阅读说明：本技术 一种实时变换数据存储方法、装置、电子设备及存储介质 (Real-time transformation data storage method and device, electronic equipment and storage medium ) 是由 许汝全 陈嘉豪 姚沣 徐志坚 陈光尧 谢睿 于 2021-04-08 设计创作，主要内容包括：本发明公开了一种实时变换数据存储方法、装置、电子设备及存储介质,用于解决现有的实时数据的存储方式数据处理效率较低的技术问题。其中,方法包括：获取实时变换数据；将实时变换数据转换为预写式日志数据块；获取预写式日志数据块的操作标识；根据操作标识合并预写式日志数据块,得到合并数据块；对合并数据块进行压缩,得到列式数据块；存储列式数据块。(The invention discloses a real-time transformation data storage method and device, electronic equipment and a storage medium, which are used for solving the technical problem of low data processing efficiency of the existing real-time data storage mode. The method comprises the following steps: acquiring real-time transformation data; converting the real-time transformation data into a pre-written log data block; acquiring an operation identifier of a pre-written log data block; merging the pre-written log data blocks according to the operation identifiers to obtain merged data blocks; compressing the merged data block to obtain a column type data block; columnar data blocks are stored.)

1. A method for real-time transformation data storage, comprising:

acquiring real-time transformation data;

converting the real-time transformation data into a pre-written log data block;

acquiring an operation identifier of the pre-written log data block;

merging the pre-written log data blocks according to the operation identifiers to obtain merged data blocks;

compressing the merged data block to obtain a column type data block;

and storing the column-wise data block.

2. The method of claim 1, wherein the step of converting the real-time transformation data into pre-written log data blocks comprises:

converting the real-time transformation data into a plurality of pre-written logs;

sequencing the plurality of pre-written logs according to a time sequence;

and partitioning the sequenced plurality of pre-written logs according to a preset partitioning time length to obtain pre-written log data blocks.

3. The method of claim 2, wherein after the step of compressing the merged data block to obtain the columnar data block, the method further comprises:

extracting a core index abstract of the pre-written log;

generating a data list based on the core index abstract;

generating a snapshot file of the pre-written log;

generating a snapshot list based on the snapshot file;

and generating a data list by adopting the data list and the snapshot list, and storing the data list.

4. The method of claim 3, further comprising:

when a data query request is received, acquiring a target data list and a target snapshot list which meet preset conditions from the data list;

determining a target data block based on the target data list and the target snapshot list;

acquiring a file path of the target data block;

loading the target data block based on the file path;

and sequencing the target data blocks according to the time sequence to obtain query data.

5. The method of claim 4, wherein the step of determining a target data block based on the target data manifest and the target snapshot manifest comprises:

determining a blocking time point according to the target snapshot list;

acquiring required time from the data query request;

determining column-wise data blocks between the blocking time point and the required time as target data blocks.

6. A real-time transformation data storage device, comprising:

the real-time transformation data acquisition module is used for acquiring real-time transformation data;

the conversion module is used for converting the real-time transformation data into a pre-written log data block;

the operation identifier acquisition module is used for acquiring the operation identifier of the pre-written log data block;

the merging module is used for merging the pre-written log data blocks according to the operation identifiers to obtain merged data blocks;

the compression module is used for compressing the merged data block to obtain a column type data block;

and the storage module is used for storing the column data blocks.

7. The apparatus of claim 6, wherein the conversion module comprises:

the pre-written log conversion submodule is used for converting the real-time transformation data into a plurality of pre-written logs;

the sequencing submodule is used for sequencing the plurality of pre-written logs according to a time sequence;

and the blocking submodule is used for blocking the sequenced plurality of pre-written logs according to the preset blocking duration to obtain pre-written log data blocks.

8. The apparatus of claim 7, further comprising:

the extraction module is used for extracting the core index abstract of the pre-written log;

the data list generating module is used for generating a data list based on the core index abstract;

the snapshot file generation module is used for generating a snapshot file of the pre-written log;

the snapshot list generating module is used for generating a snapshot list based on the snapshot file;

and the data list generating module is used for generating a data list by adopting the data list and the snapshot list and storing the data list.

9. An electronic device, comprising a processor and a memory:

the memory is used for storing program codes and transmitting the program codes to the processor;

the processor is configured to execute the real-time transformation data storage method of any one of claims 1-5 according to instructions in the program code.

10. A computer-readable storage medium for storing program code for performing the real-time transformation data storage method of any one of claims 1-5.

Technical Field

The present invention relates to the field of data storage technologies, and in particular, to a method and an apparatus for real-time transformation data storage, an electronic device, and a storage medium.

Background

With the development and popularization of information technology, more and more data are generated by users, the real-time requirement on the data is higher, and the real-time data needs to be stored on a large scale and efficiently.

Taking a game product as an example, two types of data can be included, one is a large amount of chat data, and the other is representation data of a game room. The chatting data comprises characters, pictures and audio and video, is running data, and can be continuously added along with the chatting, and the generated data can not be changed. The image data of the game room is constantly changed, and the flowing time of the room personnel is changed along with the synchronization of the game progress.

In the process of matching game players, the generated data is large, the real-time delay requirement is high, if the data processing speed is not as fast as the data production speed, the data accumulation situation occurs, the data processing delay is caused, and the long and unwarranted waiting of users is caused. In order to prevent the user from leaving in a long and unwarranted waiting process, a frame skipping mode is mainly adopted at present to directly discard the data delayed by more than a tolerance time. However, since part of data is discarded, the matching accuracy is reduced, so that the user experience is greatly reduced, and meanwhile, in order to maximize the processing speed, the process data is stored in the memory database for a short time instead of being stored permanently in real time, so that the data calculated in real time does not have reliability and integrity, and certain obstacles are caused to subsequent data analysis.

Disclosure of Invention

The invention provides a real-time transformation data storage method and device, electronic equipment and a storage medium, which are used for solving the technical problem of low data processing efficiency of the existing real-time data storage mode.

The invention provides a real-time transformation data storage method, which comprises the following steps:

acquiring real-time transformation data;

converting the real-time transformation data into a pre-written log data block;

acquiring an operation identifier of the pre-written log data block;

merging the pre-written log data blocks according to the operation identifiers to obtain merged data blocks;

compressing the merged data block to obtain a column type data block;

and storing the column-wise data block.

Optionally, the step of converting the real-time transformation data into a pre-written log data block includes:

converting the real-time transformation data into a plurality of pre-written logs;

sequencing the plurality of pre-written logs according to a time sequence;

and partitioning the sequenced plurality of pre-written logs according to a preset partitioning time length to obtain pre-written log data blocks.

Optionally, after the step of compressing the merged data block to obtain the column-wise data block, the method further includes:

extracting a core index abstract of the pre-written log;

generating a data list based on the core index abstract;

generating a snapshot file of the pre-written log;

generating a snapshot list based on the snapshot file;

and generating a data list by adopting the data list and the snapshot list, and storing the data list.

Optionally, the method further comprises:

when a data query request is received, acquiring a target data list and a target snapshot list which meet preset conditions from the data list;

determining a target data block based on the target data list and the target snapshot list;

acquiring a file path of the target data block;

loading the target data block based on the file path;

and sequencing the target data blocks according to the time sequence to obtain query data.

Optionally, the step of determining a target data block based on the target data list and the target snapshot list includes:

determining a blocking time point according to the target snapshot list;

acquiring required time from the data query request;

determining column-wise data blocks between the blocking time point and the required time as target data blocks.

The present invention also provides a real-time transformation data storage device, comprising:

the real-time transformation data acquisition module is used for acquiring real-time transformation data;

the conversion module is used for converting the real-time transformation data into a pre-written log data block;

the operation identifier acquisition module is used for acquiring the operation identifier of the pre-written log data block;

the merging module is used for merging the pre-written log data blocks according to the operation identifiers to obtain merged data blocks;

the compression module is used for compressing the merged data block to obtain a column type data block;

and the storage module is used for storing the column data blocks.

Optionally, the conversion module includes:

the pre-written log conversion submodule is used for converting the real-time transformation data into a plurality of pre-written logs;

the sequencing submodule is used for sequencing the plurality of pre-written logs according to a time sequence;

and the blocking submodule is used for blocking the sequenced plurality of pre-written logs according to the preset blocking duration to obtain pre-written log data blocks.

Optionally, the apparatus further comprises:

the extraction module is used for extracting the core index abstract of the pre-written log;

the data list generating module is used for generating a data list based on the core index abstract;

the snapshot file generation module is used for generating a snapshot file of the pre-written log;

the snapshot list generating module is used for generating a snapshot list based on the snapshot file;

and the data list generating module is used for generating a data list by adopting the data list and the snapshot list and storing the data list.

The invention also provides an electronic device comprising a processor and a memory:

the memory is used for storing program codes and transmitting the program codes to the processor;

the processor is configured to execute the real-time transformation data storage method according to instructions in the program code.

The present invention also provides a computer readable storage medium for storing program code for performing the real-time transformation data storage method as described in any one of the above.

According to the technical scheme, the invention has the following advantages:

the invention provides a real-time transformation number storage method, which comprises the following steps: acquiring real-time transformation data; converting the real-time transformation data into a pre-written log data block; acquiring an operation identifier of a pre-written log data block; merging the pre-written log data blocks according to the operation identifiers to obtain merged data blocks; compressing the merged data block to obtain a column type data block; columnar data blocks are stored. The invention improves the processing efficiency of data storage by adopting a mode of asynchronous column conversion of row log records.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

FIG. 1 is a flowchart illustrating steps of a method for real-time transformation of data storage according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating steps of a method for real-time transformation of data storage according to another embodiment of the present invention;

FIG. 3 is a schematic process diagram of a real-time transformation data storage method according to an embodiment of the present invention;

FIG. 4 is a graphical illustration of the Sigmoid algorithm;

FIG. 5 is a schematic diagram of generating a data list according to an embodiment of the present invention;

FIG. 6 is a schematic diagram of a data query process according to an embodiment of the present invention;

fig. 7 is a block diagram of a real-time transformation data storage device according to an embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a real-time transformation data storage method and device, electronic equipment and a storage medium, which are used for solving the technical problem of low data processing efficiency of the existing real-time data storage mode.

In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a real-time transformation data storage method according to an embodiment of the present invention.

The invention provides a real-time transformation data storage method, which specifically comprises the following steps:

step 101, acquiring real-time transformation data;

the real-time transformation data refers to data generated by transformation such as modification and deletion of data in the process of data access, for example, data played together in a game room flows among different tables, data is frequently read and written in the process of data access, modification and deletion of data can be performed, and the data volume is very large.

Step 102, converting the real-time transformation data into a pre-written log data block;

in an actual scene, under the conditions that a lot of real-time transformation data are generated and the requirement for real-time delay is high, if the data processing speed is not as fast as the data production speed, the data accumulation occurs, so that the data processing delay is caused, and the long and unwarranted waiting is caused for a user.

In view of the above, in one example, embodiments of the present invention employ a rank-associative storage structure for data storage. The row-column combined storage structure is that data in a table is firstly divided into a plurality of row groups, and an HDFS (Hadoop Distributed File System) is placed in each row group. Meanwhile, each row group is vertically divided into the sub-relational tables according to columns, and the data in the sub-relational tables are independently placed in the DHFS after being compressed. In this way, it can be ensured that all data in the row-column storage structure are located on the same node, so that the speed of reading records is increased, the compression effect is more obvious when compression is performed, and some columns which do not need to be accessed in the compression can be directly skipped over.

Based on the above row-column combination storage structure, in the embodiment of the present invention, the real-time transformation data needs to be converted into the pre-written log data block for recording. Wherein each real-time transformation data is recorded one line independently.

103, acquiring an operation identifier of the pre-written log data block;

step 104, merging the pre-written log data blocks according to the operation identification to obtain merged data blocks;

after the pre-written log data block is obtained, operation identifiers of the pre-written log data block, such as add (Insert), Delete (Delete), modify (Update), and the like, may be obtained, and the data is merged to obtain a merged data block as a final merged result.

It should be noted that, since the data is changed and involves operations such as adding, deleting, and modifying, all the transformed data are stateful, and only the added data is possibly modified or deleted, the real-time transformed data needs to carry time information so as to process the data according to the time information. In one example, the real-time transformation data is shown in chronological order in table 1 below:

Id	name	op_type	op_timestamp
				1	Zhang San	Insert	1604053866119
2	Li Si	Insert	1604053881118
				1	Zhang Fei	Update	1604053912118
2	Li Si	Delete	1604053932132

TABLE 1

As shown in table 1, from the time-series processing, the data generated in real time is firstly added with two records "1, zhangsan" and "2, lie four", then "1, zhangsan" is modified into "1, zhangfei", and finally the record of "2, lie four" is deleted, so that the final result of the data is shown in table 2:

id	name
		1	Zhang Fei

TABLE 2

It should be noted that, although the final result only represents the latest piece of data presenting the same Id (primary key) according to the operation, for example, only "1, zhangfei" is displayed last, when the actual deletion and modification of the data before the operation are not actually deleted, only the subsequent compression storage is not involved.

Step 105, compressing the combined data block to obtain a column type data block;

and compressing the merged data blocks obtained by the processing in a columnar storage mode to obtain columnar data blocks serving as new file pieces.

It should be noted that after the compression operation is completed, the compressed log may be identified as deleted, so as to recycle the space.

And step 106, storing the column data block.

After compression is completed, the columnar data blocks obtained through compression can be written to a distributed disk for storage.

The invention provides a real-time transformation number storage method, which comprises the following steps: acquiring real-time transformation data; converting the real-time transformation data into a pre-written log data block; acquiring an operation identifier of a pre-written log data block; merging the pre-written log data blocks according to the operation identifiers to obtain merged data blocks; compressing the merged data block to obtain a column type data block; columnar data blocks are stored. The invention improves the processing efficiency of data storage by adopting a mode of asynchronous column conversion of row log records.

Referring to fig. 2, fig. 2 is a flowchart illustrating a real-time transformation data storage method according to another embodiment of the present invention. The method specifically comprises the following steps:

step 201, acquiring real-time transformation data;

step 202, converting the real-time transformation data into a plurality of pre-written logs;

step 203, sequencing the plurality of pre-written logs according to a time sequence;

step 204, partitioning the sequenced multiple pre-written logs according to a preset partitioning duration to obtain pre-written log data blocks;

in the embodiment of the present invention, as shown in fig. 3, after the real-time transformation data is obtained, the real-time transformation data may be first converted into a format of a Write-ahead logging (Wal) for recording, where each piece of data is independent of one row. Then, sequencing the plurality of pre-written logs according to the time sequence, and blocking the sequenced pre-written logs according to a certain blocking duration to form a pre-written log data block (Wal File). In one example, the pre-written logs at each time may be grouped into a group by partitioning according to a TimeStamp (TimeStamp), forming different pre-written log data blocks.

Step 205, acquiring an operation identifier of the pre-written log data block;

step 206, merging the pre-written log data blocks according to the operation identifier to obtain merged data blocks;

in the embodiment of the present invention, as shown in fig. 3, after the pre-written log data block is obtained, operation identifiers of the pre-written log data block, such as add (Insert), Delete (Delete), modify (Update), and the like, may be obtained, and the pre-written log data block is merged according to a reverse time sequence, so as to obtain a merged data block.

Step 207, compressing the merged data block to obtain a column type data block;

at step 208, columnar data blocks are stored.

As shown in fig. 3, the merged data blocks obtained by the above processing are compressed by means of columnar storage, and the columnar data blocks can be obtained as new file pieces. After the compression is completed, the column-type data blocks obtained by the compression can be written on a distributed disk, and are uniformly distributed to different servers for persistent storage according to the load condition of the servers.

In one example, independent low priority threads may be used for data migration compression, where the migration logic takes a set of Wal file slices as input, converts all log files in each file slice into columnar format, generates new compressed file slices, writes into corresponding column spaces, and finally marks on the time axis according to the migration result. Because the asynchronous compression mode is adopted, the writing of data is not blocked.

It should be noted that, in the foregoing method, a large number of data fragments are generated for data reported in a delayed manner, and a relatively large delay is brought to both reading and writing.

Specifically, in the embodiment of the present invention, an independent low-priority thread may be created, compressed data may be merged, excessive file fragments may be reduced, and a core index summary (max, min, avg) of a data file may be extracted to generate a data list (manifest list) while merging files. Therefore, when data query is carried out, filtering can be carried out through the navigation file preferentially. The method comprises the following specific steps:

s11, extracting the core index abstract of the pre-written log;

s12, generating a data list based on the core index abstract;

s13, generating a snapshot file of the pre-written log;

s14, generating a snapshot list based on the snapshot file;

and S15, generating a data list by adopting the data list and the snapshot list, and saving the data list.

In practical application, a core index summary of the pre-written log may be extracted to generate a data list (manifest).

And then, introducing a mirror image snapshot mode to perform query optimization, and opening up an independent low-priority thread to generate a snapshot file (snapshot) of the pre-written log and a corresponding snapshot list (manifest snapshot). It should be noted that, the present invention does not perform data cleaning on the data marked as update or deletion, but retains all the data.

In practical application, different snapshot generation strategies can be selected according to the balance of data heat and storage space cost. Such as using Sigmoid algorithm for time slice interval division. Due to the storage cost, the data are different due to the requirement of heat. The more recent data is generated, the higher the fineness is needed, and the fineness is not required when the data is left in history. By adopting a Sigmoid algorithm, the limitation of snapshot time can be realized. The Sigmoid algorithm is a typical S-curve algorithm, high frequency is reserved at the early stage, and the frequency is reduced in an S-shaped curve with the time. As shown in particular in fig. 4.

In a specific example, as shown in fig. 5, after the pre-written log data block is subjected to data transfer compression, a columnar data block is formed, the columnar data block generated after compression is defragmented, a data list can be generated as a navigation file, and meanwhile, a snapshot file and a snapshot list can be generated by introducing a mirror snapshot. And (4) forming a data list and a snapshot list, and generating a data list. And the subsequent data query is convenient. After optimizing by adding navigation files and snapshot lists, the read logic changes the direct Id + time scanning data into the operation of firstly searching a data list and then inquiring a real data block. The navigation file can be recorded in a B + tree mode.

After the storage of the real-time transformation data is finished, when a user needs to inquire the stored comfort data, the method can be realized by the following steps:

s21, when a data query request is received, a target data list and a target snapshot list meeting preset conditions are obtained from the data list;

s22, determining a target data block based on the target data list and the target snapshot list;

s23, acquiring a file path of the target data block;

s24, loading the target data block based on the file path;

and S25, sequencing the target data blocks according to the time sequence to obtain query data.

In a specific implementation, as shown in fig. 6, when performing a data Query (Query), a data list needs to be queried to obtain all data lists and snapshot lists that satisfy a condition, where the preset condition may be a time condition or another condition, and may be specifically defined according to an actual need.

Then, filtering is performed through the data list and the snapshot list, so that file paths of all target data blocks meeting the conditions can be obtained, and all hit target data blocks can be loaded according to the file paths.

In one example, the process of determining the target data block is implemented by:

determining a blocking time point according to the target snapshot list; acquiring required time from the data query request; and determining the column-type data block between the blocking time point and the required time as a target data block.

Then, the target data blocks are sorted according to the time sequence, and the data records in the target data blocks can be obtained.

Further, if the latest real-time data needs to be acquired, uncompressed Wal File can be additionally queried, and a final query result is obtained after all data are integrated.

The query process based on the data structure can greatly reduce the data query range, reduce IO (input/output) overhead and finally realize the improvement of the query performance.

The embodiment of the invention adopts a mode of asynchronous column conversion of row log records, and the storage efficiency is higher. In addition, the embodiment of the invention adds the data summary navigation file and forms a multi-level data structure, thereby accelerating the efficiency of spatio-temporal analysis query. In addition, the embodiment of the invention reserves all historical data, can inquire the data at any historical moment, and effectively prevents the problem that the empty inquiry breaks down all space-time partitions by combining the snapshot.

Referring to fig. 7, fig. 7 is a block diagram illustrating a real-time transformation data storage device according to an embodiment of the present invention.

The embodiment of the invention provides a real-time transformation data storage device, which comprises:

a real-time transformation data obtaining module 701, configured to obtain real-time transformation data;

a conversion module 702, configured to convert the real-time transformation data into a pre-written log data block;

an operation identifier obtaining module 703, configured to obtain an operation identifier of the pre-written log data block;

a merging module 704, configured to merge the pre-written log data blocks according to the operation identifier to obtain merged data blocks;

a compressing module 705, configured to compress the merged data block to obtain a column-type data block;

a storage module 706 for storing the columnar data blocks.

In an embodiment of the present invention, the conversion module 702 includes:

the pre-written log conversion submodule is used for converting the real-time transformation data into a plurality of pre-written logs;

the sequencing submodule is used for sequencing the plurality of pre-written logs according to a time sequence;

and the blocking submodule is used for blocking the sequenced multiple pre-written logs according to the preset blocking duration to obtain pre-written log data blocks.

In an embodiment of the present invention, the apparatus further comprises:

the extraction module is used for extracting the core index abstract of the pre-written log;

the data list generating module is used for generating a data list based on the core index abstract;

the snapshot file generation module is used for generating a snapshot file of the pre-written log;

the snapshot list generating module is used for generating a snapshot list based on the snapshot file;

and the data list generating module is used for generating a data list by adopting the data list and the snapshot list and storing the data list.

In an embodiment of the present invention, the apparatus further comprises:

the target data list and target snapshot list acquisition module is used for acquiring a target data list and a target snapshot list which meet preset conditions from the data list when a data query request is received;

the target data block determining module is used for determining a target data block based on the target data list and the target snapshot list;

the file path acquisition module is used for acquiring a file path of the target data block;

the loading module is used for loading the target data block based on the file path;

and the query data acquisition module is used for sequencing the target data blocks according to the time sequence to obtain query data.

In an embodiment of the present invention, the target data block determining module includes:

the blocking time point determining submodule is used for determining a blocking time point according to the target snapshot list;

the required time obtaining sub-module is used for obtaining required time from the data query request;

and the target data block determining submodule is used for determining the column-type data block between the blocking time point and the required time as the target data block.

An embodiment of the present invention further provides an electronic device, where the device includes a processor and a memory:

the memory is used for storing the program codes and transmitting the program codes to the processor;

the processor is configured to execute the real-time transformation data storage method of any of the embodiments of the present invention according to instructions in the program code.

Embodiments of the present invention further provide a computer-readable storage medium, where the computer-readable storage medium is used to store a program code, and the program code is used to execute the real-time transformation data storage method according to any embodiment of the present invention.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.

Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.