of different institutions. For example, if the same user has conducted credit activities with different institutions on the blockchain, the user credit data of the different institutions may contain data targeting the user. In this way, when any institution on the blockchain performs validity analysis on user credit data uploaded to the blockchain by other institutions on the blockchain, it can perform validity analysis on the credit data of specific users in the user credit data of the other institutions based on the user credit data it maintains, where specific users refer to the same user groups targeted by the user credit data of any institution and the user credit data of the other institutions.

[0086] In practical applications, when the institution corresponding to the first user's credit data performs validity analysis on the second user's credit data, when the first user's credit data and the second user's credit data are both for the same user group, for any user in the same user group, if the credit type of the user represented by the first user's credit data and the second user's credit data are different, then the data for the user in the second user's credit data is determined to be false data, and at this time, the penalty score for the second institution can be determined; on the contrary, if the credit type of the user represented by the first user's credit data and the second user's credit data is the same, then the data for the user in the second user's credit data is determined to be true data, and at this time, the reward score for the second institution can be determined. Finally, the first scoring result for the second institution is determined based on the analysis result of the user's credit data for the second institution.

[0087] Step 210: Upload the first scoring result to the blockchain.

[0088] In the embodiments of this specification, according to the above content, it can be known that the first scoring result is specifically determined based on part or all of the data in the second user's credit data. If the score represented by the first scoring result is higher, it means that there are more real user credit data in the second user's credit data, and the validity of the second user's credit data is higher. Therefore, the first scoring result is uploaded to the blockchain, so that the blockchain can determine the validity of the second user's credit data based on the first scoring result. The second institution can be any institution on the blockchain, so the blockchain can obtain the scoring results of each institution on the blockchain, which enables the blockchain to provide users with user credit data with higher validity based on the scoring results of each institution.

[0089] In addition, in order to prevent the first institution from maliciously evaluating the credit data of the second user, the first client corresponding to the first institution can also upload the data that affects the score of the second user's credit data to the blockchain after completing the validity analysis of the second user's credit data. For example, the second user's credit data shows that Zhang San is a dishonest user, but the first user's data shows that Zhang San is a credit-granted user. At this time, the first client determines that the data about Zhang San in the second user's credit data is false data, and determines the penalty score for the second user's credit data. Therefore, the data about Zhang San of the first institution is the data that affects the score of the second user's credit data. The first client is used to upload the data about Zhang San of the first institution to the blockchain, so that relevant personnel can judge whether the first institution maliciously evaluates the second user's credit data based on the data that affects the score of the second user's credit data.

[0090] Figure 2The method in the embodiment of the present invention obtains the first user credit data maintained by the first institution, obtains the second user credit data of the second institution through the blockchain, matches the first user credit data with the second user credit data based on the first preset scoring standard, and if the matching result indicates that the first user credit data and the second user credit data both contain the same user group, then according to the matching result, a first scoring result for evaluating the validity of the user credit data of the second institution is determined, and the first scoring result is uploaded to the blockchain. Based on this, by analyzing the validity of the user credit data of each institution on the blockchain, when the user needs to access the user credit data of each institution on the blockchain, the user credit data with high validity can be provided to the user according to the analysis result, so that the present application can provide the user with more accurate user credit data; and, because more institutions can be added to the blockchain, the user credit data provided by the blockchain will also be more comprehensive, so that the present application can provide the user with more comprehensive user credit data.

[0091] based on Figure 2 The method in this specification also provides some specific implementation plans of the method, which are described below.

[0092] In the embodiments of this specification, the first user credit data may include the credit data of users of the first preset credit type, and the second user credit data may include the credit data of users of the second preset credit type, and the first preset credit type and the second preset credit type may be different. In this way, when the first user credit data and the second user credit data are both for the same first user group, since the data about the first user group in the first user credit data is real data, the data about the first user group in the second user credit data is false data. In addition, in actual applications, financial institutions generally conduct credit risk prevention and control based on blacklist data. Therefore, in order to make the embodiments of this specification more in line with actual needs and to achieve the purpose of determining whether the second user credit data is real data, the first user credit data may include the credit data of users who have been successfully granted credit; the second user credit data may include blacklist data.

[0093] Based on this, the first preset scoring standard may include the first scoring standard. Step 206 may specifically include:

[0094] The credit data of the successfully credited user is matched with the blacklist data to obtain a first matching result.

[0095] Step 208 may specifically include:

[0096] If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, a penalty score for the second institution is determined according to the first matching result.

[0097] In the embodiments of this specification, it can be understood that, in theory, all users in the user group targeted by the credit data of the successfully granted users are all successfully granted users, and all users in the user group targeted by the blacklist data are all untrustworthy users, and a user can only be one of the successfully granted users and untrustworthy users, and it is impossible for the user to be both a successfully granted user and an untrustworthy user. Therefore, when the credit data of the successfully granted users of the first institution and the blacklist data of the second institution both contain the same first user group, it can be determined that the data about the first user group in the blacklist data of the second institution is false data, and based on the false data, the penalty score for the second institution is determined.

[0098] In actual application, the credit data of the successfully granted user may be the list data of the successfully granted user. In the process of matching the credit data of the successfully granted user with the blacklist data, it may be specifically determined whether the credit data of the successfully granted user and the blacklist data contain the same user identity data. If the credit data of the successfully granted user and the blacklist data contain the same user identity data, the user group targeted by the same user identity data is the 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 user's ID number; when the user is a company, the identity data may be the company's business license registration number.

[0099] In the embodiments of this specification, the first user credit data may include the credit data of users of the first preset credit type, and the second user credit data may include the credit data of users of the second preset credit type, and the first preset credit type and the second preset credit type may be the same. In this way, when the first user credit data and the second user credit data are both for the same first user group, since the data about the first user group in the first user credit data is real data, the data about the first user group in the second user credit data is also real data. And, in actual applications, financial institutions generally carry out credit risk prevention and control based on blacklist data. Therefore, in order to make the embodiments of this specification more in line with actual needs and to achieve the purpose of determining whether the second user credit data is real data, the first user credit data may include the credit data of the dishonest user; the second user credit data may include blacklist data.

[0100] Based on this, the first preset scoring standard includes the second scoring standard; step 206 may specifically include:

[0101] The credit data of the dishonest user is matched with the blacklist data to obtain a second matching result.

[0102] Step 208 may specifically include:

[0103] If the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring criteria, the reward score for the second institution is determined according to the second matching result.

[0104] In the embodiments of this specification, the execution process of determining the reward score for the second institution based on the second scoring standard and the second matching result is similar to the execution process of "determining the penalty score for the second institution based on the first scoring standard and the first matching result" described above. The difference between the two is that when the credit data and the blacklist data of the dishonest user both contain the same second user group, the data about the second user group in the blacklist data of the second institution is determined to be real data, and the reward score for the second institution is determined based on the real data. Therefore, the execution process of "determining the reward score for the second institution based on the second scoring standard and the second matching result" can refer to the execution process of "determining the penalty score for the second institution based on the first scoring standard and the first matching result" described above, which will not be repeated here.

[0105] In the embodiments of this specification, according to the foregoing content, it can be seen that the credit data of the first institution's successfully granted users and the credit data of the untrustworthy users can both be used to evaluate the validity of the second institution's blacklist data. Therefore, if the credit data of the first institution's successfully granted users and the credit data of the untrustworthy users are used simultaneously to evaluate the validity of the second institution's blacklist data, the utilization rate of the first institution's user credit data can be improved, and the second user credit data can be analyzed more comprehensively.

[0106] Based on this, the first preset scoring standard includes a first scoring standard and a second scoring standard; the first user credit data includes credit data of successfully granted credit users and credit data of untrustworthy users; the second user credit data includes blacklist data; step 206 may specifically include:

[0107] The credit data of the successfully granted user is matched with the blacklist data to obtain a first matching result, and the credit data of the dishonest user is matched with the blacklist data to obtain a second matching result.

[0108] Step 208 may specifically include:

[0109] If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, the penalty score for the second institution is determined according to the first matching result; and if the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring standard, the reward score for the second institution is determined according to the second matching result.

[0110] The first scoring result is determined according to the penalty score and the reward score.

[0111] In the embodiments of the present specification, regarding the execution process of "matching the credit data of the successfully granted user with the blacklist data to obtain a first matching result. If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, the penalty score for the second institution is determined according to the first matching result. And, matching the credit data of the dishonest user with the blacklist data to obtain a second matching result. If the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring standard, the reward score for the second institution is determined according to the second matching result", this embodiment is the same as the aforementioned embodiment, and the details can be referred to the aforementioned embodiment, which will not be repeated here.

[0112] It should be noted that the execution order of the step "matching the credit data of the successfully granted user with the blacklist data to obtain a first matching result. If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, determine the penalty score for the second institution according to the first matching result" and the step "matching the credit data of the untrustworthy user with the blacklist data to obtain a second matching result. If the second matching result indicates that the credit data of the untrustworthy user and the blacklist data both contain the same second user group, then based on the second scoring standard, determine the reward score for the second institution according to the second matching result" is not limited in this application. This embodiment only lists an execution order of the two steps. Those skilled in the art can determine the execution order of the two steps according to actual needs, for example, first determine the reward score for the second institution, and then determine the penalty score for the second institution.

[0113] Secondly, the first scoring result may be a final score determined based on the reward score and the penalty score. For example, if the reward score is 10 points and the penalty score is -5 points, the first scoring result is 5 points. Secondly, the first scoring result may also be a data set consisting of the reward score and the penalty score. For example, if the reward score is 10 points and the penalty score is -5 points, the first scoring result is a data set consisting of 10 and -5. After obtaining the first scoring result, the blockchain sums the values ​​in the first scoring result and the current overall score of the second institution to obtain the final overall score for the second institution. Continuing with the above example, assuming that the current overall score of the second institution is 50 points, after obtaining the first scoring result, the blockchain sums the values ​​in the first scoring result and the current overall score of the second institution to obtain the final overall score for the second institution of 55 points.

[0114] In the embodiment of the present specification, the first user's credit data includes the credit data of users who have been successfully granted credit and the credit data of users who have lost their credit. Compared with the first user's credit data only including the credit data of users who have been successfully granted credit or the credit data of users who have lost their credit, the first user's credit data is richer, and then through the first user's credit data, there is an opportunity to judge the authenticity of more data in the second user's credit data, so that the analysis result of the second user's credit data is more comprehensive and accurate.

[0115] In the embodiments of this specification, it is considered that the same user may have conducted credit activities with multiple institutions. For example, the same user may have taken out loans from multiple institutions, but the user has only defaulted at some of the institutions. In this case, it is defined that the user has defaulted at the first institution and has not defaulted at the second institution. After the first institution uploads the credit data about the user to the blockchain, when the second institution performs a validity analysis on the user credit data uploaded to the blockchain by the first institution, it may be determined that the data about the user in the user credit data uploaded to the blockchain by the first institution is false data, and points are deducted from the first institution. However, the data about the user of the first institution and the second institution are both real data, which makes the scoring result of the first institution inaccurate when the second institution does not maliciously evaluate the user credit data of the first institution. In order to improve the accuracy of the scoring result of the first institution and reduce the impact of the situation that the same user has conducted credit activities with multiple institutions but has only defaulted at some institutions on the scoring results of these institutions, a step-by-step deduction method can be used to determine the penalty score for these institutions based on the false data determined for these institutions.

[0116] Based on this, based on step 208, determining the penalty score for the second institution based on the first scoring standard and the first matching result may specifically include:

[0117] The number of users included in the first user group is determined according to the first matching result.

[0118] A deduction coefficient for the second institution is determined based on the number of users.

[0119] A penalty score for the second institution is determined based on the deduction coefficient and the number of users.

[0120] In the embodiments of the present specification, a preset step deduction standard can be pre-set, and 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 to a deduction coefficient, and the deduction coefficients bound to different stages are different, and the higher the level of the stage, the higher the deduction coefficient bound to the stage. Among them, the determination of the level of the stage can be explained as follows: in a specific example, the number of users included in the first user group is divided into three stages, the first stage is the stage where 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 below), the second stage is [6,10], and the third stage is [11,15]. Then, the level of the third stage is defined to be higher than that of the second stage, and the level of the second stage is higher than that of the first stage.

[0121] Preferably, the interval length of each stage of the preset step deduction standard can be the same, and the deduction coefficients bound to each stage can constitute an arithmetic progression. In a specific example, the preset step deduction standard can be set as follows: the number of users included in the first user group is divided into N stages, the first stage is the stage where 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 each stage is represented in the same format below), and the deduction coefficient bound to the first stage is 0.1, that is, when the number of users in the first user group falls into the first stage, the deduction coefficient for the first institution is determined to be 0.1; similarly, the second stage is [11,20], and the deduction coefficient bound to the second stage is 0.2; the third stage is [21,30], and the deduction coefficient bound to the third stage is 0.3, and so on, to finally obtain the preset step deduction standard. Among them, the N value can be determined by the designer of the preset step-by-step deduction standard based on the number of users included in the second user credit data and the interval length of each stage of the preset step-by-step deduction standard. For example, the second user credit data consists of credit data of 10 users, that is, the second user credit data contains 30 users, and the interval length of each stage of the preset step-by-step deduction standard is 10, then the N value can 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].

[0122] Preferably, the interval length of each stage of the preset step deduction standard can be different, and can be set by the designer of the preset step deduction standard according to actual needs. In a specific example, the second user credit data contains 40 users, and the preset step deduction standard can be set as follows: the number of users in the first user group is divided into 3 stages, the first stage is [1, 8), and the deduction coefficient bound to the first stage is defined as 0.1; the second stage is [8, 20), and the deduction coefficient bound to the second stage is defined as 0.3; the third stage is [20, 40], and the deduction coefficient bound to the third stage is defined as 0.5.

[0123] In actual applications, after determining the number of users included in the first user group according to the first matching result, it is determined which stage of the preset step-by-step deduction standard the number of users falls into, and the deduction coefficient for the second institution is determined according to the judgment result. For example, if the number of users falls into the first stage of the preset step-by-step deduction standard, and the deduction coefficient bound to the first stage is 0.1, then the deduction coefficient for the second institution is determined to be 0.1. Finally, the number of users is multiplied by the deduction coefficient to obtain the penalty score for the second institution. Based on this, due to the use of a step-by-step deduction method, when the same user conducts credit activities with multiple institutions, but defaults only occur in some institutions, after the credit data of the user is uploaded to the blockchain by the institutions, the penalty score for the institutions determined based on the credit data of the user will also be very low, reducing the impact of the credit data of the user on the scoring results of the institutions. Also, if an institution uploads a large amount of false user credit data to the blockchain, then due to the tiered deduction method, the more false user credit data there is, the higher the deduction coefficient for the institution that uploaded the false user credit data, and the penalty score determined for the institution will also be higher, which will help improve the accuracy of the analysis results of the second user credit data.

[0124] In the embodiments of this specification, multiple institutions other than the second institution on the blockchain can analyze the second user credit data of the second institution, and when the second institution uploads the user credit data to the blockchain multiple times through the second client, the institutions other than the second institution on the blockchain can analyze the user credit data uploaded to the blockchain by the second client each time. Based on this, the number of scoring results of the second institution can be multiple, and each scoring result is used to evaluate the validity of the second user credit data. Therefore, the overall score for the second institution can be determined based on each scoring result of the second institution, and when a new scoring result for the second institution is obtained, the overall score of the second institution is updated according to the new scoring result, so as to facilitate the blockchain to quickly determine the validity of the user credit data of the second institution according to the overall score of the second institution.

[0125] Based on this, in step 208: after uploading the scoring result to the blockchain, the method of the embodiment of this specification further includes:

[0126] The overall score of the second institution is updated according to the first scoring result; the overall score is a score determined according to each scoring result of the second institution.

[0127] In the embodiment of the present specification, the overall score of the second institution is stored on the blockchain, and the overall score is determined based on the scoring results of the second institution. When the blockchain obtains the scoring results of the second institution, the obtained scoring results of the second institution and the overall score of the second institution stored on the blockchain can be summed, and the calculation result can be used as the final overall score of the second institution.

[0128] It should be noted that if the second institution is an institution that has not undergone credit data analysis, for example, the second institution is an institution that has newly joined the blockchain, before the second institution uploads user credit data to the blockchain, institutions other than the second institution on the blockchain have not scored the user credit data of the second institution. At this time, the initial overall score of the second institution can be set to 0. And, it can be understood that the scoring result of the second institution can be a positive or negative number. When the scoring result of the second institution is a positive number, it means that there is real user credit data in the second user credit data; when the scoring result of the second institution is a negative number, it means that there is false user credit data in the second user credit data.

[0129] In addition, in order to encourage each institution to upload real user credit data to the blockchain, when the overall score of the institution is lower than the preset overall score threshold, the institution will be kicked out of the blockchain. Specifically, in one example, after the blockchain completes the update of the overall score of a specific institution (the specific institution is any institution on the blockchain), it can determine whether the overall score of the specific institution is less than the preset overall score threshold. If it is determined that the overall score of the specific institution is less than the preset overall score threshold, the specific institution will be kicked out of the blockchain. In another example, the blockchain can periodically determine whether the overall score of each institution on the blockchain is less than the preset overall score threshold, and according to the judgment result, the institution with an overall score less than the preset overall score threshold will be kicked out of the blockchain.

[0130] In the embodiments of this specification, the application scenario of the above-mentioned blockchain-based credit data analysis method can be that when an institution newly joins the blockchain, other institutions on the blockchain analyze the user credit data uploaded to the blockchain by the newly joined institution.

[0131] Based on this, the second institution may be an institution newly added to the blockchain, and the second user credit data may be the user credit data initially uploaded to the blockchain by the second institution. After the second institution uploads the second user credit data to the blockchain, the blockchain obtains the second user credit data, and may send analysis instructions to the nodes corresponding to the institutions other than the second institution on the blockchain, so that the nodes corresponding to the institutions other than the second institution on the blockchain analyze the second user credit data. It should be noted that when the blockchain obtains the second user credit data, it may send analysis instructions to the nodes of at least one institution other than the second institution on the blockchain, for example, it may send analysis instructions to the nodes of all institutions other than the second institution on the blockchain, and each institution that obtains the analysis instruction through the relevant node shall serve as the first institution of the embodiment of this specification, and the node of the first institution adopts the above method of the embodiment of this specification to analyze the second user credit data.

[0132] In the embodiments of this specification, the application scenario of the above-mentioned blockchain-based credit data analysis method can be that when an institution newly joins the blockchain, the newly joined institution analyzes the user credit data uploaded to the blockchain by other institutions on the blockchain.

[0133] Based on this, the first institution may be a newly joined institution, the first user credit data may be the user credit data stored locally by the node of the first institution, and the second user credit data may be the user credit data uploaded to the blockchain by the second institution.

[0134] In actual applications, after joining the blockchain, the first institution can analyze the user credit data uploaded to the blockchain by at least one institution other than the first institution on the blockchain. For example, the user credit data uploaded to the blockchain by all institutions other than the first institution on the blockchain can be analyzed. The analysis method of the second user's credit data refers to the above embodiment and will not be repeated here.

[0135] In the embodiments of this specification, after the institution on the blockchain uploads the user credit data to the blockchain for the first time, the institution may also modify the user credit data uploaded to the blockchain, for example, adding new user credit data to the user credit data. At this time, it is necessary to analyze the newly added user credit data. Therefore, the application scenario of the above-mentioned blockchain-based credit data analysis method can also be that when an institution on the blockchain uploads the newly added user credit data to the blockchain, other institutions on the blockchain analyze the newly added user credit data.

[0136] Based on this, the second user credit data may be the newly added user credit data of the second institution on the blockchain; the newly added user credit data of the second institution on the blockchain is the user credit data of the second institution on the blockchain that has not been used to score the second institution.

[0137] In practical applications, at least one institution other than the second institution on the blockchain may analyze the credit data of the second user. For example, all institutions other than the first institution on the blockchain may analyze the credit data of the second user. In this case, each institution analyzing the credit data of the second user is defined as the first institution. The method for analyzing the credit data of the second user is described in the above embodiment and will not be repeated here.

[0138] In the embodiments of this specification, when there are a large number of institutions on the blockchain, if each institution analyzes the credit data of newly added users every time it uploads the credit data of the newly added users to the blockchain, the blockchain will need to frequently perform credit data analysis, which will consume a large amount of network resources. Therefore, in order to reduce the resource consumption of the blockchain due to credit data analysis, the credit data of newly added users uploaded to the blockchain by each institution on the blockchain can be analyzed regularly.

[0139] Based on this, when the second user credit data is the newly added user credit data of the second institution on the blockchain, the above analysis method can be executed according to the preset execution frequency. For example, the blockchain can count the user credit data uploaded to the blockchain by each institution on the blockchain within this week once a week, and for any institution on the blockchain, the blockchain sends a scoring instruction to the node of at least one institution other than the institution, so that the node receiving the scoring instruction analyzes the user credit data uploaded to the blockchain by the institution within this week. The analysis method of the second user credit data refers to the above embodiment and will not be repeated here.

[0140] In the embodiments of this specification, after an institution on a blockchain analyzes the user credit data of other institutions on the blockchain, the user credit data of the institution may change. For example, new user credit data is added to the user credit data of the institution, and the new user credit data can also be used to analyze the user credit data of other institutions on the blockchain. Therefore, the application scenario of the above-mentioned blockchain-based credit data analysis method can also be a scenario in which an institution on a blockchain analyzes the user credit data of other institutions on the blockchain, and then adds new user credit data to the user credit data of the institution, and analyzes the user credit data of other institutions on the blockchain based on the new user credit data.

[0141] Based on this, the first user credit data includes the newly added user credit data of the first institution; the newly added user credit data of the first institution is the data of the first institution that is not used to score the second institution.

[0142] In a specific example, after analyzing the second user's credit data, the client of the first institution obtains new user credit data A. At this time, the user credit data A is the first user's credit data, and the client of the first institution can analyze the second user's credit data again based on the user credit data A. The analysis method of the second user's credit data refers to the above embodiment and will not be repeated here.

[0143] In the embodiments of this specification, when any institution other than the institution corresponding to the second institution on the blockchain uses the second user credit data for credit risk control, if the control result indicates that, compared with the case where the second user credit data is not used for credit risk control, the user bad debt rate of the arbitrary institution is reduced after the second user credit data is used for credit risk control, this situation can indicate that at least part of the user credit data in the second user credit data is true. It can be seen that the second user credit data can be analyzed according to the use effect of the second user credit data.

[0144] Based on this, the method of the embodiment of this specification further includes:

[0145] Obtain first risk control result data from a third institution; the first risk control result data is risk control result data for a period of time when the second user credit data is not used for credit risk control; the third institution is an institution that uses the second user credit data for credit risk control.

[0146] Obtain second risk control result data of the third institution for the second user credit data; the second risk control result data is risk control result data when the second user credit data is used to perform credit risk control.

[0147] The first user bad debt rate of the third institution is determined based on the first risk control result data; the first user bad debt rate is the ratio of the number of successful borrowing users who fail to repay as required to the number of successful borrowing users within a period of time when the second user credit data is not used for credit risk control.

[0148] The second user bad debt rate of the third institution is determined based on the second risk control result data; the second user bad debt rate is the ratio of the number of users who have successfully borrowed money but have not repaid as required to the number of users who have successfully borrowed money when the second user credit data is used for credit risk control.

[0149] Determine whether the first user defect rate is greater than the second user defect rate.

[0150] If the first user bad rate is greater than the second user bad rate, based on a second preset scoring standard, a second scoring result for the second institution is determined according to the first risk control result data and the second risk control result data.

[0151] In the embodiments of this specification, any institution on the blockchain can perform credit risk control based only on the user credit data of an institution other than the arbitrary institution on the blockchain. Taking a third institution as an example, the third institution can perform credit risk control based only on the credit data of the second user, which enables the third institution to analyze the credit data of the second user based on the credit risk control effect.

[0152] In the actual application process, it is assumed that the third institution uses the second user's credit data for credit risk control for a period of one week. In order to avoid other factors affecting the analysis results of the second user's credit data, the corresponding period of the risk control result data obtained when the second user's credit data is not used for credit risk control is also one week, that is, the first risk control result data is the risk control result data within any week in the time period when the second user's credit data is not used for credit risk control. In addition, both the first risk control result data and the second risk control result data may include the number of users who have successfully borrowed and the number of users who have not repaid as required among the successful borrowing users, or both the first risk control result data and the second risk control result data may include the user bad debt rate determined based on the number of users who have successfully borrowed and the number of users who have not repaid as required among the successful borrowing users. If the first user bad debt rate is greater than the second user bad debt rate, it means that after using the second user's credit data for risk prevention and control, the user bad debt rate has decreased. In this case, the second reward score for the second institution can be determined.

[0153] In the embodiments of the present specification, compared to when the third institution does not use the second user's credit data for credit risk control, after the institution corresponding to the third institution uses the second user's credit data for credit risk control, if the decline in the user's bad debt rate of the third institution is greater, it indicates that the validity of the second user's credit data is higher. Therefore, in order to improve the accuracy of the analysis results of the second user's credit data, the second user's credit data may be analyzed according to the decline in the user's bad debt rate.

[0154] Based on this, step: determining a second scoring result for the second institution based on the first risk control result data and the second risk control result data based on the second preset scoring standard may specifically include:

[0155] The second user defect rate is divided by the first user defect rate to obtain a ratio.

[0156] A second reward score for the second institution is determined based on the ratio.

[0157] In the embodiments of the present specification, each ratio may correspond to a bonus score, and the smaller the ratio, the higher the bonus score corresponding to the ratio. In a specific example, when the ratio is greater than 0 and less than or equal to 0.1, the bonus score corresponding to the ratio can be set to 10 points, when the ratio is greater than 0.1 and less than or equal to 0.2, the bonus score 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 bonus score corresponding to the ratio is 8, and so on. When the ratio is greater than 0.5 and less than or equal to 0.6, the bonus score corresponding to the ratio is 5 points; . . . When the ratio is greater than 0.9 and less than or equal to 1, the bonus score corresponding to the ratio is 1 point. Assuming that the bad rate of the first user is 5% and the bad rate of the second user is 3%, the ratio is 0.6. According to the correspondence between the above ratio and the bonus score, it can be determined that the second bonus score for the second institution is 5 points.

[0158] In the embodiments of this specification, in order to ensure the real-time and comprehensiveness of the user credit data on the blockchain, each institution of the blockchain is required to promptly update the user credit data uploaded to the blockchain. In order to encourage each institution of the blockchain to promptly update the user credit data uploaded to the blockchain, a reward standard can be set to encourage each institution of the blockchain to promptly update the user credit data uploaded to the blockchain.

[0159] Based on this, the method of the embodiment of this specification further includes:

[0160] For user credit data on a verified valid blockchain, points will be awarded to the institution that uploads the verified valid user credit data on the blockchain to the blockchain for the first time.

[0161] In the embodiments of this specification, it is assumed that institutions A and B on the blockchain have successively uploaded Zhang San's credit data to the blockchain, and Zhang San's credit data has been verified as valid user credit data. Since institution A is the first to upload Zhang San's credit data to the blockchain, institution A is rewarded with points. The specific reward points can be set by technicians in this field according to actual conditions. For example, the reward points are set to 1 point.

[0162] In practical applications, the client of an institution other than institution A on the blockchain can determine the valid user credit data in the user credit data of institution A when analyzing the user credit data uploaded to the blockchain by institution A, and upload the valid user credit data to the blockchain. After obtaining the valid user credit data, the blockchain determines the institution that uploaded the valid user credit data to the blockchain for the first time, and gives points and rewards to the institution. Continuing with Zhang San as an example, the client of an institution other than institution A on the blockchain can determine that the user credit data of institution A about Zhang San is real user credit data, and upload the user credit data about Zhang San to the blockchain.

[0163] Secondly, when institutions other than institution A on the blockchain use the user credit data uploaded to the blockchain by institution A for credit risk control, they can determine the valid user credit data in the user credit data uploaded to the blockchain by institution A based on the use results, and upload the valid user credit data to the blockchain. Continuing with Zhang San as an example, and assuming that institution C uses the user credit data uploaded to the blockchain by institution A for credit risk control, and the user credit data uploaded to the blockchain by institution A is blacklist data, institution C learns that Zhang San is a dishonest user through the user credit data of institution A, but still lends money to Zhang San, and later Zhang San defaults at institution C. Therefore, institution C can determine that Zhang San is indeed a dishonest user, and institution A's credit data on Zhang San is valid user credit data, and upload Zhang San's credit data to the blockchain.

[0164] In the embodiments of this specification, by scoring and rewarding the institution that first uploads the user credit data on the verified valid blockchain to the blockchain, it is helpful to encourage the institutions of the blockchain to update the user credit data uploaded to the blockchain in a timely manner, thereby improving the real-time nature of the user credit data on the blockchain.

[0165] Based on a general inventive concept, the embodiment of this specification also provides a credit data sharing method based on blockchain. Next, a credit data sharing method based on blockchain provided in the embodiment of the specification will be specifically described in conjunction with the accompanying drawings:

[0166] Figure 3 The following is a flowchart of a credit data sharing method based on blockchain provided in the embodiments of this specification. From a program perspective, the execution subject of the process can be a node on the blockchain, or a blockchain application deployed in a node on the blockchain. Figure 3 As shown, the process may include the following steps:

[0167] Step 302: Obtain the overall score of the first institution on the blockchain; the overall score of the first institution is determined according to the scoring results of the first institution; the scoring results of the first institution are determined using the blockchain-based credit data analysis method of the embodiment of this specification.

[0168] Step 304: Obtain the overall scores of other institutions on the blockchain except the first institution.

[0169] Step 306: Determine an access order for user credit data of each institution on the blockchain based on the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution, wherein the access order is used to access user credit data of each institution on the blockchain based on the access order when a user-side device accesses user credit data of each institution on the blockchain for a fee.

[0170] In the embodiments of this specification, according to the foregoing content, it can be known that the overall score of an institution is used to indicate the validity of the user credit data of the institution. Therefore, in order to provide the user credit data to the user-side device based on the validity of the user credit data of each institution on the blockchain when the user-side device accesses the user credit data on the blockchain, the order of accessing the user credit data of each institution can be determined based on the overall score of each institution.

[0171] In practical applications, the user-side device may be a node on the blockchain. The specific process of the user-side device accessing the user credit data on the blockchain may be: the user-side device sends the user data to be matched to the blockchain, and requests the blockchain to determine the credit type of the user contained in the user data to be matched. After receiving the user data to be matched, the blockchain matches the user data to be matched with the user credit data of each institution in turn based on the access order of the user credit data of each institution determined by it, until the user data to be matched hits the user credit data on the blockchain, or the user data to be matched has been matched with the user credit data of each institution, and the matching process ends.

[0172] In a specific example, institution A on the blockchain sends Li Si's identity data to the blockchain. After receiving Li Si's identity data, the blockchain matches Li Si's identity data with the user credit data of each node on the blockchain except institution A in turn based on the access order of the user credit data of each institution determined by it. When Li Si's identity data is matched with the user credit data of institution D, the matching result is that the user credit data of institution D also contains the identity data of Li Si (that is, Li Si's identity data hits the user credit data of institution D). At this time, the blockchain can end the matching process and feedback the matching result to institution A. The matching result can be a matching result used to indicate that Li Si is a dishonest user.

[0173] In another specific example, institution A on the blockchain sends Li Si's identity data to the blockchain. After receiving Li Si's identity data, the blockchain matches Li Si's identity data with the user credit data of each institution except institution A on the blockchain in turn based on the access order of the user credit data of each institution determined by it. Moreover, after Li Si's identity data is matched with the user credit data of each institution in turn, Li Si's identity data does not hit the user credit data of any institution. At this time, the blockchain ends the matching process and feeds back the matching result to institution A. The matching result can be a matching result used to indicate that Li Si is a trusted user.

[0174] In addition, the user credit data on the blockchain can also support access by institutions that are not members of the blockchain. In this case, the user-side device can be a device of an institution that is not members of the blockchain. It should be noted that before an institution that is not members of the blockchain accesses the user credit data on the blockchain, it needs to obtain authorization and authentication. The specific authorization and authentication method can be determined by technicians in this field according to actual needs.

[0175] In the embodiments of this specification, according to the foregoing content, it can be known that the higher the overall score of an institution, the higher the validity of the user credit data of the institution. Therefore, in order to provide user credit data with higher validity to the user side device first when the user side device accesses the user credit data on the blockchain, the user side device can be allowed to give priority to accessing user credit data with higher overall scores.

[0176] Based on this, step 306 may specifically include:

[0177] Based on the sorting rule of overall scores from high to low, the institutions on the blockchain are sorted according to their overall scores, and the access order of user credit data of each institution on the blockchain is obtained.

[0178] In the embodiments of this specification, for example, in the blockchain, the overall score of institution A is 50 points, the overall score of institution B is 51 points, and the overall score of institution C is 45 points. Therefore, the order of accessing user credit data of the three institutions is: institution B, institution A, and institution C.

[0179] In the embodiments of this specification, when a user-side device accesses user credit data on a blockchain, the blockchain may, based on a determined order of access to user credit data of each institution, enable the user-side device to access user credit data of each institution on the blockchain in sequence; and, in order to protect the interests of each institution on the blockchain, the user-side device may access user credit data on the blockchain for a fee.

[0180] Based on this, after step 306, the credit data sharing method based on blockchain in the embodiment of this specification may also include:

[0181] Obtain a credit data access request sent by a first client corresponding to a second institution; the credit data access request includes user data to be matched and a payment result; the payment result is a payment result generated for a fee value determined according to the user data to be matched.

[0182] Based on the access order, the access result for the first institution is determined according to the user data to be matched and the user credit data of each institution on the blockchain.

[0183] The access result is fed back to the first client.

[0184] In the embodiments of this specification, the second institution can be an institution on the blockchain or an institution that is not a member of the blockchain. When the second institution accesses the user credit data on the blockchain, the blockchain determines the access fee for the second institution based on the user data to be matched by the second institution, and sends a payment instruction for the access fee to the client corresponding to the second institution. After the second institution completes the payment for the access fee, the second institution can access the user credit data on the blockchain. In a specific example, the access fee standard set by the blockchain is: matching the credit data of a user is charged 10 yuan, and the credit data access request sent by the first client corresponding to the second institution to the blockchain includes the credit data of two users. Therefore, the access fee for the second institution is 20 yuan.

[0185] Secondly, when the institution corresponding to the second institution accesses the user credit data on the blockchain, in order to reduce the workload of the blockchain for data matching, improve the matching efficiency of the blockchain, and make the higher the overall score of the institution, the greater the benefit to the institution, when the user data to be matched by the second institution hits the user credit data on the blockchain, the blockchain ends the access process of the second institution and feeds back the access result to the first client corresponding to the second institution.

[0186] For the access fee paid by the second institution, the access fee can be allocated to each institution according to the contribution of each institution. For example, in the blockchain, according to the ranking rule from high to low, the access order determined based on the overall score of each institution is: institution A, institution B, institution C, institution D, institution E and institution F. In the process of accessing the user credit data on the blockchain, the second institution visited institutions A, B and C in turn, and did not visit institutions D, E and F on the blockchain. Therefore, institutions A, B and C have all contributed to the access process of the second institution, while institutions D, E and F have not contributed to the access process of the second institution. At this time, the access fee of the second institution is evenly allocated to institutions A, B and C. In this way, the higher the overall score of the institution, the greater the benefit for the institution, and the blockchain reduces the process of matching the user data to be matched of the second institution with the user credit data of institutions D, E and F, thereby reducing the workload of the blockchain for data matching and improving the matching efficiency of the blockchain.

[0187] In the embodiments of this specification, when the user credit data to be matched of the second institution hits the user credit data on the blockchain, the institution corresponding to the second institution may conduct credit activities with the user targeted by the user credit data to be matched, and later determine the credit type of the user based on the performance results of the user's contract regarding the credit result. At this time, the second institution can determine whether the user credit data it hits is real user credit data based on the credit type of the user it determined, and score the user credit data it hits.

[0188] Based on this, the access result includes the identifier of a specific institution; the specific institution is an institution whose user credit data accessed by the first institution and the user data to be matched both contain the same user group. Step: After the access result is fed back to the first client, the credit data sharing method based on blockchain in the embodiment of this specification may also include:

[0189] Obtaining a scoring result for the specific institution sent by the first client; the scoring result for the specific institution is used to evaluate the validity of user credit data of the specific institution.

[0190] In a specific example, the user credit data on the blockchain is blacklist data, the user data to be matched by the second institution is Wang Wu's identity data, and the identity data of Wang Wu hits the user credit data of D institution on the blockchain. At this time, the access result includes the name of D institution, and the user credit data information about Wang Wu's identity data hitting D institution on the blockchain. After learning the access result, the institution corresponding to the second institution still lends money to Wang Wu, and later, if Wang Wu defaults at the institution corresponding to the second institution, the institution corresponding to the second institution can determine that the user credit data of Wang Wu in D institution is real user credit data based on Wang Wu's default, and determine the reward score for D institution; if Wang Wu does not default at the institution corresponding to the second institution, the institution corresponding to the second institution can determine that the user credit data of Wang Wu in D institution is false user credit data based on Wang Wu's compliance with the contract, and determine the penalty score for D institution.

[0191] In the embodiments of this specification, a blockchain institution may upload user credit data to the blockchain. After the blockchain institution uploads the user credit data to the blockchain, in order to determine the validity of the user credit data of the institution, the blockchain may notify other institutions of the blockchain to analyze the user credit data uploaded to the blockchain by the institution.

[0192] Based on this, the blockchain-based credit data sharing method of the embodiment of this specification may also include:

[0193] Obtain user credit data uploaded to the blockchain by a third party institution on the blockchain.

[0194] A scoring instruction is sent to a second client corresponding to the fourth institution on the blockchain, so that the second client adopts the blockchain-based credit data analysis method of the above embodiment of this specification to determine the scoring result for the third institution according to the user credit data uploaded to the blockchain by the third institution; the scoring result of the third institution is used to evaluate the validity of the user credit data uploaded to the blockchain by the third institution.

[0195] In the embodiments of this specification, the fourth institution is an institution on the blockchain other than the third institution. After the third institution uploads the user credit data to the blockchain, at least one institution on the blockchain other than the third institution may analyze the user credit data of the third institution. It can be seen that the number of fourth institutions is at least one. In a specific example, the third institution is an institution newly added to the blockchain. After the third institution uploads the user credit data to the blockchain for the first time, the blockchain obtains the user credit data of the third institution and sends a scoring instruction to the clients of all institutions on the blockchain other than the third institution, so that the clients of each institution on the blockchain other than the third institution analyze the user credit data uploaded to the blockchain by the third institution.

[0196] The credit data analysis method based on blockchain in the embodiment of this specification can be applied to a first institution side device, and the first institution side device can be a node of the first institution. Based on step 204, the second user credit data of the second institution is obtained through blockchain, specifically including:

[0197] Obtain the second user credit data encrypted by the first public key sent by the blockchain; the first public key is bound to the account identifier of the first institution and is used to encrypt data sent by the blockchain to the device of the first institution.

[0198] A first private key corresponding to the first public key is determined.

[0199] The first private key is used to decrypt the second user credit data encrypted by the first public key to obtain the second user credit data.

[0200] Secondly, the blockchain-based credit data analysis method of the embodiment of this specification may also include:

[0201] The data of the first institution is encrypted using a second public key; the second public key is a public key bound to the account identifier of the first institution and is used to encrypt the data sent by the first institution to the blockchain;

[0202] Uploading the encrypted data of the first institution to the blockchain;

[0203] After receiving the encrypted data of the first institution, the blockchain determines the second private key corresponding to the second public key, and uses the second private key to decrypt the encrypted data of the first institution to obtain the decrypted data of the first institution.

[0204] In the embodiments of this specification, when any organization joins the blockchain, it generates a first public key and a first private key bound to the account identifier of the organization through the local device of the organization, and sends an application to join the blockchain, and the application to join the request includes the first public key. After the blockchain obtains the application to join the request, it generates a second public key and a second private key bound to the account identifier of the organization according to the application to join the request, and feeds the second public key back to the local device of the organization, and saves the first public key and the second private key on the blockchain. In this way, when the organization interacts with the blockchain for data, it can use the public key of the other end to encrypt the transmitted file, and when the other end receives the relevant file, it can use the private key obtained from the other end to decrypt the file to ensure that the file will not be leaked during the transmission process.

[0205] In a specific example, Figure 4As shown, taking institution B as an example, when institution B joins the blockchain, the local device of institution B generates a first key pair (B.pri, B.pub) bound to the account identifier of institution B, and the local device also saves the private key B.pri locally and uploads the public key B.pub to the blockchain. After receiving the application for joining with the public key B.pub sent by the local device of the institution, the blockchain generates a second key pair (LtoB.pri, L.pub) bound to the account identifier of institution B, saves the public key B.pub and the private key LtoB.pri on the blockchain, and distributes the public key L.pub to the local device of institution B.

[0206] When institution B obtains a file from the blockchain, institution B sends a first file acquisition request carrying the account identifier of institution B to the blockchain. When the blockchain receives the file acquisition request, it determines the public key B.pub uploaded by the local device of institution B according to the account identifier of institution B, and uses the public key B.pub to encrypt the relevant file, and feeds back the relevant file encrypted by the public key B.pub to the local device of institution B, so that the local device of institution B decrypts the relevant file encrypted by the public key B.pub through the private key B.pri generated by the local device of institution B, and obtains the decrypted relevant file. Similarly, when institution B uploads a file to the blockchain, the local device of institution B encrypts the transmission file using the public key L.pub sent by the blockchain, and sends the transmission file encrypted by the public key L.pub to the blockchain, so that the blockchain uses its locally stored private key LtoB.pri to decrypt the transmission file. In this way, it is ensured that the transmission file will not be leaked when institution B transmits files with the blockchain.

[0207] In the embodiments of this specification, the blockchain can be understood as a data chain composed of multiple blocks stored sequentially. The block header of each block contains the timestamp of the block, the hash value of the previous block information and the hash value of the current block information, thereby realizing mutual verification between blocks and blocks, and forming an unalterable blockchain. Each block can be understood as a data block (a unit for storing data). As a decentralized database, the blockchain is a string of data blocks generated by using cryptographic methods to be interrelated. Each data block contains information about a network transaction, which is used to verify the validity of its information (anti-counterfeiting) and generate the next block. The chain formed by connecting blocks end to end is the blockchain. If the data in the block needs to be modified, the content of all blocks after this block needs to be modified, and the data backed up by all nodes in the blockchain network needs to be modified. Therefore, the blockchain has the characteristics of being difficult to tamper with and delete. After the data has been saved to the blockchain, it is reliable as a method to maintain the integrity of the content.

[0208] Figure 5 The embodiments of this specification provide corresponding to Figure 2 and Figure 3 A swimlane flow chart of the blockchain-based credit data analysis method and sharing method. Figure 5 As shown, the blockchain-based credit data analysis method and sharing method may involve execution entities such as a first client, a second client, and a blockchain.

[0209] Figure 5 In the example, the first institution and the second institution are both institutions that have joined the blockchain, the first institution corresponds to the first client, and the third institution corresponds to the second client.

[0210] In the credit data analysis phase, the first client can obtain the first user credit data of the first institution from the node of the first institution locally, and obtain the second user credit data uploaded to the blockchain by the second institution from the blockchain. Then, the first client matches the first user credit data with the second user credit data based on the first preset scoring standard. If the matching result indicates that the first user credit data and the second user credit data contain the same user group, the first scoring result for the second institution is determined according to the matching result, and the first scoring result is uploaded to the blockchain. The first scoring result user evaluates the validity of the second user credit data. After the blockchain obtains the first scoring result, the overall score of the second institution is updated according to the first scoring result, wherein the overall score is the score determined according to each scoring result of the second institution.

[0211] In the credit data sharing stage, after the blockchain obtains the overall score of each institution on the blockchain, it can sort the user credit data of each institution according to the overall score of each institution according to the sorting rule from high to low, and obtain the access order for the user credit data of each institution. Subsequently, when the second client sends a credit data access request containing the user data to be matched and the payment result to the blockchain, after receiving the credit data access request, the blockchain matches the user data to be matched with the user credit data of each institution in turn based on the access order, and obtains the access result, and feeds back the access result to the second client, so that the institution corresponding to the second client can know the access result through the second client.

[0212] Based on the same idea, the embodiments of this specification also provide a device corresponding to the above-mentioned blockchain-based credit data analysis method. Figure 6 The embodiments of this specification provide corresponding to Figure 2 A structural diagram of a credit data analysis device based on blockchain. Figure 6 As shown, the device may include:

[0213] The first acquisition module 602 is used to acquire the credit data of the first user of the first institution.

[0214] The second acquisition module 604 is used to acquire the credit data of the second user of the second institution through the blockchain.

[0215] The data matching module 606 is used to match the first user credit data with the second user credit data based on a first preset scoring standard to obtain a matching result.

[0216] The first scoring module 608 is used to determine a first scoring result for the second institution based on the 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; the first scoring result is used to evaluate the validity of the second user credit data.

[0217] The data uploading module 610 is used to upload the first scoring result to the blockchain.

[0218] based on Figure 6 The present specification also provides some specific implementation schemes of the device, which are described below.

[0219] In the embodiment of the present specification, the first preset scoring standard may include a first scoring standard; the first user credit data may include credit data of a user who has been successfully granted credit; and the second user credit data may include blacklist data.

[0220] The data matching module 606 may be specifically used for:

[0221] The credit data of the successfully credited user is matched with the blacklist data to obtain a first matching result.

[0222] The first scoring module 608 may be specifically used for:

[0223] If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, a penalty score for the second institution is determined according to the first matching result.

[0224] In the embodiment of this specification, the first preset scoring standard may include a second scoring standard; the first user credit data may include credit data of a dishonest user; and the second user credit data may include blacklist data.

[0225] The data matching module 606 may be specifically used for:

[0226] The credit data of the dishonest user is matched with the blacklist data to obtain a second matching result.

[0227] The first scoring module 608 may be specifically used for:

[0228] If the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring criteria, the reward score for the second institution is determined according to the second matching result.

[0229] In the embodiments of the present specification, the first preset scoring criteria may include a first scoring criteria and a second scoring criteria; the first user credit data may include credit data of successfully granted credit users and credit data of untrustworthy users; and the second user credit data may include blacklist data.

[0230] The data matching module 606 may be specifically used for:

[0231] The credit data of the successfully granted user is matched with the blacklist data to obtain a first matching result, and the credit data of the dishonest user is matched with the blacklist data to obtain a second matching result.

[0232] The first scoring module 608 may be specifically used for:

[0233] If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, the penalty score for the second institution is determined according to the first matching result; and if the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring standard, the reward score for the second institution is determined according to the second matching result.

[0234] The first scoring result is determined according to the penalty score and the reward score.

[0235] The device of the embodiment of this specification may also include:

[0236] An updating module is used to update the overall score of the second institution according to the first scoring result; the overall score is a score determined according to the scoring results of the second institution.

[0237] The device of the embodiment of this specification may also include:

[0238] The third acquisition module is used to obtain first risk control result data of a third institution; the first risk control result data is risk control result data within a period of time when the second user credit data is not used for credit risk control; the third institution is a node that uses the second user credit data for credit risk control.

[0239] The fourth acquisition module is used to obtain second risk control result data of the third institution for the second user credit data; the second risk control result data is the risk control result data when the second user credit data is used to perform credit risk control.

[0240] The first determination module is used to determine the first user bad debt rate of the third institution according to the first risk control result data; the first user bad debt rate is the ratio of the number of users who have successfully borrowed and failed to repay as required to the number of users who have successfully borrowed and failed within a period of time when the second user credit data is not used for credit risk control.

[0241] The second determination module is used to determine the second user bad debt rate of the third institution according to the second risk control result data; the second user bad debt rate is the ratio of the number of users who have successfully borrowed and failed to repay as required to the number of users who have successfully borrowed and failed when using the second user credit data for credit risk control.

[0242] A judgment module is used to judge whether the defective rate of the first user is greater than the defective rate of the second user.

[0243] The second scoring module is used to determine a second scoring result for the second institution based on the first risk control result data and the second risk control result data based on a second preset scoring standard if the first user bad rate is greater than the second user bad rate.

[0244] Based on the same idea, the embodiments of this specification also provide a device corresponding to the above-mentioned blockchain-based credit data sharing method. Figure 7 The embodiments of this specification provide corresponding to Figure 3 A structural diagram of a credit data sharing device based on blockchain. Figure 7 As shown, the device may include:

[0245] The first acquisition module 702 is used to obtain the overall score of the first institution on the blockchain; the overall score of the first institution is determined according to the scoring results of the first institution; the scoring results of the first institution are determined using the blockchain-based credit data analysis method of the embodiment of this specification.

[0246] The second acquisition module 704 is used to obtain the overall scores of other institutions on the blockchain except the first institution.

[0247] The first determination module 706 is used to determine the access order for the user credit data of each institution on the blockchain according to the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution, and the access order is used to access the user credit data of each institution on the blockchain based on the access order when the user-side device accesses the user credit data of each institution on the blockchain for a fee.

[0248] The device of the embodiment of this specification may also include:

[0249] The third acquisition module is used to obtain a credit data access request sent by the first client corresponding to the second institution; the credit data access request includes the user data to be matched and the payment result; the payment result is a payment result generated for the fee value determined based on the user data to be matched.

[0250] The second determination module is used to determine the access result for the first institution based on the access order and according to the user data to be matched and the user credit data of each node on the blockchain.

[0251] A feedback module is used to feed back the access result to the first client.

[0252] Based on the same idea, the embodiments of this specification also provide devices corresponding to the above-mentioned blockchain-based credit data analysis method.

[0253] Figure 8 The embodiments of this specification provide corresponding to Figure 2 A schematic diagram of the structure of a credit data analysis device based on blockchain. Figure 8 As shown, the device 800 may include:

[0254] at least one processor 810; and,

[0255] A memory 830 in communication with the at least one processor; wherein,

[0256] The memory 830 stores instructions 820 that can be executed by the at least one processor 810. The instructions are executed by the at least one processor 810 to enable the at least one processor 810 to:

[0257] Obtaining credit data of a first user of a first institution;

[0258] Obtain credit data of a second user of a second institution through blockchain;

[0259] Based on a first preset scoring standard, matching the first user credit data with the second user credit data to obtain a matching result;

[0260] If the 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 institution is determined based on the matching result; the first scoring result is used to evaluate the validity of the second user credit data;

[0261] The first scoring result is uploaded to the blockchain.

[0262] Based on the same idea, the embodiments of this specification also provide devices corresponding to the above-mentioned blockchain-based credit data sharing method.

[0263] Fig. 9 The embodiments of this specification provide corresponding to Figure 3 A structural diagram of a credit data sharing device based on blockchain. Fig. 9 As shown, the device 900 may include:

[0264] at least one processor 910; and,

[0265] A memory 930 in communication with the at least one processor; wherein,

[0266] The memory 930 stores instructions 920 that can be executed by the at least one processor 910. The instructions are executed by the at least one processor 910 to enable the at least one processor 910 to:

[0267] Obtaining an overall score of the first institution on the 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 institution is determined using the method according to any one of claims 1 to 10;

[0268] Obtaining the overall scores of other institutions on the blockchain except the first institution;

[0269] An access order for user credit data of each institution on the blockchain is determined based on the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution; the access order is used to access the user credit data of each institution on the blockchain based on the access order when a user-side device accesses the user credit data of each institution on the blockchain for a fee.

[0270] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments. Figure 5 As for the device shown, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0271] In the 1990s, it was very clear whether the improvement of a technology was hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0272] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, 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: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, and the memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0273] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, 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.

[0274] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0275] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0276] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0277] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0278] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0279] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0280] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0281] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0282] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0283] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0284] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0285] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. 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 blockchain, comprising: Obtaining credit data of a first user of a first institution; Obtain credit data of a second user of a second institution through blockchain; Based on a first preset scoring standard, matching the first user credit data with the second user credit data to obtain a 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, a first scoring result for the second institution is determined based on the matching result; the first scoring result is used to evaluate the validity of the second user credit data; wherein, if the first user credit data and the second user credit data contain the same user group of the same credit type, a reward score of the second institution is determined; or, if the first user credit data and the second user credit data contain the same user group of different credit types, a penalty score of the second institution is determined; the first scoring result is determined based on at least one of the reward score and the penalty score; The first scoring result is uploaded to the blockchain.

2. The method according to claim 1, wherein the first preset scoring standard includes a first scoring standard; the first user credit data includes credit data of a user who has been successfully granted credit; and the second user credit data includes blacklist data; The matching of 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 granted 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 institution according to the matching result specifically includes: If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, a penalty score for the second institution is determined according to the first matching result.

3. The method according to claim 1, wherein the first preset scoring standard includes a second scoring standard; the first user credit data includes credit data of a dishonest user; and the second user credit data includes blacklist data; The matching of 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 dishonest user with the blacklist data to obtain a second 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 institution according to the matching result specifically includes: If the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring criteria, the reward score for the second institution is determined according to the second matching result.

4. The method according to claim 1, wherein the first preset scoring standard includes a first scoring standard and a second scoring standard; the first user credit data includes credit data of successfully granted credit users and credit data of untrustworthy users; and the second user credit data includes blacklist data; The matching of 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 granted user with the blacklist data to obtain a first matching result, and matching the credit data of the dishonest user with the blacklist data to obtain a second 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 institution according to the matching result specifically includes: If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, a penalty score for the second institution is determined according to the first matching result; and if the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring standard, a reward score for the second institution is determined according to the second matching result; The first scoring result is determined according to the penalty score and the reward score.

5. The method according to claim 2 or 4, wherein determining the penalty score for the second institution based on the first scoring standard and the first matching result specifically comprises: determining the number of users included in the first user group according to the first matching result; Determining a deduction coefficient for the second institution according to the number of users; A penalty score for the second institution is determined based on the deduction coefficient and the number of users.

6. The method according to claim 1, after uploading the first scoring result to the blockchain, further comprising: updating the overall score of the second institution according to the first scoring result; The overall score is a score determined based on the scoring results of the second institution.

7. The method of claim 1, further comprising: Obtaining first risk control result data of a third institution; the first risk control result data is risk control result data for a period of time when the second user credit data is not used for credit risk control; The third institution is an institution that uses the second user credit data to perform credit risk control; Obtaining second risk control result data of the third institution for the second user's credit data; the second risk control result data is risk control result data when the second user's credit data is used to perform credit risk control; Determine the first user bad debt rate of the third institution according to the first risk control result data; The first user bad debt rate is the ratio of the number of users who have successfully borrowed money and failed to repay as required to the number of users who have successfully borrowed money over a period of time when the second user credit data is not used for credit risk control; Determining the second user bad debt rate of the third institution according to the second risk control result data; The second user bad debt rate is the ratio of the number of users who have successfully borrowed money but have not repaid as required to the number of users who have successfully borrowed money when the second user credit data is used for credit risk control; Determine whether the first user defect rate is greater than the second user defect rate; If the first user bad rate is greater than the second user bad rate, based on a second preset scoring standard, a second scoring result for the second institution is determined according to the first risk control result data and the second risk control result data.

8. According to the method as claimed in claim 1, the second user credit data includes the user credit data initially uploaded to the blockchain by the second institution, or the newly added user credit data on the blockchain by the second institution; the newly added user credit data on the blockchain by the second institution is the user credit data on the blockchain of the second institution that is not used to score the second user credit data.

9. The method as claimed in claim 1, wherein the first user credit data includes newly added user credit data of the first institution; the newly added user credit data of the first institution is data of the first institution that is not used to score the second user credit data.

10. The method of claim 1, further comprising: For user credit data on a verified valid blockchain, points will be awarded to the institution that uploads the verified valid user credit data on the blockchain to the blockchain for the first time.

11. The method according to claim 1, wherein obtaining the credit data of the second user of the second institution through the blockchain specifically comprises: Obtaining credit data of a second user encrypted with the first public key and sent by the blockchain; The first public key is a public key bound to the account identifier of the first institution and is used to encrypt data sent by the blockchain to the device of the first institution; Determining a first private key corresponding to the first public key; The first private key is used to decrypt the second user credit data encrypted by the first public key to obtain the second user credit data.

12. The method of claim 11, further comprising: The data of the first institution is encrypted using a second public key; the second public key is a public key bound to the account identifier of the first institution and is used to encrypt the data sent by the first institution to the blockchain; Uploading the encrypted data of the first institution to the blockchain; After receiving the encrypted data of the first institution, the blockchain determines the second private key corresponding to the second public key, and uses the second private key to decrypt the encrypted data of the first institution to obtain the decrypted data of the first institution.

13. A credit data sharing method based on blockchain, comprising: Get the overall score of the first institution on the 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 institution is determined by the method according to any one of claims 1 to 10; Obtaining the overall scores of other institutions on the blockchain except the first institution; An access order for user credit data of each institution on the blockchain is determined based on the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution; the access order is used to access the user credit data of each institution on the blockchain based on the access order when a user-side device accesses the user credit data of each institution on the blockchain for a fee.

14. The method of claim 13, wherein determining the order of accessing user credit data of each institution on the blockchain according to the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution comprises: Based on the sorting rule of overall scores from high to low, the institutions on the blockchain are sorted according to the overall scores of the institutions on the blockchain to obtain an access order for the institutions on the blockchain.

15. The method according to claim 13, after determining the order of accessing the user credit data of each institution on the blockchain according to the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution, further comprising: Obtaining a credit data access request sent by the second institution through the first client; The credit data access request includes the user data to be matched and the payment result; The payment result is a payment result generated for the fee value determined according to the to-be-matched user data; Based on the access order, determining the access result for the first institution according to the to-be-matched user data and the user credit data of each institution on the blockchain; The access result is fed back to the first client.

16. The method according to claim 15, wherein the access result includes an identifier of a specific institution; the specific institution is an institution whose user credit data accessed by the first institution and the user data to be matched both contain the same user group; After feeding back the access result to the first client, the method further includes: Obtaining a rating result for the specific institution sent by the first client; The scoring result for the specific institution is used to evaluate the validity of the user credit data of the specific institution.

17. The method of claim 13, further comprising: Obtain user credit data uploaded to the blockchain by a third party institution on the blockchain; Send a scoring instruction to a second client corresponding to the fourth institution on the blockchain, so that the second client adopts the method described in any one of claims 1 to 5 to determine the scoring result for the third institution based on the user credit data uploaded to the blockchain by the third institution; the scoring result of the third institution is used to evaluate the validity of the user credit data uploaded to the blockchain by the third institution.

18. A credit data analysis device based on blockchain, comprising: A first acquisition module, acquiring credit data of a first user of a first institution; A second acquisition module, which acquires credit data of a second user of a second institution through blockchain; A data matching module, configured to match the first user credit data with the second user credit data based on a first preset scoring standard to obtain a matching result; a first scoring module, wherein if the 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 institution is determined according to the matching result; the first scoring result is used to evaluate the validity of the second user credit data; wherein, if the first user credit data and the second user credit data contain the same user group of the same credit type, a reward score of the second institution is determined; or, if the first user credit data and the second user credit data contain the same user group of different credit types, a penalty score of the second institution is determined; the first scoring result is determined based on at least one of the reward score and the penalty score; A data uploading module is used to upload the first scoring result to the blockchain.

19. The device of claim 18, wherein the first preset scoring standard comprises a first scoring standard; the first user credit data comprises credit data of a user who has been successfully granted credit; and the second user credit data comprises blacklist data; The data matching module is specifically used for: Matching the credit data of the successfully granted user with the blacklist data to obtain a first matching result; The first scoring module is specifically used for: If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, a penalty score for the second institution is determined according to the first matching result.

20. The device of claim 18, wherein the first preset scoring standard includes a second scoring standard; the first user credit data includes credit data of a dishonest user; and the second user credit data includes blacklist data; The data matching module is specifically used for: Matching the credit data of the dishonest user with the blacklist data to obtain a second matching result; The first scoring module is specifically used for: If the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring criteria, the reward score for the second institution is determined according to the second matching result.

21. The device of claim 18, wherein the first preset scoring standard includes a first scoring standard and a second scoring standard; the first user credit data includes credit data of successfully granted credit users and credit data of untrustworthy users; and the second user credit data includes blacklist data; The data matching module is specifically used for: Matching the credit data of the successfully granted user with the blacklist data to obtain a first matching result, and matching the credit data of the dishonest user with the blacklist data to obtain a second matching result; The first scoring module is specifically used for: If the first matching result indicates that the credit data of the successfully granted user and the blacklist data both contain the same first user group, then based on the first scoring standard, a penalty score for the second institution is determined according to the first matching result; and if the second matching result indicates that the credit data of the dishonest user and the blacklist data both contain the same second user group, then based on the second scoring standard, a reward score for the second institution is determined according to the second matching result; The first scoring result is determined according to the penalty score and the reward score.

22. A credit data sharing device based on blockchain, comprising: A first acquisition module, used to obtain the overall score of the first institution on the 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 institution is determined by the method according to any one of claims 1 to 10; A second acquisition module, used to obtain the overall scores of other institutions on the blockchain except the first institution; The first determination module is used to determine an access order for user credit data of each institution on the blockchain based on the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution, wherein the access order is used to access the user credit data of each institution on the blockchain based on the access order when the user-side device accesses the user credit data of each institution on the blockchain for a fee.

23. The apparatus of claim 22, further comprising: A third acquisition module, used to acquire a credit data access request sent by the second institution through the first client; The credit data access request includes the user data to be matched and the payment result; The payment result is a payment result generated for the fee value determined according to the to-be-matched user data; A second determination module is used to determine the access result for the first institution based on the access order and according to the to-be-matched user data and the user credit data of each institution on the blockchain; A feedback module is used to feed back the access result to the first client.

24. A credit data analysis device based on blockchain, comprising: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Obtaining credit data of a first user of a first institution; Obtain credit data of a second user of a second institution through blockchain; Based on a first preset scoring standard, matching the first user credit data with the second user credit data to obtain a 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, a first scoring result for the second institution is determined based on the matching result; the first scoring result is used to evaluate the validity of the second user credit data; wherein, if the first user credit data and the second user credit data contain the same user group of the same credit type, a reward score of the second institution is determined; or, if the first user credit data and the second user credit data contain the same user group of different credit types, a penalty score of the second institution is determined; the first scoring result is determined based on at least one of the reward score and the penalty score; The first scoring result is uploaded to the blockchain.

25. A credit data sharing device based on blockchain, comprising: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Obtaining an overall score of the first institution on the 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 institution is determined using the method according to any one of claims 1 to 10; Obtaining the overall scores of other institutions on the blockchain except the first institution; An access order for user credit data of each institution on the blockchain is determined based on the overall score of the first institution and the overall scores of other institutions on the blockchain except the first institution. The access order is used to access the user credit data of each institution on the blockchain based on the access order when a user-side device accesses the user credit data of each institution on the blockchain for a fee.

Images