CN116860836A - Data management system and data management method - Google Patents
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
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- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/22—Indexing; Data structures therefor; Storage structures
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
Abstract
The invention relates to the technical field of data processing, and discloses a data management system and a data management method, wherein the system comprises the following components: the system comprises a base module, an expansion module and a service module; the basic module is used for acquiring detail data and business data corresponding to the target user; the expansion module is used for obtaining statistical index data based on detail data and business data; the service module is used for processing the detail data, the service data and the statistical index data according to the service requirement, obtaining and outputting the processed target service data for data sharing. According to the invention, detail data, service data and statistical index data obtained based on the detail data and the service data are processed according to service requirements, and processed target service data are obtained and output for data sharing, so that the technical problem that data sharing is difficult due to the fact that service and organization architecture are used as data organization in data management in the prior art is solved.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a data management system and a data management method.
Background
With the development of information technology and the increase of the enterprise scale, the enterprise operation range tends to be diversified, and due to the large amount and variety of operation data of enterprises, standardized management of data is required. In the current data management technology, a business and an organization architecture are generally used as an organization form of data, but the data management in this way easily causes difficulty in data sharing, which is further unfavorable for data management.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide a data management system and a data management method, and aims to solve the technical problem that in the prior art, data is difficult to share because data management is generally carried out in a business and organization architecture as an organization form of data.
To achieve the above object, the present invention provides a data management system comprising: the system comprises a base module, an expansion module and a service module;
the basic module is used for acquiring detail data and business data corresponding to the target user;
the expansion module is used for obtaining statistical index data based on the detail data and the service data;
the service module is used for processing the detail data, the service data and the statistical index data according to service requirements, obtaining and outputting processed target service data for data sharing.
Optionally, the base module is further configured to receive user data generated by a target user based on a product, and determine detail data and service data in the user data according to a data attribute;
the basic module is further used for storing the detail data and the service data to a preset database, and the detail data and the service data are respectively stored in different positions in the preset database;
the expansion module is further used for obtaining statistical index data based on the detail data and the service data in the preset database.
Optionally, the expansion module is further configured to perform statistics on the detail data and the service data based on a paradigm modeling manner and a preset statistical index;
the expansion module is also used for obtaining statistical index data according to the statistical result.
Optionally, the expansion module is further configured to correlate a user information table;
and the expansion module is further used for counting the detail data and the service data based on a paradigm modeling mode, a preset statistical index and the user information table when the association is successful.
Optionally, the data management system further comprises: a dimension module;
the dimension module is used for outputting dimension data to the service module;
the service module is further configured to correlate the statistical index data with the dimension data to obtain a target data wide table, where the target data wide table is used to support a service requirement.
In addition, in order to achieve the above object, the present invention also provides a data management method based on the above data management system, where the data management method includes:
the basic module acquires detail data and business data corresponding to a target user;
the expansion module obtains statistical index data based on the detail data and the business data;
and the service module processes the detail data, the service data and the statistical index data according to service requirements to obtain and output processed target service data so as to share the data.
Optionally, the step of obtaining detail data and service data corresponding to the target user by the base module includes:
the base module receives user data generated by a target user based on a product;
and the basic module determines detail data and business data in the user data according to the data attribute.
Optionally, after the step of determining the detail class data and the service class data in the user data according to the data attribute, the base module further includes:
the basic module stores the detail data and the service data into a preset database, and the detail data and the service data are respectively stored in different positions in the preset database;
the step of the expansion module obtaining statistical index data based on the detail class data and the service class data comprises the following steps:
and the expansion module obtains statistical index data based on the detail data and the service data in the preset database.
Optionally, before the step of obtaining the statistical index data based on the detail class data and the service class data, the expansion module further includes:
the expansion module correlates the user information table;
the step of the expansion module obtaining statistical index data based on the detail class data and the service class data comprises the following steps:
and when the association is successful, the expansion module counts the detail data and the business data based on a normal form modeling mode, a preset statistical index and the user information table, and obtains statistical index data according to a statistical result.
Optionally, the service module processes the detail data, the service data and the statistical index data according to service requirements, and obtains and outputs processed target service data, so as to perform the step of data sharing, and then further includes:
the service module associates the statistical index data with dimension data to obtain a target data wide table, wherein the target data wide table is used for supporting service requirements, and the dimension data is stored in the dimension module.
In the invention, a base module, an extension module and a service module are disclosed; the basic module is used for acquiring detail data and business data corresponding to the target user; the expansion module is used for obtaining statistical index data based on detail data and business data; the service module is used for processing detail data, service data and statistical index data according to service requirements, obtaining and outputting processed target service data for data sharing; according to the invention, the service module processes the detail data, the service data and the statistical index data obtained based on the detail data and the service data to obtain and output the processed target service data for data sharing, so that the technical problem of difficult data sharing caused by the fact that the service and the organization structure are used as the organization form of the data in the data management in the prior art is solved.
Drawings
FIG. 1 is a block diagram of a first embodiment of a data management system according to the present invention;
FIG. 2 is a block diagram illustrating a second embodiment of a data management system according to the present invention;
FIG. 3 is a flowchart of a first embodiment of a data management method according to the present invention based on the data management system;
fig. 4 is a flowchart of a second embodiment of a data management method based on the data management system according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, but not all embodiments, all embodiments obtained by persons skilled in the art based on the embodiments in the present invention without making creative efforts, belong to the protection scope of the present invention.
It should be noted that the descriptions of "first," "second," etc. in the embodiments of the present invention are for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
Referring to fig. 1, fig. 1 is a block diagram illustrating a first embodiment of a data management system according to the present invention.
As shown in fig. 1, the data management system of the present embodiment includes: base module 100, extension module 200, and service module 300.
The base module 100 is configured to obtain detail data and service data corresponding to a target user.
It should be noted that, the target user may be a user who provides data and needs to perform data management, for example, a user who may use or manage products on an enterprise internet bank, an enterprise APP, and a third party channel, which is not limited in this embodiment.
It should be appreciated that the detail class data may be data related to user behavior events, such as: start-up software behavior data, login behavior data, etc., which is not limited in this embodiment.
It will be appreciated that the service class data may be related data for a user to transact a service, for example: user transfer service manager information, etc., which is not limited in this embodiment.
In a specific implementation, taking contact data as an example, if business data of an enterprise internet banking, an enterprise APP or a third party channel flows into a data warehouse, data can be cleaned and processed by using a data script, so that detail data and business data of the base module 100 can be obtained.
The expansion module 200 is configured to obtain statistical index data based on the detail class data and the service class data.
It should be noted that, the statistical index data may be data obtained by using the data in the base module 100 to perform statistics on various behavior data. In practical application, the data in the basic module 100 is utilized in the extension module 200 to respectively count various behavior data, so that single statistical index data such as start software statistics, login statistics, click statistics and the like can be obtained.
Further, in order to improve the data processing efficiency, the base module 100 is further configured to receive user data generated by a target user based on a product, and determine detail data and service data in the user data according to a data attribute; the base module 100 is further configured to store the detail data and the service data in a preset database, where the detail data and the service data are respectively stored in different positions in the preset database; the expansion module 200 is further configured to obtain statistical index data based on the detail class data and the service class data in the preset database.
It should be appreciated that the user data described above may be data generated by the target user for all products on the enterprise network banking, enterprise APP and third party channels.
It is understood that the above-mentioned preset database may be a space for storing data in the base module 100.
In a specific implementation, the data types include structured data and unstructured data, and since the data structures generated by the systems are different, and part of the systems have service data and part of the systems do not have service data, in order to be compatible with the data generated by all the systems, the subsequent growth data can be directly collected into the existing storage space, and the data is divided into detail data and service data in the base module 100. Meanwhile, in order to reduce the upper-layer statistics logic and obtain the standardized structure data, in the base module 100, all source data can be cleaned and personalized processed by utilizing the mass data processing capability of the large data platform according to the data structure provided by each system, so as to obtain standard structured data and provide a unified standard data source for the expansion module 200. In addition, in the base module 100, the data generated by the target user on the product can be identified according to the data attribute, and the specific operation behavior data of the user and the business information of the user are stored separately, namely, detail data and business data are stored separately, so that the data boundary of the base module 100 is clear, the screening data range of the user is reduced, and the data redundancy is reduced.
Further, the expansion module 200 is further configured to perform statistics on the detail data and the service data based on a paradigm modeling manner and a preset statistical index; the expansion module is also used for obtaining statistical index data according to the statistical result.
It should be noted that, the foregoing paradigm modeling manner may be a manner in which a user performs data warehouse modeling. The model in the model modeling mode, namely an Entity Relation (ER) model, is a data warehouse framework of a hub from top to bottom (EDW-DM) proposed by father Inmon of the data warehouse, combines a data model of a business system from the perspective of a relational database, and is used for designing a 3NF model at the enterprise level to realize the modeling of the data warehouse.
It should be understood that, in the foregoing preset statistical index, that is, the statistical index corresponding to the data in the base module 100, in practical application, the extension module 200 may separate the user behavior statistical index from the detailed information of the user, the product, the client, etc. in a normal form modeling manner, which is favorable for reducing data redundancy and facilitating unified maintenance of the detailed information of the user, the product, etc. Taking the user behavior data as an example, the user behavior data includes data such as a start software behavior, a login behavior, a click behavior, and an access page behavior, and the expansion module 200 can respectively count various types of user behavior data by using the data in the base module 100, so as to obtain single statistical index data such as start software statistics, login statistics, and click statistics.
It can be understood that the expansion module 200 can obtain the statistical index data by processing the detail data and the service data, so that the detail data, the service data and the statistical index data are obtained, and the data is layered clearly, so that the data management and the use are convenient.
Further, in order to add the dimension of the user to the statistics to perform data statistics, the expansion module 200 is further configured to correlate the user information table; the expansion module 200 is further configured to, when the association is successful, perform statistics on the detail data and the service data based on a paradigm modeling manner, a preset statistical index and the user information table.
It is to be understood that the above-mentioned user information table may be a table storing user information, and the embodiment does not limit the user information specifically stored in the user information table.
In a specific implementation, if the service needs to add the user dimension in the statistics, the user information table may be associated in the statistics process, so as to implement statistics on the user dimension. Meanwhile, in the embodiment, detailed information such as statistical indexes, users, products and the like can be respectively stored in a model modeling mode, different business requirements can be met, and once the processing is finished, the existing statistical data can be reused in the same follow-up requirement, so that the unification of the caliber of the statistical data information item can be realized.
The service module 300 is configured to process the detail data, the service data, and the statistics index data according to service requirements, obtain and output processed target service data, so as to perform data sharing.
In a specific implementation, in this embodiment, a big data storage and calculation platform is used as a carrier, and a standard and a management method for data construction are unified, so as to create a unified operation data platform, where the data platform includes: base module 100, extension module 200, and service module 300. The service module 300 can provide a gate for external service for the whole operation bazaar, so that all requirements can be supported by the service module 300, and management and data sharing of external data are facilitated. Specifically, the service module 300 may process the detail data, the business data and the statistical index data according to the business requirement, and provide data service to the outside, so as to realize sharing data and managing the use authority.
The embodiment discloses a base module, an expansion module and a service module; the basic module is used for acquiring detail data and business data corresponding to the target user; the expansion module is used for obtaining statistical index data based on detail data and business data; the service module is used for processing detail data, service data and statistical index data according to service requirements, obtaining and outputting processed target service data for data sharing; according to the invention, the service module processes the detail data, the service data and the statistical index data obtained based on the detail data and the service data to obtain and output the processed target service data for data sharing, so that the technical problem of difficult data sharing caused by the fact that the service and the organization structure are used as the organization form of the data in the data management in the prior art is solved. Meanwhile, the service module can process detail data, business data and statistical index data according to business requirements, and provide data service to the outside.
Referring to fig. 2, fig. 2 is a block diagram illustrating a second embodiment of a data management system according to the present invention. Based on the first embodiment of the data management system, the present invention is presented based on a second embodiment of the data management system.
As shown in fig. 2, the data management system further includes: dimension module 400.
The dimension module 400 is configured to output dimension data to the service module.
It should be noted that, the dimension module 400 may be used to store common dimension information, and in addition, the dimension module 400 may function as supplementary information in the entire data stream.
The service module 300 is further configured to correlate the statistical index data with the dimension data to obtain a target data wide table, where the target data wide table is used to support a service requirement.
It should be understood that the target data width table may be a data width table obtained by associating statistical index data with dimension data and complementing fact information.
In a specific implementation, since the dimension module 400 plays a role of storing common dimension information and supplementary information in the whole data stream, the service module 300 in this embodiment may use the statistical index data in the extension module 200 to correlate the dimension data in the dimension module 400 and complement the fact information, thereby obtaining a large wide table, where the large wide table in the service module 300 may support most of the service requirements.
The embodiment discloses a dimension module which is used for outputting dimension data to a service module; the service module is also used for correlating the statistical index data with the dimension data to obtain a target data wide table, wherein the target data wide table is used for supporting the service requirement. According to the embodiment, the statistical index data in the expansion module and the dimension data in the dimension module are associated through the service module, so that the target data wide table is obtained to support service requirements, the support of most of service requirements is realized, and the user experience is improved.
Based on the embodiments of the data management system, a first embodiment of the data management method based on the data management system of the present invention is provided.
Referring to fig. 3, fig. 3 is a flowchart illustrating a first embodiment of a data management method according to the present invention based on the above data management system.
In this embodiment, the data management method based on the data management system includes the following steps:
step S10: and the basic module acquires detail data and business data corresponding to the target user.
It should be noted that, the execution body of the method of this embodiment may be a data management device that can manage data generated by all products of the user on the enterprise network bank, the enterprise APP and the third party channels, or other data management systems that can implement the same or similar functions and include the data management device. The data management method provided in this embodiment and the following embodiments will be specifically described with a data management system (hereinafter referred to as a system).
Further, the step S10 may include: the base module receives user data generated by a target user based on a product; and the basic module determines detail data and business data in the user data according to the data attribute.
Step S20: the expansion module obtains statistical index data based on the detail class data and the service class data.
Further, after the step of determining the detail class data and the service class data in the user data according to the data attribute, the base module further includes: the basic module stores the detail data and the service data into a preset database, and the detail data and the service data are respectively stored in different positions in the preset database; the step S20 includes: and the expansion module obtains statistical index data based on the detail data and the service data in the preset database.
It should be noted that, in the basic module, the data generated by the user on the product can be identified according to the data attribute, and the specific operation behavior data of the user and the business information of the user are stored separately, so that the data boundary in the basic module is clear, the screening data range of the user is reduced, and the data redundancy is reduced.
Step S30: and the service module processes the detail data, the service data and the statistical index data according to service requirements to obtain and output processed target service data so as to share the data.
In a specific implementation, the basic module can identify data generated by a target user on a product according to data attributes, and store the specific operation behavior data of the user and the business information of the user separately, namely store detail data and business data separately, so that the data boundary of the basic module is clear, the screening data range of the user is reduced, and the data redundancy is reduced. The expansion module can respectively count various user behavior data by utilizing the data in the basic module, so that single starting software statistics, login statistics, click statistics and other statistics index data are obtained, so that bright and fine data, business data and statistics index data are obtained, and the data are layered clearly, so that the management and the use are convenient. In addition, the service module can process detail data, business data and statistical index data according to business requirements and provide data services to the outside so as to realize sharing data and managing use rights.
The embodiment discloses that a basic module acquires detail data and business data corresponding to a target user; the expansion module obtains statistical index data based on detail data and business data; the service module processes the detail data, the service data and the statistical index data according to the service requirement, and obtains and outputs the processed target service data so as to share the data; according to the invention, the service module processes the detail data, the service data and the statistical index data obtained based on the detail data and the service data to obtain and output the processed target service data for data sharing, so that the technical problem of difficult data sharing caused by the fact that the service and the organization structure are used as the organization form of the data in the data management in the prior art is solved. Meanwhile, the service module can process detail data, business data and statistical index data according to business requirements, and provide data service to the outside.
Referring to fig. 4, fig. 4 is a flowchart illustrating a second embodiment of a data management method according to the present invention based on the above data management system. Based on the above-described first embodiment, a second embodiment of the data management method of the present invention based on the above-described data management system is presented.
Based on the first embodiment, in order to add a dimension to the statistics for data statistics, in this embodiment, before step S20, the method further includes:
step S020: and the expansion module correlates the user information table.
Correspondingly, the step S20 includes:
step S20': and when the association is successful, the expansion module counts the detail data and the business data based on a normal form modeling mode, a preset statistical index and the user information table, and obtains statistical index data according to a statistical result.
Further, after the step S30, the method further includes: the service module associates the statistical index data with dimension data to obtain a target data wide table, wherein the target data wide table is used for supporting service requirements, and the dimension data is stored in the dimension module.
In a specific implementation, if the service needs to add the dimension of the user in statistics, the user information table can be associated in the statistics process, so that detail data and service data can be counted based on a paradigm modeling mode, a preset statistical index and the user information table, and statistical index data is obtained according to a statistical result, thereby realizing statistics of the dimension of the user. In addition, the service module can use the statistical index data in the expansion module to correlate the dimension data in the dimension module and complement the fact information, so that a large wide table is obtained to support most of service demands.
The embodiment discloses that an expansion module correlates a user information table; and when the association is successful, counting detail data and business data based on a paradigm modeling mode, a preset statistical index and a user information table, and obtaining statistical index data according to a statistical result, so that the dimension of a user can be added in the statistics. Meanwhile, in the embodiment, the statistical index data in the expansion module and the dimension data in the dimension module are associated through the service module to obtain the target data wide table so as to support service requirements, so that the support of most of service requirements is realized, and the user experience is improved.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
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 invention 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. read-only memory/random-access memory, magnetic disk, optical disk), comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. A data management system, the data management system comprising: the system comprises a base module, an expansion module and a service module;
the basic module is used for acquiring detail data and business data corresponding to the target user;
the expansion module is used for obtaining statistical index data based on the detail data and the service data;
the service module is used for processing the detail data, the service data and the statistical index data according to service requirements, obtaining and outputting processed target service data for data sharing.
2. The data management system of claim 1, wherein the base module is further configured to receive user data generated by a target user based on a product, and determine detail class data and business class data in the user data according to data attributes;
the basic module is further used for storing the detail data and the service data to a preset database, and the detail data and the service data are respectively stored in different positions in the preset database;
the expansion module is further used for obtaining statistical index data based on the detail data and the service data in the preset database.
3. The data management system of claim 1, wherein the expansion module is further configured to perform statistics on the detail class data and the business class data based on a paradigm modeling approach and a preset statistical indicator;
the expansion module is also used for obtaining statistical index data according to the statistical result.
4. The data management system of claim 3, wherein the expansion module is further configured to associate a user information table;
and the expansion module is further used for counting the detail data and the service data based on a paradigm modeling mode, a preset statistical index and the user information table when the association is successful.
5. The data management system of claim 1, wherein the data management system further comprises: a dimension module;
the dimension module is used for outputting dimension data to the service module;
the service module is further configured to correlate the statistical index data with the dimension data to obtain a target data wide table, where the target data wide table is used to support a service requirement.
6. A data management method based on the data management system according to any one of claims 1 to 5, characterized in that the data management method comprises:
the basic module acquires detail data and business data corresponding to a target user;
the expansion module obtains statistical index data based on the detail data and the business data;
and the service module processes the detail data, the service data and the statistical index data according to service requirements to obtain and output processed target service data so as to share the data.
7. The data management method as claimed in claim 6, wherein the step of the base module obtaining detail class data and service class data corresponding to the target user comprises:
the base module receives user data generated by a target user based on a product;
and the basic module determines detail data and business data in the user data according to the data attribute.
8. The data management method according to claim 7, wherein after the step of determining detail class data and service class data in the user data according to data attributes, the base module further comprises:
the basic module stores the detail data and the service data into a preset database, and the detail data and the service data are respectively stored in different positions in the preset database;
the step of the expansion module obtaining statistical index data based on the detail class data and the service class data comprises the following steps:
and the expansion module obtains statistical index data based on the detail data and the service data in the preset database.
9. The data management method according to claim 6, wherein before the step of the expansion module obtaining statistical index data based on the detail class data and the service class data, further comprising:
the expansion module correlates the user information table;
the step of the expansion module obtaining statistical index data based on the detail class data and the service class data comprises the following steps:
and when the association is successful, the expansion module counts the detail data and the business data based on a normal form modeling mode, a preset statistical index and the user information table, and obtains statistical index data according to a statistical result.
10. The data management method according to claim 6, wherein the service module processes the detail class data, the service class data and the statistical index data according to service requirements, and obtains and outputs processed target service class data, so as to perform data sharing, and further comprises:
the service module associates the statistical index data with dimension data to obtain a target data wide table, wherein the target data wide table is used for supporting service requirements, and the dimension data is stored in the dimension module.
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| CN202310957663.1A CN116860836A (en) | 2023-07-31 | 2023-07-31 | Data management system and data management method |
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