CN111783125A - Cloud platform data dynamic configuration method for big data - Google Patents
Cloud platform data dynamic configuration method for big data Download PDFInfo
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- CN111783125A CN111783125A CN202010651135.XA CN202010651135A CN111783125A CN 111783125 A CN111783125 A CN 111783125A CN 202010651135 A CN202010651135 A CN 202010651135A CN 111783125 A CN111783125 A CN 111783125A
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- 238000012216 screening Methods 0.000 claims description 18
- 238000013500 data storage Methods 0.000 abstract description 3
- 238000013507 mapping Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 3
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/602—Providing cryptographic facilities or services
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
Abstract
The invention discloses a cloud platform data dynamic configuration method for big data, which comprises the following steps of S1 inputting a user name and a static password, S2 unlocking and sending the dynamic password through the static password, S3 inputting the dynamic password, then inputting a calling requirement, S4 analyzing the data priority sequence of the requirement by a database, S5 dynamically configuring the data by the database and then outputting the data, in S1 and S2, unlocking a user operation interface while inputting the user name and the static password, sending the dynamic password to a receiving device after the static password is successfully input, unlocking a search interface after the dynamic password is input, entering the user operation interface through the static password input, triggering the dynamic password to be sent, entering the data calling interface after the dynamic password is successfully input, combining the static password and the dynamic password to input the password more diversification, the safety of data storage is improved, and the retrieval is convenient.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a cloud platform data dynamic configuration method for big data.
Background
Configuration refers to an operation of creating data that enables normal operation of equipment and setting the data to the equipment, and therefore, configuration is also commonly referred to as "data configuration", and operations such as adding, deleting, modifying, querying, storing, backing up, and restoring are performed on the data operated by the equipment, that is, configuration management, which is an important tool for effective management and maintenance of the equipment;
however, in the data configuration of the current big data cloud platform, data is often called by inputting a fixed single password, the data configuration is not comprehensive enough and has poor timeliness, the data security is not high, and the data loss is easily caused.
Disclosure of Invention
The invention provides a cloud platform data dynamic configuration method for big data, which can effectively solve the problems that in the data configuration of the existing big data cloud platform provided in the background art, data is often called by inputting a fixed single password, the data configuration is not comprehensive enough and has poor timeliness, the data security is not high, and the data loss is easily caused.
In order to achieve the purpose, the invention provides the following technical scheme: a cloud platform data dynamic configuration method for big data comprises the following steps:
s1, inputting a user name and a static password;
s2, unlocking and sending the dynamic password through the static password;
s3, inputting a dynamic password and then inputting a calling requirement;
s4, analyzing the data priority sequence of the requirement by the database;
and S5, dynamically configuring the data by the database and outputting the data.
Preferably, in S1 and S2, the user operation interface is unlocked while the user name and the static password are input, the dynamic password is sent to the receiving device after the static password is successfully input and identified, the search interface is unlocked after the dynamic password is input, and the search operation is performed on the search interface.
Preferably, the priority order of the database in S4 is analyzed, after the requirement is analyzed, the classification data in the service database is first matched, then the data repetition rate and the search record in the classification database are compared, and the corresponding classification database is output to the output interface.
Preferably, the process of dynamically configuring data in S5 includes building a background micro service project, classifying data resources, defining a dynamic data source, and configuring the data source in a database.
Preferably, when defining the dynamic data source, first add a database identifier class, identify the classified data in the database, derive a dynamic data source from the data source, control data connection release by using DDSTimer, manage the data source by using DDSHolder, provide functions of adding, clearing and querying the data source, implement dynamic switching of database connection, and clear the idle data source.
Preferably, the cloud platform data dynamic configuration method for big data needs to control a system to operate, the system comprises a demand input module and a service database, the demand input module is connected with the service database, the service database is connected with a classification selection module, and the classification selection module is connected with a real-time updating module;
the service database is connected with a data screening module, and the data screening module is connected with a cloud storage database;
the service database is connected with the priority selection module, the priority selection module is connected with the cloud computing module, the cloud computing module is respectively connected with the recording module and the character set module, and the recording module is connected with the requirement input module.
Preferably, the data of the real-time updating module is received and then enters a classification selection module to classify the updating data, the classification selection module compares the classified data with corresponding classification data in a business database, replaces a part to be updated in the corresponding classification data, transmits the original classification data to a cloud storage database to be stored, and marks the data before and after updating.
Preferably, the requirement input module transmits the input requirement to the service database, the service database selectively transmits the data to the data screening module, the data screening module screens the update record of the selected data in the service database, and the data with the update record is called out from the cloud storage database and is output together with the data before update.
Preferably, the requirement input module transmits the input requirement records to the recording module for record backup, when the classification data in the service database is selected, the characters in the character machine module are compared, the cloud computing module judges the repetition condition, the priority selection module is used for selecting, meanwhile, the search records of the recording module are compared, and the final analysis data is output.
Compared with the prior art, the invention has the beneficial effects that:
the static password is input into a user operation interface to trigger the sending of the dynamic password, the dynamic password is input into a data calling interface after being successfully input, the static password and the dynamic password are combined to input the password, the input of the password is more diversified, the safety of data storage is improved, and the password is convenient to call;
the data which are updated in real time are classified through a classification selection module, then enter a database for classification storage, and are repeatedly updated in the database are subjected to replacement identification, original data are transmitted to a cloud storage database for identification storage, the data can be screened through a data screening module when the data are called, the updated data are called, dynamic configuration of the data can synchronously call the updated data and the original data, so that the data configuration is more comprehensive, the timeliness of the data is improved, the data can be backed up by using the cloud storage database, and the cloud storage database can be started to continue to work after a service database fails;
when the data is called, the data is compared and configured by the cloud computing module and the priority selection module, so that the data output sequence is more reasonable, and the user can conveniently and quickly inquire the data.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
In the drawings:
FIG. 1 is a flow chart of the operation of the present invention;
FIG. 2 is a flow chart of the data configuration of the present invention;
fig. 3 is a data retrieval block diagram of the inventive service database.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Example 1: as shown in fig. 1-2, the present invention provides a technical solution, a method for dynamically configuring cloud platform data for big data, including the following steps:
s1, inputting a user name and a static password;
s2, unlocking and sending the dynamic password through the static password, unlocking a user operation interface while inputting a user name and the static password, sending the dynamic password to receiving equipment after the static password is successfully input and identified, unlocking a search interface after the dynamic password is input, and performing search operation on the search interface;
s3, inputting a dynamic password and then inputting a calling requirement;
s4, analyzing the data priority sequence of the demand by the database, after analyzing the demand, firstly matching the classification data in the service database, then comparing the data repetition rate and the search record in the classification database, and outputting the corresponding classification database to an output interface;
s5, outputting data after dynamically configuring the data by the database, wherein the process of dynamically configuring the data comprises the steps of building a background micro-service project, classifying data resources, defining a dynamic data source and configuring the data source in the database;
the method for building the background micro-service project specifically comprises the following steps: the method comprises the steps of data source configuration, the relevant classes of the dynamic data sources realized by the method, the mapping relation between management project codes and IP and names of a database, a database access interface of mybatis, a mapping model, a restful interface issued by a micro-service pair and a configuration database JDBC parameter.
When defining the dynamic data source, firstly adding a database identification class, identifying classification data in the database, deriving a dynamic data source by using the data source, controlling data connection release by using DDSTImer, managing the data source by using DDSHOlder, providing functions of adding, clearing and inquiring the data source, realizing dynamic switching of database connection and clearing an idle data source.
Example 2: as shown in fig. 3, the present invention provides a technical solution, in which a cloud platform data dynamic configuration method for big data requires a control system to operate, the system includes a demand input module and a service database, the demand input module is connected with the service database, the service database is connected with a classification selection module, and the classification selection module is connected with a real-time update module;
the service database is connected with the data screening module, and the data screening module is connected with the cloud storage database;
the service database is connected with the priority selection module, the priority selection module is connected with the cloud computing module, the cloud computing module is respectively connected with the recording module and the character set module, and the recording module is connected with the requirement input module.
The data of the real-time updating module is received and then enters a classification selection module to classify the updating data, the classification selection module classifies the data and then compares the classified data with corresponding classification data in a business database, a part to be updated in the corresponding classification data is replaced, the original classification data is transmitted to a cloud storage database to be stored, and the data before and after updating is marked.
The requirement input module transmits the input requirement to the service database, the service database selectively transmits the data to the data screening module, the data screening module screens the update records of the selected data in the service database, and the data with the update records are called out from the cloud storage database and are output together.
The method comprises the steps that a demand input module transmits input demand records to a recording module for record backup, when classification data in a service database are selected, character comparison in a character machine module is firstly carried out, repetition conditions are judged through a cloud computing module, a priority selection module is used for selecting, search records of the recording module are compared at the same time, and final analysis data are output.
The working principle and the using process of the invention are as follows: firstly, inputting a user name and a static password; the method comprises the steps of unlocking and sending a dynamic password through a static password, unlocking a user operation interface while inputting a user name and the static password, sending the dynamic password to receiving equipment after the static password is successfully input and identified, unlocking a search interface after the dynamic password is input, carrying out search operation on the search interface, directly triggering the sending of the dynamic password by the input of the static password, improving the safety of data storage by utilizing the combined input of the dynamic password and the static password, and being convenient to call;
inputting a dynamic password and then inputting a calling requirement;
the database analyzes the data priority sequence of the demand, after the demand is analyzed, firstly, the classification data in the service database is matched, then, the data repetition rate and the search record in the classification database are compared, and the corresponding classification database is output to an output interface;
the method comprises the steps that a database outputs data after dynamically configuring the data, data source configuration is built, the self-realized dynamic data source related class manages the mapping relation between project codes and IP and names of the database, a mybatis database access interface, a mapping model, a restful interface issued by micro service to the outside, a background micro service project of JDBC parameters of the database is configured, data resource classification is carried out, then a database identification class is added, identification is carried out on classified data in the database, a dynamic DataSource is derived by utilizing the DataSource, data connection release is controlled by utilizing DDSTImer, the data source is managed by utilizing DDSHOlder, functions of adding, removing and inquiring the data source are provided, dynamic switching of database connection is realized, an idle data source is removed to define a dynamic data source, and the data source is configured in the database;
when the real-time dynamic data is updated, the data of the real-time updating module is received and enters a classification selection module to classify the updating data, the classification selection module compares the classified data with the corresponding classification data in the business database, replaces the part to be updated in the corresponding classification data, transmits the original classification data to the cloud storage database for storage, and marks the data before and after updating, when the data screening module screens the data, a requirement input module transmits the input requirement to the business database, the business database selectively transmits the data to the data screening module, the data selected in the business database is screened by the data screening module for updating record, the data with the updating record is called out from the cloud storage database and is output together, and the real-time updating data is classified by the classification selection module, the data are classified and stored in the database, the updated repeated data in the database are subjected to replacement identification, the original data are transmitted to the cloud storage database for identification storage, the data can be screened through the data screening module when the data are called, the updated data are called, the dynamic configuration of the data is facilitated, the backup effect of the cloud storage database on the data can be realized, and the cloud storage database can be started to continue to work after the service database fails;
when the cloud computing module processes data, the demand input module transmits input demand records to the recording module for record backup, when classification data in a service database is selected, character comparison in the character machine module is firstly carried out, repetition conditions are judged through the cloud computing module, the priority selection module is used for selecting, search records of the recording module are compared at the same time, and final analysis data are output, the selection of the module of the priority selection module realizes that data of different data sources are assembled through from clauses, where is used for screening based on specified conditions, where is used for dividing data into a plurality of groups, then aggregation functions are used for computing, and the groups are screened through haiving clauses, expressions are computed, and finally orderby is used for sorting all results, and comparison configuration is carried out through the cloud computing module and the priority selection module, the data output sequence is more reasonable, and the user can conveniently and quickly inquire the data.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. A cloud platform data dynamic configuration method for big data is characterized in that: the method comprises the following steps:
s1, inputting a user name and a static password;
s2, unlocking and sending the dynamic password through the static password;
s3, inputting a dynamic password and then inputting a calling requirement;
s4, analyzing the data priority sequence of the requirement by the database;
and S5, dynamically configuring the data by the database and outputting the data.
2. The method of claim 1, wherein in S1 and S2, the user interface is unlocked when a user name and a static password are input, the dynamic password is sent to the receiving device after the static password is successfully input, and the search interface is unlocked after the dynamic password is input, so that the search operation is performed on the search interface.
3. The method according to claim 1, wherein in step S4, the database is analyzed according to priority, and after the requirement is analyzed, the classification data in the service database is first matched, and then the data repetition rate and the search record in the classification database are compared, and the corresponding classification database is output to the output interface.
4. The cloud platform data dynamic configuration method for big data according to claim 1, wherein the flow of data dynamic configuration in S5 includes building a background micro service project, classifying data resources, defining a dynamic data source, and configuring a data source in a database.
5. The method for dynamically configuring cloud platform data for big data according to claim 4, wherein when a dynamic data source is defined, a database identifier class is added first, classification data in the database is identified, a dynamic data source is derived by using the data source, data connection release is controlled by using DDSTImer, the data source is managed by using DDSHOlder, functions of adding, removing and querying the data source are provided, dynamic switching of database connection is realized, and an idle data source is removed.
6. The method for dynamically configuring the cloud platform data for the big data according to any one of claims 1 to 5, wherein the method for dynamically configuring the cloud platform data for the big data requires a control system to operate, the system comprises a demand input module and a service database, the demand input module is connected with the service database, the service database is connected with a classification selection module, and the classification selection module is connected with a real-time update module;
the service database is connected with a data screening module, and the data screening module is connected with a cloud storage database;
the service database is connected with the priority selection module, the priority selection module is connected with the cloud computing module, the cloud computing module is respectively connected with the recording module and the character set module, and the recording module is connected with the requirement input module.
7. The method according to claim 6, wherein the real-time update module receives the data and then classifies the update data in the classification selection module, the classification selection module classifies the data and then compares the classified data with corresponding classification data in the service database, replaces a part to be updated in the corresponding classification data, transmits the original classification data to the cloud storage database for storage, and marks the data before and after updating.
8. The dynamic configuration method for the cloud platform data of the big data as claimed in claim 6, wherein the demand input module transmits the input demand to the service database, the service database selectively transmits the data to the data screening module, the data screening module screens the update records of the selected data in the service database, and the data with the update records is called out from the cloud storage database and is output together with the data before update.
9. The method as claimed in claim 8, wherein the requirement input module transmits the input requirement records to the recording module for record backup, when selecting the classified data in the service database, the repetition is determined by the character comparison in the character machine module, the selection is performed by the priority selection module, and simultaneously the search records of the recording module are compared to output the final analysis data.
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| CN112732433A (en) * | 2021-03-30 | 2021-04-30 | 骊阳(广东)节能科技股份有限公司 | Data processing system capable of carrying out priority allocation |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112732433A (en) * | 2021-03-30 | 2021-04-30 | 骊阳(广东)节能科技股份有限公司 | Data processing system capable of carrying out priority allocation |
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Application publication date: 20201016 |