Check-in filing approval method and system based on trusted data space

Abstract

The invention discloses a check-in filing approval method and system based on a credible data space, and relates to the technical field related to credible certification, and the method comprises the steps: loading a business processing log of the credible data space, executing frequency statistics, and obtaining a plurality of high-frequency data attribute sets. The high-frequency data attribute sets are traversed, a plurality of joint vouchers are constructed, and the joint vouchers are generated by a plurality of data providers involved in the high-frequency data attribute sets through common signing and issuing by means of the block chain technology. And when the preset approval node of the preset task type or the preset task type is triggered, calling a plurality of corresponding joint vouchers to execute verification, and when the verification is passed, based on the plurality of corresponding high-frequency data attribute sets, calling associated data to execute check-in filing approval. The technical problems of difficulty in improving the approval efficiency and poor data security of the check-in filing approval method based on the trusted data space in the prior art are solved. The check-in filing examination and approval efficiency is improved, and the technical effects of data verification safety are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of trusted authentication technology, and particularly relates to a check-in filing approval method and system based on a trusted data space. BACKGROUND

[0002] The trusted data space is a data flow utilization infrastructure based on consensus rules, connecting multiple parties to realize data resource sharing. In the traditional trusted data space, the information of a data providing node is stored as a whole for access. However, in the check-in filing approval, a large number of nodes are involved, which leads to repeated data verification work, thereby greatly reducing the approval efficiency, and the data security problem of data leakage exists in multiple data retrieval.

[0003] Therefore, the check-in filing approval method based on the trusted data space in the prior art has the technical problems of difficult improvement of approval efficiency and poor data security. SUMMARY

[0004] The present application provides a check-in filing approval method and system based on a trusted data space, which solves the technical problems of difficult improvement of approval efficiency and poor data security of the check-in filing approval method based on the trusted data space in the prior art. The check-in filing approval efficiency is improved, the data is trusted and traceable, and the technical effect of verifying the data security is ensured.

[0005] The present application provides a check-in filing approval method based on a trusted data space, which comprises: loading a business processing log of a trusted data space, performing frequency statistics, and obtaining a plurality of high-frequency data attribute sets, wherein any one of the plurality of high-frequency data attribute sets is uniquely associated with a preset approval node of a preset task type or a preset task type; traversing the plurality of high-frequency data attribute sets to construct a plurality of joint credentials, wherein the joint credentials are generated by a plurality of data providers involved in the high-frequency data attribute set using blockchain technology; when the preset approval node of the preset task type or the preset task type is triggered, the corresponding plurality of joint credentials are retrieved to perform verification, and when the verification is passed, the associated data is retrieved based on the corresponding plurality of high-frequency data attribute sets to perform check-in filing approval.

[0006] In an implementation, a business processing log of a trusted data space is loaded, frequency statistics are performed, and a plurality of high-frequency data attribute sets are obtained, including: performing task-level frequency statistics on the business processing log to obtain high-frequency approval tasks; deleting log data related to the high-frequency approval tasks from the business processing log to obtain an updated business processing log; performing node-level frequency statistics on the updated business processing log to obtain a plurality of first high-frequency data attribute sets; traversing the high-frequency approval tasks to extract a plurality of second high-frequency data attribute sets; and adding the plurality of first high-frequency data attribute sets and the plurality of second high-frequency data attribute sets to the plurality of high-frequency data attribute sets.

[0007] In an implementation, performing task-level frequency statistics on the business processing log to obtain high-frequency approval tasks includes: counting a first trigger number of a first approval task according to the business processing log of the trusted data space; and adding the first approval task to the high-frequency approval tasks when the first trigger number is greater than or equal to a trigger number threshold.

[0008] In an implementation, performing node-level frequency statistics on the updated business processing log to obtain a plurality of first high-frequency data attribute sets includes: extracting a plurality of business processing logs of a first approval node from the updated business processing log; extracting a data attribute set of the plurality of business processing logs, performing enumeration combination to obtain a plurality of groups of data attributes, wherein a total number of data attributes ≥ a number of combined attributes ≥ 2, the plurality of groups of data attributes correspond to a plurality of approval task types; traversing the plurality of groups of data attributes to count a plurality of supports of the plurality of business processing logs; extracting a group of data attributes with a support greater than or equal to a support threshold from the plurality of supports, and storing the group of data attributes in association with a corresponding approval task type and the first approval node to add the group of data attributes to the plurality of first high-frequency data attribute sets.

[0009] In an implementation, extracting a group of data attributes with a support greater than or equal to a support threshold from the plurality of supports includes: extracting a group of first-level data attributes with a support greater than or equal to a support threshold from the plurality of supports; obtaining a first-level approval task type of the group of first-level data attributes from the updated business processing log; classifying the group of first-level data attributes according to the first-level approval task type to obtain a plurality of clusters of first-level data attribute groups; and traversing the plurality of clusters of first-level data attribute groups, and deleting a child data attribute group when a data attribute group with a containing relationship is found to obtain the data attribute group.

[0010] In an implementation, the plurality of high-frequency data attribute sets are traversed to construct a plurality of joint credentials, including: extracting a first high-frequency data attribute set from the plurality of high-frequency data attribute sets, wherein the first high-frequency data attribute set is associated with a preset approval node of a preset task type or a preset task type; obtaining a plurality of data providers of the first high-frequency data attribute set; receiving a comprehensive data set from the plurality of data providers based on the first high-frequency data attribute set and the preset approval node of the preset task type or the preset task type through a trusted data space; constructing a first joint credential for the comprehensive data set in the trusted data space, adding the first joint credential to the plurality of joint credentials, and sending a corresponding public key of the first joint credential to a corresponding approval node.

[0011] In an implementation, the corresponding plurality of joint credentials are called to perform verification, and when the verification passes, the associated data is called to perform check-in and file establishment approval based on the corresponding plurality of high-frequency data attribute sets, including: selecting a target joint credential based on the triggered preset approval node of the preset task type or the preset task type; receiving a to-be-verified public key from a demand approval node to perform verification on the target joint credential; when the verification passes: if the triggered is the preset approval node of the preset task type, setting the data set of the target joint credential as the associated data; if the triggered is the preset task type, crawling demand data attributes based on the demand approval node, and selecting the associated data from the data set of the target joint credential.

[0012] The application also provides a check-in and file establishment approval system based on a trusted data space, including: a high-frequency data attribute acquisition module, configured to load business processing logs of a trusted data space, perform frequency statistics, and obtain a plurality of high-frequency data attribute sets, wherein any one of the plurality of high-frequency data attribute sets is uniquely associated with a preset approval node of a preset task type or a preset task type; a joint credential construction module, configured to traverse the plurality of high-frequency data attribute sets to construct a plurality of joint credentials, wherein a joint credential is generated by a plurality of data providers involved in a high-frequency data attribute set using blockchain technology; and a verification module, configured to call the corresponding plurality of joint credentials to perform verification when the preset approval node of the preset task type or the preset task type is triggered, and when the verification passes, call associated data to perform check-in and file establishment approval based on the corresponding plurality of high-frequency data attribute sets.

[0013] The application provides a check-in and file establishment approval method and system based on a trusted data space. A business processing log of the trusted data space is loaded, frequency statistics are performed, and a plurality of high-frequency data attribute sets are obtained. Any one of the plurality of high-frequency data attribute sets is uniquely associated with a preset approval node of a preset task type or the preset task type. The plurality of high-frequency data attribute sets are traversed, and a plurality of joint credentials are constructed. The joint credentials are generated by a plurality of data providers involved in the high-frequency data attribute sets by using a blockchain technology. When the preset approval node of the preset task type or the preset task type is triggered, the corresponding plurality of joint credentials are called to perform verification. When the verification is passed, the corresponding plurality of high-frequency data attribute sets are called to perform check-in and file establishment approval based on associated data. The technical problem of low approval efficiency and poor data security in the prior art is solved. The check-in and file establishment approval efficiency is improved, the data is trusted and traceable, and the technical effect of verifying data security is ensured. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments of the present application will be briefly introduced. The flowchart is used to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, according to the needs, various steps can be processed in reverse order or at the same time. Meanwhile, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1 The method for check-in and file establishment approval based on a trusted data space provided by the embodiments of the present application is shown in the flowchart.

[0016] Figure 2 The structure of the check-in and file establishment approval system based on a trusted data space provided by the embodiments of the present application is shown in the schematic diagram.

[0017] Explanation of reference numerals: high-frequency data attribute acquisition module 11, joint credential construction module 12, and verification module 13. DETAILED DESCRIPTION

[0018] The above description is only a summary of the technical solutions of the present application. In order to more clearly understand the technical means of the present application, the embodiments of the present application can be implemented according to the content of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described.

[0019] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings, and the described embodiments should not be regarded as limiting the present application. All other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of protection of the present application.

[0020] In the following description, "some embodiments" are referred to, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict. The term "first\second" referred to only distinguishes similar objects, and does not represent a specific order of the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0021] The embodiments of the present application provide a check-in filing approval method and system based on a trusted data space, as shown in Figure 1 The method comprises the following steps: Load the business processing log of the trusted data space, perform frequency statistics, and obtain a plurality of high-frequency data attribute sets, wherein any one of the plurality of high-frequency data attribute sets is uniquely associated with a preset approval node of a preset task type or a preset task type.

[0022] Specifically, the business processing log of the trusted data space is loaded, and the trusted data space is a data circulation infrastructure based on consensus rules, connecting multiple subjects to realize data resource sharing. The historical check-in filing approval related operation data, i.e. the business processing log, is stored in the trusted data space, including approval task type, involved approval node, called data attribute, processing time, processing result, etc. Then, the number of triggers of each preset task type is counted, and a plurality of high-frequency data attribute sets, such as age, gender, name, identity information and other demand information, are obtained. Any one of the plurality of high-frequency data attribute sets is uniquely associated with a preset approval node of a preset task type or a preset task type.

[0023] The method provided in the embodiments of the present application further includes: performing task-level frequency statistics on the service processing log to obtain high-frequency approval tasks; deleting log data related to the high-frequency approval tasks from the service processing log to obtain an updated service processing log; performing node-level frequency statistics on the updated service processing log to obtain a plurality of first high-frequency data attribute sets; traversing the high-frequency approval tasks to extract a plurality of second high-frequency data attribute sets; and adding the plurality of first high-frequency data attribute sets and the plurality of second high-frequency data attribute sets into the plurality of high-frequency data attribute sets.

[0024] Loading the service processing log of the trusted data space, performing frequency statistics, and obtaining a plurality of high-frequency data attribute sets include: after obtaining the service processing log, performing task-level frequency statistics on the service processing log, the task-level frequency statistics being statistics of the frequency of a preset task category in the service processing log, and adding a task type meeting a frequency requirement to high-frequency approval tasks. Further, log data related to the high-frequency approval tasks is deleted from the service processing log to obtain remaining service processing log, and an updated service processing log is obtained. At this time, the updated service processing log only contains non-frequent approval task categories. Further, node-level frequency statistics is performed on the updated service processing log to obtain a plurality of first high-frequency data attribute sets. Further, a plurality of second high-frequency data attribute sets are extracted by traversing the high-frequency approval tasks, the second high-frequency data attribute sets being all data attribute combinations called in the approval process of the tasks extracted from the log data corresponding to the high-frequency approval tasks, and being directly associated with the high-frequency approval tasks. Finally, the plurality of first high-frequency data attribute sets and the plurality of second high-frequency data attribute sets are added to the plurality of high-frequency data attribute sets. At this time, the plurality of high-frequency data attribute sets cover all data attribute sets of the high-frequency approval tasks and the frequent high-frequency data attribute sets of the non-high-frequency approval tasks. This ensures that the data requirements of the high-frequency tasks with the largest approval amount are covered, and the high-frequency data requirements of the key nodes in the non-high-frequency tasks are avoided to be missed, ensuring comprehensive coverage of subsequent joint credentials and reducing the risk of data loss during approval.

[0025] The method provided in the embodiments of the present application further includes: according to the service processing log of the trusted data space, statistics of a first trigger number of a first approval task; when the first trigger number is greater than or equal to a trigger number threshold, adding the first approval task into the high-frequency approval tasks.

[0026] The task-level frequency statistics are performed to obtain high-frequency approval tasks, including: according to the obtained business processing log of the trusted data space, the first trigger number of a first approval task is counted from the processing log, the first approval task being a random one of the preset approval task types. Then, it is judged whether the first trigger number of the first approval task is greater than or equal to a trigger number threshold, and if yes, the first approval task is added to the high-frequency approval tasks, otherwise, no processing is performed. The trigger number threshold is a pre-set judgment threshold of a frequent item, and when being greater than or equal to the trigger number threshold, the corresponding approval task is a high-frequency approval task.

[0027] The method provided by the embodiment of the application further includes: extracting a plurality of business processing logs of the first approval node from the updated business processing log; extracting a data attribute set of the plurality of business processing logs, performing enumeration combination, and obtaining a plurality of groups of data attributes, wherein the total number of data attributes ≥ the number of combined attributes ≥ 2, and the plurality of groups of data attributes correspond to a plurality of approval task types; traversing the plurality of groups of data attributes, counting a plurality of support degrees of the plurality of business processing logs; extracting a group of data attributes with a support degree greater than or equal to a support degree threshold from the plurality of support degrees, and storing the group of data attributes in association with the corresponding approval task type and the first approval node, and adding the group of data attributes to the first high-frequency data attribute set.

[0028] Specifically, the node-level frequency statistics are performed on the updated business processing log to obtain a first high-frequency data attribute set, including: extracting a plurality of business processing logs of a first approval node from the obtained updated business processing log, the first approval node being an approval node of a random approval task in the updated business processing log. Further, a data attribute set of the plurality of business processing logs is extracted, the data attribute set being demand information when a corresponding business node is processed, such as age, gender, name, identity information and other demand information. Enumeration combination is performed on the data attribute set to mine the associated high frequency among a plurality of attributes, wherein the total number of data attributes ≥ the number of combined attributes ≥ 2, that is, the number of combined attributes of each group of data attributes is less than or equal to the total number of data attributes and greater than or equal to 2. For example, the data attribute set includes three data attributes A, B and C, at this time, the number of combined attributes is greater than or equal to 2 and less than or equal to 3, at this time, the obtained plurality of groups of data attributes are A+B, A+C, B+C and A+B+C, that is, four groups of data attributes. And the plurality of groups of data attributes correspond to a plurality of approval task types. Further, each group of data attributes in the plurality of groups of data attributes is traversed to count a plurality of support degrees of the plurality of business processing logs. The support degree refers to the frequency of a group of data attributes appearing in the plurality of business processing logs of the first approval node, and is a core index for judging whether the attribute group is high-frequency. For example, the A+B attribute group appears 18 times in 20 logs, and the support degree is 0.9.

[0029] Finally, the data attribute group with a support degree greater than or equal to a support degree threshold value is extracted from the plurality of support degrees, and is stored in association with a corresponding approval task type and a first approval node, and is added to the plurality of first high-frequency data attribute sets. By extracting the data attribute group with a support degree greater than or equal to a support degree threshold value from the plurality of support degrees, that is, the combined data attribute, it can be accurately positioned to those data attribute groups that frequently appear in a specific approval node and have a key influence on the approval task. These data attribute groups not only reflect the high-frequency demand in the business processing process, but also provide a strong basis for subsequent approval process optimization and decision support. For example, in the individual business registration approval process, if it is found that the operator identity information and the individual business license number data attribute group show a high support degree in multiple approval nodes, then this combination can be considered as a key verification item in the approval process to improve the efficiency and accuracy of the approval. At the same time, storing these high-frequency data attribute sets in association with the corresponding approval task type and the first approval node helps to build a more perfect and efficient data processing and analysis system, and provides solid data support for subsequent frequency statistics and business optimization.

[0030] The method provided by the embodiment of the application further includes: extracting a first data attribute group with a support degree greater than or equal to a support degree threshold value from the plurality of support degrees; obtaining a first approval task type of the first data attribute group from the updated business processing log; classifying the first data attribute group according to the first approval task type to obtain a plurality of clusters of first data attribute groups; and traversing the plurality of clusters of first data attribute groups, deleting a child data attribute group when a data attribute group with a containing relationship is found, and obtaining the data attribute group.

[0031] Specifically, extracting the data attribute group with a support degree greater than or equal to a support degree threshold value from the plurality of support degrees includes: extracting the support degree corresponding to each data attribute group, obtaining a plurality of support degrees, extracting a first data attribute group with a support degree greater than or equal to a support degree threshold value from the plurality of support degrees, and the support degree threshold value is a preset judgment data attribute group support degree for an approval node. When greater than the support degree threshold value, the corresponding data attribute group has a higher support degree for the approval node of the approval task type, and is a high-frequency demand in the processing process of the approval node of the approval task type. Further, the first approval task type corresponding to the first data attribute group is obtained from the updated business processing log. Then, the first data attribute group is classified according to the first approval task type, the corresponding relationship between the first approval task type and one or more first data attribute groups is obtained, and all first data attribute groups corresponding to each task type form a plurality of clusters of first data attribute groups.

[0032] Finally, the plurality of primary data attribute groups are traversed, and when a data attribute group with a containing relationship exists, a sub-data attribute group is deleted to obtain the data attribute group. The data attribute groups in the relationship are data attribute groups that have a containing relationship between different data attribute groups, such as an X data attribute group containing A and B attributes, and a Y data attribute group containing A, B, and C attributes. Since the data attributes of the Y data attribute group contain the data attributes of the X data attribute group, the X and Y data attribute groups are in a containing relationship, and at this time, the sub-data attribute group, that is, the X data attribute group, is deleted, and the data attribute group is obtained, thereby ensuring that the data attribute group obtained finally does not have redundancy, thereby reducing the cost of building and storing credentials, and at the same time, improving the efficiency of credential retrieval during approval.

[0033] The plurality of high-frequency data attribute sets are traversed to construct a plurality of joint credentials, wherein the joint credentials are generated by a plurality of data providers involved in the high-frequency data attribute set using blockchain technology; when the preset approval node of the preset task type or the preset task type is triggered, the corresponding plurality of joint credentials are retrieved to perform verification, and when the verification is passed, the associated data is retrieved based on the corresponding plurality of high-frequency data attribute sets to perform check-in and file establishment approval.

[0034] Specifically, the plurality of high-frequency data attribute sets are traversed to construct a plurality of joint credentials based on a plurality of data providers in a trusted data space, and the joint credentials are generated by a plurality of data providers involved in the high-frequency data attribute set using blockchain technology. When the preset approval node of the preset task type or the preset task type is triggered, the corresponding plurality of joint credentials are retrieved to perform verification, and when the joint credential verification is passed, the associated data is retrieved based on the corresponding plurality of high-frequency data attribute sets to perform check-in and file establishment approval. Through the trusted data space, data security is ensured, the blockchain technology is used to ensure that the credentials are not tamperable, and the high-frequency statistics are used to reduce data query redundancy during approval, and finally, efficient, trusted, and compliant check-in and file establishment approval is realized.

[0035] The method provided by the embodiment of the application further includes: extracting a first high-frequency data attribute set from the plurality of high-frequency data attribute sets, wherein the first high-frequency data attribute set is associated with a preset approval node of a preset task type or a preset task type; obtaining a plurality of data providers of the first high-frequency data attribute set; receiving a comprehensive data set from the plurality of data providers based on the first high-frequency data attribute set and the preset approval node of the preset task type or the preset task type through a trusted data space; constructing a first joint credential for the comprehensive data set in the trusted data space, adding the first joint credential to the plurality of joint credentials, and sending a corresponding public key of the first joint credential to a corresponding approval node.

[0036] The plurality of high-frequency data attribute sets are traversed to construct a plurality of joint credentials, including: extracting a first high-frequency data attribute set from the plurality of high-frequency data attribute sets, the first high-frequency data attribute set being a subset of the plurality of high-frequency data attribute sets and being a frequent data attribute group of non-high-frequency approval tasks obtained through node-level frequency statistics. And the first high-frequency data attribute set is associated with a preset approval node of a preset task type or a preset task type.

[0037] Subsequently, a plurality of data providers of the first high-frequency data attribute set are obtained, the data providers being data providers determined based on data attributes, such as a park property company providing a business premises lease contract number, and personal identity information being provided by a personal user. Different data attributes correspond to specific data providers. Further, based on the first high-frequency data attribute set and the preset approval node of the preset task type or the preset task type, a comprehensive data set is received from the plurality of data providers through a trusted data space. The comprehensive data set received from the plurality of data providers through the trusted data space based on the first high-frequency data attribute set and the preset approval node of the preset task type or the preset task type is an encrypted data set provided by the plurality of data providers involved in the first high-frequency data attribute set according to attribute requirements, containing core attribute data and additional verification information, which is the original data for constructing the joint credential.

[0038] Further, in the blockchain node of the trusted data space, a preset multi-party joint signature smart contract is called based on the Schnorr signature algorithm optimization, supporting dynamic node adjustment. The hash value of the comprehensive data set is taken as a signature invitation message, and the signature invitation is sent to each data provider. After receiving the invitation, each provider node generates its own partial signature and uploads it to the smart contract. The smart contract aggregates all partial signatures to generate the final multi-party joint signature, and packs the comprehensive data set encryption content, multi-party signature, generation timestamp, and associated object identifier into the first joint credential, stores it in the blockchain distributed ledger to generate a blockchain credential, and further completes the construction of the first joint credential. The first joint credential is added to the plurality of joint credentials, and the corresponding public key of the first joint credential is sent to the corresponding approval node, which is the preset approval node associated with the first high-frequency data attribute set, or all approval nodes contained in the associated preset task type. The first high-frequency data attribute set is only associated with the approval node, and other nodes have no access right, which avoids the leakage of public keys and improves the security of data.

[0039] The method provided by the embodiment of the application further includes: selecting a target joint credential based on the preset approval node or the preset task type of the triggered preset task type; receiving a to-be-verified public key from a demand approval node, and performing verification on the target joint credential; when the verification passes: if the triggered is the preset approval node of the preset task type, setting a data set of the target joint credential as the associated data; and if the triggered is the preset task type, based on the demand approval node, crawling demand data attributes, and sorting the associated data from the data set of the target joint credential.

[0040] The method provided by the embodiment of the application further includes: selecting a target joint credential based on the preset approval node or the preset task type of the triggered preset task type; receiving a to-be-verified public key from a demand approval node, and performing verification on the target joint credential; when the verification passes: if the triggered is the preset approval node of the preset task type, setting a data set of the target joint credential as the associated data; and if the triggered is the preset task type, based on the demand approval node, crawling demand data attributes, and sorting the associated data from the data set of the target joint credential.

[0041] Subsequently, a to-be-verified public key is received from a demand approval node, that is, a node currently performing approval, and verification is performed on the target joint credential. The demand approval node uses the to-be-verified public key to perform multi-layer verification on the target joint credential, and if all the multi-layer verifications pass, it is determined that the verification passes. The multi-layer verification includes signature validity verification, data integrity verification, and validity period verification. In the signature validity verification, the multi-party signature of the target credential is decrypted by using the to-be-verified public key, and it is verified whether the signature is a legal signature of the data provider. If the signature is tampered with, the verification fails. In the data integrity verification, a hash value of the encrypted data set of the target credential is calculated, and is compared with a data set hash value recorded in the credential. If the two hash values are inconsistent, it indicates that the data is tampered with, and the verification fails. In the validity period verification, it is checked whether the timestamp of the credential is within a preset validity period. If the validity period is exceeded, the verification fails. When the verification passes, if the triggered is the preset approval node of the preset task type, because the demand data attributes of the preset approval node completely match the data set of the target joint credential, the encrypted data set of the target joint credential is directly set as the associated data. If the triggered is the preset task type, corresponding demand data attributes are crawled according to different demand approval nodes, and the associated data is sorted from the data set of the target joint credential, so as to ensure that the data is accurately matched with the approval demand and avoid invalid data retrieval.

[0042] In the foregoing, the method for checking in and building a file based on a trusted data space according to an embodiment of the application is described in detail. Figure 1 The method for checking in and building a file based on a trusted data space according to an embodiment of the application is described in detail. Figure 2 The system for checking in and building a file based on a trusted data space according to an embodiment of the application is described.

[0043] The check-in filing approval system based on the trusted data space according to the embodiment of the present application solves the technical problem that the check-in filing approval method based on the trusted data space in the prior art has low approval efficiency and poor data security. The check-in filing approval efficiency is improved, the data is ensured to be trusted and traceable, and the technical effect of ensuring the data security is achieved. The check-in filing approval system based on the trusted data space comprises a high-frequency data attribute acquisition module 11, a joint certificate construction module 12, and a verification module 13.

[0044] The high-frequency data attribute acquisition module 11 is configured to load the business processing log of the trusted data space, perform frequency statistics, and obtain a plurality of high-frequency data attribute sets, wherein any one of the plurality of high-frequency data attribute sets is uniquely associated with a preset approval node of a preset task type or a preset task type. The joint certificate construction module 12 is configured to traverse the plurality of high-frequency data attribute sets and construct a plurality of joint certificates, wherein the joint certificates are generated by a plurality of data providers involved in the high-frequency data attribute sets using blockchain technology. The verification module 13 is configured to, when the preset approval node of the preset task type or the preset task type is triggered, call the corresponding plurality of joint certificates to perform verification, and when the verification is passed, call the associated data to perform check-in filing approval based on the corresponding plurality of high-frequency data attribute sets.

[0045] In the following, the specific configuration of the high-frequency data attribute acquisition module 11 will be described in detail. The high-frequency data attribute acquisition module 11 can further comprise: loading the business processing log of the trusted data space, performing frequency statistics, and obtaining a plurality of high-frequency data attribute sets, comprising: performing task-level frequency statistics on the business processing log to obtain high-frequency approval tasks; deleting log data involved in the high-frequency approval tasks from the business processing log to obtain updated business processing log; performing node-level frequency statistics on the updated business processing log to obtain a plurality of first high-frequency data attribute sets; traversing the high-frequency approval tasks to extract a plurality of second high-frequency data attribute sets; and adding the plurality of first high-frequency data attribute sets and the plurality of second high-frequency data attribute sets to the plurality of high-frequency data attribute sets.

[0046] In the following, the specific configuration of the high-frequency data attribute acquisition module 11 will be described in detail. The high-frequency data attribute acquisition module 11 can further comprise: performing task-level frequency statistics on the business processing log to obtain high-frequency approval tasks, comprising: according to the business processing log of the trusted data space, counting the first trigger number of the first approval task; when the first trigger number is greater than or equal to a trigger number threshold, adding the first approval task to the high-frequency approval tasks.

[0047] Below, the specific configuration of the high-frequency data attribute acquisition module 11 will be described in detail. The high-frequency data attribute acquisition module 11 further comprises: node-level frequency statistics on the update business processing log to obtain a plurality of first high-frequency data attribute sets, including: extracting a plurality of business processing logs of a first approval node from the update business processing log; extracting a data attribute set of the plurality of business processing logs, performing enumeration combination to obtain a plurality of groups of data attributes, wherein the total number of data attributes > the number of combined attributes > 2, and the plurality of groups of data attributes correspond to a plurality of approval task types; traversing the plurality of groups of data attributes to count a plurality of support degrees of the plurality of business processing logs; extracting a group of data attributes with a support degree greater than or equal to a support degree threshold from the plurality of support degrees, and storing the group of data attributes in association with the corresponding approval task type and the first approval node, and adding the group of data attributes to the plurality of first high-frequency data attribute sets.

[0048] Below, the specific configuration of the high-frequency data attribute acquisition module 11 will be described in detail. The high-frequency data attribute acquisition module 11 further comprises: extracting a group of data attributes with a support degree greater than or equal to a support degree threshold from the plurality of support degrees, including: extracting a first-level group of data attributes with a support degree greater than or equal to a support degree threshold from the plurality of support degrees; obtaining a first-level approval task type of the first-level group of data attributes from the update business processing log; classifying the first-level group of data attributes according to the first-level approval task type to obtain a plurality of clusters of first-level groups of data attributes; traversing the plurality of clusters of first-level groups of data attributes, and when there is a group of data attributes with a containing relationship, deleting a child group of data attributes to obtain the group of data attributes.

[0049] Below, the specific configuration of the joint credential construction module 12 will be described in detail. The joint credential construction module 12 further comprises: traversing the plurality of high-frequency data attribute sets to construct a plurality of joint credentials, including: extracting a first high-frequency data attribute set from the plurality of high-frequency data attribute sets, wherein the first high-frequency data attribute set is associated with a preset approval node of a preset task type or a preset task type; obtaining a plurality of data providers of the first high-frequency data attribute set; receiving a comprehensive data set from the plurality of data providers based on the first high-frequency data attribute set and the preset approval node of the preset task type or the preset task type through a trusted data space; constructing a first joint credential for the comprehensive data set in the trusted data space, adding the first joint credential to the plurality of joint credentials, and sending a corresponding public key of the first joint credential to a corresponding approval node.

[0050] The specific configuration of the verification module 13 will be described in detail below. The verification module 13 further comprises: calling the corresponding joint credentials to perform verification, and when the verification is passed, calling the associated data to perform the check-in filing approval based on the corresponding high-frequency data attribute set, comprising: selecting a target joint credential based on the triggered preset approval node of the preset task type or the preset task type; receiving a to-be-verified public key from a demand approval node, and performing verification on the target joint credential; when the verification is passed: if the triggered is the preset approval node of the preset task type, setting the data set of the target joint credential as the associated data; and if the triggered is the preset task type, based on the demand approval node, crawling demand data attributes, and sorting the associated data from the data set of the target joint credential.

[0051] The check-in filing approval system based on the trusted data space provided by the embodiment of the application can execute the check-in filing approval method based on the trusted data space provided by any embodiment of the application, and has the corresponding function modules and beneficial effects of the execution method.

[0052] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and each unit and module included is only divided according to the functional logic, but is not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific name of each functional unit is only for easy mutual distinction, and does not limit the protection scope of the present application.

[0053] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent replacement and improvement made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A check-in profiling approval method based on a trusted data space, characterized in that, The method comprises the following steps: loading a business processing log of a trusted data space, performing frequency statistics to obtain a plurality of high-frequency data attribute sets, wherein any one of the plurality of high-frequency data attribute sets is uniquely associated with a preset approval node of a preset task type or the preset task type; iterating through the plurality of high-frequency data attribute sets to construct a plurality of joint credentials, wherein the joint credentials are generated by a plurality of data providers involved in the high-frequency data attribute sets using blockchain technology; when the preset approval node of the preset task type or the preset task type is triggered, the corresponding plurality of joint credentials are called to perform verification, and when the verification is passed, the associated data is called to perform check-in and file approval based on the corresponding plurality of high-frequency data attribute sets.

2. The method of claim 1, wherein, Loading a business processing log of a trusted data space, performing frequency statistics to obtain a plurality of high-frequency data attribute sets, comprising: performing task-level frequency statistics on the business processing log to obtain a high-frequency approval task; deleting log data related to the high-frequency approval task from the business processing log to obtain an updated business processing log; performing node-level frequency statistics on the updated business processing log to obtain a plurality of first high-frequency data attribute sets; iterating through the high-frequency approval task to extract a plurality of second high-frequency data attribute sets; adding the plurality of first high-frequency data attribute sets and the plurality of second high-frequency data attribute sets to the plurality of high-frequency data attribute sets.

3. The method of claim 2, wherein, Performing task-level frequency statistics on the business processing log to obtain a high-frequency approval task, comprising: According to the business processing log of the trusted data space, the first trigger number of the first approval task is counted; when the first trigger number is greater than or equal to the trigger number threshold, the first approval task is added to the high-frequency approval task.

4. The method of claim 2, wherein, Performing node-level frequency statistics on the updated business processing log to obtain a plurality of first high-frequency data attribute sets, comprising: extracting a plurality of business processing logs of a first approval node from the updated business processing log; extracting data attribute sets of the plurality of business processing logs, performing enumeration combination to obtain a plurality of data attribute sets, wherein the total number of data attributes ≥ the number of combined attributes ≥ 2, and the plurality of data attribute sets correspond to a plurality of approval task types; iterating through the plurality of data attribute sets to count a plurality of support degrees of the plurality of business processing logs; extracting a data attribute group with a support degree greater than or equal to a support degree threshold from the plurality of support degrees, and storing the data attribute group in association with a corresponding approval task type and a first approval node, and adding the data attribute group to the plurality of first high-frequency data attribute sets.

5. The method of claim 4, wherein, Extracting a data attribute group with a support degree greater than or equal to a support degree threshold from the plurality of support degrees, comprising: extracting a first-level data attribute group with a support degree greater than or equal to a support degree threshold from the plurality of support degrees; obtaining a first-level approval task type of the first-level data attribute group from the updated business processing log; classifying the first-level data attribute group according to the first-level approval task type to obtain a plurality of clusters of first-level data attribute groups; iterating through the plurality of clusters of first-level data attribute groups, and deleting a child data attribute group when a data attribute group with a containing relationship is found.

6. The method of claim 1, wherein, iterating through the plurality of high-frequency data attribute sets, constructing a plurality of joint credentials, including: extracting a first high-frequency data attribute set from the plurality of high-frequency data attribute sets, wherein the first high-frequency data attribute set is associated with a preset approval node of a preset task type or a preset task type; obtaining a plurality of data providers of the first high-frequency data attribute set; receiving a comprehensive data set from the plurality of data providers based on the first high-frequency data attribute set and the preset approval node of the preset task type or the preset task type through a trusted data space; constructing a first joint credential for the comprehensive data set in the trusted data space, adding the first joint credential to the plurality of joint credentials, and sending a corresponding public key of the first joint credential to a corresponding approval node.

7. The method of claim 6, wherein, When the verification is passed, based on the corresponding plurality of high-frequency data attribute sets, the associated data is executed to enter the approval for building a file, including: selecting a target joint credential based on the triggered preset approval node of the preset task type or the preset task type; receiving a to-be-verified public key from a demand approval node to execute verification on the target joint credential; When the verification is passed: if the triggered is the preset approval node of the preset task type, the data set of the target joint credential is set as the associated data; if the triggered is the preset task type, based on the demand approval node, the demand data attribute is crawled, and the associated data is sorted from the data set of the target joint credential.

8. A check-in profiling approval system based on a trusted data space, characterized in that, The system includes: a high-frequency data attribute acquisition module configured to load a business processing log of a trusted data space, perform frequency statistics, and obtain a plurality of high-frequency data attribute sets, wherein any one of the plurality of high-frequency data attribute sets is uniquely associated with a preset approval node of a preset task type or a preset task type; a joint credential construction module configured to iterate through the plurality of high-frequency data attribute sets to construct a plurality of joint credentials, wherein the joint credentials are generated by a plurality of data providers involved in the high-frequency data attribute set using blockchain technology; a verification module configured to, when the preset approval node of the preset task type or the preset task type is triggered, call the corresponding plurality of joint credentials to perform verification, and when the verification is passed, based on the corresponding plurality of high-frequency data attribute sets, call the associated data to execute the approval for entering and building a file.

9. The system of claim 8, wherein, The high-frequency data attribute acquisition module performs steps including: performing task-level frequency statistics on the business processing log to obtain a high-frequency approval task; deleting log data involved in the high-frequency approval task from the business processing log to obtain an updated business processing log; performing node-level frequency statistics on the updated business processing log to obtain a plurality of first high-frequency data attribute sets; iterating through the high-frequency approval task to extract a plurality of second high-frequency data attribute sets; adding the plurality of first high-frequency data attribute sets and the plurality of second high-frequency data attribute sets to the plurality of high-frequency data attribute sets.

10. The system of claim 9, wherein, The high-frequency data attribute acquisition module performs steps further including: statistically counting a first trigger number of a first approval task according to a business processing log of a trusted data space; When the first trigger number of times is greater than or equal to a trigger number of times threshold, the first examination and approval task is added to the high-frequency examination and approval task.

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