CN110851486A - Data storage method and device - Google Patents
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Abstract
The application provides a data storage method and a data storage device, wherein the method comprises the following steps: receiving data to be stored; determining first class data and second class data in the data to be stored; performing data mining on the second type data according to a preset rule to obtain target data; storing the first type of data and the target data in a relational database. By adopting the technical scheme, the problem that the follow-up database access pressure is large due to data redundancy in the data storage process in the related technology is solved, data to be stored are mined according to the preset rules and then stored after mining and sorting, data redundancy is avoided, meanwhile, connection between data is established, and follow-up rapid reading is facilitated.
Description
Technical Field
The application relates to the field of electric appliances, in particular to a data storage method and device.
Background
In the related art, management after data access is an important task. The database is generally used for storage, and the database management is all database management activities after the database is designed, including database model creation, data loading, database system daily maintenance activities and the like, and simultaneously is activities taken on the database during the database system operation, including data service, performance supervision, database reorganization, database reconstruction, database integrity control and security control, database recovery and the like.
The user electricity consumption data is fed by using an Access database, the Access database is a desktop database, the desktop database is very good in use and high in efficiency when a small amount of data and a single machine are processed, but the desktop database is a small-sized database, has limitations and can greatly influence the efficiency when massive data are processed. When the database is too large, the performance generally deteriorates by more than hundred meters; and theoretically support 255 concurrent users, but in practice not so much at all, if accessed in a read-only manner, approximately 100 users, if edited concurrently, approximately 10-20 users; too many records, millions of single-table records, can become poor performance, and if the design is poor, the limit is reduced; can not be compiled into an executable file, and an Access operating environment must be installed for use.
Aiming at the problem that the subsequent database access pressure is high due to data redundancy in the data storage process in the related technology, no effective solution is available at present.
Disclosure of Invention
The embodiment of the application provides a data storage method and a data storage device, which are used for at least solving the problem of high subsequent database access pressure caused by data redundancy in the data storage process in the related technology.
According to an embodiment of the present application, there is provided a data storage method including: receiving data to be stored; determining first class data and second class data in the data to be stored; performing data mining on the second type data according to a preset rule to obtain target data; storing the first type of data and the target data in a relational database.
Optionally, determining the first type of data and the second type of data in the data to be stored includes: determining whether the data to be stored accords with a storage rule; and determining the data to be stored which accords with the storage rule as first class data, and determining the data to be stored which does not accord with the storage rule as second class data.
Optionally, performing data mining on the second type of data according to a preset rule to obtain target data, including: and analyzing and processing the second-class data by using a first model to obtain target data, wherein the first model is trained by using multiple groups of data through machine learning, and each group of data in the multiple groups of data comprises power utilization data in an initial state and corresponding power utilization data in a storage state.
Optionally, the second type data is analyzed and processed by using a first model, and the method includes at least one of the following steps: classifying a plurality of parameters in the second type of data according to different states of the electric equipment; performing regression analysis on the parameters in the second type of data; establishing an association between a plurality of parameters in the second class of data; aggregating a plurality of parameters in the second class of data.
Optionally, storing the first type of data and the target data in a relational database includes: and storing the first type of data and the target data to a Structured Query Language (SQL) server database.
Optionally, after storing the first type of data and the target data in a structured query language SQL server database, when detecting that data stored in the SQL server database is changed, recording a change operation in a log.
Optionally, after the change operation is recorded in the log, the method further includes: monitoring the log; and under the condition that the log is monitored to have abnormity, performing transaction rollback on the log with the abnormity.
According to another embodiment of this document, there is also provided a data storage device including: the receiving module is used for receiving data to be stored; the determining module is used for determining first class data and second class data in the data to be stored; the data mining module is used for performing data mining on the second type of data according to a preset rule to obtain target data; and the storage module is used for storing the first type of data and the target data into a relational database.
According to a further embodiment of the present application, there is also provided a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the steps of any of the above method embodiments when executed.
According to yet another embodiment of the present application, there is also provided an electronic device, comprising a memory in which a computer program is stored and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Receiving data to be stored through the application; determining first class data and second class data in the data to be stored; performing data mining on the second type data according to a preset rule to obtain target data; storing the first type of data and the target data in a relational database. By adopting the technical scheme, the problem that the follow-up database access pressure is large due to data redundancy in the data storage process in the related technology is solved, data to be stored are mined according to the preset rules and then stored after mining and sorting, data redundancy is avoided, meanwhile, connection between data is established, and follow-up rapid reading is facilitated.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a block diagram of a hardware structure of a computer terminal of a data storage method according to an embodiment of the present application;
FIG. 2 is a flow chart of a data storage method according to an embodiment of the present application;
fig. 3 is a flow chart diagram of a power consumption data management method according to the present document.
Detailed Description
The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Example one
The method provided by the first embodiment of the present application may be executed in a computer terminal or a similar computing device. Taking an example of the data storage method running on a computer terminal, fig. 1 is a hardware structure block diagram of the computer terminal according to the embodiment of the present application. As shown in fig. 1, the computer terminal 10 may include one or more (only one shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, and optionally may also include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the computer terminal. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to a data storage method in the embodiment of the present application, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In this embodiment, a data storage method operating in the computer terminal is provided, and fig. 2 is a flowchart of the data storage method according to the embodiment of the present application, and as shown in fig. 2, the flowchart includes the following steps:
step S202, receiving data to be stored;
step S204, determining a first class of data and a second class of data in the data to be stored;
step S206, performing data mining on the second type data according to a preset rule to obtain target data;
and step S208, storing the first type data and the target data into a relational database.
The scheme of the application file can be applied to a scene of storing electricity utilization data, and the electricity utilization equipment can be household appliances such as an air conditioner and the like, but is not limited to the household appliances.
Through the steps, receiving data to be stored; determining first class data and second class data in the data to be stored; performing data mining on the second type data according to a preset rule to obtain target data; storing the first type of data and the target data in a relational database. By adopting the technical scheme, the problem that the follow-up database access pressure is large due to data redundancy in the data storage process in the related technology is solved, data to be stored are mined according to the preset rules and then stored after mining and sorting, data redundancy is avoided, meanwhile, connection between data is established, and follow-up rapid reading is facilitated.
Optionally, determining the first type of data and the second type of data in the data to be stored includes: determining whether the data to be stored accords with a storage rule; and determining the data to be stored which accords with the storage rule as first class data, and determining the data to be stored which does not accord with the storage rule as second class data.
Optionally, performing data mining on the second type of data according to a preset rule to obtain target data, including: and analyzing and processing the second-class data by using a first model to obtain target data, wherein the first model is trained by using multiple groups of data through machine learning, and each group of data in the multiple groups of data comprises power utilization data in an initial state and corresponding power utilization data in a storage state. The electricity consumption data in the initial state in this embodiment refers to an initial state transmitted from an electricity meter or the like to a database, that is, an operation such as data sorting or data mining has not yet been performed. The corresponding stored electricity consumption data refers to data subjected to data mining or data arrangement, namely data of the final storage relational database.
Optionally, the second type data is analyzed and processed by using a first model, and the method includes at least one of the following steps:
classifying a plurality of parameters in the second type of data according to different states of the electric equipment; the different states of the powered device may include an on/off state of the powered device, or the powered device being in a sleep mode, etc.
Performing regression analysis on the parameters in the second type of data;
establishing an association between a plurality of parameters in the second type of data may be establishing an association between different data, for example, by time sequence, or establishing an association between data belonging to the same device.
Aggregating a plurality of parameters in the second class of data.
Optionally, storing the first type of data and the target data in a relational database includes: and storing the first type of data and the target data to a Structured Query Language (SQL) server database.
Optionally, after storing the first type of data and the target data in a structured query language SQL server database, when detecting that data stored in the SQL server database is changed, recording a change operation in a log.
Optionally, after the change operation is recorded in the log, the method further includes: monitoring the log; and under the condition that the log is monitored to have abnormity, performing transaction rollback on the log with the abnormity.
The log exception can adopt a log exception detection technology in the related technology, and in the case of determining that the log exception exists, the data operation causing the log exception is rejected, the data operation is cancelled, and the log is restored to the state before updating or before the data operation.
The following description is given in detail with reference to another embodiment of the present document.
The method and the system adopt the artificial intelligence algorithm to manage the electricity utilization data of the user, carry out deep data processing mining on the data which do not meet the requirements by using the machine learning artificial intelligence algorithm through methods such as classification, regression analysis, clustering and association rules, effectively reduce the redundancy of the data, store the data in the relational database SQL server database, and facilitate later use.
The data is stored in the SQL server database, so that the data management and analysis of the SQLserver database are more flexible, and the data management and analysis method is easy to use, is suitable for the scalability of a distributed organization, the data warehouse function for decision support, the close association integration with many other server software, good cost performance and the like; when the SQL server database operates data, the correct operation of increasing, deleting, modifying and checking the database data is ensured through transaction rollback and log monitoring; meanwhile, the electricity utilization data of the user is managed by adopting an artificial intelligence algorithm, the redundancy of the data is effectively reduced, and the disaster tolerance of the data is improved by the SQL server backup function.
The implementation manner of the present document may be as follows, and fig. 3 is a schematic flow chart of the electricity consumption data management method according to the present document, and as shown in fig. 3, the method includes the following steps:
step 1), data access:
step 2), judging whether the data meet requirements after data access, processing the data which do not meet the requirements by using an artificial intelligent algorithm such as deep learning and the like, turning to step 3, directly storing the data which meet the requirements, and turning to step 4;
step 3), algorithm processing, namely processing data by using artificial intelligent algorithms such as deep learning and the like, and specifically comprises the following steps: classification, regression analysis, clustering, association rules, and the like; the classification method mainly processes qualitative data such as on-off state and the like, regression analysis mainly processes quantitative data such as current, voltage and the like, clustering, association rules and the like mainly perform aggregate classification on the data, the sufficiency of data use is ensured, and meanwhile, the redundancy of the data is reduced;
step 4), data storage: storing the data meeting the requirements in a relational database, such as an SQL server database and the like, and ensuring the structure and format of the data;
step 5), data operation: performing related operation on data stored in an SQL server database;
step 6), any data change operation in the database is recorded in the log, and some problems are solved through log monitoring, such as checking some unexpected modifications; checking whether the log is abnormal or not, and turning to the step 7 under the condition that the log is detected to be abnormal;
and 7), performing transaction rollback on the log with the exception, and rolling back all updates to a state before the transaction starts, so as to ensure normal use of the database.
By adopting the technical scheme, the following beneficial effects are realized:
1) the database is more flexible in data management and analysis;
2) the data are processed by artificial intelligence, so that the redundancy of the data is reduced;
3) the database is managed by artificial intelligence, so that the disaster tolerance of the database is improved, and the correct operation of the database on data is ensured.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present application.
Example two
In this embodiment, a data storage device is further provided, and the data storage device is used to implement the foregoing embodiments and preferred embodiments, and the description of the data storage device is omitted. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
According to another embodiment of this document, there is also provided a data storage device including:
the receiving module is used for receiving data to be stored;
the determining module is used for determining first class data and second class data in the data to be stored;
the data mining module is used for performing data mining on the second type of data according to a preset rule to obtain target data;
and the storage module is used for storing the first type of data and the target data into a relational database.
By adopting the technical scheme, the data to be stored is received; determining first class data and second class data in the data to be stored; performing data mining on the second type data according to a preset rule to obtain target data; storing the first type of data and the target data in a relational database. The problem that the follow-up database access pressure is high due to data redundancy in the data storage process in the related technology is solved, data to be stored are mined according to preset rules and stored after mining and sorting, data redundancy is avoided, connection among data is established, and follow-up rapid reading is facilitated.
Optionally, the determining module is further configured to determine whether the data to be stored conforms to a storage rule; and determining the data to be stored which accords with the storage rule as first class data, and determining the data to be stored which does not accord with the storage rule as second class data.
Optionally, the data mining module is further configured to analyze and process the second type of data by using a first model to obtain target data, where the first model is trained by machine learning using multiple sets of data, and each set of data in the multiple sets of data includes power consumption data in an initial state and power consumption data in a corresponding storage state.
Optionally, the second type data is analyzed and processed by using a first model, and the method includes at least one of the following steps: classifying a plurality of parameters in the second type of data according to different states of the electric equipment; performing regression analysis on the parameters in the second type of data; establishing an association between a plurality of parameters in the second class of data; aggregating a plurality of parameters in the second class of data.
Optionally, the storage module is further configured to store the first type of data and the target data in a structured query language SQL server database.
Optionally, after storing the first type of data and the target data in the SQL server database, the storage module is further configured to record a change operation in a log when detecting that the data stored in the SQL server database is changed.
Optionally, after the storage module records the change operation in a log, the storage module is further configured to monitor the log; and under the condition that the log is monitored to have abnormity, performing transaction rollback on the log with the abnormity.
It should be noted that, the above modules may be implemented by software or hardware, and for the latter, the following may be implemented, but not limited to: the modules are all positioned in the same processor; alternatively, the modules are respectively located in different processors in any combination.
EXAMPLE III
Embodiments of the present application also provide a storage medium. Alternatively, in the present embodiment, the storage medium may be configured to store program codes for performing the following steps:
s1, receiving data to be stored;
s2, determining a first class of data and a second class of data in the data to be stored;
s3, performing data mining on the second type data according to a preset rule to obtain target data;
s4, storing the first type data and the target data in a relational database.
Optionally, the storage medium is further arranged to store program code for performing the steps of:
optionally, in this embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Embodiments of the present application further provide an electronic device comprising a memory having a computer program stored therein and a processor configured to execute the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
s1, receiving data to be stored;
s2, determining a first class of data and a second class of data in the data to be stored;
s3, performing data mining on the second type data according to a preset rule to obtain target data;
s4, storing the first type data and the target data in a relational database.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation manners, and this embodiment is not described herein again.
It will be apparent to those skilled in the art that the modules or steps of the present application described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present application is not limited to any specific combination of hardware and software.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A method of storing data, comprising:
receiving data to be stored;
determining first class data and second class data in the data to be stored;
performing data mining on the second type data according to a preset rule to obtain target data;
storing the first type of data and the target data in a relational database.
2. The method of claim 1, wherein determining the first type of data and the second type of data in the data to be stored comprises:
determining whether the data to be stored accords with a storage rule;
and determining the data to be stored which accords with the storage rule as first class data, and determining the data to be stored which does not accord with the storage rule as second class data.
3. The method of claim 1, wherein performing data mining on the second type data according to a preset rule to obtain target data comprises:
and analyzing and processing the second-class data by using a first model to obtain target data, wherein the first model is trained by using multiple groups of data through machine learning, and each group of data in the multiple groups of data comprises power utilization data in an initial state and corresponding power utilization data in a storage state.
4. The method of claim 3, wherein the analyzing the second type of data using the first model comprises at least one of:
classifying a plurality of parameters in the second type of data according to different states of the electric equipment;
performing regression analysis on the parameters in the second type of data;
establishing an association between a plurality of parameters in the second class of data;
aggregating a plurality of parameters in the second class of data.
5. The method of claim 1, wherein storing the first type of data and the target data in a relational database comprises:
and storing the first type of data and the target data to a Structured Query Language (SQL) server database.
6. The method of claim 5, wherein after storing the first type of data and the target data in a Structured Query Language (SQL) server database, the method further comprises:
and when detecting that the data stored in the SQL server database is changed, recording the change operation in a log.
7. The method of claim 6, wherein after logging the change operation, the method further comprises:
monitoring the log;
and under the condition that the log is monitored to have abnormity, performing transaction rollback on the log with the abnormity.
8. A data storage device, comprising:
the receiving module is used for receiving data to be stored;
the determining module is used for determining first class data and second class data in the data to be stored;
the data mining module is used for performing data mining on the second type of data according to a preset rule to obtain target data;
and the storage module is used for storing the first type of data and the target data into a relational database.
9. A storage medium, in which a computer program is stored, wherein the computer program is arranged to perform the method of any of claims 1 to 7 when executed.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and wherein the processor is arranged to execute the computer program to perform the method of any of claims 1 to 7.
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Application publication date: 20200228 |