CN117094827A

CN117094827A - Data generation method, device, computer equipment and storage medium

Info

Publication number: CN117094827A
Application number: CN202310682397.6A
Authority: CN
Inventors: 龚官岱
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2023-06-09
Filing date: 2023-06-09
Publication date: 2023-11-21

Abstract

The embodiment of the application belongs to the field of big data and the field of financial science and technology, and relates to a data generation method, a device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring asset processing data generated by a target data asset in each blood edge processing process; acquiring team information of a processing team corresponding to the target data asset in each blood margin processing process based on the asset processing data; marking target data assets based on team information respectively, and generating ID information of the target assets corresponding to each blood margin processing process; constructing asset nodes based on the ID information; acquiring processing logic information and asset behavior data of each asset node based on asset processing data; a target blood-edge relationship graph is generated based on the asset nodes, the processing logic information, and the asset behavior data. In addition, the application also relates to a blockchain technology, and the target blood relationship graph can be stored in the blockchain. The application can be applied to asset management scenes in the financial field, and effectively improves the management efficiency of data assets.

Description

Data generation method, device, computer equipment and storage medium

Technical Field

The present application relates to the field of big data and the field of financial technology, and in particular, to a data generating method, apparatus, computer device, and storage medium.

Background

The processing logic of the data asset, the data flow direction is an important component of big data, data development and data management, and is an indispensable tool for big data platforms constructed by financial enterprises such as insurance companies, banks and the like. The perfect data processing system is an indispensable ring in data management, and has obvious use for data security, trust and data quality. The life cycle of the data asset in enterprises, companies and departments can be identified by accurately grasping the processing and processing of the data asset in each link. The rapid positioning data processing and processing has great effects on companies, even whole enterprises, such as cost reduction, more accurate positioning and easier management of data.

The existing data blood-edge tool obtains corresponding table and field information by analyzing the data processing code, but does not record the behavior data of the asset data, so that in the processing process of the data asset, when a business faces the behavior data which needs to know the asset data clearly, the traditional data blood-edge tool cannot solve the problem, the data can only be obtained by manually analyzing the data processing code, the data query efficiency of the data asset is low, and the management efficiency of the data asset is low.

Disclosure of Invention

The embodiment of the application aims to provide a data generation method, a device, computer equipment and a storage medium, which are used for solving the technical problems that in the existing processing process of data assets, when businesses face behavior data which need to know asset data clearly, the traditional data blood-source tool cannot solve, the data can only be obtained by manually analyzing data processing codes, the data query efficiency of the data assets is low, and the management efficiency of the data assets is low.

In order to solve the above technical problems, an embodiment of the present application provides a data generating method, which adopts the following technical schemes:

acquiring asset processing data generated by a target data asset in each blood edge processing process;

based on the asset processing data, respectively acquiring team information of a processing team corresponding to the target data asset in each blood margin processing process;

labeling the target data asset based on each team information respectively, and generating ID information of the target asset corresponding to each blood margin processing process respectively;

constructing asset nodes respectively corresponding to the ID information;

Based on the asset processing data, processing logic information and asset behavior data of each asset node are respectively obtained;

and generating a target blood relationship graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data.

Further, the step of generating a target blood-edge relationship graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data specifically includes:

generating an asset blood edge relation graph corresponding to all the asset nodes based on the processing logic information;

constructing asset behavior nodes respectively corresponding to the asset behavior data;

generating a one-to-one correspondence between the asset behavior nodes and the asset nodes based on the association between the asset behavior data and the asset nodes;

and adding each asset behavior node to each matched asset node in the asset blood edge relation graph correspondingly based on the one-to-one correspondence, and obtaining the target blood edge relation graph.

Further, after the step of obtaining the target blood edge relationship graph after adding each of the asset behavior nodes to each of the matched asset nodes in the asset blood edge relationship graph based on the one-to-one correspondence, the method further includes:

Acquiring a first asset behavior node in the target blood relationship graph; wherein the first asset behavior node is any one of all the asset behavior nodes;

judging whether the first asset behavior node is a sensitive node or not;

and if so, carrying out encryption processing on the first asset behavior node in the target blood relationship graph.

Further, the step of determining whether the first asset behavior node is a sensitive node specifically includes:

acquiring a designated asset node corresponding to the first asset behavior node;

acquiring first ID information corresponding to the designated asset node;

calling a preset grade data table;

performing query processing on the grade data table based on the first ID information, and acquiring a designated grade corresponding to the first ID information from the grade data table;

judging whether the appointed level is higher than a preset level;

if yes, determining the first asset behavior node as a sensitive node, otherwise, determining the first asset behavior node as a non-sensitive node.

Further, the step of encrypting the first asset behavior node in the target blood relationship graph specifically includes:

Acquiring second ID information corresponding to the first asset behavior node;

acquiring a target grade corresponding to the second ID information based on the second ID information and the grade data table;

acquiring a target encryption rule corresponding to the target grade;

and encrypting the first asset behavior node in the target blood relationship graph based on the target encryption rule.

Further, after the step of encrypting the first asset behavior node in the target blood relationship graph, the method further includes:

receiving a data viewing request triggered by a user for a second asset behavior node in the target blood relationship graph; the second asset behavior node is any sensitive node in the target blood relationship graph, and the data viewing request carries user information of the user;

performing authority verification on the user based on the user information;

if the authority verification is passed, acquiring appointed decryption information corresponding to the second asset behavior node;

the specified decryption information is shown in the target blood relationship diagram.

Further, after the step of generating the target blood edge relationship graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data, the method further includes:

Acquiring operation information corresponding to the target blood edge relation graph;

judging whether a changed designated node exists in the target blood edge relation graph or not based on the operation information;

if yes, setting the node information of the designated node in the target blood relationship graph to be in a failure state.

In order to solve the above technical problems, the embodiment of the present application further provides a data generating device, which adopts the following technical scheme:

the first acquisition module is used for acquiring asset processing data generated in each blood edge processing process of the target data asset;

the second acquisition module is used for respectively acquiring team information of a processing team corresponding to the target data asset in each blood margin processing process based on the asset processing data;

the first generation module is used for respectively labeling the target data assets based on the team information and generating ID information of the target assets respectively corresponding to each blood margin processing process;

the construction module is used for constructing asset nodes respectively corresponding to the ID information;

the third acquisition module is used for respectively acquiring processing logic information and asset behavior data of each asset node based on the asset processing data;

And the second generation module is used for generating a target blood edge relation graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

constructing asset nodes respectively corresponding to the ID information;

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

constructing asset nodes respectively corresponding to the ID information;

Compared with the prior art, the embodiment of the application has the following main beneficial effects:

firstly, acquiring asset processing data generated in each blood margin processing process of a target data asset; then, based on the asset processing data, team information of a processing team corresponding to the target data asset in each blood margin processing process is respectively obtained; labeling the target data assets based on the team information respectively, and generating ID information of the target assets corresponding to each blood margin processing process respectively; subsequently constructing asset nodes respectively corresponding to the ID information; further based on the asset processing data, processing logic information and asset behavior data of each asset node are respectively obtained; and finally, generating a target blood-edge relation graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data. According to the embodiment of the application, the processing logic information and the asset behavior data of the asset node corresponding to the target data asset are counted by acquiring the asset processing data generated in each blood edge processing process of the target data asset acquired in advance, so that the target blood edge relation diagram corresponding to the target data asset can be quickly and accurately generated according to the asset node, the processing logic information and the asset behavior data, the generation efficiency and the generation intelligence of the target blood edge relation diagram are improved, a user can quickly find out the required data asset information by using the target blood edge relation diagram, the data query efficiency of the data asset is improved, and the user can perform tracing analysis and influence analysis on the data asset based on the target blood edge relation diagram, so that the management efficiency of the data asset is improved.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a data generation method according to the present application;

FIG. 3 is a schematic diagram of a data generating apparatus according to an embodiment of the present application;

FIG. 4 is a schematic structural diagram of one embodiment of a computer device in accordance with the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the data generating method provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the data generating apparatus is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow chart of one embodiment of a data generation method according to the present application is shown. The order of the steps in the flowchart may be changed and some steps may be omitted according to various needs. The data generation method provided by the embodiment of the application can be applied to any scene needing asset management, and can be applied to products of the scenes, such as financial asset data management in the field of financial insurance. The data generation method comprises the following steps:

In step S201, asset processing data generated during each blood edge processing of a target data asset is acquired.

In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the data generating method operates may acquire the asset processing data through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The target data asset may be a table or a field. The asset processing data at least comprises team information, processing logic information and asset behavior data of a processing team corresponding to each blood edge processing process. Taking a business scenario of processing asset data of a bank as an example, the target data assets may refer to assets such as interest, credit, mortgage, stock, currency, investment, funds, portfolio, pension, and the like.

Step S202, based on the asset processing data, team information of a processing team corresponding to the target data asset in each blood edge processing process is obtained respectively.

In this embodiment, the team information of the processing team corresponding to the target data asset in each blood edge processing process may be obtained by extracting information from the asset processing data based on the team ID type information. For example, if the asset processing data includes a data record of processing the data asset a in the t group of the intelligent platform team, team information of the processing team corresponding to the data asset a in the t group blood margin processing process of the intelligent platform team may be obtained as the t group.

Step S203, performing labeling processing on the target data asset based on each team information, and generating ID information of the target asset corresponding to each blood edge processing procedure.

In this embodiment, a mapping relationship between team information and ID information may be preset for different team information at random, and then, based on the mapping relationship, labeling processing may be performed on the target data asset based on each team information, so as to generate ID information of the target asset corresponding to each blood edge processing procedure. The ID information is the unique ID of the asset identification, and the exclusive format of the intelligent platform is different from the format of the core team and the business team, so that the intelligent platform has uniqueness. For example, the ID information corresponding to the t group is AI01, and the ID information corresponding to the y group is AI0102.

Step S204, constructing asset nodes corresponding to the ID information respectively.

In this embodiment, a one-to-one correspondence of asset nodes may be generated for each ID information, each asset node being identified using its corresponding ID information. In the process of processing the blood edges of the data assets, the node residence time of the data assets in the corresponding asset nodes is synchronously recorded, and after the blood edge processing is finished, the behavior data of the data assets such as how many times the data assets are referenced in the nodes, how many times the data assets are accessed, access details and the like are recorded.

Step S205, based on the asset processing data, processing logic information and asset behavior data of each asset node are respectively acquired.

In this embodiment, the asset processing data further records processing logic information and asset behavior data generated during each processing of the target data asset. The processing logic information and the asset behavior data corresponding to the ID information, that is, the processing logic information and the asset behavior data corresponding to each of the asset nodes, may be extracted from the processing logic information and the asset behavior data generated in each of the blood edge processing processes of the target data asset based on the correspondence between the ID information and the asset nodes. The asset behavior data may include, among other things, node dwell time of the data asset, how many times the data asset is used at the node (number of uses), how many times it is referenced (number of references), how many times it is accessed (number of accesses), and data such as access details, screening logic, etc. The asset behavior data corresponds to business attributes and technical attributes possessed by the asset node.

Step S206, generating a target blood edge relation graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data.

In this embodiment, the specific implementation process of generating the target blood-edge relationship graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data will be described in further detail in the following specific embodiments, which will not be described herein.

Firstly, acquiring asset processing data generated in each blood margin processing process of a target data asset; then, based on the asset processing data, team information of a processing team corresponding to the target data asset in each blood margin processing process is respectively obtained; labeling the target data assets based on the team information respectively, and generating ID information of the target assets corresponding to each blood margin processing process respectively; subsequently constructing asset nodes respectively corresponding to the ID information; further based on the asset processing data, processing logic information and asset behavior data of each asset node are respectively obtained; and finally, generating a target blood-edge relation graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data. According to the application, the asset processing data generated in each blood edge processing process of the target data asset collected in advance is obtained, so that the processing logic information and the asset behavior data of the asset node corresponding to the target data asset are counted, the target blood edge relation diagram corresponding to the target data asset can be quickly and accurately generated according to the asset node, the processing logic information and the asset behavior data, the generation efficiency and the generation intelligence of the target blood edge relation diagram are improved, a user can quickly find out the required data asset information by using the target blood edge relation diagram, the data query efficiency of the data asset is improved, and the user can perform source analysis and influence analysis on the data asset based on the target blood edge relation diagram, so that the management efficiency of the data asset is improved.

In some alternative implementations, step S206 includes the steps of:

and generating an asset blood edge relation graph corresponding to all the asset nodes based on the processing logic information.

In this embodiment, by analyzing the processing logic information, processing logic and processing logic of each asset node may be obtained, and further, an upstream-downstream relationship between each asset node may be analyzed based on the obtained processing logic and processing logic. The upstream-downstream relationship may specifically refer to a blood-based relationship of inheritance of data that exists between the asset nodes. And obtaining the blood edge relation among the asset nodes, and further generating an asset blood edge relation graph corresponding to all the asset nodes based on the blood edge relation. The generated asset blood-edge relationship graph is a graph (a mesh structure chart) containing the blood-edge relationship among the asset nodes.

And constructing asset behavior nodes respectively corresponding to the asset behavior data.

In this embodiment, for each asset behavior data, a one-to-one corresponding asset behavior node may be generated, and each asset behavior node is identified using its corresponding asset behavior data, that is, each asset behavior node includes the corresponding asset behavior data.

And generating a one-to-one correspondence between the asset behavior nodes and the asset nodes based on the association relationship between the asset behavior data and the asset nodes.

In this embodiment, the asset behavior nodes may be considered secondary nodes to the matching asset nodes in the asset blood relationship graph.

Generating an asset blood edge relation graph corresponding to all asset nodes based on the processing logic information; then constructing asset behavior nodes respectively corresponding to the asset behavior data; then, based on the association relation between the asset behavior data and the asset nodes, generating a one-to-one correspondence relation between the asset behavior nodes and the asset nodes; and then, based on the one-to-one correspondence, adding each asset behavior node to each matched asset node in the asset blood edge relationship graph to obtain the target blood edge relationship graph, so that the target blood edge relationship graph corresponding to the target data asset is quickly and accurately generated, and the generation efficiency and the generation intelligence of the target blood edge relationship graph are improved.

In some optional implementations of this embodiment, after the step of obtaining the target blood edge relationship graph after adding each of the asset behavior nodes to each of the matched asset nodes in the asset blood edge relationship graph based on the one-to-one correspondence, the electronic device may further perform the following steps:

and acquiring a first asset behavior node in the target blood relationship graph.

In this embodiment, the first asset behavior node is any one node of all the asset behavior nodes.

And judging whether the first asset behavior node is a sensitive node or not.

In this embodiment, the foregoing specific implementation process of determining whether the first asset behavior node is a sensitive node will be described in further detail in the following specific embodiments, which will not be described herein.

In this embodiment, if the first asset behavior node is a non-sensitive node, that is, the first asset behavior node is a common node, the first asset behavior node is not encrypted, and is directly displayed in the target blood-edge relationship graph. The above specific implementation process of the encryption processing on the first asset behavior node in the target blood relationship graph will be described in further detail in the following specific embodiments, which will not be described herein.

The method comprises the steps of obtaining a first asset behavior node in the target blood relationship graph; then judging whether the first asset behavior node is a sensitive node or not; and if so, carrying out encryption processing on the first asset behavior node in the target blood relationship graph. After the target blood-edge relation diagram corresponding to the target data asset is generated, the sensitive nodes in the target blood-edge relation diagram are intelligently encrypted, so that a user without accessing the sensitive nodes cannot directly look up the related data of the sensitive nodes from the target blood-edge relation diagram, the normalization and the intelligence of information display in the target blood-edge relation diagram are effectively improved, and the safety of sensitive information is ensured.

In some optional implementations, the determining whether the first asset behavior node is a sensitive node includes the steps of:

and acquiring a designated asset node corresponding to the first asset behavior node.

In this embodiment, the specified asset node corresponding to the first asset behavior node may be obtained based on the correspondence relationship between the asset behavior node and the asset node.

And acquiring first ID information corresponding to the designated asset node.

And calling a preset grade data table.

In this embodiment, the level data table is a data table which is constructed in advance according to actual service usage requirements and stores a plurality of ID information and level information corresponding to each ID information one by one. The ID information may include a first level, a second level, and a third level, where the first level corresponds to a level of high importance, the second level corresponds to a level of medium importance, and the third level corresponds to a level of low importance. If the processing team belongs to a high-importance level or a medium-importance level, the asset behavior node corresponding to the ID information of the processing team belongs to a sensitive node, the sensitive data in the sensitive node needs to be encrypted or stealth processed in the blood relationship diagram, and is not displayed if not necessary, and if the processing team is accessed by a user, the related responsible person needs to be notified. And the processing team belongs to a low-importance level, the asset behavior node corresponding to the ID information of the processing team belongs to a non-sensitive node, namely a common node, and behavior data of the non-sensitive asset, namely detailed data of the asset behavior node, can be processed according to a preset data management method, such as direct display, deletion or migration and the like.

And carrying out query processing on the grade data table based on the first ID information, and acquiring a designated grade corresponding to the first ID information from the grade data table.

And judging whether the appointed level is higher than a preset level.

In this embodiment, the preset level may be set according to actual use requirements, for example, may be set to a third level.

In this embodiment, if the specified level is not higher than a preset level, the first asset behavior node is determined to be a non-sensitive node.

The method comprises the steps of obtaining a designated asset node corresponding to the first asset behavior node; then obtaining first ID information corresponding to the appointed asset node; then calling a preset grade data table; subsequently, inquiring the grade data table based on the first ID information, acquiring a designated grade corresponding to the first ID information from the grade data table, and judging whether the designated grade is higher than a preset grade or not; if yes, determining the first asset behavior node as a sensitive node, otherwise, determining the first asset behavior node as a non-sensitive node. The application can acquire the grade information of the first ID information corresponding to the designated asset node based on the use of the grade data table, and further can accurately determine whether the first asset behavior node belongs to the sensitive node based on the comparison result of the grade information and the preset grade.

In some optional implementations, the encrypting the first asset behavior node in the target blood relationship graph includes the steps of:

and acquiring second ID information corresponding to the first asset behavior node.

And acquiring a target grade corresponding to the second ID information based on the second ID information and the grade data table.

In this embodiment, the level data table may be subjected to a query process based on the second ID information, and a target level corresponding to the second ID information may be acquired from the level data table.

And acquiring a target encryption rule corresponding to the target grade.

In this embodiment, encryption rules corresponding to medium importance and high importance are preset for sensitive nodes of medium importance and high importance, respectively. Specifically, for sensitive nodes with medium importance, encryption processing can be performed on the sensitive nodes in a mosaic processing mode; for sensitive nodes with high importance, the sensitive nodes can be encrypted by adopting a preset encryption algorithm to carry out encryption operation. The selection of the encryption algorithm is not particularly limited, and may be set according to actual use requirements.

The method comprises the steps of obtaining second ID information corresponding to the first asset behavior node; then, based on the second ID information and the grade data table, acquiring a target grade corresponding to the second ID information; then, a target encryption rule corresponding to the target grade is obtained; and subsequently, encrypting the first asset behavior node in the target blood relationship graph based on the target encryption rule. According to the application, the target grade of the second ID information is obtained, and then the target encryption rule corresponding to the target grade is intelligently adopted to encrypt the first asset behavior node in the target blood edge relation graph, so that the processing intelligence and the processing normalization of encrypting the sensitive node in the target blood edge relation graph are improved.

In some optional implementations of this embodiment, after the step of encrypting the first asset behavior node in the target blood relationship graph, the electronic device may further perform the following steps:

And receiving a data viewing request triggered by a user for a second asset behavior node in the target blood relationship graph.

In this embodiment, the second asset behavior node is any one of the sensitive nodes in the target blood-edge relationship graph, and the data viewing request carries user information of the user.

And carrying out authority verification on the user based on the user information.

In this embodiment, a user permission level corresponding to user information may be obtained, and then a viewing permission level corresponding to a data viewing operation of the sensitive node may be obtained, and if the user permission level is greater than the viewing permission level, it is indicated that the user has permission to view the detail data of the sensitive node, and it is determined that the user passes permission verification; and if the authority level of the user is smaller than the checking authority level, indicating that the user does not have the authority to check the detail data of the sensitive node, and judging that the user fails the authority verification. The authority authentication scheme of the embodiment can also be applied to government institutions, academic systems, financial institutions (such as banks and the like).

And if the authority verification is passed, acquiring the appointed decryption information corresponding to the second asset behavior node.

In this embodiment, in the process of encrypting the asset behavior node belonging to the sensitive node, decryption information of the asset behavior node is also stored in advance.

In this embodiment, for a user applying for accessing the encrypted information of the sensitive node, the method may further record, in addition to the behavior data of the user, the access time of the user and the access duration, so as to enable subsequent tracing processing for referring to the encrypted information of the sensitive node.

Receiving a data checking request which is triggered by a user and is for a second asset behavior node in the target blood relationship graph; performing authority verification on the user based on the user information; if the authority verification is passed, acquiring appointed decryption information corresponding to the second asset behavior node; the specified decryption information is subsequently shown in the target blood relationship graph. When receiving a data checking request for the sensitive node in the target blood relationship graph, the application can display decryption information corresponding to the sensitive node to the user only when ensuring that the user has the authority to check the detail data of the sensitive node, thereby improving the processing standardization of the data checking request for the sensitive node.

In some optional implementations of this embodiment, after step S206, the electronic device may further perform the following steps:

and acquiring operation information corresponding to the target blood edge relation graph.

In this embodiment, the operation information may include operation information such as addition, deletion, and modification of the nodes in the target blood edge relationship graph triggered by the user.

And judging whether a changed designated node exists in the target blood edge relation graph or not based on the operation information.

In this embodiment, the type of the above specified node may be an asset node or an asset behavior node.

In this embodiment, the failure state refers to that node information of a designated node is not deleted, and the designated node is not identified and displayed in the target blood edge relationship graph.

The application obtains the operation information corresponding to the target blood edge relation diagram; then, based on the operation information, judging whether a changed designated node exists in the target blood edge relation graph; if yes, setting the node information of the designated node in the target blood edge relation graph as a failure state, realizing intelligent failure processing on the designated node with change, so as to complete intelligent simplified processing on the target blood edge relation graph, reduce the display of useless data in the target blood edge relation graph, and improve the consulting experience of users.

It should be emphasized that, to further ensure the privacy and security of the target blood-edge relationship graph, the target blood-edge relationship graph may also be stored in a node of a blockchain.

The blockchain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.

The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a data generating apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus is particularly applicable to various electronic devices.

As shown in fig. 3, the data generating apparatus 300 according to the present embodiment includes: a first acquisition module 301, a second acquisition module 302, a first generation module 303, a construction module 304, a third acquisition module 305, and a second generation module 306. Wherein:

a first acquiring module 301, configured to acquire asset processing data generated during each blood edge processing of a target data asset;

a second obtaining module 302, configured to obtain team information of a processing team corresponding to the target data asset in each of the blood edge processing processes based on the asset processing data;

a first generating module 303, configured to perform labeling processing on the target data asset based on each team information, and generate ID information of the target asset corresponding to each blood edge processing procedure;

a construction module 304, configured to construct asset nodes corresponding to the ID information respectively;

A third obtaining module 305, configured to obtain processing logic information and asset behavior data of each of the asset nodes based on the asset processing data;

and a second generating module 306, configured to generate a target blood edge relationship graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data generating method in the foregoing embodiment one by one, which is not described herein again.

In some alternative implementations of the present embodiment, the second generating module 306 includes:

the first generation sub-module is used for generating an asset blood edge relation graph corresponding to all the asset nodes based on the processing logic information;

a construction sub-module for constructing asset behavior nodes corresponding to the asset behavior data respectively;

the second generation sub-module is used for generating a one-to-one correspondence between the asset behavior nodes and the asset nodes based on the association relationship between the asset behavior data and the asset nodes;

and the third generation sub-module is used for correspondingly adding each asset behavior node to each matched asset node in the asset blood edge relationship graph based on the one-to-one correspondence, and obtaining the target blood edge relationship graph.

In some optional implementations of this embodiment, the second generating module 306 further includes:

the first acquisition submodule is used for acquiring a first asset behavior node in the target blood-edge relation graph; wherein the first asset behavior node is any one of all the asset behavior nodes;

the judging submodule is used for judging whether the first asset behavior node is a sensitive node or not;

and the encryption sub-module is used for carrying out encryption processing on the first asset behavior node in the target blood relationship graph if the first asset behavior node is in the target blood relationship graph.

In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the data generating method in the foregoing embodiment one by one, and are not described herein again.

In some optional implementations of this embodiment, the determining submodule includes:

a first obtaining unit, configured to obtain a specified asset node corresponding to the first asset behavior node;

a second acquiring unit configured to acquire first ID information corresponding to the specified asset node;

The calling unit is used for calling a preset grade data table;

a query unit configured to perform query processing on the level data table based on the first ID information, and acquire a specified level corresponding to the first ID information from the level data table;

the judging unit is used for judging whether the appointed level is higher than a preset level;

and the determining unit is used for determining the first asset behavior node as a sensitive node if yes, and determining the first asset behavior node as a non-sensitive node if not.

In some optional implementations of this embodiment, the encryption submodule includes:

a third obtaining unit, configured to obtain second ID information corresponding to the first asset behavior node;

a fourth acquisition unit configured to acquire a target level corresponding to the second ID information based on the second ID information and the level data table;

a fifth acquisition unit configured to acquire a target encryption rule corresponding to the target level;

and the encryption unit is used for carrying out encryption processing on the first asset behavior node in the target blood relationship graph based on the target encryption rule.

the receiving sub-module is used for receiving a data viewing request which is triggered by a user and is used for a second asset behavior node in the target blood relationship graph; the second asset behavior node is any sensitive node in the target blood relationship graph, and the data viewing request carries user information of the user;

the verification sub-module is used for carrying out authority verification on the user based on the user information;

the second obtaining sub-module is used for obtaining the appointed decryption information corresponding to the second asset behavior node if the authority verification is passed;

and the display sub-module is used for displaying the specified decryption information in the target blood relationship diagram.

In some optional implementations of the present embodiment, the data generating apparatus further includes:

A fourth obtaining module, configured to obtain operation information corresponding to the target blood edge relationship graph;

the judging module is used for judging whether a changed designated node exists in the target blood edge relation graph or not based on the operation information;

and the setting module is used for setting the node information of the designated node in the target blood edge relation graph to be in a failure state if the target blood edge relation graph is in the failure state.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a data generating method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.

The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the data generating method.

The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.

in the embodiment of the application, firstly, asset processing data generated in each blood margin processing process of a target data asset is acquired; then, based on the asset processing data, team information of a processing team corresponding to the target data asset in each blood margin processing process is respectively obtained; labeling the target data assets based on the team information respectively, and generating ID information of the target assets corresponding to each blood margin processing process respectively; subsequently constructing asset nodes respectively corresponding to the ID information; further based on the asset processing data, processing logic information and asset behavior data of each asset node are respectively obtained; and finally, generating a target blood-edge relation graph corresponding to all the asset nodes based on the asset nodes, the processing logic information and the asset behavior data. According to the embodiment of the application, the processing logic information and the asset behavior data of the asset node corresponding to the target data asset are counted by acquiring the asset processing data generated in each blood edge processing process of the target data asset acquired in advance, so that the target blood edge relation diagram corresponding to the target data asset can be quickly and accurately generated according to the asset node, the processing logic information and the asset behavior data, the generation efficiency and the generation intelligence of the target blood edge relation diagram are improved, a user can quickly find out the required data asset information by using the target blood edge relation diagram, the data query efficiency of the data asset is improved, and the user can perform tracing analysis and influence analysis on the data asset based on the target blood edge relation diagram, so that the management efficiency of the data asset is improved.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the data generation method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. A data generation method, comprising the steps of:

constructing asset nodes respectively corresponding to the ID information;

2. The method of claim 1, wherein the step of generating a target blood-edge relationship graph corresponding to all the asset nodes based on the asset nodes, the processing logic information, and the asset behavior data, specifically comprises:

3. The data generating method according to claim 2, further comprising, after the step of obtaining the target blood edge relationship map after adding each of the asset behavior nodes to each of the matched asset nodes in the asset blood edge relationship map based on the one-to-one correspondence, the step of:

judging whether the first asset behavior node is a sensitive node or not;

4. A method of generating data according to claim 3, wherein the step of determining whether the first asset behavior node is a sensitive node comprises:

acquiring first ID information corresponding to the designated asset node;

calling a preset grade data table;

judging whether the appointed level is higher than a preset level;

5. The method of claim 4, wherein the step of encrypting the first asset behavior node in the target blood relationship graph specifically comprises:

acquiring second ID information corresponding to the first asset behavior node;

acquiring a target encryption rule corresponding to the target grade;

6. The data generation method according to claim 3, wherein after the step of encrypting the first asset behavior node in the target blood relationship graph, further comprising:

performing authority verification on the user based on the user information;

7. The data generation method according to claim 1, further comprising, after the step of generating target blood-edge relationship maps corresponding to all the asset nodes based on the asset nodes, the processing logic information, and the asset behavior data:

8. A data generating apparatus, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the data generation method of any of claims 1 to 7.

10. A computer-readable storage medium, having stored thereon computer-readable instructions which, when executed by a processor, implement the steps of the data generation method of any of claims 1 to 7.