CN114328515A - Data storage method based on composite distribution 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 distribution algorithm. The data storage method comprises the following steps: establishing a database according to the age, and establishing different types of database engine tables in the database, wherein the database engine tables comprise: collecting time and sequence ID; 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 allocation algorithm comprises: an exact allocation algorithm, a range allocation algorithm, and a multiple allocation algorithm comprising an exact allocation algorithm and a range allocation algorithm; according to the configuration rule, the data to be stored are distributed to database engine tables of corresponding types so as to realize data storage; the problems of difficulty in reading and writing, access overtime, difficulty in maintenance and further 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 distribution algorithm.
Background
Photovoltaic power generation is a technology of directly converting light energy into electric energy by using the photovoltaic effect of a semiconductor interface. The installed capacity of photovoltaic as a new energy is continuously increased, and some small disturbances can influence the normal operation of photovoltaic power generation, so that the stable operation state of photovoltaic power station equipment needs to be mastered all the time. However, obtaining information about stable operation of photovoltaic power station equipment requires analyzing and processing information data of each point location of the power equipment. However, the photovoltaic power station has many equipment points and large data volume.
Currently, a mysql database, an Oracle database and the like are generally used for storing point position data of photovoltaic power station equipment. However, under the condition that the photovoltaic power station equipment points have many points and the data volume is large, data reading and writing are difficult, access is overtime and maintenance is difficult due to the bottleneck problem of an engine, and further, the point data distribution and storage of the photovoltaic power station equipment points are difficult, for example, a mysql database has poor data reading and writing performance and long access time when the data volume of a single table exceeds 1000 ten thousand.
Disclosure of Invention
The application provides a data storage method based on a composite distribution algorithm, and aims to solve the problems of difficult reading and writing, access overtime, difficult maintenance and further difficult data storage in the prior art.
The embodiment of the application is realized as follows:
the embodiment of the application provides a data storage method based on a composite allocation algorithm, which comprises the following steps: establishing a database according to the age, and establishing different types of database engine tables in the database, wherein the database engine tables comprise: collecting time and sequence ID; 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 exact allocation algorithm, a range allocation algorithm, and a multiple allocation algorithm comprising an exact allocation algorithm and a range allocation algorithm; and distributing the data to be stored to a database engine table of a corresponding type 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 includes: designing structures of different types of database engine tables, and establishing corresponding database engine table scripts according to the structures of the different types of database engine tables; establishing different types of database engine tables according to the database engine table script; wherein the sequence ID is generated according to a snowflake algorithm and has 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 data to be stored to obtain a distribution algorithm corresponding to the data to be stored; if the distribution algorithm corresponding to the data to be stored is an accurate distribution algorithm, performing distribution calculation on the data to be stored through the accurate distribution algorithm, and determining that a configuration rule corresponding to the data to be stored is an accurate distribution rule; if the distribution algorithm corresponding to the data to be stored is a range distribution algorithm, performing distribution calculation on the data to be stored through the range distribution algorithm, and determining a configuration rule corresponding to the data to be stored as a range distribution rule; and if the distribution algorithm corresponding to the data to be stored is a multiple distribution algorithm, performing distribution calculation on the data to be stored through the multiple distribution algorithm, and determining that the configuration rule corresponding to the data to be stored is a multiple distribution rule.
In some embodiments, the specific step of allocating the data to be stored to the database engine tables of the corresponding types according to the configuration rule includes: if the configuration rule corresponding to the data to be stored is an accurate distribution rule, accurately distributing the data to be stored according to the accurate distribution rule and the time category to obtain a time rule corresponding to the data to be stored, and distributing the data to be stored to a database engine table under the time rule; the time categories comprise a year category, a month category, a day category and a time category, the time rules corresponding to the year categories are database date and year limit rules, the time rules corresponding to the month categories are database date and month rules, the time rules corresponding to the day categories are database date and day rules, and the time rules corresponding to the time categories are database date and hour rules.
The specific step of allocating the data to be stored to the database engine table of the corresponding type according to the configuration rule includes: if the configuration rule corresponding to the data to be stored is a range distribution rule, carrying out range distribution on the data to be stored according to the time category according to the range distribution rule to obtain a time limit range corresponding to the data to be stored; and distributing the data to be stored to a database engine table under the time limit range according to the time limit range.
According to the range distribution rule, carrying out range distribution on the data to be stored according to time categories, and further comprising: a range configuration switch for setting a range allocation limit; setting the highest limit and the lowest limit of range distribution according to the range configuration switch.
The beneficial effects of the present application reside in that, the present application provides a data storage method based on a composite allocation algorithm, wherein a database is established according to the age, and different types of database engine tables are established in the database, wherein the database engine tables include: collecting time and sequence ID; 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; according to the configuration rule, the data to be stored are distributed to the database engine tables of the corresponding types to realize data storage, so that a large photovoltaic power station can store the data at the minimum cost, data storage support is provided for intelligent analysis of photovoltaic equipment, and the problems of high data storage cost, difficulty in data storage reading and writing, long access time and the like of operating equipment of the photovoltaic power station are solved.
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Specifically, in order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments are briefly described below, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without any creative effort.
FIG. 1 is a schematic flow chart illustrating a data storage method based on a composite allocation algorithm according to an embodiment of the present application;
fig. 2 is a schematic diagram illustrating the determination of the data configuration rule based on the preset composite allocation algorithm according to the 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 are 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 various embodiments of the present application is defined solely by the claims. Features illustrated or described in connection with one exemplary embodiment may be combined with 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 "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," or the like, 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, the particular features, structures, or characteristics shown or described in connection with one embodiment may be combined, in whole or in part, with the 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.
Flow charts are used herein to illustrate operations performed by systems according to some embodiments of the present application. It should be expressly understood that the operations of the flow diagrams may be performed out of order, with precision. Rather, these operations may be performed in the reverse order or simultaneously. Also, one or more other operations may be added to the flowchart. 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 established according to the age, and different types of database engine tables are established in the database, wherein the database engine tables comprise: acquisition time and sequence ID.
In some embodiments, the specific step of establishing different types of database engine tables in the database includes: designing structures of different types of database engine tables, and establishing corresponding database engine table scripts according to the structures of the different types of database engine tables; establishing different types of database engine tables according to the database engine table script; wherein the sequence ID is generated according to a snowflake algorithm and has uniqueness.
The method comprises the steps that a sequence ID field and a collecting time field are included before structures of different types of database engine tables are designed, data of the sequence ID field are generated by relying on a snowflake algorithm, uniqueness is achieved, and the collecting time and a snowflake algorithm time sequence are kept consistent. The database needs to be manually established, the database engine table script is well compiled and established, and the database engine table script is uploaded into a program through the program to establish different types of database engine tables.
Fig. 2 is a schematic diagram illustrating the determination of the data configuration rule based on the preset composite allocation algorithm according to the embodiment of the present application.
In step 102, performing allocation calculation on data to be stored based on a preset composite allocation algorithm, and determining a configuration rule corresponding to the data to be stored; wherein the composite allocation algorithm comprises: a precision allocation algorithm, a range allocation algorithm, and a multiple allocation algorithm including a precision allocation algorithm and a range allocation 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 data to be stored to obtain a distribution algorithm corresponding to the data to be stored; if the distribution algorithm corresponding to the data to be stored is an accurate distribution algorithm, performing distribution calculation on the data to be stored through the accurate distribution algorithm, and determining that a configuration rule corresponding to the data to be stored is an accurate distribution rule; if the distribution algorithm corresponding to the data to be stored is a range distribution algorithm, performing distribution calculation on the data to be stored through the range distribution algorithm, and determining a configuration rule corresponding to the data to be stored as a range distribution rule; and if the distribution algorithm corresponding to the data to be stored is a multiple distribution algorithm, performing distribution calculation on the data to be stored through the multiple distribution algorithm, and determining that the configuration rule corresponding to the data to be stored is a multiple distribution rule.
The method comprises the steps of reversely analyzing data to be stored based on acquisition time and sequence ID, performing distribution calculation on the data to be stored, determining configuration rules corresponding to the data to be stored, selecting the storage modes according to the data volume of different scene equipment by using different storage modes according to the configuration rules, and performing data storage by using different configuration rules; and the sequence ID reverse analysis can obtain time information consistent with the acquisition time.
In some embodiments, the specific step of allocating the data to be stored to the database engine tables of the corresponding types according to the configuration rule includes: if the configuration rule corresponding to the data to be stored is an accurate distribution rule, accurately distributing the data to be stored according to the accurate distribution rule and the time category to obtain a time rule corresponding to the data to be stored, and distributing the data to be stored to a database engine table under the time rule; the time categories comprise a year category, a month category, a day category and a time category, the time rules corresponding to the year categories are database date and year limit rules, the time rules corresponding to the month categories are database date and month rules, the time rules corresponding to the day categories are database date and day rules, and the time rules corresponding to the time categories are database date and hour rules.
According to the sequence ID and the acquisition time, the data to be stored are accurately distributed in terms of year, month and day, the data to be stored are accurately distributed to a time rule containing year, month, day and hour according to an accurate distribution rule, field data are obtained, and the field data are accurately matched to a database engine table under the time rule.
For example: distributing according to the day, and finally accurately arranging the data to a database engine table distributed according to the day time rule according to the acquisition time or the sequence ID; the precise allocation algorithm supports independent use of the sequence ID and the acquisition time, or compatible modification, or can be independently used and can be upgraded into a combined mode, but the table allocation rule cannot be upgraded or downgraded after being selected. If the rule is required to be modified, the table is deleted to carry out table re-initialization configuration, and then the corresponding mode of the selected configuration is returned.
In some embodiments, a field content value is acquired by a database age equivalent judgment method, a data table Date and month rule equivalent judgment method, a data table Date and day rule equivalent judgment method, or a data table Date and hour rule equivalent judgment method, time matching is performed after time conversion, data format compatibility is performed on a time field, long type timestamps and time content of character string types are uniformly subjected to type conversion, and a util tool is used for value judgment after the conversion into Date.
In some embodiments, the specific step of allocating the data to be stored to the database engine tables of the corresponding types according to the configuration rule includes: if the configuration rule corresponding to the data to be stored is a range distribution rule, carrying out range distribution on the data to be stored according to the time category according to the range distribution rule to obtain a time limit range corresponding to the data to be stored; and distributing the data to be stored to a database engine table under the time limit range according to the time limit range.
According to the sequence ID and the acquisition time, a determined range allocation rule is adopted to perform year, month, day and time range allocation on data to be stored, the data are allocated to time categories including year, month, day and hour to perform time range span allocation, theoretically, the time categories can span one year, but the use is not suggested, so that the resource is over-used, the influence on server resources is large, different configuration rules are used, and all related database engine tables covered by the range can be allocated according to the range allocation rule. Note that: the sequence and acquisition time may also be used separately.
In some embodiments, performing range allocation on the data to be stored according to the time category according to the range allocation rule further includes: a range configuration switch for setting a range allocation limit; setting the highest limit and the lowest limit of range distribution according to the range configuration switch.
When the range distribution is carried out on the data to be stored, a range configuration switch is also provided, the range distribution size can be set, and if used, the value is <, >, and < >. The specific values used by the range configuration switch now demarcate the upper and lower bounds (i.e., the highest and lowest bounds), and instead of scanning all the tables in the database engine, the upper and lower bounds are isolated according to the specific values in the range configuration range switch.
For example, using the day allocation rule, where the value specified in the range configuration switch is, for example, 5, then >2021-11-14 is used, then the time range to which all database engine tables included in 2021-11-14 to 2021-11-19 are allocated, and vice versa. When the method is used specifically, the method is used specifically according to the type of equipment, the number of point locations, the number of data volumes and the specific scene.
In some embodiments, a data date boundary determination method and a database engine table date range determination method are included in the range allocation rules to obtain the field content values.
The data date boundary judging method judges the range rule according to the date dividing boundary used by configuration, all the range rules use the same configuration, and the boundary judging method processes uniformly no matter which rule is used, and does not support single rule and single configuration. After the limit configuration value is taken, performing upper and lower limit limitation on the data to be stored: if > > >, the operation of + threshold is performed, and if < >, the operation of-threshold is performed.
For example, setting the threshold to 5, calculating for the month range threshold: if yes, acquiring the data content of the current configuration field, performing month +5, returning all database engine tables in the current month and +5 range, executing operations such as query and write-in, and returning; obtaining the data content of the current configuration field, performing month-5, returning all database engine tables in the current month and-5 range, executing operations such as query and write-in, and returning. Calculating for the day range cutoff: acquiring the data content of the current configuration field, calculating day +5, returning all database engine tables in the current date and +5 range, executing operations such as query and write-in, and returning; and < < <, acquiring the data content of the current configuration field, calculating day-5, returning to the current date and all database engine tables in the range of-5, executing operations such as query and write-in, and returning. Calculate for hour range cutoff: if yes, acquiring the data content of the current configuration field, performing hour +5, returning all database engine tables in the current hour and +5 range, executing operations such as query and write-in, and returning; and < < <, acquiring the data content of the current configuration field, performing hour-5, returning all database engine tables in the current hour and in the range of-5, executing operations such as query and write-in, and returning.
In some embodiments, the database engine table date range determining method performs time range determination according to the data to be stored and the configuration rule, and the reading device performs matching according to the configuration usage rule, including two cases: a. with a definite boundary, the method returns all data within all boundaries; b. and returning to call the boundary judgment method without definite boundary to obtain the boundary and returning all data in the boundary.
In some embodiments, the specific step of allocating the data to be stored to the database engine tables of the corresponding types according to the configuration rule includes: and if the configuration rule corresponding to the data to be stored is a multiple allocation rule, allocating the data to be stored to a database engine table of a corresponding type according to the multiple allocation rule so as to realize data storage.
The method has the advantages that the precise allocation and the range allocation are compatible in a composite mode, the two algorithm rules are compatible, the storage function is stronger, if a more complex business scene is needed, the algorithm rule is suggested to be used, and the composite allocation algorithm is suitable for the more complex upstream and downstream business scene and supports other tool components to be compatible.
The beneficial effects of the present application reside in that, the present application provides a data storage method based on a composite allocation algorithm, wherein a database is established according to the age, and different types of database engine tables are established in the database, wherein the database engine tables include: collecting time and sequence ID; 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; according to the configuration rule, the data to be stored are distributed to the database engine tables of the corresponding types to realize data storage, so that a large photovoltaic power station can store the data at the minimum cost, data storage support is provided for intelligent analysis of photovoltaic equipment, and the problems of high data storage cost, difficulty in data storage reading and writing, long access time and the like of operating equipment of the photovoltaic power station are solved.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. 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 over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various 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 C, 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, partly on the user's computer, 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 network format, 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, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose 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 that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
The entire contents of each patent, patent application publication, and other material cited in this application, such as articles, books, specifications, publications, documents, and the like, are hereby incorporated by reference into this application. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Claims (6)
1. A data storage method based on a composite allocation algorithm is characterized by comprising the following steps:
establishing a database according to the age, and establishing different types of database engine tables in the database, wherein the database engine tables comprise: collecting time and sequence ID;
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 allocation algorithm comprises: an exact allocation algorithm, a range allocation algorithm, and a multiple allocation algorithm comprising an exact allocation algorithm and a range allocation algorithm;
and distributing the data to be stored to a database engine table of a corresponding type according to the configuration rule so as to realize data storage.
2. The data storage method based on the composite allocation algorithm as claimed in claim 1, wherein the specific steps of establishing database engine tables of different types in the database comprise: designing structures of different types of database engine tables, and establishing corresponding database engine table scripts according to the structures of the different types of database engine tables; establishing different types of database engine tables according to the database engine table script; wherein the sequence ID is generated according to a snowflake algorithm and has uniqueness.
3. The data storage method based on the composite distribution algorithm according to claim 1, wherein the specific step of determining the configuration rule corresponding to the data to be stored comprises:
performing distribution algorithm analysis on data to be stored to obtain a distribution algorithm corresponding to the data to be stored; if the distribution algorithm corresponding to the data to be stored is an accurate distribution algorithm, performing distribution calculation on the data to be stored through the accurate distribution algorithm, and determining that a configuration rule corresponding to the data to be stored is an accurate distribution rule; if the distribution algorithm corresponding to the data to be stored is a range distribution algorithm, performing distribution calculation on the data to be stored through the range distribution algorithm, and determining a configuration rule corresponding to the data to be stored as a range distribution rule; and if the distribution algorithm corresponding to the data to be stored is a multiple distribution algorithm, performing distribution calculation on the data to be stored through the multiple distribution algorithm, and determining that the configuration rule corresponding to the data to be stored is a multiple distribution rule.
4. The data storage method based on the composite distribution algorithm according to claim 3, wherein the specific step of distributing the data to be stored to the database engine tables of the corresponding types according to the configuration rule comprises:
if the configuration rule corresponding to the data to be stored is an accurate distribution rule, accurately distributing the data to be stored according to the accurate distribution rule and the time category to obtain a time rule corresponding to the data to be stored, and distributing the data to be stored to a database engine table under the time rule; the time categories comprise a year category, a month category, a day category and a time category, the time rules corresponding to the year categories are database date and year limit rules, the time rules corresponding to the month categories are database date and month rules, the time rules corresponding to the day categories are database date and day rules, and the time rules corresponding to the time categories are database date and hour rules.
5. The data storage method based on the composite distribution algorithm according to claim 3, wherein the specific step of distributing the data to be stored to the database engine tables of the corresponding types according to the configuration rule comprises:
if the configuration rule corresponding to the data to be stored is a range distribution rule, carrying out range distribution on the data to be stored according to the time category according to the range distribution rule to obtain a time limit range corresponding to the data to be stored; and distributing the data to be stored to a database engine table under the time limit range according to the time limit range.
6. The data storage method based on the composite allocation algorithm according to claim 5, wherein the range allocation is performed on the data to be stored according to the time category according to the range allocation rule, and further comprising: a range configuration switch for setting a range allocation limit; setting the highest limit and the lowest limit of range distribution according to the range configuration switch.
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