CN111125103A - Data processing method and device and computer readable storage medium - Google Patents
Data processing method and device and computer readable storage medium Download PDFInfo
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- CN111125103A CN111125103A CN201911307764.4A CN201911307764A CN111125103A CN 111125103 A CN111125103 A CN 111125103A CN 201911307764 A CN201911307764 A CN 201911307764A CN 111125103 A CN111125103 A CN 111125103A
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Abstract
A data processing method, a device and a computer readable storage medium comprise: acquiring data from each department service system and storing the data in a database; carrying out standardization processing on data stored in a database; classifying the data after the standardized processing according to the attribute of the data, and storing the classified data in a database; and processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply. Because the data based on the service indexes for deploying the services come from the service systems of all departments, cross-department data sharing is realized, and the data utilization rate is improved.
Description
Technical Field
The present disclosure relates to computer technologies, and in particular, to a data processing method, an apparatus, and a computer-readable storage medium.
Background
Each department of a government organization often generates a series of service indexes based on some data, and then deploys service applications according to the generated service indexes.
However, due to the lack or delay of the large data platform construction, data sharing across departments cannot be realized, and therefore, in the related art, the data based on the business index generated by each department is only the data of the department, so that the data utilization rate is low.
Disclosure of Invention
The application provides a data processing method, a data processing device and a computer readable storage medium, so that cross-department data sharing is realized, and the data utilization rate is improved.
The application provides a data processing method, which comprises the following steps:
acquiring data from each department service system and storing the data in a database;
carrying out standardization processing on data stored in a database;
classifying the data after the standardized processing according to the attribute of the data, and storing the classified data in the database;
and processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
After processing each type of data according to the pre-constructed general service requirement to obtain the service index of each type of data for each department to apply, the method further comprises the following steps:
acquiring actual business requirements of a target department;
and selecting service indexes of different types of data according to the actual service requirements, and deploying the service based on the selected service indexes.
The acquiring of the data from each department service system and the storing of the data in the database includes:
acquiring data from each department business system according to a pre-established data acquisition rule;
and storing the obtained data in the database according to a pre-established data storage strategy.
The types of the data include: metadata, code data and content data, wherein each data type has a corresponding standardized mode; the normalizing the data stored in the database comprises the following steps:
and carrying out standardization processing on the data stored in the database according to a standardization mode corresponding to the type to which the data belongs.
Storing the classified data in a database comprises:
modeling the storage mode of each type of data through a dimensional modeling method to obtain a storage model of each type of data;
and storing each type of data in the database according to the corresponding storage model.
The dimension modeling method comprises the following steps: a star model building method.
The database is a Hadoop database based on a distributed system infrastructure.
The present application further provides a server, including:
the acquisition module is used for acquiring data from each department service system and storing the data in a database;
the preprocessing module is used for carrying out standardization processing on the data stored in the database;
the storage module is used for classifying the standardized data according to the attributes of the data and storing the classified data in the database;
and the processing module is used for processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
The present application also provides a data processing apparatus, including: a processor and a memory, wherein the memory has stored therein the following commands executable by the processor:
acquiring data from each department service system and storing the data in a database;
carrying out standardization processing on data stored in a database;
classifying the data after the standardized processing according to the attribute of the data, and storing the classified data in the database;
and processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
The present application further provides a computer-readable storage medium having stored thereon computer-executable instructions for performing the steps of:
acquiring data from each department service system and storing the data in a database;
carrying out standardization processing on data stored in a database;
classifying the data after the standardized processing according to the attribute of the data, and storing the classified data in the database;
and processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
Compared with the related art, the method comprises the following steps: acquiring data from each department service system and storing the data in a database; carrying out standardization processing on data stored in a database; classifying the data after the standardized processing according to the attribute of the data, and storing the classified data in a database; and processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply. Because the data based on the service indexes for deploying the services come from the service systems of all departments, cross-department data sharing is realized, and the data utilization rate is improved.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The accompanying drawings are included to provide an understanding of the present disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the examples serve to explain the principles of the disclosure and not to limit the disclosure.
FIG. 1 is a schematic flow chart of a data processing method in the prior art;
fig. 2 is a schematic flowchart of a data processing method according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of a star model according to an embodiment of the present disclosure;
FIG. 4 is a schematic flow chart illustrating data processing according to a star model according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a server according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a data processing platform according to an embodiment of the present application.
Detailed Description
The present application describes embodiments, but the description is illustrative rather than limiting and it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the embodiments described herein. Although many possible combinations of features are shown in the drawings and discussed in the detailed description, many other combinations of the disclosed features are possible. Any feature or element of any embodiment may be used in combination with or instead of any other feature or element in any other embodiment, unless expressly limited otherwise.
The present application includes and contemplates combinations of features and elements known to those of ordinary skill in the art. The embodiments, features and elements disclosed in this application may also be combined with any conventional features or elements to form a unique inventive concept as defined by the claims. Any feature or element of any embodiment may also be combined with features or elements from other inventive aspects to form yet another unique inventive aspect, as defined by the claims. Thus, it should be understood that any of the features shown and/or discussed in this application may be implemented alone or in any suitable combination. Accordingly, the embodiments are not limited except as by the appended claims and their equivalents. Furthermore, various modifications and changes may be made within the scope of the appended claims.
Further, in describing representative embodiments, the specification may have presented the method and/or process as a particular sequence of steps. However, to the extent that the method or process does not rely on the particular order of steps set forth herein, the method or process should not be limited to the particular sequence of steps described. Other orders of steps are possible as will be understood by those of ordinary skill in the art. Therefore, the particular order of the steps set forth in the specification should not be construed as limitations on the claims. Further, the claims directed to the method and/or process should not be limited to the performance of their steps in the order written, and one skilled in the art can readily appreciate that the sequences may be varied and still remain within the spirit and scope of the embodiments of the present application.
In the related art, the data processing process is of a chimney type, as shown in fig. 1, assuming that the departments include: the system comprises a social security bureau, a meteorological bureau and a public security bureau, wherein the social security bureau needs to develop business application of social security topics, the meteorological bureau needs to develop business application of the meteorological topics, and the public security bureau needs to develop business application of population topics; the method comprises the steps that a social security bureau obtains data from a business System database of the social security bureau, the business application is developed through analysis of an analysis System, a weather bureau obtains data from the business System database of the meteorological bureau, the business application is developed through analysis of the analysis System, a public security bureau obtains data from the business System database of the public security bureau, and the business application is developed through analysis of the analysis System, wherein the business System is based on the Relational Database (RDBMS), and the analysis System is based on the RDBMS and Massively Parallel Processing (MPP). Therefore, barriers exist among the three departments, and data sharing cannot be achieved through the data.
An embodiment of the present application provides a data processing method, as shown in fig. 2, including:
and step 101, acquiring data from each department service system and storing the data in a database.
In an exemplary embodiment, obtaining data from each of the department business systems and storing the data in a database includes:
first, data from each department business system is acquired according to a pre-established data acquisition rule.
And secondly, storing the obtained data in a database according to a pre-established data storage strategy.
In one illustrative example, the types of data include: metadata, code data and content data, there being a corresponding standardized way for each data type. The data stored in the database is subjected to standardization processing, and the standardization processing comprises the following steps:
and carrying out standardization processing on the data stored in the database according to a standardization mode corresponding to the type to which the data belongs.
And 103, classifying the standardized data according to the attributes of the data, and storing the classified data in a database.
In one illustrative example, storing the categorized data in a database includes:
firstly, modeling is respectively carried out on the storage mode of each type of data through a dimensional modeling method, and a storage model of each type of data is obtained.
And secondly, storing each type of data in a database according to a corresponding storage model.
In one illustrative example, a dimensional modeling method includes: a star model building method.
In an illustrative example, as shown in FIG. 3, the fact tables and dimension tables constitute a star model. The star schema is a multidimensional data relationship, which is composed of a Fact Table (Fact Table) and a set of Dimension tables (Dimension Table). Each dimension table has one dimension as a primary key, and the primary keys of all these dimensions are combined into the primary key of the fact table. The non-primary key attributes of Fact tables are called facts (Fact), which are typically numeric values or other data that can be calculated. The model is an unnormalized structure, each dimension in a multi-dimensional data set is connected with a fact table, and a gradual change dimension does not exist, so that the data has certain redundancy, and because of the redundancy of the data, many statistical queries do not need to be connected externally, so that the efficiency is higher in general. The star structure does not need to consider a plurality of normalization factors, and the design and the implementation are simple.
In an illustrative example, taking population subjects as an example, as shown in fig. 4, various types of applications are supported by designing a population fact table (comprising a population type base table and a population model base table) and various dimension tables (comprising a dictionary table, a subject library date dimension table, a time dimension table, a high-frequency word road dimension table, an age dimension table and a population type dimension table) and calculating and processing according to business rules to form various index data (comprising a high-liquidity community population flow ratio, a population male-female gender ratio of a population type, population quantity analysis of different ethnic groups of the population type and population quantity and proportion analysis of the population type of each population).
And 104, processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
In an exemplary embodiment, after processing each type of data according to a pre-constructed general service requirement to obtain a service index of each type of data for each department to apply, the method further includes:
firstly, the actual business requirements of a target department are obtained.
And secondly, selecting service indexes of different types of data according to actual service requirements, and deploying the service based on the selected service indexes.
In one illustrative example, the database is a Hadoop database based on a distributed system infrastructure.
In an exemplary embodiment, the advantages of the Hadoop database compared with the conventional relational database are shown in table 1 below, so that the problems of insufficient computing processing capacity, single type of processed data, high expansion cost and the like in the face of massive data are solved.
TABLE 1
According to the data processing method provided by the embodiment of the application, because the data based on the service indexes for deploying the service is data from each department service system, cross-department data sharing is realized, and the data utilization rate is improved.
An embodiment of the present application further provides a server, as shown in fig. 5, where the cargo server 2 includes:
and the acquisition module 21 is used for acquiring data from each department service system and storing the data in a database.
And the preprocessing module 22 is used for carrying out standardization processing on the data stored in the database.
And the storage module 23 is configured to classify the standardized data according to the attribute of the data, and store the classified data in the database.
And the processing module 24 is configured to process each type of data according to a pre-constructed general service requirement, so as to obtain a service index of each type of data for each department to apply.
In an exemplary embodiment, the obtaining module 21 is further configured to obtain an actual business requirement of the target department.
The processing module 24 is further configured to select service indexes of different types of data according to actual service requirements, and deploy a service based on the selected service indexes.
In an exemplary embodiment, the obtaining module 21 is specifically configured to:
acquiring data from each department business system according to a pre-established data acquisition rule;
and storing the obtained data in a database according to a pre-established data storage strategy.
In one illustrative example, the types of data include: metadata, code data and content data, there being a corresponding standardized way for each data type.
The preprocessing module 22 is specifically configured to perform normalization processing on the data stored in the database according to a normalization manner corresponding to the type to which the data belongs.
In an exemplary embodiment, the storage module 23 is specifically configured to:
and respectively modeling the storage mode of each type of data by a dimensional modeling method to obtain a storage model of each type of data.
And storing each type of data in a database according to the corresponding storage model.
In one illustrative example, a dimensional modeling method includes: a star model building method.
In one illustrative example, the database is a Hadoop database based on a distributed system infrastructure.
According to the server provided by the embodiment of the application, because the data based on the service indexes for deploying the service is data from each department service system, cross-department data sharing is realized, and the data utilization rate is improved.
In practical applications, the obtaining module 21, the preprocessing module 22, the storage module 23, and the Processing module 24 are all implemented by a Central Processing Unit (CPU), a microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like in a server.
An embodiment of the present application further provides a data processing apparatus, including: a processor and a memory, wherein the memory has stored therein a computer program which, when executed by the processor, implements the processing of the method as set forth in any one of the above.
An embodiment of the present application further provides a storage medium, where the storage medium stores computer-executable commands, and the computer-executable commands are used for executing the processing of any one of the methods described above.
An embodiment of the present application further provides a data processing platform, as shown in fig. 6, including: the data convergence layer comprises a buffer layer and a proximity layer; the storage structure based on comprises: hadoop Distributed File System (HDFS) non/semi-structured storage and HDFS/HIVE data storage.
And the buffer layer is used for acquiring data (corresponding to the data of each department service system in the embodiment) from a source database, wherein the source database comprises an Oracle database, a MySQL database and a SQL Server database.
And the proximity layer is used for storing data according to the data storage strategy.
And the standard layer is used for formulating a platform standardization management standard, realizing metadata standardization, code data standardization and content data standardization processing, and keeping the data structure basically consistent with the convergence layer.
And the theme layer is used for designing a model by a dimensional modeling method and storing data in the designed model. Wherein, the theme layer can include: topics such as population, legal people, events, education, civil life, government affairs service, weather, economic industry, residential construction, traffic, internet of things, video analysis, operation and maintenance and the like; wherein, the population: the method comprises the following steps that actual population basic information, living information, employment information, birth information, social security information, social relationship information and death population information exist in the scope of the current-level government; weather: meteorological data within the scope of the current-level government comprises rainfall, wind speed, wind direction, temperature, humidity, air pressure, PM2.5 and the like; building: building, house and public facility component information in the scope of the current-level government; economic industry: total industrial value, industrial investment, total retail amount, tax, total import and export amount, investment and service trade information in the scope of the current government; government affair service: administrative permission, administrative payment, administrative confirmation, administrative collection and other service projects within the scope of the current-level government; event: emergency data concerned by the government of the level, such as three-prevention emergency, group emergency and the like; operation and maintenance: the user information and the business process approval information of the level government IT operation and maintenance platform and the government OA system; a legal person: various operation license numbers, establishment dates, operation ranges, operation addresses, registered capital, actual income capital, regional street community affiliation and other legal person basic information in the scope of the government; shareholder information such as shareholder certificate number, shareholder name, funding amount, funding proportion and the like; event contents such as administrative spot check results and the like; and (3) education: basic information of all education institutions, charging information of the education institutions, information of employees, information of students and notification and announcement information related to education in the scope of the current-level government; the method comprises the following steps: the information of the case of complaints and the information of the reporting person of the civilian life in the scope of the government of the current level; traffic: traffic running road condition information, intersection delay information, traffic flow information, bus running speed, track, passenger flow information, high-speed rail station passenger flow information, traffic safety information, network contracted vehicle running amount and distribution information in the scope of the current-level government; video analysis, namely analyzing data of the video in the scope of the current government district; the Internet of things: this grade government side slope sensor early warning information.
The special topic layer is used for slightly processing the data of the topic layer according to different topics to form a base wide table with finer granularity, and then associating various dimension tables to be further cleaned, filtered, combined and summarized into a summary index table, namely, a multi-dimensional CUBE (which is equivalent to processing each type of data according to the pre-constructed general service requirement in the embodiment to obtain the service index of each type of data for each department to apply).
The index summary table is obtained through a data sharing layer (including shared pre-database sharing, Application Programming Interface (API) Interface sharing and a distributed log system Kafka) to perform various business applications, wherein the business applications include: the system comprises a population structure, economy and social flow analysis and display, an event high-incidence, trend, attention crowd, academic degree analysis and display, a legal credit, operation and operation risk analysis and display, traffic key vehicle monitoring, operation monitoring and road safety analysis and display, a 3D large screen display, a regional commission office business system and a government online open platform.
The information processing system provided by the embodiment of the application integrates data of all government departments, breaks through the barriers of city data departments, realizes digital city data resource sharing and solves the problem of information isolated island; through the treatment and fusion of the big data platform, the accuracy and the reliability of data are improved, and high-quality basic data are provided for data analysis.
It will be understood by those of ordinary skill in the art that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof. In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
Claims (10)
1. A data processing method, comprising:
acquiring data from each department service system and storing the data in a database;
carrying out standardization processing on data stored in a database;
classifying the data after the standardized processing according to the attribute of the data, and storing the classified data in the database;
and processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
2. The method according to claim 1, wherein after processing each type of data according to the pre-constructed general service requirement to obtain the service index of each type of data for each department, the method further comprises:
acquiring actual business requirements of a target department;
and selecting service indexes of different types of data according to the actual service requirements, and deploying the service based on the selected service indexes.
3. The method of claim 1, wherein the obtaining and storing data from each department service system in a database comprises:
acquiring data from each department business system according to a pre-established data acquisition rule;
and storing the obtained data in the database according to a pre-established data storage strategy.
4. The method of claim 1, wherein the type of data comprises: metadata, code data and content data, wherein each data type has a corresponding standardized mode; the normalizing the data stored in the database comprises the following steps:
and carrying out standardization processing on the data stored in the database according to a standardization mode corresponding to the type to which the data belongs.
5. The method of claim 1, wherein storing the categorized data in a database comprises:
modeling the storage mode of each type of data through a dimensional modeling method to obtain a storage model of each type of data;
and storing each type of data in the database according to the corresponding storage model.
6. The method of claim 5, wherein the dimensional modeling method comprises: a star model building method.
7. The method of claim 1 or 3 or 4 or 5, wherein the database is a Hadoop database based on a distributed system infrastructure.
8. A server, comprising:
the acquisition module is used for acquiring data from each department service system and storing the data in a database;
the preprocessing module is used for carrying out standardization processing on the data stored in the database;
the storage module is used for classifying the standardized data according to the attributes of the data and storing the classified data in the database;
and the processing module is used for processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
9. A data processing apparatus, comprising: a processor and a memory, wherein the memory has stored therein the following commands executable by the processor:
acquiring data from each department service system and storing the data in a database;
carrying out standardization processing on data stored in a database;
classifying the data after the standardized processing according to the attribute of the data, and storing the classified data in the database;
and processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
10. A computer-readable storage medium having computer-executable instructions stored thereon for performing the steps of:
acquiring data from each department service system and storing the data in a database;
carrying out standardization processing on data stored in a database;
classifying the data after the standardized processing according to the attribute of the data, and storing the classified data in the database;
and processing each type of data according to the pre-constructed general service requirements to obtain the service index of each type of data for each department to apply.
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| CN112559742A (en) * | 2020-12-08 | 2021-03-26 | 北京伟杰东博信息科技有限公司 | Classified storage method and system thereof |
| CN115222374A (en) * | 2022-09-21 | 2022-10-21 | 智慧齐鲁(山东)大数据科技有限公司 | Government affair data service system based on big data processing |
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