CN114328515B - A data storage method based on composite allocation algorithm - Google Patents
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Abstract
The application relates to the technical field of photovoltaic power generation data storage, in particular to a data storage method based on a composite allocation algorithm. The data storage method comprises the steps of establishing a database according to years, and establishing different types of database engine tables in the database, wherein the database engine tables comprise acquisition time and sequence IDs, carrying out distribution calculation on data to be stored based on a preset compound distribution algorithm, and determining a configuration rule corresponding to the data to be stored, wherein the compound distribution algorithm comprises an accurate distribution algorithm, a range distribution algorithm and a compound distribution algorithm comprising the accurate distribution algorithm and the range distribution algorithm, and distributing the data to be stored to the corresponding types of database engine tables according to the configuration rule so as to realize data storage, so that the problems of difficulty in reading and writing, overtime access, difficulty in maintenance and difficulty in data storage in the prior art are solved.
Description
Technical Field
The application relates to the technical field of photovoltaic power generation data storage, in particular to a data storage method based on a composite allocation algorithm.
Background
Photovoltaic power generation is a technology that uses the photovoltaic effect of a semiconductor interface to directly convert light energy into electrical energy. The installed capacity of the photovoltaic as a new energy is continuously increased, and the normal operation of the photovoltaic power generation can be influenced by some small disturbance, so that the stable operation state of the photovoltaic power station equipment needs to be mastered at any time. However, the information about the stable operation of the photovoltaic power station equipment is acquired, and the information data of each point position of the power equipment is required to be analyzed and processed. However, the photovoltaic power station has a large number of equipment points and large data volume.
At present, point location data storage of photovoltaic power station equipment is generally performed by using a mysql database, an Oracle database and the like. However, under the condition of large data volume due to the fact that the number of the photovoltaic power station equipment points is large, data reading and writing are difficult due to the bottleneck problem of an engine, access is overtime, maintenance is difficult, and further distribution and storage of the photovoltaic power station equipment point data are difficult, for example, a mysql database is caused, and when the single-table data volume exceeds 1000 ten thousand, the data reading and writing performance is poor, and the access time is long.
Disclosure of Invention
The application provides a data storage method based on a composite allocation algorithm, which aims to solve the problems of difficult reading and writing, overtime access, difficult maintenance and difficult data storage in the prior art.
Embodiments of the present application are implemented as follows:
The embodiment of the application provides a data storage method based on a compound allocation algorithm, which comprises the steps of establishing a database according to years, establishing different types of database engine tables in the database, wherein the database engine tables comprise acquisition time and sequence IDs, carrying out allocation calculation on data to be stored based on a preset compound allocation algorithm, and determining a configuration rule corresponding to the data to be stored, wherein the compound allocation algorithm comprises an accurate allocation algorithm, a range allocation algorithm and a compound allocation algorithm comprising the accurate allocation algorithm and the range allocation algorithm, and distributing the data to be stored to the corresponding types of database engine tables according to the configuration rule so as to realize data storage.
In some embodiments, the specific step of establishing different types of database engine tables in the database comprises the steps of designing structures of different types of database engine tables, establishing corresponding database engine table scripts according to the structures of the different types of database engine tables, and establishing different types of database engine tables according to the database engine table scripts, wherein the sequence IDs are generated according to a snowflake algorithm and have uniqueness.
In some embodiments, the specific step of determining the configuration rule corresponding to the data to be stored includes performing distribution algorithm analysis on the data to be stored to obtain a distribution algorithm corresponding to the data to be stored, performing distribution calculation on the data to be stored through the accurate distribution algorithm if the distribution algorithm corresponding to the data to be stored is the accurate distribution algorithm, determining the configuration rule corresponding to the data to be stored as the accurate distribution rule, performing distribution calculation on the data to be stored through the range distribution algorithm if the distribution algorithm corresponding to be stored is the range distribution algorithm, determining the configuration rule corresponding to the data to be stored as the range distribution rule, performing distribution calculation on the data to be stored through the multiple distribution algorithm if the distribution algorithm corresponding to be stored is the multiple distribution algorithm, and determining the configuration rule corresponding to the data to be stored as the multiple distribution rule.
In some embodiments, the specific step of distributing the data to be stored to the database engine table of the corresponding type according to the configuration rule includes that if the configuration rule corresponding to the data to be stored is an accurate distribution rule, the time rule corresponding to the data to be stored is obtained by accurately distributing the data to be stored according to a time category according to the accurate distribution rule, and the data to be stored is distributed to the database engine table under the time rule, wherein the time category includes a year category, a month category, a day category and a time category, the time rule corresponding to the year category is a database date year rule, the time rule corresponding to the month category is a database date month rule, the time rule corresponding to the day category is a database date day rule, and the time rule corresponding to the time category is a database date hour rule.
The specific step of distributing the data to be stored to the database engine table of the corresponding type according to the configuration rule comprises the steps of distributing the data to be stored to the database engine table of the corresponding type according to the time category by performing range distribution on the data to be stored according to the range distribution rule if the configuration rule corresponding to the data to be stored is a range distribution rule, and distributing the data to be stored to the database engine table under the range according to the range.
And carrying out range allocation on the data to be stored according to the range allocation rule and the time category, and further comprising a range configuration switch for setting a range allocation limit, wherein the range configuration switch is used for setting the highest limit and the lowest limit of the range allocation.
The data storage method based on the composite allocation algorithm has the advantages that a database is built according to years, and database engine tables of different types are built in the database, wherein the database engine tables comprise acquisition time and serial IDs, allocation calculation is carried out on data to be stored based on a preset composite allocation algorithm, configuration rules corresponding to the data to be stored are determined, the data to be stored are allocated to the database engine tables of corresponding types according to the configuration rules, so that data storage is achieved, a large photovoltaic power station can store data at minimum cost, data storage support is provided for intelligent analysis of photovoltaic equipment, and the problems that the photovoltaic power station is high in data storage cost, difficult in data storage and reading and writing, long in access time and the like are solved.
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In particular, in order to more clearly illustrate the technical solutions of the present application, the drawings that are necessary for the embodiments will be briefly described, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
FIG. 1 is a schematic flow chart of a data storage method based on a composite allocation algorithm according to an embodiment of the present application;
Fig. 2 is a schematic diagram of determining a data configuration rule based on a preset composite allocation algorithm according to an embodiment of the present application.
Detailed Description
Certain exemplary embodiments will now be described to provide an overall understanding of the principles of the structure, function, manufacture, and use of the devices and methods disclosed herein. One or more examples of these embodiments have been illustrated in the accompanying drawings. Those of ordinary skill in the art will understand that the devices and methods specifically described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the present application is defined solely by the claims. The features illustrated or described in connection with one exemplary embodiment may be combined with the features of other embodiments. Such modifications and variations are intended to be included within the scope of the present application.
Reference throughout this specification to "multiple embodiments," "some embodiments," "one embodiment," or "an embodiment," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. Thus, appearances of the phrases "in various embodiments," "in some embodiments," "in at least one other embodiment," or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Thus, a particular feature, structure, or characteristic shown or described in connection with one embodiment may be combined, in whole or in part, with features, structures, or characteristics of one or more other embodiments without limitation. Such modifications and variations are intended to be included within the scope of the present application.
A flowchart is used in the present application to illustrate the operations performed by a system according to some embodiments of the present application. It should be expressly understood that the operations of the flowcharts may be performed out of order with precision. Rather, these operations may be performed in reverse order or concurrently. Also, one or more other operations may be added to the flow chart. One or more operations may be removed from the flowchart.
Fig. 1 shows a flow chart of a data storage method based on a composite allocation algorithm according to an embodiment of the present application.
In step 101, a database is built according to the age, and different types of database engine tables are built in the database, wherein the database engine tables comprise acquisition time and sequence IDs.
In some embodiments, the specific step of establishing different types of database engine tables in the database comprises the steps of designing structures of different types of database engine tables, establishing corresponding database engine table scripts according to the structures of the different types of database engine tables, and establishing different types of database engine tables according to the database engine table scripts, wherein the sequence IDs are generated according to a snowflake algorithm and have uniqueness.
Before designing the structures of different types of database engine tables, the method comprises two fields of sequence ID and acquisition time, wherein the sequence ID field data is generated by relying on a snowflake algorithm, and the sequence ID field data has uniqueness, and the acquisition time and the snowflake algorithm time sequence are consistent. The database is manually established, the database engine table script is compiled, and the database engine table script is uploaded to a program to establish different types of database engine tables.
Fig. 2 is a schematic diagram of determining a data configuration rule based on a preset composite allocation algorithm according to an embodiment of the present application.
In step 102, distribution calculation is performed on data to be stored based on a preset composite distribution algorithm, and a configuration rule corresponding to the data to be stored is determined, wherein the composite distribution algorithm comprises an accurate distribution algorithm, a range distribution algorithm and a composite distribution algorithm comprising the accurate distribution algorithm and the range distribution algorithm.
In some embodiments, the specific step of determining the configuration rule corresponding to the data to be stored includes performing distribution algorithm analysis on the data to be stored to obtain a distribution algorithm corresponding to the data to be stored, performing distribution calculation on the data to be stored through the accurate distribution algorithm if the distribution algorithm corresponding to the data to be stored is the accurate distribution algorithm, determining the configuration rule corresponding to the data to be stored as the accurate distribution rule, performing distribution calculation on the data to be stored through the range distribution algorithm if the distribution algorithm corresponding to be stored is the range distribution algorithm, determining the configuration rule corresponding to the data to be stored as the range distribution rule, performing distribution calculation on the data to be stored through the multiple distribution algorithm if the distribution algorithm corresponding to be stored is the multiple distribution algorithm, and determining the configuration rule corresponding to the data to be stored as the multiple distribution rule.
The method comprises the steps of reversely analyzing data to be stored based on acquisition time and a sequence ID, carrying out distribution calculation on the data to be stored, determining a configuration rule corresponding to the data to be stored, selecting a storage mode according to the data quantity of different scene equipment by using different storage modes through the configuration rule, and storing the data by using different configuration rules, wherein the sequence ID can obtain time information consistent with the acquisition time through reversely analyzing the sequence ID.
In some embodiments, the specific step of distributing the data to be stored to the database engine table of the corresponding type according to the configuration rule includes that if the configuration rule corresponding to the data to be stored is an accurate distribution rule, the time rule corresponding to the data to be stored is obtained by accurately distributing the data to be stored according to a time category according to the accurate distribution rule, and the data to be stored is distributed to the database engine table under the time rule, wherein the time category includes a year category, a month category, a day category and a time category, the time rule corresponding to the year category is a database date year rule, the time rule corresponding to the month category is a database date month rule, the time rule corresponding to the day category is a database date day rule, and the time rule corresponding to the time category is a database date hour rule.
And precisely distributing the data to be stored to a time rule comprising year, month, day and hour according to a precise distribution rule, acquiring field data, and precisely matching the field data to a database engine table under the time rule.
For example, the method is distributed daily, and finally the sequence ID is accurate to the position under a database engine table distributed according to a daily rule according to the acquisition time or the sequence ID, and the accurate distribution algorithm supports the independent use of the sequence ID and the acquisition time or compatible modification, can be independently used and can be upgraded into a combined mode, but the table distribution rule cannot be upgraded or downgraded after being selected. If the rule must be modified, returning to the re-table initialization configuration by deleting the table, and then returning to the selection of the configuration corresponding mode.
In some embodiments, the field content value is obtained through a database annual equivalent judging method, a data table Date and month rule equivalent judging method, or a data table Date and hour rule equivalent judging method, the sequence ID is reversely analyzed, time matching is carried out after the conversion time, data format compatibility is carried out on the time field, a long type time stamp and the time content of the character string type are carried out, the type conversion is carried out uniformly, and the value judgment is carried out by using an util tool after the conversion to Date.
In some embodiments, the step of distributing the data to be stored to the database engine table of the corresponding type according to the configuration rule includes distributing the data to be stored according to a time category to obtain a time limit range corresponding to the data to be stored according to the range distribution rule if the configuration rule corresponding to the data to be stored is a range distribution rule, and distributing the data to be stored to the database engine table under the time limit range according to the time limit range.
According to the sequence ID and the acquisition time, the data to be stored are subjected to annual, monthly, daily and temporal range allocation by adopting a determined range allocation rule, and the time range span allocation is performed to the time categories including year, month, day and hour, theoretically, the time range span can span one year, but the use is not recommended, so that the excessive use of resources is caused, the influence on the server resources is large, different configuration rules are used, and the range allocation rule can be used in all relevant database engine tables covered by the range. Note that the sequence and acquisition time may also be used separately.
In some embodiments, the range allocation rule is used for carrying out range allocation on the data to be stored according to the time category, and the range allocation method further comprises a range configuration switch used for setting range allocation limit and setting the highest limit and the lowest limit of range allocation according to the range configuration switch.
When the range allocation is performed on the data to be stored, a range configuration switch is further provided, and the range allocation size can be set, if the range allocation size is used, <, <=, and < =. The specific values used by the range configuration switch demarcate the upper and lower bounds (i.e., the highest and lowest bounds) at which point not all tables in the database engine are scanned, but the upper and lower bounds are isolated based on the specific values in the range configuration range switch.
For example, the data to be stored is assigned a range using the day assignment rule, where the value specified in the range configuration switch is, for example, 5, then >2021-11-14 is used at this time, then the time range that would be assigned is 2021-11-14 to 2021-11-19 inclusive database engine tables, and vice versa. When the device is specifically used, the device is specifically used in a specific scene according to the number of the point positions and the number of the data volume.
In some embodiments, the data date boundary determination method and the database engine table date range determination method are included in the range allocation rule to obtain the field content value.
In the data date boundary judging method, range rule judgment is carried out according to date dividing limits used by configuration, all range rules use the same configuration, no matter which rule is used, the boundary judging method uniformly processes, and single rule and single configuration are not supported. And after the limit configuration value is taken, limiting the upper limit and the lower limit of the data to be stored, wherein if the limit configuration value is greater than the limit value, the limit value is + and if the limit configuration value is less than the limit value, the limit value is-operated.
For example, setting the critical value to be 5, calculating > = according to the critical value of the month range, acquiring the data content of the current configuration field, carrying out the operation of the monta+5, returning to all database engine tables in the current month and +5 range, carrying out the operation of inquiring, writing and the like, carrying out the operation of returning < < =, acquiring the data content of the current configuration field, carrying out the operation of the monta-5, returning to all database engine tables in the current month and-5 range, carrying out the operation of inquiring, writing and the like, and carrying out the operation of returning. And (3) calculating a daily range critical value of > =, acquiring the data content of the current configuration field, performing day+5 calculation, returning to all database engine tables within the current date and +5 range, performing operations such as inquiring, writing and the like, returning < <=, acquiring the data content of the current configuration field, performing day-5 calculation, returning to all database engine tables within the current date and-5 range, performing operations such as inquiring, writing and the like, and returning. And calculating the critical value of the hour range, namely, acquiring the data content of the current configuration field, carrying out the operations of the hour+5, returning to all database engine tables in the current hour and the hour +5 range, carrying out the operations of inquiring, writing and the like, carrying out the operations of returning, namely, acquiring the data content of the current configuration field, carrying out the hour-5, returning to all database engine tables in the current hour and the hour-5 range, carrying out the operations of inquiring, writing and the like, and carrying out the operations of returning.
In some embodiments, the method for judging the date range of the database engine table comprises the steps of judging the time range according to the data to be stored and the configuration rule, and configuring the usage rule of the reading equipment to match, wherein the method comprises the following two cases that a, the method with the clear limit returns all data in all limits, b, the method without the clear limit returns to call the boundary judging method, the boundary is obtained, and all data in the boundary is returned.
In some embodiments, the allocating the data to be stored to the database engine table of the corresponding type according to the configuration rule includes allocating the data to be stored to the database engine table of the corresponding type according to the multiple allocation rule if the configuration rule corresponding to the data to be stored is the multiple allocation rule, so as to realize data storage.
The method comprises the steps of carrying out compound compatibility on accurate allocation and range allocation, carrying out compatibility on the two algorithm rules, enabling the storage function to be more powerful, and if a more complex service scene is needed, suggesting that the algorithm rule is used, wherein the compound allocation algorithm is applicable to the more complex upstream and downstream service scene and supports compatibility with other tool components.
The data storage method based on the composite allocation algorithm has the advantages that a database is built according to years, and database engine tables of different types are built in the database, wherein the database engine tables comprise acquisition time and serial IDs, allocation calculation is carried out on data to be stored based on a preset composite allocation algorithm, configuration rules corresponding to the data to be stored are determined, the data to be stored are allocated to the database engine tables of corresponding types according to the configuration rules, so that data storage is achieved, a large photovoltaic power station can store data at minimum cost, data storage support is provided for intelligent analysis of photovoltaic equipment, and the problems that the photovoltaic power station is high in data storage cost, difficult in data storage and reading and writing, long in access time and the like are solved.
Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.
The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, C#, VB NET, python, and the like, a conventional programming language such as the C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, for example, software as a service (SaaS).
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of example, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure does not imply that the subject application requires more features than are set forth in the claims. Indeed, less than all of the features of a single embodiment disclosed above.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited herein is hereby incorporated by reference in its entirety. Except for the application history file that is inconsistent or conflicting with this disclosure, the file (currently or later attached to this disclosure) that limits the broadest scope of the claims of this disclosure is also excluded. It is noted that the description, definition, and/or use of the term in the appended claims controls the description, definition, and/or use of the term in this application if there is a discrepancy or conflict between the description, definition, and/or use of the term in the appended claims.
Claims (2)
1. A data storage method based on a composite allocation algorithm, comprising:
Establishing a database according to years, and establishing different types of database engine tables in the database, wherein the database engine tables comprise acquisition time and sequence IDs;
Performing distribution calculation on data to be stored based on a preset composite distribution algorithm, and determining a configuration rule corresponding to the data to be stored, wherein the composite distribution algorithm comprises an accurate distribution algorithm, a range distribution algorithm and a composite distribution algorithm comprising the accurate distribution algorithm and the range distribution algorithm;
the specific step of determining the configuration rule corresponding to the data to be stored comprises the following steps:
The method comprises the steps of carrying out distribution algorithm analysis on data to be stored to obtain a distribution algorithm corresponding to the data to be stored, carrying out distribution calculation on the data to be stored through the accurate distribution algorithm if the distribution algorithm corresponding to the data to be stored is an accurate distribution algorithm, determining that a configuration rule corresponding to the data to be stored is an accurate distribution rule;
according to the configuration rule, distributing the data to be stored to a database engine table of a corresponding type to realize data storage, including:
If the configuration rule corresponding to the data to be stored is an accurate allocation rule, accurately allocating the data to be stored according to the accurate allocation rule according to a time category to obtain a time rule corresponding to the data to be stored, and allocating the data to be stored to a database engine table under the time rule, wherein the time category comprises a year category, a month category, a day category and a time category, the time rule corresponding to the year category is a database date year rule, the time rule corresponding to the month category is a database date month rule, the time rule corresponding to the day category is a database date day rule, and the time rule corresponding to the time category is a database date hour rule;
The range allocation device also comprises a range configuration switch for setting a range allocation limit, wherein the range configuration switch is used for setting the highest limit and the lowest limit of range allocation according to the range;
The specific steps of distributing the data to be stored to the corresponding type of database engine table according to the configuration rule comprise:
And if the configuration rule corresponding to the data to be stored is a range allocation rule, performing range allocation on the data to be stored according to the range allocation rule and the time category to obtain a time limit range corresponding to the data to be stored, and allocating the data to be stored to a database engine table under the time limit range according to the time limit range.
2. The data storage method based on the compound allocation algorithm according to claim 1, wherein the specific step of establishing different types of database engine tables in the database comprises the steps of designing structures of the different types of database engine tables, establishing corresponding database engine table scripts according to the structures of the different types of database engine tables, and establishing different types of database engine tables according to the database engine table scripts, wherein the sequence IDs are generated according to a snowflake algorithm and have uniqueness.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104516894A (en) * | 2013-09-27 | 2015-04-15 | 国际商业机器公司 | Method and device for managing time series database |
| CN112052259A (en) * | 2020-09-28 | 2020-12-08 | 深圳前海微众银行股份有限公司 | Data processing method, apparatus, equipment and computer storage medium |
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|---|---|---|---|---|
| US8239369B2 (en) * | 2008-03-20 | 2012-08-07 | DBSophic, Ltd. | Method and apparatus for enhancing performance of database and environment thereof |
| US8849771B2 (en) * | 2010-09-02 | 2014-09-30 | Anker Berg-Sonne | Rules engine with database triggering |
| CN110175210A (en) * | 2019-04-26 | 2019-08-27 | 厦门市美亚柏科信息股份有限公司 | A kind of data distributing method, device, system and storage medium |
| CN111125089B (en) * | 2019-11-05 | 2023-09-26 | 远景智能国际私人投资有限公司 | Time sequence data storage method, device, server and storage medium |
| CN112905587B (en) * | 2019-12-04 | 2024-05-14 | 北京金山云网络技术有限公司 | Database data management method, device and electronic equipment |
| CN110968647A (en) * | 2019-12-23 | 2020-04-07 | 广东电力交易中心有限责任公司 | Data storage method, apparatus, computer equipment and storage medium |
-
2021
- 2021-12-22 CN CN202111578208.8A patent/CN114328515B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104516894A (en) * | 2013-09-27 | 2015-04-15 | 国际商业机器公司 | Method and device for managing time series database |
| CN112052259A (en) * | 2020-09-28 | 2020-12-08 | 深圳前海微众银行股份有限公司 | Data processing method, apparatus, equipment and computer storage medium |
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