Data management platform, method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a data management platform, a data management method and device, equipment and a storage medium. The data management platform comprises a data integration module used for integrating data of a plurality of departments by establishing a data lake; the data association module is connected with the data integration module and is used for associating the department data through a block chain technology to obtain association information of the department data; the data analysis module is connected with the data association module and used for analyzing the department data based on the association information to obtain a data analysis result; and the data display module is connected with the data analysis module and is used for displaying the data analysis result through a display interface, so that a user processes the department data on the display page based on the data analysis result. According to the invention, data is processed through the data management platform, so that the labor cost is reduced while the data risk is reduced.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a data management platform, a data management method, a data management device, a computer equipment, and a computer-readable storage medium. Background Art

[0002] At present, the business of financial companies such as banks usually involves multiple lines, departments and systems, and each department has accumulated a large amount of data. However, this data is usually stored in a decentralized manner. Therefore, data is difficult to communicate, and some sensitive data cannot be shared across departments, which may lead to data duplication, inefficient use and other potential risks.

[0003] However, the processing of data in existing financial data management platforms often relies on professional staff to write corresponding codes for processing, which has low processing efficiency and, to a certain extent, poor privacy protection of data. At the same time, the development cost of existing financial data management platforms is high, and the labor cost is also high. Summary of the invention

[0004] The present application provides a data management platform, a data management method, a data management device, a computer device and a computer-readable storage medium, which are intended to process data through the data management platform, thereby reducing data risks and labor costs.

[0005] To achieve the above objectives, the present application provides a data management platform, including:

[0006] Data integration module, used to integrate data from several departments by building a data lake;

[0007] A data association module, connected to the data integration module, is used to associate the department data through blockchain technology to obtain association information of the department data;

[0008] A data analysis module, connected to the data association module, for analyzing the department data based on the association information to obtain a data analysis result;

[0009] The data display module is connected to the data analysis module and is used to display the data analysis results through a display interface so that the user can process the department data based on the data analysis results on the display page.

[0010] To achieve the above objectives, the present application also provides a data management method, comprising:

[0011] By building a data lake to integrate data from several departments;

[0012] The department data are associated with each other through blockchain technology to obtain associated information of the department data;

[0013] Analyze the department data based on the associated information to obtain data analysis results;

[0014] The data analysis result is displayed through a display interface, so that the user can process the department data based on the data analysis result on the display page.

[0015] To achieve the above objectives, the present application also provides a data management device, including:

[0016] An integration module, wherein the integration module is used to integrate data of several departments by establishing a data lake;

[0017] An association module, the association module is used to associate the department data through blockchain technology to obtain association information of the department data;

[0018] An analysis module, the analysis module is used to analyze the department data based on the association information to obtain a data analysis result;

[0019] A display module is used to display the data analysis result through a display interface, so that the user can process the department data based on the data analysis result on the display page.

[0020] In addition, to achieve the above-mentioned purpose, the present application also provides a computer device, which includes a memory and a processor; the memory is used to store a computer program; the processor is used to execute the computer program and implement the steps of any one of the data management methods provided in the embodiments of the present application when executing the computer program.

[0021] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the processor implements the steps of any one of the data management methods provided in the embodiments of the present application.

[0022] The data management platform, data management method, data management device, computer equipment and computer-readable storage medium disclosed in the embodiment of the present application. Among them, the data management platform includes a data integration module, which is used to integrate several department data by establishing a data lake; a data association module, which is connected to the data integration module, and is used to associate department data through blockchain technology to obtain association information of department data; a data analysis module, which is connected to the data association module, is used to analyze department data based on association information to obtain data analysis results; a data display module, which is connected to the data analysis module, is used to display the data analysis results through a display interface, so that users can process department data based on the data analysis results on the display page. The data management platform provided by the present application integrates multi-department data through a data lake, and uses blockchain technology to realize the secure association of data to ensure the credibility and non-tamperability of data. Efficient data analysis based on association information can help users quickly obtain valuable analysis results, and is displayed through an intuitive interface to facilitate user processing and decision-making. The platform not only improves the security and efficiency of data management, but also enhances the ability of cross-departmental collaboration and information sharing, improves overall work efficiency, and reduces labor costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0024] Figure 1 It is a scenario diagram of a data management method provided in an embodiment of the present application;

[0025] Figure 2 It is a structural diagram of a data management platform provided in an embodiment of the present application;

[0026] Figure 3 It is a flowchart of a data management method provided in an embodiment of the present application;

[0027] Figure 4 is a schematic block diagram of a data management device provided in an embodiment of the present application;

[0028] Figure 5 It is a schematic block diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions. In addition, although the functional modules are divided in the device schematic, in some cases, the module division can be different from that in the device schematic.

[0031] The term "and / or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0032] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0033] like Figure 1 As shown, the data management method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. The application environment includes a terminal device 110 and a server 120, wherein the terminal device 110 can communicate with the server 120 through a network. Specifically, the server 120 can integrate several department data by establishing a data lake; associate the department data through blockchain technology to obtain the association information of the department data; analyze the department data based on the association information to obtain the data analysis result; display the data analysis result through a display interface so that the user processes the department data based on the data analysis result on the display page, thereby sending the data analysis result to the terminal device 110. Among them, the server 120 can be an independent server, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms. The terminal device 110 can be a smart phone, a tablet computer, a laptop, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server may be connected directly or indirectly via wired or wireless communication, which is not limited in this application.

[0034] See also Figure 2 , Figure 2 Schematic diagram of a data management platform provided in an embodiment of the present application. Figure 2 As shown, the data management platform includes a data integration module 11 , a data association module 12 , a data analysis module 13 and a data display module 14 .

[0035] The data integration module 11 is used to integrate several department data by establishing a data lake; the data association module 12 is connected to the data integration module 11, and is used to associate the department data through blockchain technology to obtain the association information of the department data; the data analysis module 13 is connected to the data association module 12, and is used to analyze the department data based on the association information to obtain the data analysis results; the data display module 14 is connected to the data analysis module 13, and is used to display the data analysis results through the display interface, so that the user can process the department data based on the data analysis results on the display page.

[0036] It should be noted that a data lake is a large repository that can store structured and unstructured data, allowing users to put data from different sources and formats into it without having to predefine the structure of the data.

[0037] For example, the data integration module 11 can collect and store data from several departments through a data lake, avoid data island problems, and centrally store data from different departments on a unified platform.

[0038] In other embodiments, the data integration module 11 can also be responsible for preprocessing data for subsequent data association and analysis. Preprocessing may include data cleaning, deduplication, format standardization, etc., which are not limited in this application.

[0039] It should be noted that blockchain is a decentralized, tamper-proof distributed ledger technology. Using it for data association can ensure that the association process of data from different departments is transparent, secure, and traceable.

[0040] For example, the data association module 12 can find the correlation between the data of each department based on the smart contract or preset rules of the blockchain. For example, a certain data of department A has a business or logical connection with a certain data of department B, and the blockchain can be responsible for recording and verifying these connections. The obtained association information may include but is not limited to which data is related, the strength or frequency of the correlation, and the business logic behind these associations (such as transactions, user behavior, etc.).

[0041] For example, the data analysis module 13 can perform in-depth analysis using the association information provided by the data association module 12. The analysis can use various data mining and machine learning techniques, including but not limited to: statistical analysis, trend prediction, association rule mining, cluster analysis, and the like.

[0042] For example, the data analysis module 13 can analyze the potential patterns, trends, anomalies, etc. of the data according to different business needs to provide valuable insights for users. For example, the data analysis module 13 can analyze the data of multiple departments to find cross-departmental efficiency problems, potential collaboration opportunities, or detect bottlenecks in business processes.

[0043] For example, the data display module 14 can display the data analysis results through a display interface. The display interface may include a variety of visualization methods such as charts, data tables, and heat maps to help users intuitively understand the analysis results. The data display module 14 may also include interactive functions, allowing users to operate on the data based on the displayed analysis results. For example, the user can choose to filter certain data, adjust analysis parameters, or directly issue instructions through the system to correct data or optimize business processes when problems are found. In addition, the data display module 14 can also support real-time data updates to ensure that users can see the latest analysis results and data dynamics.

[0044] In the above implementation mode, the data management platform stores the data of multiple departments in the data lake through the data integration module 11, and uses the data association module 12 to ensure the transparency and security of data association through blockchain technology. The data analysis module 13 then analyzes these related data, and finally presents the analysis results to the user intuitively through the data display module 14, so as to help the user further process and optimize the data and business processes between departments.

[0045] The data management platform disclosed in the embodiment of the present application includes a data integration module 11, which is used to integrate several department data by establishing a data lake; a data association module 12, which is connected to the data integration module 11, and is used to associate department data through blockchain technology to obtain association information of department data; a data analysis module 13, which is connected to the data association module 12, and is used to analyze department data based on association information to obtain data analysis results; a data display module 14, which is connected to the data analysis module 13, is used to display the data analysis results through a display interface, so that users can process department data based on the data analysis results on the display page. The data management platform provided by the present application integrates multi-department data through a data lake, and uses blockchain technology to achieve secure association of data to ensure the credibility and non-tamperability of data. Efficient data analysis based on association information can help users quickly obtain valuable analysis results, and is displayed through an intuitive interface to facilitate user processing and decision-making. The platform not only improves the security and efficiency of data management, but also enhances the ability of cross-departmental collaboration and information sharing, improves overall work efficiency, and reduces labor costs.

[0046] Optionally, the data management platform also includes a data governance module (not shown), which is respectively connected to the data integration module 11, the data association module 12 and the data analysis module 13, and is used to govern the life cycle of department data so that the risk value of the department data is lower than a first preset threshold.

[0047] For example, the data governance module can perform the following operations when governing the life cycle of departmental data: Data creation: When data is first created, the data governance module can check whether its source is legal, whether it complies with the company's internal data standards, and whether it has sufficient metadata support (such as data source, data owner, etc.); Data storage: The data governance module can ensure that all data is stored in a secure storage environment, and adopt appropriate encryption technology and access control measures to prevent unauthorized access; Data use and sharing: When data is used or shared, the data governance module can track who is accessing the data, what the data is used for, and ensure that the use and sharing behaviors comply with regulatory requirements; Data destruction: At the end of the data life cycle, the data governance module can securely destroy the data to ensure that sensitive data does not fall into the hands of criminals or is improperly used.

[0048] For example, the data governance module can also continuously monitor the risk value of the data to ensure that the data risk is within the safety threshold, that is, within the first preset threshold. Once it is detected that the data risk value is close to or exceeds the first preset threshold, corresponding emergency measures can be triggered, such as suspending data operations, issuing warnings, or even requiring manual intervention.

[0049] In the above implementation mode, by connecting the data integration module 11, the data association module 12 and the data analysis module 13, the data governance module ensures that the operation of each module complies with the data security and compliance requirements, while minimizing the data risks.

[0050] Optionally, the data integration module 11 is further configured to manage the department data by adopting privacy computing technology so that the privacy value of the department data is higher than a second preset threshold.

[0051] The second preset threshold is used to measure the level of data privacy protection. Through privacy computing technology, the data privacy value of each department is higher than the second preset threshold, ensuring that the data will not be exposed to unauthorized users or systems during data integration and sharing.

[0052] It should be noted that the core goal of privacy computing technology is to ensure that data can be calculated, analyzed or used without exposing the original data. It is widely used in many fields, especially in scenarios where data needs to be shared across organizations or departments but data privacy must be protected. Specifically in the data integration module, privacy computing technology can be used to process sensitive data from different departments, so that data privacy is still guaranteed even in the process of data integration and sharing.

[0053] For example, the data integration module 11 can improve the privacy of the data through privacy computing technology to ensure that its privacy value is higher than the second preset threshold. Among them, the privacy value usually reflects the possibility of data being leaked during use and integration. Through privacy computing, the risk of data leakage can be reduced, thereby improving the privacy value. The privacy value being higher than the second preset threshold means that the privacy risk in the data processing process is within a controllable range and can meet the privacy protection requirements of enterprises, industries or regulations.

[0054] The above implementation method, by introducing privacy computing technology into the data integration module 11, can effectively protect the data privacy of each department during the data processing and integration process and improve the privacy value of the data.

[0055] Optionally, the data association module 12 is also used to create a knowledge graph through blockchain technology, and associate department data through the knowledge graph to obtain association information of the department data; wherein the department data includes at least one of customer information, transaction history information and product data information.

[0056] It should be noted that the knowledge graph is a graph structure used to represent and store data and its relationships. It represents knowledge in the real world through nodes (entities) and edges (relationships). The knowledge graph can not only describe the entities of each data item (such as customer information, transaction history information, product data information), but also show the relationship between them. This association is not limited to simple logical connections, but can also show more complex semantic associations.

[0057] For example, the data association module 12 can use the knowledge graph to display the association information of each department's data. Through blockchain technology, each node and relationship in the knowledge graph has the characteristic of being tamper-proof, ensuring the reliability and authenticity of the data association information.

[0058] In the above implementation, through the knowledge graph created by blockchain technology, the data association module 12 can efficiently and securely associate department data to obtain associated information. The knowledge graph has significant advantages in displaying complex relationships, enhancing semantic understanding and data visualization.

[0059] Optionally, the data analysis module 13 is further configured to generate warning information when determining that the data analysis result is abnormal; the data display module 14 is further configured to display the warning information on the display page to prompt the user that the data analysis result is abnormal.

[0060] For example, the data analysis module 13 may perform detailed data analysis based on the association information provided by the data association module 12. The data analysis process may include statistical analysis, pattern recognition, trend analysis, etc. Through these methods, the data analysis module 13 can evaluate the normal range or expected behavior of the data. When the data analysis module 13 finds that certain data deviates from the preset normal range during the analysis process, it will be regarded as abnormal data. For example: there are unusual changes in the customer's transaction behavior (such as sudden large transactions or frequent returns); there are abnormal fluctuations in product data (such as a sharp drop in sales data of a certain product). Once the data analysis result is determined to be abnormal, the data analysis module 13 will immediately generate an early warning message. The purpose of the early warning message is to inform the user that there may be risks or problems in certain data, which helps the user to respond quickly to abnormal situations and avoid potential risk expansion.

[0061] Optionally, the data display module 14 is further used to generate a risk report based on the data analysis results, and display the risk report on the display page.

[0062] For example, after completing the data analysis, the data display module 14 will not only display the warning information, but also generate a detailed risk report. The report will cover data anomalies, trend predictions, descriptions of potential risks, etc. based on the data analysis results. The generation of the risk report is usually combined with the risk model in the system to evaluate and predict the risks that may arise in the future.

[0063] In the above embodiment, through the cooperation of the data analysis module 13 and the data display module 14, not only can the data be analyzed in depth, but also early warning information can be generated in time when data anomalies are found, and the user can be informed through the display page. Based on further analysis, a detailed risk report can also be generated to help users understand the potential risks and problems in the data.

[0064] Please continue reading Figure 3 , Figure 3 Schematic diagram of a data management method provided in an embodiment of the present application. Figure 3 As shown, the data management method can be implemented through steps S11 to S14.

[0065] Step S11: Integrate data from several departments by establishing a data lake.

[0066] Step S12: The department data is associated through blockchain technology to obtain the associated information of the department data.

[0067] Step S13: Analyze the department data based on the associated information to obtain data analysis results.

[0068] Step S14: Displaying the data analysis results through a display interface, so that the user can process the department data based on the data analysis results on the display page.

[0069] The above implementation method describes the complete process from data collection to data correlation analysis and result display. Through these steps, cross-departmental data can be integrated, blockchain technology can be used to ensure data security and reliability, and valuable business insights can be provided through analysis. Ultimately, users can obtain analysis results through the display interface and make data-driven decisions based on them to improve enterprise operational efficiency.

[0070] See also Figure 4 , Figure 4 1 is a schematic block diagram of a data management device provided in an embodiment of the present application. The data management device can be configured in a server to execute the aforementioned data management method.

[0071] like Figure 4 As shown, the data management device 200 includes: an integration module 201 , an association module 202 , an analysis module 203 and a display module 204 .

[0072] An integration module 201, used for integrating data of several departments by establishing a data lake;

[0073] An association module 202, used to associate the department data through blockchain technology to obtain association information of the department data;

[0074] An analysis module 203 is used to analyze the department data based on the association information to obtain a data analysis result;

[0075] The display module 204 is used to display the data analysis result through a display interface, so that the user can process the department data based on the data analysis result on the display page.

[0076] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and modules and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0077] The method and apparatus of the present application can be used in many general or special computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer terminal devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices.

[0078] Exemplarily, the above method and apparatus may be implemented in the form of a computer program. The computer program may be implemented in Figure 5 Runs on the computer device shown.

[0079] See also Figure 5 , Figure 5 1 is a schematic diagram of a computer device provided in an embodiment of the present application. The computer device may be a server.

[0080] like Figure 5 As shown, the computer device includes a processor, a memory and a network interface connected through a system bus, wherein the memory may include a volatile storage medium, a non-volatile storage medium and an internal memory.

[0081] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any data management method.

[0082] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0083] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any data management method.

[0084] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that the structure of the computer device is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0085] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0086] Among them, in some embodiments, the processor is used to integrate several department data by establishing a data lake; associate the department data through blockchain technology to obtain association information of the department data; analyze the department data based on the association information to obtain data analysis results; display the data analysis results through a display interface, so that the user can process the department data based on the data analysis results on the display page.

[0087] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions, and when the program instructions are executed, any one of the data management methods provided in the embodiments of the present application is implemented.

[0088] The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the computer device.

[0089] Furthermore, the computer-readable storage medium may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function, and the like.

[0090] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A data management platform, characterized in that: The data management platform includes: Data integration module, used to integrate data from several departments by building a data lake; A data association module, connected to the data integration module, is used to associate the department data through blockchain technology to obtain association information of the department data; A data analysis module, connected to the data association module, for analyzing the department data based on the association information to obtain a data analysis result; The data display module is connected to the data analysis module and is used to display the data analysis results through a display interface so that the user can process the department data based on the data analysis results on the display page.

2. The data management platform according to claim 1, characterized in that: The data management platform also includes a data governance module, which is connected to the data integration module, the data association module and the data analysis module respectively, and is used to govern the life cycle of the department data so that the risk value of the department data is lower than a first preset threshold.

3. The data management platform according to claim 1, characterized in that: The data integration module is also used to manage the department data using privacy computing technology so that the privacy value of the department data is higher than a second preset threshold.

4. The data management platform according to claim 1, characterized in that: The data association module is also used to create a knowledge graph through the blockchain technology, and associate the department data through the knowledge graph to obtain association information of the department data; wherein the department data includes at least one of customer information, transaction history information and product data information.

5. The data management platform according to claim 1, characterized in that: The data analysis module is further configured to generate warning information when determining that the data analysis result is abnormal; the data display module is further configured to display the warning information on the display page to prompt the user that the data analysis result is abnormal.

6. The data management platform according to claim 1, characterized in that: The data display module is also used to generate a risk report based on the data analysis result, and display the risk report on the display page.

7. A data management method, characterized in that: The data management method comprises: By building a data lake to integrate data from several departments; The department data are associated with each other through blockchain technology to obtain associated information of the department data; Analyze the department data based on the associated information to obtain data analysis results; The data analysis result is displayed through a display interface, so that the user can process the department data based on the data analysis result on the display page.

8. A data management device, characterized in that: The data management device comprises: An integration module, wherein the integration module is used to integrate data of several departments by establishing a data lake; An association module, the association module is used to associate the department data through blockchain technology to obtain association information of the department data; An analysis module, the analysis module is used to analyze the department data based on the association information to obtain a data analysis result; A display module is used to display the data analysis result through a display interface, so that the user can process the department data based on the data analysis result on the display page.

9. A computer device, characterized in that: include: A memory and a processor; wherein the memory is connected to the processor for storing a program; the processor is used to implement the steps of the data management method as described in claim 7 by running the program stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, which, when executed by a processor, causes the processor to implement the steps of the data management method as claimed in claim 7.

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