Credit data processing

Abstract

Provided in the embodiments of the present disclosure are a credit data processing method and apparatus. The credit data processing method comprises: after acquiring a transaction for performing data correction on disputed data in each piece of credit data stored in a consortium blockchain, performing transaction verification on the transaction; after the transaction verification is passed, generating a data correction message on the basis of the disputed data and a proof material of the disputed data; distributing the data correction message to a trusted node of a trusted institution that has established a trusted relationship with an institution to which a consortium blockchain node belongs, so as to perform consensus processing between the consortium blockchain node and the trusted node on the basis of the data correction message; and after the consensus processing succeeds, on the basis of the proof material, performing data correction processing on the disputed data, so as to obtain each piece of target credit data.

Description

Credit data processing TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, and particularly relates to credit data processing. BACKGROUND

[0002] With the continuous development of Internet technology and the continuous progress of social information, more and more online services provide different services for users with different credit data in the process of user access, so that credit data is becoming more and more important to users. However, in the process of collecting and managing credit data of users, due to the diversity of collection methods, the collected credit data of users may have data errors. Therefore, the collection and management of credit data bring great pressure and challenges to credit institutions. SUMMARY

[0003] One or more embodiments of the present disclosure provide a credit data processing method applied to a consortium chain node, the method comprising: acquiring a transaction of data correction on disputed data in each credit data stored by a consortium chain, and performing transaction verification on the transaction. After the transaction verification is passed, a data correction message is generated according to the disputed data and proof materials of the disputed data. The data correction message is distributed to a trusted node of a trusted institution having a trusted relationship with an institution to which the consortium chain node belongs, so as to perform consensus processing of the consortium chain node and the trusted node based on the data correction message. The trusted relationship is established after the trusted institution acquires the each credit data and credit verification of the trusted institution is passed. If the consensus processing is successful, each target credit data is obtained by performing data correction processing on the disputed data according to the proof materials.

[0004] One or more embodiments of the present disclosure provide a credit data processing device running in a consortium chain node, the device comprising: a transaction acquisition module configured to acquire a transaction of data correction on disputed data in each credit data stored by a consortium chain, and perform transaction verification on the transaction. A message generation module configured to generate a data correction message according to the disputed data and proof materials of the disputed data after the transaction verification is passed. A message distribution module configured to distribute the data correction message to a trusted node of a trusted institution having a trusted relationship with an institution to which the consortium chain node belongs, so as to perform consensus processing of the consortium chain node and the trusted node based on the data correction message. The trusted relationship is established after the trusted institution acquires the each credit data and credit verification of the trusted institution is passed. A data correction module configured to obtain each target credit data by performing data correction processing on the disputed data according to the proof materials if the consensus processing is successful.

[0005] One or more embodiments of the present disclosure provide a credit data processing device, comprising: a processor; and a memory configured to store computer executable instructions which, when executed, cause the processor to: acquire a transaction of data correction on disputed data in each credit data stored by a consortium chain, and perform transaction verification on the transaction. After the transaction verification passes, generate a data correction message according to the disputed data and proof materials of the disputed data. Distribute the data correction message to a trusted node of a trusted institution that has a trusted relationship with an institution to which a consortium chain node belongs, so as to perform consensus processing of the consortium chain node and the trusted node based on the data correction message. The trusted relationship is established after the trusted institution acquires the each credit data and credit verification of the trusted institution passes. If the consensus processing is successful, data correction processing is performed on the disputed data according to the proof materials to obtain each target credit data.

[0006] One or more embodiments of the present disclosure provide a computer readable storage medium for storing computer executable instructions which, when executed, implement the following steps: acquiring a transaction of data correction on disputed data in each credit data stored by a consortium chain, and performing transaction verification on the transaction. After the transaction verification passes, generating a data correction message according to the disputed data and proof materials of the disputed data. Distributing the data correction message to a trusted node of a trusted institution that has a trusted relationship with an institution to which a consortium chain node belongs, so as to perform consensus processing of the consortium chain node and the trusted node based on the data correction message. The trusted relationship is established after the trusted institution acquires the each credit data and credit verification of the trusted institution passes. If the consensus processing is successful, data correction processing is performed on the disputed data according to the proof materials to obtain each target credit data. BRIEF DESCRIPTION OF DRAWINGS

[0007] In order to more clearly illustrate the technical solutions in the one or more embodiments of the present disclosure or the related art, the drawings needed to be used in the embodiment or related art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present disclosure, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings;

[0008] FIG. 1 is a schematic diagram of a credit data processing method implementation environment provided by one or more embodiments of the present disclosure;

[0009] FIG. 2 is a credit data processing method processing flowchart provided by one or more embodiments of the present disclosure;

[0010] FIG. 3 is a credit data processing method processing flowchart applied to a credit data scene provided by one or more embodiments of the present disclosure;

[0011] FIG. 4 is a schematic diagram of an embodiment of a credit data processing device according to one or more embodiments of the present disclosure;

[0012] FIG. 5 is a structural schematic diagram of a credit data processing device according to one or more embodiments of the present disclosure. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the technical solutions in the one or more embodiments of the present disclosure, the technical solutions in the one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings of the one or more embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the one or more embodiments of the present disclosure, all other embodiments obtained by those skilled in the art without creative labor should fall within the protection scope of the present disclosure.

[0014] The credit data processing method provided by the one or more embodiments of the present disclosure can be applied to the implementation environment of the alliance chain. Referring to FIG. 1, the implementation environment at least includes an alliance chain node 101 for processing credit data, and further includes a trusted node 102 of a trusted institution that establishes a trusted relationship with the institution to which the alliance chain node 101 belongs.

[0015] The alliance chain node 101 and the trusted node 102 can belong to the same alliance chain.

[0016] In the implementation environment, the alliance chain node 101 obtains a transaction for data correction of disputed data in each credit data stored in the alliance chain, performs transaction verification on the transaction, and generates a data correction message according to the disputed data and the proof materials of the disputed data after the transaction verification is passed. The data correction message is distributed to the trusted node 102 of the trusted institution that establishes a trusted relationship with the institution to which the alliance chain node 101 belongs, so as to perform consensus processing of the alliance chain node 101 and the trusted node 102 based on the data correction message. After the consensus processing is successful, the disputed data is corrected according to the proof materials to obtain each target credit data. The trusted relationship is established after the trusted institution obtains each credit data and the credit verification of the trusted institution is passed.

[0017] The one or more embodiments of the credit data processing method provided by the present disclosure are as follows: referring to FIG. 2, the credit data processing method provided by the present embodiment can be applied to an alliance chain node, and specifically includes steps S202 to S208.

[0018] In step S202, a transaction for data correction of disputed data in each credit data stored in the alliance chain is obtained, and transaction verification is performed on the transaction.

[0019] The alliance chain in this embodiment is an alliance chain to which the alliance chain node belongs, and the alliance chain can be composed of alliance chain nodes of multiple parties. Specifically, the multiple parties can include a credit data management institution and a credit data acquisition institution, where the credit data acquisition institution can be a credit data purchasing institution or a credit data purchasing and selling institution; the credit data management institution can be a specific resource institution or a credit management organization in the specific resource institution, such as a specific financial institution or a credit management department or a credit investigation management department in the specific financial institution. In addition, the multiple parties can also include users, that is, users to which the credit data belong can also have user nodes on the alliance chain. This embodiment can be applied to an alliance chain node, which can be a verification node, and the alliance chain node can be one or more.

[0020] Optionally, the alliance chain is composed of at least one of the following alliance chain nodes: an alliance chain node of a specific resource institution, an alliance chain node of a credit management organization in a specific resource institution, an alliance chain node of a credit data acquisition institution, and an alliance chain node of a user to which credit data belong.

[0021] The credit data in this embodiment refers to data related to the credit of a user or an institution, and the credit data can be credit investigation data of the user or the institution. The disputed data in each credit data refers to disputed data in each credit data, and specifically can be data in each credit data that is disputed, such as data in the credit data that a user believes to be incorrect, and this part of data that is incorrect is the disputed data in the credit data.

[0022] In specific implementation, a credit correction platform can be provided, and a user or an institution can correct disputed data in credit data through the credit correction platform. Specifically, after receiving a data correction request submitted by a user for disputed data in credit data, the data correction request can carry disputed data in credit data submitted by the user and proof materials of the disputed data, the credit correction platform can generate a data correction request for correcting the disputed data based on the disputed data in credit data of the user and the proof materials of the disputed data, and send the generated data correction request to an alliance chain node. Optionally, a transaction corresponding to the credit data in each credit data is generated after a user or an institution submits a data correction request for disputed data in the credit data, and the transaction carries the disputed data in the credit data and the proof materials of the disputed data. The user herein can be a user to which the credit data belong, and the institution can be an institution that purchases or purchases and sells the credit data. Optionally, a transaction is generated after a target object corresponding to each credit data submits a data correction request for the disputed data, and the target object can be a user to which each credit data belong or an institution that purchases or purchases and sells each credit data.

[0023] In a specific implementation process, since the alliance chain nodes receive the data correction request for the disputed data in the credit data of the user in a scattered manner, in order to improve the data correction efficiency and convenience of performing data correction, the transaction of performing data correction on the disputed data in each credit data in a plurality of credit data can be performed in batches; it should be noted that in the embodiment, the transaction of performing data correction on the disputed data in each credit data can be a transaction for performing data correction on the disputed data in each credit data. The data correction in the embodiment can be error correction.

[0024] In actual application, in the process of the user accessing his own credit data, the institution accessing its own credit data, or the institution accessing the purchased credit data, it can be found that there is an error or inconsistent data in the credit data. In view of this, in order to improve the convenience and efficiency of data correction on the credit data and avoid increasing the time cost of data correction through offline mode, the alliance chain can be introduced in the embodiment to connect each link of data correction through the alliance chain, thereby effectively improving the efficiency of data correction.

[0025] After obtaining the transaction of performing data correction on the disputed data in each credit data stored in the alliance chain, in order to improve the effectiveness of the transaction, the transaction can be transaction verified. Specifically, in the case that the number of proof materials of the disputed data is greater than a preset number threshold, the reference proof material of the proof material is obtained from the management institution of the proof material, and in the case that the reference proof material matches the proof material, it is determined that the transaction verification is passed. In an optional implementation provided in the embodiment, in the process of transaction verification of the transaction, the following operations are performed: detecting whether the number of proof materials of the disputed data is greater than a preset number threshold; if yes, obtaining the reference proof material of the proof material from the management institution of the proof material, and in the case that the reference proof material matches the proof material, determining that the transaction verification is passed.

[0026] The management institution of the proof material can be the source institution of the proof material, such as a financial institution (bank) or a credit card institution. The disputed data refers to the data in dispute in the credit data, and the proof material of the disputed data refers to the material proving that the disputed data is in dispute, such as the disputed data in the credit data being the error data marked by the user or the institution in the credit data. The proof material of the disputed data can be the initial proof material uploaded by the target object corresponding to each credit data.

[0027] On this basis, if the number of the proof materials of the disputed data is less than or equal to the preset number threshold, and the material type of the proof materials of the disputed data does not contain the preset material type, the auxiliary proof materials of the proof materials are obtained from the management mechanism, and in the case that the total number of the auxiliary proof materials and the proof materials is greater than the preset number threshold, it is determined that the transaction verification is passed. Specifically, if the execution result after detecting whether the number of the proof materials of the disputed data is greater than the preset number threshold is no, in an optional implementation of the embodiment, the following operation is performed: detecting whether the material type of the proof materials of the disputed data contains the preset material type; if no, the auxiliary proof materials of the proof materials are obtained from the management mechanism, and in the case that the total number of the auxiliary proof materials and the proof materials is greater than the preset number threshold, it is determined that the transaction verification is passed.

[0028] The material type of the auxiliary proof materials of the proof materials can be different from the material type of the proof materials, or can be the same as the material type of the proof materials. The total number refers to the total number of the auxiliary proof materials and the proof materials. The material type of the proof materials can include a basic attribute material type, a residence material type, a resource material type, a payment material type, and / or a disciplinary material type. The proof materials corresponding to the basic attribute material type can be materials composed of information such as name, occupation, and identity. The proof materials corresponding to the residence material type can be materials composed of information such as self-housing, mortgage, or rental. The proof materials corresponding to the payment material type can be materials composed of information such as payment of charges. The proof materials corresponding to the resource material type can be a loan material type and a resource flow material type. The preset material type can be a material type set in advance.

[0029] Specifically, in the case that the material type of the proof materials of the disputed data contains the preset material type, it can be determined that the transaction verification is passed.

[0030] In addition, in the process of transaction verification of the transaction, the material type of the proof materials of the disputed data can also be detected first to determine whether it contains the preset material type. If it contains, the transaction verification can be directly determined to be passed, or the reference proof materials of the proof materials are obtained from the management mechanism of the proof materials, and in the case that the reference proof materials match the proof materials, it is determined that the transaction verification is passed. If the material type of the proof materials does not contain the preset material type, it is detected whether the number of the proof materials of the disputed data is greater than the preset number threshold. If it is greater, it is determined that the transaction verification is passed. If it is less than or equal to, the auxiliary proof materials of the proof materials are obtained from the management mechanism, and in the case that the total number of the auxiliary proof materials and the proof materials is greater than the preset number threshold, it is determined that the transaction verification is passed.

[0031] In actual implementation, before accessing the alliance chain, the target object can be authenticated to improve the security of data access, and after the authentication is passed, the target object is opened to access the corresponding credit data; in an optional implementation provided by the embodiment, on the basis of the above transaction generating after the target object corresponding to each credit data submits the data correction request of the dispute data, before the transaction verification of the transaction of correcting the dispute data in each credit data stored in the alliance chain is performed, the following operation is further performed: the service certificate of the target object transmitted through the data access interface is acquired, and the credit evaluation index of the target object is queried in the alliance chain based on the object identifier of the target object; the identity of the target object is verified based on the service certificate and the credit evaluation index, and after the identity verification is passed, the target object is opened to access the corresponding credit data.

[0032] The service certificate can be a digital certificate; the target object can be a user to which the credit data belongs, or an institution that purchases or collects the credit data. For the case that the target object is a user, the corresponding credit data can be the credit data of the user, and for the case that the target object is an institution, the corresponding credit data can be the credit data purchased or collected by the institution.

[0033] Specifically, in the process of verifying the identity of the target object based on the service certificate and the credit evaluation index, the service certificate can be verified, and after the verification is passed, it is detected whether the credit evaluation index is greater than an index threshold, if yes, it is determined that the identity verification is passed; if the verification of the service certificate is not passed or the credit evaluation index is less than or equal to the index threshold, it is determined that the identity verification is not passed; in the process of verifying the service certificate, it can be verified whether the validity period of the service certificate is expired.

[0034] After the above identity verification of the target object based on the service certificate and the credit evaluation index, and after the access permission of the corresponding credit data of the target object is opened, in an optional implementation provided by the embodiment, the following operation is further performed: the data access request of the credit data transmitted through the interface call based on the access permission is acquired; in response to the data access request, the credit data is queried in the alliance chain and is returned to mark the dispute data in the credit data.

[0035] In step S204, a data correction message is generated according to the dispute data and the proof materials of the dispute data.

[0036] The transaction of correcting the disputed data in the credit data stored in the alliance chain is obtained, and the transaction is verified. In this step, after the transaction verification is passed, a data correction message is generated based on the disputed data and the proof materials of the disputed data. If the transaction verification fails, no processing is performed.

[0037] The data correction message described in this embodiment includes a message indicating whether the disputed data in each credit data is corrected, that is, the data correction message indicates whether the disputed data in each credit data is corrected. That is, the data correction message can be yes or no. Yes means agreeing to correct the disputed data in the credit data, and no means disagreeing to correct the disputed data in the credit data.

[0038] The proof materials of the disputed data can include initial proof materials uploaded by the target object corresponding to each credit data, or can include auxiliary proof materials obtained by the alliance chain node from a management agency of the proof materials of the disputed data.

[0039] In specific implementation, in the process of generating a data correction message based on the disputed data and the proof materials of the disputed data, the type of data correction of the disputed data can be determined based on the proof materials of the disputed data, and the data correction message is generated based on the type of data correction. The type of data correction of the disputed data includes a positive correction type and a negative correction type. The positive correction type includes a type of correcting the disputed data, and the negative correction type includes a type of not correcting the disputed data. Therefore, the data correction message includes a positive correction message and a negative correction message.

[0040] In step S206, the data correction message is distributed to a trusted node of a trusted agency having a trusted relationship with an agency to which the alliance chain node belongs, so that consensus processing of the alliance chain node and the trusted node is performed based on the data correction message.

[0041] In this step, in order to more efficiently and safely determine whether to correct the disputed data, a consensus mechanism can be introduced in this embodiment, and the alliance chain node and the trusted node jointly participate in the data correction. Specifically, the alliance chain node distributes the data correction message to a trusted node of a trusted agency having a trusted relationship with an agency to which the alliance chain node belongs, and performs consensus processing of the alliance chain node and the trusted node based on the data correction message. Specifically, the consensus processing of the alliance chain node and the trusted node is performed based on the data correction message for data correction.

[0042] The trusted node in the embodiment can be a consortium chain node of a consortium chain, and specifically can be a trusted node in a trusted node list, such as a UNL (Unique Node List, trusted node list), which is of the consortium chain node. Optionally, the trusted relationship is established after the trusted agency obtains the credit data and the credit verification of the trusted agency is passed.

[0043] In actual application, before the consortium chain node and the trusted node perform consensus processing on the disputed data in each credit data, the agencies corresponding to other nodes on the consortium chain may or may not purchase each credit data, and the credit indicators of the agencies corresponding to other nodes are also uneven. In view of this, in order to improve the flexibility and effectiveness of subsequent consensus processing, a trusted relationship between the agency to which the consortium chain node belongs and the trusted agency can be established, and the consensus processing of the consortium chain node and the trusted node is performed based on the trusted relationship. In an optional implementation provided in the embodiment, the trusted relationship is established by: screening a purchase agency that purchases the credit data from a plurality of preset agencies; detecting whether the credit indicator of the purchase agency is greater than an indicator threshold, and if so, regarding the purchase agency whose credit indicator is greater than the indicator threshold as the trusted agency, and establishing the trusted relationship between the trusted agency and the agency.

[0044] The acquisition agency of each credit data includes a purchase agency or a purchase agency of each credit data, and the purchase agency or the purchase agency of each credit data can be a commercial bank, a loan agency, or a merchant, etc. The plurality of preset agencies can be agencies corresponding to other verification nodes on the consortium chain except the consortium chain node. Here, if the credit indicator of the purchase agency is less than or equal to the indicator threshold, no processing is performed.

[0045] In specific implementation, the data correction message can be broadcast to each trusted node in the trusted node list of the consortium chain node, and on this basis, the consensus processing of the consortium chain node and the trusted node on the data correction can be performed based on the data correction message, that is, the consortium chain node can use the data correction message to perform consensus processing on the data correction together with the trusted node. The consensus processing in the embodiment can be a consensus processing process of all nodes including the consortium chain node and the trusted node.

[0046] In a specific implementation process, in the consensus processing of the alliance chain node and the trusted node based on the data correction message, in order to improve the effectiveness of the consensus and reduce the time cost of the consensus process, a preset number threshold can be set. In the consensus process, the dialogue processing of the alliance chain node and the trusted node for data correction based on the data correction message, that is, the discussion, can be iteratively performed. If the number of positive correction results in the data correction results of the alliance chain node and the trusted node after the dialogue processing is greater than the preset number threshold, it is determined that the consensus processing is successful. The data correction result can be a data correction result generated by the alliance chain node and the trusted node in the consensus processing. For example, the alliance chain node and x trusted nodes jointly perform consensus processing for data correction based on the data correction message, that is, the alliance chain node and 3 trusted nodes discuss data correction. If the number of positive correction results in the data correction results of all nodes, that is, the alliance chain node and x trusted nodes, is greater than the preset number threshold, it is determined that the consensus processing is successful.

[0047] In addition, the above-mentioned embodiment can be applied to the alliance chain node. In actual use, there are multiple alliance chain nodes. In this regard, in order to improve the flexibility of data correction, in an optional implementation provided by the embodiment, in the consensus processing of the alliance chain node and the trusted node based on the data correction message, the following operations are performed: based on the data correction message of each alliance chain node in the multiple alliance chain nodes, the consensus processing of each alliance chain node and the corresponding trusted node is performed; the data correction result of each alliance chain node and the corresponding trusted node after the consensus processing is determined; if the multiple trusted nodes are the same and the data correction results of the multiple trusted nodes are inconsistent, the data correction results of the multiple trusted nodes are excluded. If the number of positive correction results in the remaining data correction results is greater than the number threshold, it is determined that the consensus processing is successful.

[0048] For example, the multiple alliance chain nodes are 3 alliance chain nodes. Each alliance chain node in the 3 alliance chain nodes performs consensus processing for data correction with each trusted node in the trusted node list of the alliance chain node based on the respective data correction message. The data correction result of each alliance chain node in the 3 alliance chain nodes and the data correction result of each trusted node in the trusted node list of each alliance chain node after the consensus processing are determined. If multiple trusted nodes in the trusted node list are the same and the data correction results of the multiple trusted nodes are inconsistent (for example, a trusted node in the trusted node list of the first alliance chain node is the same as a trusted node in the trusted node list of the second alliance chain node, but the data correction results of the two trusted nodes are inconsistent), the data correction result of the trusted node is excluded. If the number of positive correction results in the remaining data correction results is greater than the number threshold, it is determined that the consensus processing is successful.

[0049] In addition, if the plurality of trusted nodes are the same and the data correction results of the plurality of trusted nodes are inconsistent, the weight of the data correction result of each trusted node can be determined according to the level of the alliance chain node corresponding to each trusted node in the plurality of trusted nodes, the data correction result corresponding to the weight whose arrangement position is before the preset position is taken as the data correction result of the trusted node, and if the number of positive correction results in the data correction result of the trusted node and the remaining data correction results is greater than the number threshold, it is determined that the consensus processing is successful.

[0050] It should be noted that, after step S204 is executed and before step S206 is executed, the following operation can be performed: filtering the purchase institutions that purchase the credit data from the plurality of preset institutions; detecting whether the credit index of the purchase institution is greater than the index threshold, and if so, taking the purchase institution whose credit index is greater than the index threshold as the trusted institution, and establishing a trusted relationship between the trusted institution and the institution.

[0051] In step S208, the disputed data is corrected according to the proof material to obtain each target credit data.

[0052] The data correction message is distributed to the trusted nodes of the trusted institutions that establish a trusted relationship with the institution to which the alliance chain node belongs, and the consensus processing of the alliance chain node and the trusted node is performed based on the data correction message. In this step, if the consensus processing is successful, the disputed data is corrected according to the proof material to obtain each target credit data, and if the consensus processing fails, a transaction block can be generated according to the transaction and the consensus failure result, and the transaction block is stored in the block chain.

[0053] In an optional implementation provided by the embodiment, in the process of correcting the disputed data according to the proof material to obtain each target credit data, the following operation is performed: extracting key data from the proof material according to the disputed data; correcting the disputed data in the credit data based on the key data to obtain the target credit data.

[0054] The key data can be data related to the disputed data in the proof material, that is, the correct data corresponding to the disputed data extracted from the proof material.

[0055] In actual implementation, because the data of the alliance chain is tamper-proof, in order to maintain the tamper-proof characteristic of the data of the alliance chain and to trace the data correction process, a transaction block can be generated according to each target credit data and a corresponding transaction, that is, a new transaction block is generated and stored in the alliance chain; in an optional implementation of the embodiment, after the data correction process of the disputed data according to the proof materials is performed to obtain each target credit data, the following operation is further performed: each transaction block is generated according to the target credit data and the corresponding transaction, and the transaction blocks are stored in the alliance chain.

[0056] The corresponding transaction refers to the transaction of the credit data corresponding to each target credit data.

[0057] Specifically, each transaction block can be generated according to each target credit data and a corresponding transaction, that is, a new transaction block is generated for each target credit data, or a transaction block can be generated according to each target credit data and a corresponding transaction, that is, a same transaction block is generated for all target credit data and corresponding transactions.

[0058] It should be noted that, in order to improve the efficiency of data correction of the disputed data in the credit data, the smart contract deployed by the alliance chain can be called and executed, and the smart contract is called after detecting that the number of proof materials of the disputed data is greater than a number threshold.

[0059] After the transaction blocks are generated or the target credit data are obtained, the smart contract can be called and executed to generate a data correction success result and return to a target object (a user or an institution); the target object can access the data correction process and / or the data correction result of the disputed data in the credit data through the credit correction platform, or access the data correction process and / or the data correction result of the disputed data in the credit data through interface calling.

[0060] The alliance chain in the embodiment can further include a management and control node. In the entire process of credit data processing of the alliance chain node, that is, in the processes of transaction verification, generation of a data correction message, message sharing and consensus processing, and data correction processing, the management and control node can interrupt or not interrupt, in the case of not interrupting, the management and control node can interact with the alliance chain node or a trusted node of the alliance chain node offline, and in the case of interrupting, the management and control node and the alliance chain node can jointly process the credit data, that is, the opinion of the management and control node can be referred to in the credit data processing process.

[0061] It should be further pointed out that each optional implementation and each feasible execution manner in steps S202 to S208 provided by the embodiment can be independently executed as needed, or can be combined with each other and referred to each other, and each specific execution step in each optional implementation or each feasible execution manner can also be independently executed, which is not specifically limited by the embodiment.

[0062] In summary, the one or more credit data processing methods provided by the embodiment first acquire a transaction of data correction on disputed data in each credit data stored by the alliance chain, perform transaction verification on the transaction, and generate a data correction message for data correction according to the disputed data and the proof materials of the disputed data after the transaction verification is passed. Secondly, the data correction message is distributed to each trusted node in the trusted node list of the alliance chain node, and consensus processing of the alliance chain node and the trusted node is performed based on the data correction message. If the consensus processing is successful, the disputed data is corrected according to the proof materials to obtain each target credit data. If the consensus processing fails, a transaction block is generated according to the transaction of each credit data and the consensus failure result, and the transaction block is stored in the block chain. In this way, the entire process of data correction of disputed data is completed through the alliance chain, the time of data correction of disputed data is shortened, and the efficiency and convenience of data correction are improved. At the same time, through the consensus processing of the alliance chain node and each trusted node in the trusted node list of the alliance chain node, the effectiveness and accuracy of data correction are improved.

[0063] The credit data processing method provided by the embodiment is further described below by taking the application of the credit data processing method provided by the embodiment in a credit data scene as an example. Referring to FIG. 3, the credit data processing method applied in the credit data scene specifically includes the following steps.

[0064] The embodiment can be applied to an alliance chain node.

[0065] In step S302, a transaction of data correction on disputed data in each credit data stored by the alliance chain is acquired, and transaction verification is performed on the transaction.

[0066] In step S304, a data correction message is generated according to the disputed data and the proof materials of the disputed data after the transaction verification is passed.

[0067] In step S306, the data correction message is distributed to each trusted node in the trusted node list of the alliance chain node, and consensus processing of the alliance chain node and each trusted node is performed based on the data correction message for data correction.

[0068] Optionally, the trusted institutions to which the trusted nodes in the trusted node list belong establish a trusted relationship with the institution to which the alliance chain node belongs, and the trusted relationship is established after the trusted institutions obtain the credit data and the credit verification of the trusted institutions is passed.

[0069] In step S308, if the consensus processing is successful, key data is extracted from the proof material according to the disputed data.

[0070] In step S310, the disputed data in the credit data is modified based on the key data to obtain target credit data.

[0071] In step S312, each transaction block is generated according to the target credit data and the corresponding transaction, and each transaction block is stored in the alliance chain.

[0072] It should be noted that any one or combination of steps S302 to S312 can be replaced by the corresponding technical means provided in steps S202 to S208 according to the needs of implementation and deployment, which will not be described here.

[0073] The credit data processing device provided in the present disclosure is implemented, for example, as follows: in the above-mentioned embodiments, a credit data processing method is provided, and a credit data processing device corresponding thereto is also provided, which will be described below with reference to the accompanying drawings.

[0074] Referring to FIG. 4, a schematic diagram of an embodiment of a credit data processing device provided in the present embodiment is shown.

[0075] Since the device embodiment corresponds to the method embodiment, the description is relatively simple, and the related parts can be referred to the corresponding description of the above-mentioned method embodiment. The device embodiments described below are only schematic.

[0076] The credit data processing device provided in the present embodiment runs in an alliance chain node, and includes: a transaction acquisition module 402 configured to acquire a transaction for modifying disputed data in each credit data stored in an alliance chain, and perform transaction verification on the transaction; a message generation module 404 configured to generate a data modification message according to the disputed data and proof material of the disputed data after the transaction verification is passed; a message distribution module 406 configured to distribute the data modification message to a trusted node of a trusted institution that establishes a trusted relationship with an institution to which the alliance chain node belongs, so as to perform consensus processing of the alliance chain node and the trusted node based on the data modification message; the trusted relationship is established after the trusted institutions obtain the credit data and the credit verification of the trusted institutions is passed; and a data modification module 408 configured to perform data modification processing on the disputed data according to the proof material to obtain target credit data if the consensus processing is successful.

[0077] The credit data processing device provided by the disclosure implements, for example, the credit data processing method described above. Based on the same technical concept, one or more embodiments of the disclosure also provide a credit data processing device for executing the credit data processing method provided above. FIG. 5 is a structural schematic diagram of a credit data processing device provided by one or more embodiments of the disclosure.

[0078] The credit data processing device provided by the present embodiment includes, as shown in FIG. 5, the credit data processing device can have great differences due to different configurations or performances, and can include one or more processors 501 and memories 502. The memories 502 can store one or more storage applications or data. The memories 502 can be temporary storage or persistent storage. The applications stored in the memories 502 can include one or more modules (not shown in the figure), and each module can include a series of computer executable instructions in the credit data processing device. Furthermore, the processor 501 can be configured to communicate with the memory 502 and execute a series of computer executable instructions in the memory 502 on the credit data processing device. The credit data processing device can also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input / output interfaces 505, one or more keyboards 506, and the like.

[0079] In one specific embodiment, the credit data processing device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and the one or more programs can include one or more modules, and each module can include a series of computer executable instructions in the credit data processing device, and the one or more processors configured to execute the one or more programs include computer executable instructions for: obtaining a transaction for data correction of disputed data in each credit data stored in a consortium chain, and performing transaction verification on the transaction; after the transaction verification is passed, generating a data correction message according to the disputed data and the proof materials of the disputed data; distributing the data correction message to a trusted node of a trusted institution that has a trusted relationship with an institution to which a consortium chain node belongs, so as to perform consensus processing of the consortium chain node and the trusted node based on the data correction message; the trusted relationship is established after the trusted institution obtains the each credit data and credit verification of the trusted institution is passed; if the consensus processing is successful, data correction processing is performed on the disputed data according to the proof materials to obtain each target credit data.

[0080] The computer readable storage medium provided by the present disclosure implements, for example, a credit data processing method as described above. Based on the same technical concept, one or more embodiments of the present disclosure also provide a computer readable storage medium.

[0081] The computer readable storage medium provided by the present embodiment is used to store computer executable instructions, and the computer executable instructions, when executed, implement the following steps: obtaining a transaction for data correction of disputed data in each credit data stored by a consortium chain, and performing transaction verification on the transaction; after the transaction verification is passed, generating a data correction message according to the disputed data and proof materials of the disputed data; distributing the data correction message to a trusted node of a trusted institution that has a trusted relationship with an institution to which a consortium chain node belongs, so as to perform consensus processing of the consortium chain node and the trusted node based on the data correction message; the trusted relationship is established after the trusted institution obtains the each credit data and credit verification of the trusted institution is passed; and if the consensus processing is successful, data correction processing is performed on the disputed data according to the proof materials to obtain each target credit data.

[0082] It should be noted that the embodiments of the computer readable storage medium in the present disclosure and the embodiments of the credit data processing method in the present disclosure are based on the same inventive concept, so the specific implementation of the embodiments can refer to the foregoing implementation of the corresponding method, and the repeated parts will not be described again.

[0083] Each of the embodiments in the present disclosure is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the difference from other embodiments, such as the device embodiment, the equipment embodiment and the computer readable storage medium embodiment, which are similar to the method embodiment, so the description is relatively simple. The related content in the device embodiment, the equipment embodiment and the computer readable storage medium embodiment can be referred to the part of the description of the method embodiment.

[0084] The above describes specific embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than the order in which they are recited and still achieve desirable results. In addition, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0085] In the 1930s, it was clear to distinguish whether an improvement in a technology was in hardware (e.g., improvement in circuit structure of diodes, transistors, switches, etc.) or in software (e.g., improvement in method flow). However, as technology has evolved, many improvements in method flow today can be considered as direct improvements in 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 an improvement in a method flow cannot be implemented by a hardware entity module. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by user programming of the device. A digital system is "integrated" on a PLD by the designer programming it, rather than by asking a chip manufacturer to design and fabricate a custom integrated circuit chip. Moreover, instead of manually fabricating an integrated circuit chip, this programming is now mostly implemented by "logic compiler" software, which is similar to a software compiler used in program development, and the original code before compilation is also written in a specific programming language, which is called a hardware description language (HDL), and there are many HDLs, 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., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that only a slight logical programming of the method flow in the above-mentioned hardware description languages and programming into an integrated circuit can easily obtain a hardware circuit that implements the logical method flow.

[0086] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to being implemented in pure computer readable program code, the controller can also be implemented to perform the same functions in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers, etc. by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can even be considered as both a software module implementing a method and a structure within a hardware component.

[0087] The systems, apparatuses, modules or units illustrated by the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can 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.

[0088] For the sake of brevity, the above apparatuses are described in functional form in various units. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0089] Those skilled in the art will appreciate that one or more embodiments of the disclosure can be provided as a method, a system or a computer program product. Therefore, one or more embodiments of the disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Moreover, the disclosure can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage and the like) containing computer usable program code.

[0090] The computer program instructions can also be loaded onto a computer or other programmable test processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0091] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable test processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks.

[0092] These computer program instructions can also be loaded onto a computer or other programmable test processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0093] In one typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0094] The memory can include non-persistent memory and / or volatile memory, such as random access memory (RAM) and / or cache memory, non-volatile memory, such as read-only memory (ROM), EPROM, and / or flash memory, etc. The memory is an example of computer-readable media.

[0095] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory media such as modulated data signals and carriers.

[0096] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to encompass non-exclusive inclusion, such that processes, methods, articles or devices that include a series of features are not limited to those features, but also include other features not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the feature defined by the statement "comprising a" does not exclude the presence of another identical feature in the process, method, article or device comprising the feature.

[0097] One or more embodiments of the present disclosure can be described in the general context of computer-executable instructions being executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform particular tasks or implement particular abstract data types. One or more embodiments of the present disclosure can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices that are connected through a communication network. In a distributed computing environment, program modules can be located in both local and remote computer storage media, including storage devices.

[0098] Various embodiments in the present disclosure are described in a progressive manner, and the same or similar parts between various embodiments can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0099] The above merely provides an example of the present disclosure, and is not intended to limit the present disclosure. The present disclosure can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present disclosure shall be included in the scope of claims of the present disclosure.

Claims

1. A credit data processing method applied to a node of a consortium chain, the method comprising: obtaining a transaction for data correction of disputed data in each credit data stored in the consortium chain, and performing transaction verification on the transaction; generating a data correction message based on the disputed data and proof materials of the disputed data after the transaction verification is passed; distributing the data correction message to a trusted node of a trusted institution that has a trusted relationship with an institution to which the node of the consortium chain belongs, so as to perform consensus processing between the node of the consortium chain and the trusted node based on the data correction message; the trusted relationship being established after the trusted institution obtains the each credit data and credit verification of the trusted institution is passed; and performing data correction processing on the disputed data based on the proof materials to obtain each target credit data if the consensus processing is successful. 2.The credit data processing method of claim 1, wherein the trusted relationship is established by: screening a purchase institution that purchases the each credit data from a plurality of preset institutions; and detecting whether a credit index of the purchase institution is greater than an index threshold value, and if so, establishing a trusted relationship between the trusted institution and the institution, wherein the trusted institution is a purchase institution whose credit index is greater than the index threshold value. 3.The credit data processing method of claim 1, wherein the transaction verification comprises: detecting whether a number of the proof materials of the disputed data is greater than a preset number threshold value; and if so, obtaining a reference proof material of the proof materials from a management institution of the proof materials, and determining that the transaction verification is passed if the reference proof material matches the proof materials. 4.The credit data processing method of claim 3, wherein if the detection result of whether the number of the proof materials of the disputed data is greater than the preset number threshold value is negative, the following operations are performed: detecting whether a material type of the proof materials of the disputed data contains a preset material type; and if not, obtaining an auxiliary proof material of the proof materials from the management institution, and determining that the transaction verification is passed if a total number of the auxiliary proof material and the proof materials is greater than the preset number threshold value. 5.The credit data processing method of claim 1, wherein the data correction processing on the disputed data based on the proof materials to obtain each target credit data comprises: extracting key data from the proof materials based on the disputed data; and performing data correction on the disputed data in the each credit data based on the key data to obtain the each target credit data. 6.The credit data processing method of claim 1, wherein the credit data processing method is executed by calling a smart contract deployed in the consortium chain; and the smart contract is called after detecting that the number of the proof materials of the disputed data is greater than a number threshold value. 7.The credit data processing method of claim 1, wherein the transaction is generated after a target object corresponding to the each credit data submits a data correction request for the disputed data. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ The transaction obtaining disputed data in each credit data stored in the alliance chain for data correction, before the transaction verification step is performed on the transaction, further comprises: Obtain the service certificate of the target object transmitted through the data access interface, and query the credit evaluation index of the target object based on the object identifier of the target object in the alliance chain; Based on the service certificate and the credit evaluation index, the identity of the target object is verified, and after the identity verification, the access permission of the corresponding credit data is opened to the target object.

8. The credit data processing method according to claim 7, after the step of verifying the identity of the target object based on the service certificate and the credit evaluation index, and after the identity verification, the access permission of the corresponding credit data is opened to the target object, further comprising: Obtain the data access request of the credit data transmitted through the interface call based on the access permission; In response to the data access request, the credit data is queried in the alliance chain and is returned to mark the disputed data in the credit data.

9. The credit data processing method according to claim 1, after the step of obtaining each target credit data by correcting the disputed data according to the proof material if the consensus processing is successful, further comprising: According to the each target credit data and the corresponding transaction, each transaction block is generated, and the each transaction block is stored to the alliance chain.

10. The credit data processing method according to claim 1, wherein the consensus processing of the alliance chain node and the trusted node based on the data correction message comprises: Based on the data correction message of each alliance chain node in a plurality of alliance chain nodes, the consensus processing of each alliance chain node and the corresponding trusted node is performed; Determine the data correction result of each alliance chain node and the corresponding trusted node after consensus processing; If the data correction results of the plurality of trusted nodes are inconsistent and the same, the data correction results of the plurality of trusted nodes are excluded, and in the case that the number of positive correction results in the remaining data correction results is greater than the number threshold, it is determined that the consensus processing is successful.

11. A credit data processing apparatus running in an alliance chain node, the apparatus comprising: A transaction obtaining module configured to obtain a transaction for correcting disputed data in each credit data stored in an alliance chain, and to perform transaction verification on the transaction; A message generation module configured to generate a data correction message based on the disputed data and the proof material of the disputed data after the transaction verification is passed; A message distribution module configured to distribute the data correction message to a trusted node of a trusted institution having a trusted relationship with the institution to which the alliance chain node belongs, so as to perform consensus processing of the alliance chain node and the trusted node based on the data correction message; The trusted relationship is established after the trusted institution obtains the each credit data and the credit verification of the trusted institution is passed. a data correction module configured to, if the consensus processing is successful, perform data correction processing on the disputed data according to the proof material to obtain each target credit data.

12. A credit data processing device, comprising: a processor; and a memory configured to store computer executable instructions which, when executed, cause the processor to: obtain a transaction for correcting disputed data in each credit data stored in a consortium chain, and perform transaction verification on the transaction; after the transaction verification is passed, generate a data correction message according to the disputed data and proof material of the disputed data; distribute the data correction message to a trusted node of a trusted institution that has a trusted relationship with an institution to which a consortium chain node belongs, so as to perform consensus processing between the consortium chain node and the trusted node based on the data correction message; the trusted relationship is established after the trusted institution obtains the credit data and credit verification of the trusted institution is passed; if the consensus processing is successful, perform data correction processing on the disputed data according to the proof material to obtain each target credit data.

13. A computer readable storage medium for storing computer executable instructions which, when executed, implement the steps of the method of claim 1.

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