Credit investigation data processing method and device

Abstract

The embodiment of the specification provides a credit investigation data processing method and device, wherein the credit investigation data processing method comprises the following steps: in the credit investigation data processing process of the credit investigation node of the credit investigation trusted data space, first, the signing process and contract authority synchronization of the credit investigation digital contract of the credit investigation client are performed; in the case that the encrypted credit investigation data uploaded by the credit investigation client is obtained, the encrypted credit investigation data is transmitted into the trusted execution environment to obtain the credit investigation data by performing the structural conversion on the encrypted credit investigation data, the credit investigation data is transmitted to the virtual isolation space in the form of the secret state to perform the user interaction confirmation, and the target credit investigation data obtained by the confirmation is encrypted and stored as evidence, so that the safe processing and storage of the credit investigation data are realized within the credit investigation signing range.

Description

Technical Field

[0001] This document relates to the field of data processing technology, and in particular to a credit data processing method and apparatus. Background Technology

[0002] With the continuous development of internet technology and big data applications, users are paying increasing attention to credit and credit reporting. The establishment and improvement of credit and credit reporting systems are also constantly advancing. Service platforms that provide credit and credit reporting services to users have emerged. Many service platforms provide users with various credit and credit reporting services such as credit assessment and credit reporting inquiries. However, as the number of service providers increases, the competition among them is becoming increasingly fierce. Exploring new service methods around credit and credit reporting has become a key focus for service providers. Summary of the Invention

[0003] This specification provides one or more embodiments of a credit reporting data processing method applied to a credit reporting node in a trusted credit reporting data space. The method includes: generating a credit reporting digital contract based on a user credit reporting request submitted by a credit reporting client, and performing contract signing processing and contract permission synchronization for the credit reporting digital contract; acquiring encrypted credit reporting data uploaded by the credit reporting client and transmitting the encrypted credit reporting data to a trusted execution environment, whereby the unstructured data contained in the encrypted credit reporting data undergoes a structured transformation to obtain credit reporting data; transmitting the credit reporting data in encrypted form to a virtual isolated space allocated to the credit reporting client for user interaction confirmation of the credit reporting data, and encrypting and storing the confirmed target credit reporting data.

[0004] This specification provides one or more embodiments of a credit reporting data processing device, operating on a credit reporting node in a trusted credit reporting data space. The device includes: a contract processing module configured to generate a credit reporting digital contract based on a user credit reporting request submitted by a credit reporting client, and to perform contract signing processing and contract permission synchronization for the credit reporting digital contract; a structured conversion module configured to acquire encrypted credit reporting data uploaded by the credit reporting client, and to transmit the encrypted credit reporting data to a trusted execution environment, whereby the unstructured data contained in the encrypted credit reporting data is structured to obtain credit reporting data; and a data confirmation module configured to transmit the credit reporting data in encrypted form to a virtual isolated space allocated to the credit reporting client for user interaction confirmation of the credit reporting data, and to encrypt and store the confirmed target credit reporting data.

[0005] This specification provides one or more embodiments of a credit data processing device, including: a processor; and a memory configured to store computer-executable instructions, which, when executed, cause the processor to: generate a credit digital contract based on a user credit request submitted by a credit client, and perform signing processing and contract permission synchronization of the credit digital contract; acquire encrypted credit data uploaded by the credit client, and transmit the encrypted credit data to a trusted execution environment to perform a structured transformation on the unstructured data contained in the encrypted credit data to obtain credit data; transmit the credit data in encrypted form to a virtual isolated space allocated to the credit client for user interaction confirmation of the credit data, and encrypt and store the confirmed target credit data.

[0006] This specification provides one or more embodiments of a computer-readable storage medium for storing computer-executable instructions. When executed, these instructions implement the following process: generating a credit reporting digital contract based on a user credit reporting request submitted by a credit reporting client, and performing contract signing processing and contract permission synchronization for the credit reporting digital contract; acquiring encrypted credit reporting data uploaded by the credit reporting client and transmitting the encrypted credit reporting data to a trusted execution environment, whereby the unstructured data contained in the encrypted credit reporting data undergoes a structuring transformation to obtain credit reporting data; transmitting the credit reporting data in encrypted form to a virtual isolated space allocated to the credit reporting client for user interaction confirmation of the credit reporting data, and encrypting and storing the confirmed target credit reporting data. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in one or more embodiments of this specification or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0008] Figure 1 A schematic diagram illustrating the implementation environment of a credit data processing method provided in one or more embodiments of this specification;

[0009] Figure 2 A flowchart illustrating a credit data processing method provided in one or more embodiments of this specification;

[0010] Figure 3 A flowchart illustrating a credit data processing method applied in a credit reporting scenario, provided for one or more embodiments of this specification;

[0011] Figure 4A schematic diagram of an embodiment of a credit data processing device provided in one or more embodiments of this specification;

[0012] Figure 5 This is a schematic diagram of the structure of a credit data processing device provided for one or more embodiments of this specification. Detailed Implementation

[0013] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this specification, and not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this document.

[0014] The credit data processing method provided in one or more embodiments of this specification is applicable to the implementation environment of a trusted credit data space. (Refer to...) Figure 1 The implementation environment includes at least:

[0015] Credit information trusted data space 100, credit information node 101, credit information client 102;

[0016] Among them, the trusted credit data space 100 is used to provide the infrastructure for credit processing by integrating technical means and rule mechanisms and connecting the processing agencies of all parties involved in credit processing; the credit node 101 refers to the system node of the credit-related processing of the trusted credit data space by the credit platform or credit agency.

[0017] Credit node 101 is equipped with a trusted execution environment 101-1 and a virtual isolation space 101-2. The trusted execution environment 101-1 refers to an independent security area built with hardware, while the virtual isolation space 101-2 refers to an isolation environment at the software level used to process sensitive data. It can open data access permissions under the control of data permissions to ensure that data is not leaked or abused, such as a data sandbox.

[0018] Credit node 101 can also be configured with data conversion model 101-3 and rights generation model 101-4; wherein, data conversion model 101-3 is used to convert unstructured data into structured data, and rights generation model 101-4 is used to match corresponding credit rights recommended to users based on credit data;

[0019] In addition, the credit information trusted data space 100 can also be equipped with a blockchain 103, which is used to store relevant data or processing logs involved in the credit information processing process.

[0020] In this implementation environment, during the credit data processing based on the trusted credit data space 100, firstly, a credit digital contract is generated based on the user credit request submitted by the credit client 102 to the credit node 101, and the signing of the credit digital contract and the synchronization of contract permissions are performed. On this basis, the credit node 101 obtains the encrypted credit data uploaded by the credit client 102 and transmits the encrypted credit data to the trusted execution environment 101-1. In the trusted execution environment 101-1, the unstructured data contained in the encrypted credit data is structured to obtain the credit data. Further, the credit data is transmitted in encrypted form to the virtual isolation space 101-2 allocated to the credit client 102 for user interaction confirmation of the credit data, and the confirmed target credit data is stored on the blockchain 103 for notarization. In this way, the secure processing and notarization of credit data are achieved within the scope of the credit signing agreement.

[0021] It should be noted that, considering that the encrypted credit data, unstructured data, structured credit data, and other related data involved in this specification may, to some extent, constitute user privacy, authorization from the user must be obtained before acquiring or processing such data to ensure that the data collection operation complies with relevant data management regulations. For example, authorization can be granted by generating a credit digital contract and signing the credit digital contract, or other methods can be used. This embodiment does not limit the specific methods used here.

[0022] This specification provides one or more embodiments of a credit data processing method as follows:

[0023] Reference Figure 2 The credit data processing method provided in this embodiment can be applied to credit nodes in the trusted credit data space. The method specifically includes steps S202 to S206.

[0024] Step S202: Generate a credit reporting digital contract based on the user credit reporting request submitted by the credit reporting client, and perform the signing process of the credit reporting digital contract and synchronize the contract permissions.

[0025] The trusted data space for credit reporting described in this embodiment refers to an infrastructure system that integrates technical means and rule mechanisms and connects with various processing agencies involved in credit reporting. Specifically, it uses credit reporting data as the core resource to achieve cross-domain resource interaction, and relies on technologies such as privacy computing and blockchain to build a trusted processing environment that ensures that credit reporting data is "usable but not visible". It also supports applications in related scenarios such as access control and data storage.

[0026] A credit reporting node refers to a system node in which a credit reporting platform or credit reporting agency participates in relevant credit reporting processing within the trusted credit reporting data space. Specifically, in this embodiment, a credit reporting node can be a server or service terminal for credit reporting services or applications provided by a credit reporting platform or credit reporting agency to users. The credit reporting service terminal accesses the trusted credit reporting data space and performs specific processing procedures for credit reporting services or applications within the environment provided by the trusted credit reporting data space.

[0027] In practice, the credit reporting client submits a user credit reporting request to the credit reporting node for uploading credit reporting data. The credit reporting node generates a credit reporting digital contract based on the submitted user credit reporting request, that is, generates a credit reporting digital contract for uploading credit reporting data. After generating the credit reporting digital contract, it interacts with the credit reporting client to sign the contract and obtain an authorized digital contract. After obtaining the authorized digital contract, the contract permissions are synchronized, so that the credit reporting node can perform corresponding credit reporting processing based on the synchronized contract permissions.

[0028] Among them, the credit reporting digital contract refers to the credit reporting digital contract in which the user authorizes the credit reporting node. Specifically, it is a structured digital contract in which the user authorizes the credit reporting node for the credit reporting data upload process. The credit reporting digital contract can record the scope of authorization, the authorization period and / or the authorized prohibited behaviors.

[0029] In the specific execution process, before generating the credit digital contract based on the user credit request submitted by the credit reporting client, user identity authentication can also be performed to improve the security of credit processing. During the identity authentication process, the identity information entered by the user through the credit reporting client can be used for authentication. Specifically, the identity information carried by the user's credit request can be used to call the identity authentication node of the trusted credit data space to authenticate the identity information. Alternatively, the identity authentication engine or identity authentication interface of a third-party institution or platform connected to the trusted credit data space can be called to authenticate the identity information.

[0030] After identity authentication is successful, a credit information digital contract can be generated, authorizing the user to submit credit information to the credit reporting node during the credit data upload process. During the signing process, the credit information digital contract is sent to the credit reporting client, where the user confirms authorization of the credit information digital contract. Based on the user's authorization confirmation, the credit information digital contract is electronically signed to obtain the authorized digital contract. Furthermore, the authorized digital contract can be stored on the blockchain for notarization. After the signing process is completed, the authorization processing result can be pushed to the credit reporting client. The authorization processing result includes the notarization number of the authorized digital contract. The user can query the authorized digital contract based on the notarization number and can also modify or revoke the authorization permissions in the authorized digital contract.

[0031] After the signing process is completed, in order for the credit reporting node to process user credit data within the authorized scope based on the authorized digital contract, the contract permissions of the credit reporting node are synchronized. Specifically, the authorization permissions recorded in the authorized digital contract can be synchronized to the credit reporting node's permission pool, so that the credit reporting node can process the user's credit data accordingly based on the synchronized contract permissions. Alternatively, the contract permissions of the authorized digital contract can be not synchronized, and the credit reporting node can query permissions when processing the user's credit data. In this case, the signing process of the credit reporting digital contract and the synchronization of contract permissions can be replaced by obtaining the authorized digital contract through the signing process of the credit reporting digital contract.

[0032] Step S204: Obtain the encrypted credit data uploaded by the credit reporting client, and transmit the encrypted credit data to a trusted execution environment, so as to perform a structured transformation on the unstructured data contained in the encrypted credit data in the trusted execution environment to obtain credit data.

[0033] In practice, after authorizing credit reporting through the signing of a credit reporting digital contract, the credit reporting client uploads credit reporting data to the credit reporting node. During the upload process, the credit reporting client encrypts the credit reporting data to obtain encrypted credit reporting data, and then uploads the encrypted credit reporting data to the credit reporting node. Correspondingly, the credit reporting node receives the encrypted credit reporting data uploaded by the credit reporting client and transmits the encrypted credit reporting data to a trusted execution environment for corresponding processing.

[0034] Here, encrypted credit data refers to encrypted credit data obtained after encrypting the credit data from the credit reporting client. Specifically, the credit data can be unstructured credit data, structured credit data, or a combination of unstructured and structured data. Correspondingly, encrypted credit data can be encrypted credit data obtained by encrypting unstructured or structured data, or it can be encrypted credit data obtained by encrypting unstructured and structured data.

[0035] If the encrypted credit data contains unstructured data, or if the encrypted credit data is unstructured, the encrypted credit data can be passed to a trusted execution environment to perform a structured transformation on the unstructured data contained in the encrypted credit data to obtain the credit data. Alternatively, the unstructured data obtained by decrypting the encrypted credit data can be performed a structured transformation to obtain the credit data.

[0036] Furthermore, if the encrypted credit data does not contain unstructured data (i.e., it contains structured data) or is structured data, the encrypted credit data can be passed to a trusted execution environment for appropriate processing.

[0037] Specifically, to improve the efficiency and accuracy of structuring unstructured data, a data transformation model can be used to convert unstructured data into structured data. In this case, the structuring transformation performed in a trusted execution environment includes: inputting unstructured data into a data transformation model to perform structuring transformation of credit data. Here, the data transformation model can be a data transformation model deployed in a trusted execution environment, or a data transformation model passed into a trusted execution environment.

[0038] In the specific execution process, during the structured transformation of credit data by the data transformation model, unstructured data can be converted into credit text data, and key semantic fragments can be extracted from the credit text data and combined to obtain the credit data, i.e., structured credit data. Here, the data transformation model can be a multimodal transformation model, such as a multimodal transformation model using an encoder-decoder architecture, which can convert unstructured data of multiple modalities into structured credit data. Alternatively, the data transformation model can also be a large language model, such as a large language model using a Transformer architecture. In this case, the process of inputting unstructured data into the data transformation model for structured transformation of credit data in a trusted execution environment can be replaced by inputting unstructured data and transformation task text into a large language model for structured transformation of credit data.

[0039] In one optional implementation method provided in this embodiment, the structured transformation of credit data is achieved in the following way:

[0040] Unstructured data of various data types are converted into credit reporting text data, and semantic encoding is performed on each credit reporting text data to obtain semantic encoding features;

[0041] Discrete semantic fragments are obtained by performing key semantic identification and semantic association on each semantic coding feature, and the discrete semantic fragments are merged and credit data is generated based on the merging results.

[0042] The structured transformation of credit data can employ a multimodal transformation model with a neural network architecture. Specifically, within the multimodal transformation model, during processing, unstructured data of various data types can be transformed through a modality classification transformation unit. For example, a vision-to-text transformation unit (composed of ViT (Vision Transformer) and a generative decoder) can convert unstructured image data into credit text data, and a speech-to-text transformation unit (composed of CNN (Recurrent Neural Network) and a Transformer encoder) can convert unstructured speech data into credit text data. Furthermore, a key semantic extraction unit (composed of BiLSTM and CRF neural network) can semantically encode each credit text data to obtain semantic encoded features. Finally, a cross-modal semantic association unit can perform key semantic recognition and semantic association on each semantic encoded feature to obtain discrete semantic fragments. The cross-modal semantic association unit consists of a multi-head attention module. The system consists of an attention unit, a semantic similarity calculation network, and a graph neural network (GNN). Furthermore, it merges discrete semantic segments through a semantic segment fusion unit and a structured generation unit, and generates credit data based on the fusion results. The semantic segment fusion unit can use a cross-attention fusion network, and the structured generation unit can consist of a fully connected layer and a structured output layer.

[0043] To further improve the accuracy of structuring unstructured data, a data conversion model can be set for each data mode of the unstructured data. In this case, after obtaining the encrypted credit data uploaded by the credit reporting client, the data conversion models corresponding to each data type of the unstructured data can be passed to a trusted execution environment. The unstructured data of each data type can then be input into the corresponding data conversion model for structuring within the trusted execution environment. In this scenario, obtaining credit data by structuring the unstructured data contained in the encrypted credit reporting data within the trusted execution environment includes: inputting the unstructured data of each data type into the corresponding data conversion model for structuring within the trusted execution environment; or, this process of obtaining credit data by structuring the unstructured data contained in the encrypted credit reporting data within the trusted execution environment can be replaced by: inputting the unstructured data of each data type into the corresponding data conversion model for structuring within the trusted execution environment. Here, the data conversion model corresponding to each data type can adopt an architecture similar to the multimodal data conversion model mentioned above. The difference is that the data conversion model corresponding to each data type only contains the conversion unit of the current data type (modality), while the multimodal data conversion model contains conversion units of data types (modality) of multiple modalities.

[0044] Specifically, in one optional implementation of this embodiment, after the operation of obtaining encrypted credit data uploaded by the credit reporting client is executed, the method further includes: according to the data type identification of unstructured data obtained by the credit reporting client, the data conversion model corresponding to the data type is passed into the trusted execution environment, so as to input the unstructured data of each data type into the corresponding data conversion model for structure conversion in the trusted execution environment.

[0045] The data types of unstructured data can be identified by the credit reporting client. Alternatively, after the encrypted credit reporting data is passed into a trusted execution environment, data types can be identified to obtain each data type, and the corresponding data conversion model can be passed into the trusted execution environment. In this case, the unstructured data of each data type is input into the corresponding data conversion model for structured conversion in the trusted execution environment. This process of obtaining credit reporting data by performing structured conversion on the unstructured data contained in the encrypted credit reporting data in the trusted execution environment includes: identifying data types in the trusted execution environment to obtain each data type, and passing the corresponding data conversion model into the trusted execution environment for structured conversion. Alternatively, this process of obtaining credit reporting data by performing structured conversion on the unstructured data contained in the encrypted credit reporting data in the trusted execution environment can be replaced by: identifying data types in the trusted execution environment to obtain each data type, and passing the corresponding data conversion model into the trusted execution environment for structured conversion.

[0046] In practical applications, the encrypted credit data uploaded by users through the credit reporting client can be encrypted credit data obtained by encrypting unstructured data, encrypted credit data obtained by encrypting structured data, or encrypted credit data obtained by encrypting both unstructured and structured data. In order to improve the flexibility of users uploading credit data, interfaces for uploading structured data and unstructured data can be opened to users respectively.

[0047] In one optional implementation of this embodiment, after the signing of the credit reporting digital contract and the synchronization of contract permissions, the following operations are performed:

[0048] Allocate virtual isolation spaces to credit reporting clients, generate data collection pages in the virtual isolation spaces, and send them to the credit reporting clients;

[0049] If the unstructured data control configured on the data collection page is triggered, it indicates that the user wants to upload unstructured data, execute the operation of obtaining encrypted credit data uploaded by the credit reporting client, and pass the encrypted credit data into the trusted execution environment.

[0050] In addition, if the structured data control configured on the data collection page is triggered, it indicates that the user wants to upload structured data. In order to improve the efficiency of users uploading structured data, structured data can be uploaded through the credit data template. Specifically, the credit data template can be sent to the credit client to upload structured data through the credit data template.

[0051] Specifically, in one optional implementation of this embodiment, after allocating a virtual isolation space to the credit reporting client, generating a data collection page in the virtual isolation space, and sending it to the credit reporting client, the following operations are performed:

[0052] If the structured data control configured on the data collection page is triggered, the encrypted identity data uploaded by the credit reporting client is obtained and passed into the trusted execution environment to determine the credit reporting data template that matches the encrypted identity data;

[0053] Send a credit data template to the credit reporting client to collect structured credit data in the client's encrypted space and encrypt it.

[0054] In practical applications, there may be situations where users want to upload both structured and unstructured data simultaneously. To address this, the aforementioned process for uploading and processing unstructured data can be combined with the process for uploading and processing structured data. For example, if encrypted credit data contains both unstructured and structured data, instead of passing the encrypted credit data to a trusted execution environment and then performing a structured transformation on the unstructured data within the trusted execution environment to obtain the credit data, the process could be: performing a structured transformation on the unstructured data within the encrypted credit data to obtain structured credit data, and then merging the obtained structured credit data with the structured data within the encrypted credit data to obtain the final credit data.

[0055] Step S206: The credit data is transmitted in encrypted form to the virtual isolation space allocated to the credit client for user interaction confirmation of the credit data, and the confirmed target credit data is encrypted and stored.

[0056] To enhance users' awareness of the uploaded credit data, the credit data can be interactively confirmed with users through a secure and isolated space. During the user interaction confirmation process, users can directly submit confirmation commands for the credit data obtained after structured transformation, and can also modify the credit data. The credit data confirmed by the user is called the target credit data. Based on obtaining the target credit data through user interaction confirmation, the target credit data can also be encrypted and stored.

[0057] Specifically, during the user interaction confirmation process, when the user only uploads structured data, the structured data can be transmitted in encrypted form to a virtual isolation space for user interaction confirmation of the structured data, and the target credit data obtained after the structured data confirmation can be encrypted and stored as evidence; whereby, the virtual isolation space refers to an isolation environment at the software level used to process sensitive data, which can open data usage permissions under the control of data permissions to ensure that data is not leaked or misused, such as a data sandbox.

[0058] When users only upload unstructured data, the structured data obtained by converting the unstructured data into structured data can be transmitted in encrypted form to a virtual isolated space for user interaction confirmation of the structured data, and the target credit data obtained after confirmation can be encrypted and stored as evidence.

[0059] When a user uploads both structured and unstructured data, the credit data obtained by merging the uploaded structured data with the structured data obtained by converting the unstructured data into structured data can be transmitted in encrypted form to a virtual isolated space for user interaction confirmation of the credit data. The target credit data obtained after confirmation is then encrypted and stored as evidence.

[0060] In specific implementation, to enhance users' enthusiasm for participating in credit reporting services or applications provided by credit reporting nodes, credit reporting rights can be issued to users to increase their participation. Specifically, in one optional implementation of this embodiment, after transmitting credit reporting data in encrypted form to a virtual isolated space allocated to the credit reporting client for user interaction confirmation, the following operations are performed: the target credit reporting data and the rights generation model are input into a trusted execution environment, so that the target credit reporting data is input into the rights generation model in the trusted execution environment to generate credit reporting rights and obtain the credit reporting rights recommended to the user; the trusted execution environment here can be the trusted execution environment that performs the structure transformation as described above, or it can be a newly allocated trusted execution environment (second trusted execution environment); the credit reporting rights can be credit-related rights provided to users during the access to credit reporting services or applications;

[0061] Specifically, in the process of generating credit rights through the rights generation model, in order to ensure that the credit rights issued to users are more closely matched with the user's uploaded credit data and the credit data itself, the generation of credit rights can be based on the data value of the credit data. In one optional implementation method provided in this embodiment, credit rights generation includes:

[0062] The data evaluation results are obtained by conducting a data dimension assessment on the target credit data, and the data value is obtained by conducting a data value assessment based on the data evaluation results and the target credit data.

[0063] Credit rights are obtained through matching credit rights based on rights matching parameters, data evaluation results, and / or the value of credit data. Rights matching parameters refer to relevant data or parameters that can be used for rights matching, such as the scope of authorization for digital contracts and the frequency of credit data updates.

[0064] Specifically, in the process of credit rights processing, the rights generation model can obtain data evaluation results by evaluating the data dimensions of the target credit data through the dimensional evaluation unit of the rights generation model. The dimensional evaluation unit can be composed of a lightweight Transformer encoding and a multi-dimensional scoring network. Then, the data acquisition cost is calculated through the value calculation unit to obtain the data acquisition cost. The value calculation unit can specifically include a cross-attention network, a feature splicing layer, a multilayer perceptron (MLP) regression network, and a value calibration layer. Finally, the credit rights are matched through the feature alignment unit and the rights matching unit to obtain credit rights. The feature alignment unit can be composed of a Transformer Cross-Attention layer and a feature normalization module, and the rights matching unit can be composed of a graph neural network (GNN) and a rule matching engine.

[0065] Furthermore, credit rights can be generated based on the authenticity of credit data and the difficulty (cost) of obtaining it, thereby making the credit rights issued to users more aligned with the authenticity and cost of obtaining the credit data. In another optional implementation of this embodiment, credit rights generation includes:

[0066] The authenticity of the target credit data is verified to obtain an authenticity score, and the data acquisition cost is rated based on the authenticity score and the target credit data to obtain the data acquisition cost.

[0067] Credit benefits are obtained by matching authenticity scores and / or data acquisition costs with rights matching rules.

[0068] Specifically, in the process of credit rights processing, the rights generation model can verify the authenticity of the target credit data through its authenticity verification unit to obtain an authenticity score. The authenticity verification unit can be composed of a binary classification neural network and a scoring mapping layer. Then, the data acquisition cost can be rated through a feature fusion unit and a cost rating unit to obtain the data acquisition cost. The feature fusion unit can be composed of a cross-attention network and a feature concatenation layer. The cost rating unit can be composed of a multi-class MLP network and a level mapping layer. Finally, the credit rights can be obtained through matching by a rule parsing unit and a rights matching unit. The rule parsing unit can be composed of a rule compiler and a conditional featureization module. The rights matching unit can be composed of a rule matching engine and an attention matching network.

[0069] In addition, the two optional implementation methods provided above can be combined in any way to obtain new implementation methods for generating credit rights. For example, generating credit rights includes: verifying the authenticity of the target credit data to obtain an authenticity score, and assessing the data value based on the authenticity score and the target credit data to obtain the credit data value, and matching credit rights based on rights matching parameters, authenticity score and / or credit data value to obtain credit rights; or, generating credit rights includes: assessing the data value of the target credit data to obtain the credit data value, and assessing the data acquisition cost based on the credit data value and the target credit data to obtain the data acquisition cost, and matching the credit data value and / or data acquisition cost with rights matching rules to obtain credit rights.

[0070] In this embodiment, during the credit data processing in the trusted credit data space, to ensure data security and compliance in the specific processing steps, the specific processing steps can be controlled. Specifically, each processing step can be controlled, such as the contract signing process, the structure conversion process, the user interaction confirmation process, and the encrypted evidence storage process. Furthermore, any one or more processing steps can be controlled as needed according to the actual scenario.

[0071] Specifically, in one optional implementation of this embodiment, after obtaining the encrypted credit data uploaded by the credit reporting client and transmitting the encrypted credit data to the trusted execution environment, the following management and control methods are used:

[0072] The system parses the user's authorized digital contract and generates an executable control policy based on the obtained authorized contract information, so as to control the processing within the virtual isolation space through the executable control policy;

[0073] Optionally, the control processing includes: verifying authorization permissions for structured data conversion, user interaction confirmation, and / or encrypted evidence storage; furthermore, the control processing also includes: verifying authorization permissions for the processing of data conversion models, credit rights generation models, and / or rights matching models. Here, the executable control policy refers to the permission policy generated based on the data permissions or processing permissions in the authorization contract information. Authorization permission verification includes checking whether the data processing permissions or data scope permissions of the current processing process are within the permission scope of the executable control policy generated based on the authorization contract information.

[0074] In specific implementation, during the credit data processing based on the trusted credit data space, to ensure that the credit data processing process is recorded and traceable, the data and processing logs during the credit data processing process can also be stored as evidence. For example, the data or processing logs involved in the contract signing process, the structured conversion process, and / or the user interaction confirmation process can be stored as evidence. In one optional implementation of this embodiment, the confirmed target credit data is encrypted and stored as evidence, including:

[0075] Encrypting target credit data to obtain evidence-based credit data, and / or encrypting authorized digital contracts to obtain evidence-based contracts;

[0076] The evidence storage of credit data, evidence storage contracts, contract processing logs, structured conversion logs and / or interaction confirmation logs is carried out on the blockchain.

[0077] In practical applications, after users upload structured and / or unstructured data through the credit reporting client, they can also upload the credit reporting data a second time after the previous upload. The credit reporting data uploaded a second time can also be structured and / or unstructured data. In one optional implementation of this embodiment, if the second encrypted credit reporting data (secondary encrypted credit reporting data) uploaded by the credit reporting client is obtained, the evidence-based credit reporting data is queried from the blockchain; the second encrypted credit reporting data and the evidence-based credit reporting data are passed to the second trusted execution environment allocated to the credit reporting client, so as to perform data merging processing based on the second encrypted credit reporting data and the evidence-based credit reporting data to obtain merged credit reporting data.

[0078] During the secondary upload of credit data, to prevent the data permissions of the secondary credit data upload from exceeding the authorized scope of the authorized digital contract, the evidence storage contract can be queried from the blockchain and permission verification can be performed based on the evidence storage contract. This ensures that the secondary credit data upload process is within the authorized scope of the authorized digital contract. Specifically, in one optional implementation of this embodiment, before the operation of querying the evidence storage credit data from the blockchain is executed, the following steps are also included:

[0079] Query the evidence storage contract from the blockchain and perform permission verification based on the evidence storage contract;

[0080] If the verification is successful, proceed with the operation of querying the stored credit information data from the blockchain;

[0081] If the verification fails, a second digital contract is generated and the signing process of the second digital contract is performed, and the contract permissions are synchronized. Alternatively, a second digital contract is generated and the signing process of the second digital contract is performed to obtain a second authorized digital contract and update the authorized digital contract.

[0082] Similar to the above-mentioned method of issuing credit rights to users during the credit data upload process, here, the corresponding credit rights can also be issued to users during the secondary credit data upload process, so as to further enhance users' enthusiasm for participating in credit services or credit applications. In an optional implementation method provided in this embodiment, if the second encrypted credit data uploaded by the credit client is obtained, and the stored credit data is queried from the blockchain;

[0083] The second encrypted credit data, the evidence-based credit data, and the rights matching model are passed into the second trusted execution environment allocated to the credit reporting client. The data is then merged based on the second encrypted credit data and the evidence-based credit data to obtain merged credit data. Finally, credit rights matching is performed based on the merged credit data to obtain second credit rights.

[0084] Specifically, the process of obtaining second credit rights by matching credit rights based on merged credit data can be implemented in the following ways:

[0085] The authenticity of the merged credit data is verified to obtain an authenticity score, and the data acquisition cost is rated based on the authenticity score and the merged credit data to obtain the data acquisition cost; the authenticity score, data acquisition cost and rights matching rules are matched to obtain the second credit rights;

[0086] or,

[0087] The data evaluation results are obtained by conducting a data dimension assessment on the merged credit data, and the credit data value is obtained by conducting a data value assessment based on the data evaluation results and the merged credit data; credit rights are matched based on rights matching parameters, data evaluation results and / or credit data value to obtain secondary credit rights.

[0088] In addition, the two optional implementation methods provided above can be combined in any way to obtain new implementation methods. For example, the second credit right can be obtained by matching credit rights based on the merged credit data, including: verifying the authenticity of the merged credit data to obtain an authenticity score, and assessing the data value based on the authenticity score and the merged credit data to obtain the credit data value, and matching credit rights based on the rights matching parameters, the authenticity score and / or the credit data value to obtain the second credit right; or, the second credit right can be obtained by matching credit rights based on the merged credit data, including: assessing the data value of the merged credit data to obtain the credit data value, and assessing the data acquisition cost based on the credit data value and the merged credit data to obtain the data acquisition cost, and matching the credit data value and / or the data acquisition cost with the rights matching rules to obtain the second credit right.

[0089] In summary, the credit data processing method provided in this embodiment, during the credit data processing process at the credit node in the trusted credit data space, firstly generates a credit digital contract based on the user credit request submitted by the credit client, and performs contract signing and contract permission synchronization to provide a basis for authorization in subsequent credit data processing. Upon obtaining encrypted credit data uploaded by the credit client, the encrypted credit data is transmitted to a trusted execution environment. In this environment, the unstructured data contained in the encrypted credit data undergoes a structured transformation to obtain the actual credit data, ensuring the security of credit data processing at the credit node. Furthermore, the credit data is transmitted in encrypted form to a virtual isolation space allocated to the credit client for user interaction confirmation. This virtual isolation space ensures data security during data interaction between the credit node and the credit client. Finally, the confirmed target credit data is encrypted and stored, thereby achieving end-to-end notarization and traceability of the credit data processing process based on the notarized credit data.

[0090] The following example uses a credit data processing method provided in this embodiment to illustrate its application in a credit reporting scenario. Figure 3 The credit data processing method provided in this embodiment will be further explained below. Figure 3 The credit data processing method applied to credit reporting scenarios includes the following steps.

[0091] Step S302: Generate a credit reporting digital contract based on the user credit reporting request submitted by the credit reporting client, and perform the signing process for the credit reporting digital contract.

[0092] Step S304: Allocate a virtual isolation space to the credit reporting client, generate a data collection page in the virtual isolation space, and send it to the credit reporting client.

[0093] Step S306: Obtain the encrypted unstructured data uploaded after the unstructured data control configured on the data collection page of the credit reporting client is triggered.

[0094] Step S308: The encrypted unstructured data and the data transformation model are passed into the trusted execution environment to input the encrypted unstructured data into the data transformation model for structured transformation to obtain structured data.

[0095] Step S310: Transmit the structured data in encrypted form to the virtual isolation space for user interaction confirmation of the structured data.

[0096] Step S312: The confirmed target credit data is transmitted to the trusted execution environment in encrypted form.

[0097] Step S314: Pass the rights generation model into the trusted execution environment to input the target credit data into the rights generation model to generate credit rights and obtain the credit rights recommended to the user.

[0098] Step S316: Encrypt the target credit data to obtain the stored credit data.

[0099] Step S318: The evidence-based credit data, structured conversion logs, and interaction confirmation logs are stored on the blockchain.

[0100] It should be noted that any one or more steps in steps S302 to S318 can be combined with any one or more steps in steps S202 to S206 to form a new implementation method according to the needs of implementation and deployment. In addition, any one or more technical features in steps S302 to S318 can be selected and combined with any one or more technical features provided in steps S202 to S206 to form a new implementation method according to the actual deployment needs. Alternatively, any one or more technical features in steps S302 to S318 can be replaced with any one or more technical features provided in steps S202 to S206 to form a new implementation method according to the actual deployment needs. These will not be elaborated on here.

[0101] This specification provides an embodiment of a credit data processing device as follows:

[0102] In the above embodiments, a credit data processing method is provided, and correspondingly, a credit data processing device is also provided, which will be described below with reference to the accompanying drawings.

[0103] Reference Figure 4 The diagram illustrates an embodiment of a credit data processing device provided in this embodiment.

[0104] Since the apparatus embodiments correspond to the method embodiments, the descriptions are relatively simple. For relevant parts, please refer to the corresponding descriptions of the method embodiments provided above. The apparatus embodiments described below are merely illustrative.

[0105] This embodiment provides a credit data processing device that can operate at a credit node in a trusted credit data space. The device includes:

[0106] The contract processing module 402 is configured to generate a credit digital contract based on the user credit request submitted by the credit client, and to perform the contract signing processing and contract permission synchronization of the credit digital contract.

[0107] The structure conversion module 404 is configured to acquire the encrypted credit data uploaded by the credit client and transmit the encrypted credit data to a trusted execution environment, so as to perform structure conversion on the unstructured data contained in the encrypted credit data in the trusted execution environment to obtain credit data.

[0108] The data confirmation module 406 is configured to transmit the credit data in encrypted form to a virtual isolation space allocated to the credit client for user interaction confirmation of the credit data, and to encrypt and store the confirmed target credit data.

[0109] For ease of description, the above devices are described by dividing them into various modules or units based on their functions. Of course, when implementing one or more of these specifications, the functions of each module or unit can be implemented in the same or different software and / or hardware, or a module that performs the same function can be implemented by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed.

[0110] The following is an example of a credit data processing device provided in this manual:

[0111] Corresponding to the credit data processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a credit data processing device, which is used to execute the credit data processing method provided above. Figure 5 This is a schematic diagram of the structure of a credit data processing device provided for one or more embodiments of this specification.

[0112] This embodiment provides a credit data processing device, including:

[0113] like Figure 5As shown, device 500 mainly consists of a communication interface 502, a user interface 504, a processor 506, and a data storage 508. These components are interconnected and communicate with each other via a system bus, network, or other connection mechanism 510. The communication interface 502 enables device 500 to communicate with other devices, access networks, and transmission networks via analog or digital modulation. For example, the communication interface 502 may include a chipset and antenna for wireless communication with a radio access network or access point. Furthermore, the communication interface 502 can be a wired interface such as Ethernet, Token Ring, or a USB port, or a wireless interface such as Wi-Fi, Bluetooth, Global Positioning System (GPS), or a wide-area wireless interface (e.g., WiMAX or LTE). Of course, the communication interface 502 can also support other forms of physical layer interfaces and standard or proprietary communication protocols. The communication interface 502 may also include multiple physical communication interfaces, such as Wi-Fi, Bluetooth, and wide-area wireless interfaces. The user interface 504 includes receiving user input and providing output to the user. Therefore, user interface 504 may include input components such as a keypad, keyboard, touch-sensitive or presence-sensitive panel, computer mouse, trackball, joystick, microphone, still camera, and video camera, and output components such as a display screen (which may be combined with a touch-sensitive panel), CRT, LCD, LED, display using DLP technology, printer, and other similar devices known or developed in the future. User interface 504 may also generate auditory output via speakers, speaker jacks, audio output ports, audio output devices, headphones, and other similar devices known or developed in the future. In some embodiments, user interface 504 may include software, circuitry, or other forms of logic capable of transmitting and receiving data to and from external user input / output devices. Additionally or alternatively, device 500 may support remote access from other devices via communication interface 502 or another physical interface (not shown). User interface 504 may be configured to receive user input, the position and movement of which may be indicated by indicators or cursors described herein. User interface 504 may also be configured as a display device for rendering or displaying text fragments.

[0114] Processor 506 may include one or more general-purpose processors and / or special-purpose processors. Data storage 508 may include one or more volatile and / or non-volatile storage components and may be integrated wholly or partially with processor 506. Data storage 508 may include removable and non-removable components.

[0115] Processor 506 is capable of executing program instructions 518 (e.g., compiled or uncompiled program logic and / or machine code) stored in data storage 508 to perform the various functions described herein. Data storage 508 may contain a non-transitory computer-readable medium on which program instructions are stored, which, when executed by device 500, enable device 500 to perform any methods, processes, or functions disclosed in this specification and / or the accompanying drawings. Execution of program instructions 518 by processor 506 may result in processor 506 using data 512. For example, program instructions 518 may include an operating system 522 (e.g., an operating system kernel, device drivers, and / or other modules) installed on device 500 and one or more application programs 520 (e.g., a browser, social application, or game application). Similarly, data 512 may include operating system data 516 and application data 514. Operating system data 516 is primarily accessible to operating system 522, while application data 514 is primarily accessible to one or more application programs 520. Application data 514 may reside in a file system visible or hidden from the user of device 500. Application 520 can communicate with operating system 522 through one or more application programming interfaces (APIs). These APIs facilitate application 520 in reading and / or writing application data 514, transmitting or receiving information via communication interface 502, and receiving or displaying information on user interface 504. In some terms, application 520 may be simply referred to as "app". Furthermore, application 520 can be downloaded to device 500 through one or more online app stores or app markets. However, applications can also be installed on device 500 in other ways, such as through a web browser or a physical interface on device 500 (e.g., a USB port).

[0116] In one specific embodiment, the credit data processing device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions for the credit data processing device, and is configured to be executed by one or more processors. The one or more programs include computer-executable instructions for performing the following:

[0117] A credit reporting digital contract is generated based on the user credit reporting request submitted by the credit reporting client, and the signing of the credit reporting digital contract and the synchronization of contract permissions are carried out; the credit reporting client corresponds to or cooperates with the credit reporting nodes in the trusted credit reporting data space;

[0118] The encrypted credit data uploaded by the credit reporting client is obtained, and the encrypted credit data is transmitted to a trusted execution environment, so as to perform a structured transformation on the unstructured data contained in the encrypted credit data in the trusted execution environment to obtain credit data.

[0119] The credit data is transmitted in encrypted form to a virtual isolated space allocated to the credit client for user interaction confirmation of the credit data, and the confirmed target credit data is encrypted and stored as evidence.

[0120] This specification provides an embodiment of a computer-readable storage medium as follows:

[0121] Corresponding to the credit data processing method described above, and based on the same technical concept, one or more embodiments of this specification also provide a computer-readable storage medium.

[0122] The computer-readable storage medium provided in this embodiment is used to store computer-executable instructions, which, when executed, implement the following process:

[0123] A credit reporting digital contract is generated based on the user credit reporting request submitted by the credit reporting client, and the signing of the credit reporting digital contract and the synchronization of contract permissions are carried out; the credit reporting client corresponds to or cooperates with the credit reporting nodes in the trusted credit reporting data space;

[0124] The encrypted credit data uploaded by the credit reporting client is obtained, and the encrypted credit data is transmitted to a trusted execution environment, so as to perform a structured transformation on the unstructured data contained in the encrypted credit data in the trusted execution environment to obtain credit data.

[0125] The credit data is transmitted in encrypted form to a virtual isolated space allocated to the credit client for user interaction confirmation of the credit data, and the confirmed target credit data is encrypted and stored as evidence.

[0126] It should be noted that the embodiments of a computer-readable storage medium described in this specification and the embodiments of a credit data processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0127] This specification provides an example of a computer program product as follows:

[0128] Corresponding to the credit data processing method described above, based on the same technical concept, one or more embodiments of this specification also provide a computer program product.

[0129] A computer program product includes a computer program / instructions that, when executed by a processor, perform the following steps:

[0130] A credit reporting digital contract is generated based on the user credit reporting request submitted by the credit reporting client, and the signing of the credit reporting digital contract and the synchronization of contract permissions are carried out; the credit reporting client corresponds to or cooperates with the credit reporting nodes in the trusted credit reporting data space;

[0131] The encrypted credit data uploaded by the credit reporting client is obtained, and the encrypted credit data is transmitted to a trusted execution environment, so as to perform a structured transformation on the unstructured data contained in the encrypted credit data in the trusted execution environment to obtain credit data.

[0132] The credit data is transmitted in encrypted form to a virtual isolated space allocated to the credit client for user interaction confirmation of the credit data, and the confirmed target credit data is encrypted and stored as evidence.

[0133] It should be noted that the embodiments of a computer program product described in this specification and the embodiments of a credit data processing method described in this specification are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding method described above, and the repeated parts will not be described again.

[0134] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments. For example, the device embodiment, equipment embodiment and computer-readable storage medium embodiment are all similar to the method embodiment, so the description is relatively simple. When reading the relevant content of the device embodiment, equipment embodiment and computer-readable storage medium embodiment, please refer to the description of the method embodiment.

[0135] While one or more embodiments of this specification provide method steps as described in the embodiments or flowcharts, it is understood that the order of steps listed in the embodiments or flowcharts is merely one possible execution order among many steps, and does not represent the only execution order. Therefore, when the claims involve method steps, any changes or adjustments to the order of such steps, or the parallelism between steps, are also within the scope of protection of the claims. This specification uses specific terms to describe embodiments of this specification. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "an embodiment," "one embodiment," or "an alternative embodiment" mentioned twice or more in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0136] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0137] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using hardware physical modules. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program and "integrate" a digital system onto a PLD themselves, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must also be written in a specific programming language, called a Hardware Description Language (HDL). 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, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0138] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered 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 be considered as both software modules implementing the method and structures within the hardware component.

[0139] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0140] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, when implementing the embodiments of this specification, the functions of each unit can be implemented in one or more software and / or hardware.

[0141] Those skilled in the art will understand that one or more embodiments of this specification can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this specification may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this specification may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0142] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0143] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0144] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0145] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0146] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0147] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable 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, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0148] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising at least one…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0149] One or more embodiments of this specification can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a particular task or implement a particular abstract data type. One or more embodiments of this specification can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0150] The above description is merely an embodiment of this document and is not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A credit investigation data processing method applied to a service end of a credit investigation platform for providing credit investigation services to users, which accesses a credit investigation credible data space, characterized in that, The method includes: Generate a credit reporting digital contract based on the user credit reporting request submitted by the credit reporting client, and obtain an authorized digital contract by signing the credit reporting digital contract, and synchronize the authorization permissions of the authorized digital contract with the permission pool of the server. A virtual isolation space is allocated to the credit reporting client. A data collection page is generated in the virtual isolation space and sent to the credit reporting client. If the unstructured data control configured on the data collection page is triggered, encrypted credit reporting data containing unstructured data uploaded by the credit reporting client is obtained. Based on the data type identification of the unstructured data by the credit reporting client, the data conversion model corresponding to the data type is passed into the trusted execution environment. The encrypted credit reporting data is also passed into the trusted execution environment so that the unstructured data is input into the corresponding data conversion model in the trusted execution environment for structure conversion to obtain credit reporting data. The credit data is transmitted in encrypted form to a virtual isolated space allocated to the credit client for user interaction confirmation and to obtain target credit data. The target credit data and the rights generation model are then passed into the trusted execution environment. The target credit data is input into the rights generation model, and the target credit data is evaluated based on the authorized permissions to obtain a data evaluation result. Based on the data evaluation result and the target credit data, a data value evaluation is performed to obtain the credit data value. Based on the rights matching parameters, the data evaluation result, and the credit data value, credit rights are matched to obtain credit rights and recommended to the user.

2. The credit information data processing method of claim 1, wherein, The trusted execution environment deploys a data transformation model; the structured transformation includes inputting the unstructured data into the data transformation model to perform structured transformation of the credit data.

3. The credit information data processing method of claim 2, wherein, The structured transformation of the credit data is achieved in the following way: Unstructured data of various data types are converted into credit reporting text data, and semantic encoding is performed on each credit reporting text data to obtain semantic encoding features; Discrete semantic fragments are obtained by performing key semantic identification and semantic association on each semantic encoding feature, and the discrete semantic fragments are merged and the credit data is generated based on the merging result.

4. The credit data processing method according to claim 1, characterized in that, After the step of matching credit rights based on the rights matching parameters, the data evaluation results, and the value of the credit data to obtain credit rights and recommending them to the user, the process also includes: The target credit data is encrypted to obtain evidence-based credit data, and the authorized digital contract is encrypted to obtain evidence-based contract; The aforementioned evidence-based credit data, the evidence-based contract, the contract processing log, the structured conversion log, and the interaction confirmation log are stored on the blockchain.

5. The credit data processing method according to claim 4, characterized in that, Also includes: If the second encrypted credit data uploaded by the credit reporting client is obtained, the stored credit data is queried from the blockchain; The second encrypted credit data, the evidence-based credit data, and the rights matching model are passed to the second trusted execution environment allocated to the credit client, so as to perform data merging processing based on the second encrypted credit data and the evidence-based credit data to obtain merged credit data, and perform credit rights matching based on the merged credit data to obtain second credit rights.

6. The credit data processing method according to claim 5, characterized in that, The matching of credit reporting rights includes: The authenticity of the merged credit data is verified to obtain an authenticity score, and the data acquisition cost is rated based on the authenticity score and the merged credit data to obtain the data acquisition cost. The second credit benefit is obtained by matching the authenticity score, the data acquisition cost, and the rights matching rules.

7. The credit data processing method according to claim 6, characterized in that, Before the operation of querying the stored credit data from the blockchain is executed, it also includes: The evidence storage contract is queried from the blockchain, and authorization verification is performed based on the evidence storage contract; If the verification is successful, the operation of querying the stored credit data from the blockchain will be performed; If the verification fails, a second digital contract is generated and the signing process and contract permissions are synchronized.

8. The credit data processing method according to claim 1, characterized in that, Also includes: If the structured data control configured on the data collection page is triggered, the encrypted identity data uploaded by the credit reporting client is obtained and passed to the trusted execution environment to determine the credit reporting data template that matches the encrypted identity data; The credit data template is sent to the credit reporting client to collect structured credit data and encrypt it in the encrypted space of the credit reporting client.

9. The credit data processing method according to claim 8, characterized in that, Also includes: The encrypted structured data obtained by the credit reporting client through encryption processing of structured credit reporting data is obtained, and the encrypted structured data is transmitted to the trusted execution environment to perform data merging processing on the structured credit reporting data and the structured data obtained by structure transformation; or, The structured credit data is transmitted in encrypted form to the virtual isolation space for user interaction confirmation of the structured credit data, and the confirmed structured credit data is encrypted and stored as evidence.

10. A credit data processing device, operating on the server side of a credit reporting platform that provides credit reporting services to users and accesses a trusted credit data space, characterized in that, The device includes: The contract signing module is configured to generate a credit digital contract based on the user credit request submitted by the credit client, and to perform the contract signing process of the credit digital contract to obtain an authorization digital contract, and to synchronize the authorization permissions of the authorization digital contract with the permission pool of the server. The structured conversion module is configured to allocate a virtual isolation space to the credit reporting client, generate a data collection page in the virtual isolation space and send it to the credit reporting client. If the unstructured data control configured on the data collection page is triggered, it acquires encrypted credit reporting data containing unstructured data uploaded by the credit reporting client. Based on the data type identification of the unstructured data obtained by the credit reporting client, it passes the data conversion model corresponding to the data type to the trusted execution environment and passes the encrypted credit reporting data to the trusted execution environment. In the trusted execution environment, the unstructured data is input into the corresponding data conversion model for structured conversion to obtain credit reporting data. The data confirmation module is configured to transmit the credit data in encrypted form to a virtual isolated space allocated to the credit client for user interaction confirmation of the credit data and to obtain target credit data. The target credit data and the rights generation model are then passed into the trusted execution environment. The target credit data is input into the rights generation model. Based on the authorized permissions, the target credit data is evaluated in terms of data dimensions to obtain a data evaluation result. Based on the data evaluation result and the target credit data, a data value evaluation is performed to obtain the credit data value. Finally, based on the rights matching parameters, the data evaluation result, and the credit data value, credit rights are matched to obtain credit rights and recommended to the user.

11. A credit data processing device, characterized in that, include: processor; And, a memory configured to store computer-executable instructions, which, when executed, cause the processor to: Generate a credit reporting digital contract based on the user credit reporting request submitted by the credit reporting client, and obtain an authorized digital contract by signing the credit reporting digital contract, and synchronize the authorization permissions of the authorized digital contract with the permission pool of the server. A virtual isolation space is allocated to the credit reporting client. A data collection page is generated in the virtual isolation space and sent to the credit reporting client. If the unstructured data control configured on the data collection page is triggered, encrypted credit reporting data containing unstructured data uploaded by the credit reporting client is obtained. Based on the data type identification of the unstructured data by the credit reporting client, the data conversion model corresponding to the data type is passed into the trusted execution environment. The encrypted credit reporting data is also passed into the trusted execution environment so that the unstructured data is input into the corresponding data conversion model in the trusted execution environment for structure conversion to obtain credit reporting data. The credit data is transmitted in encrypted form to a virtual isolated space allocated to the credit client for user interaction confirmation and to obtain target credit data. The target credit data and the rights generation model are then passed into the trusted execution environment. The target credit data is input into the rights generation model, and the target credit data is evaluated based on the authorized permissions to obtain a data evaluation result. Based on the data evaluation result and the target credit data, a data value evaluation is performed to obtain the credit data value. Based on the rights matching parameters, the data evaluation result, and the credit data value, credit rights are matched to obtain credit rights and recommended to the user.

12. A computer-readable storage medium for storing computer-executable instructions, characterized in that, When the computer-executable instructions are executed, they implement the steps of the method of claim 1.

Images