Data synchronization and processing method of cloud table

Abstract

The invention aims to provide the data synchronization and processing method of the cloud table, which is convenient and quick, efficient, low in cost and high in security. The method comprises the following steps: step 100, realizing data synchronization of real-time collaboration online spreadsheets, and establishing a database; step 200, dividing the database into a plurality of complementary databases for accommodating different data, and establishing a complementary communication relationship for all the complementary databases to realize capacity compensation; step 300, splitting the data into a plurality of same keywords, and sequentially storing the plurality of keywords and the key value corresponding to each keyword as a data set in the complementary database; step 400, performing data processing on the same keyword of different data sets, and performing summarization processing on different keywords in the same data set; and step 500, extracting the data set in the database and displaying the data set in a cloud table form. The method is suitable for the technical field of data synchronization of online spreadsheets and cloud processing of databases.

Description

Technical Field

[0001] The invention relates to a data synchronization and processing method for a cloud table. Background Art

[0002] Cloud computing is a type of distributed computing, which means breaking down a huge data computing program into countless small programs through the network "cloud", and then processing and analyzing these small programs through a system composed of multiple servers to obtain the results and return them to the user. In the early days of cloud computing, simply put, it was simple distributed computing, solving task distribution and merging calculation results. Therefore, cloud computing is also called grid computing. Through this technology, tens of thousands of data can be processed in a very short time (a few seconds), thereby achieving powerful network services.

[0003] The existing system method of using cloud computing to realize cloud grid table display still has the following defects: (1) The security of cloud grid table is not enough. Anyone can modify the table content in the cloud grid table, so there is a hidden danger of data content modification; (2) The system differentiation of cloud storage requires manual operation, which leads to slow data processing speed when the cloud disk is full, affecting the stability and speed of data processing.

[0004] Real-time collaborative online spreadsheets are a complex project. First of all, the spreadsheet itself needs to use very complex data structures to store various related functions. The most basic one is a two-dimensional data structure composed of rows and columns. We can determine the data location of a cell through the row number and column number. Cells also have cell-related data structures, such as strings / arrays. At the same time, there are data structures for additional functions, such as filter settings / sort settings, etc. In essence, all the functions of the online spreadsheet are to modify the underlying data structure of the online spreadsheet, and these modifications need to be synchronized to the server and other clients participating in the collaboration.

[0005] The common practice in online table development is the same as that of ordinary web applications. An API is developed for each function. For example, if the content of a cell needs to be modified, an API called setCell will be developed and a transmission data structure specifically for modifying cell data will be defined. Similarly, if a function of adding a row is to be added, an addRow API will be added. During this period, a series of processes such as API design, technical review, technical development, front-end and back-end joint debugging will be carried out.

[0006] As you can see, according to the traditional web application development method, each API design is a time-consuming and labor-intensive process, and the functions of a mature online spreadsheet application are calculated in hundreds or thousands. Unimaginable time is consumed in API design. At the same time, high complexity also brings problems with software stability, high bug rates, and high human maintenance costs. Summary of the invention

[0007] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a convenient, fast, efficient, low-cost and highly secure cloud table data synchronization and processing method.

[0008] The technical solution adopted by the present invention is: the present invention comprises the following steps: Step 100, realizing data synchronization of real-time collaborative online electronic spreadsheets and establishing a database; Step 200: Divide the database into a plurality of complementary databases for accommodating different data, and establish a complementary connectivity relationship among all the complementary databases to achieve capacity compensation; Step 300: split the data into multiple identical keywords, and sequentially store the multiple keywords and the key value corresponding to each keyword as a data set in the complementary database; Step 400: perform data processing on the same keyword in different data sets, and perform summary processing on different keywords in the same data set; Step 500: extract the data set in the database and display it in the form of a cloud table; In step 100, the steps of implementing data synchronization of real-time collaborative online electronic forms include: Step 101, obtaining snapshot data of the table to be modified; Step 102: construct a path symbol, a command symbol, and a content symbol by operating the command queue to modify the JSON format data of the snapshot; Step 103: Implement the parsing logic of the operation command queue obtained in step 102 on the client and the server respectively, send the operation command queue obtained in step 102, realize data synchronization, and use the synchronized data as the database; In step 200, the capacity of each complementary database is the same, and the capacities of the multiple complementary databases are complementary to each other so that the complementary database in use has a stable storage capacity. The specific implementation steps for establishing the capacity complementary relationship between the multiple complementary databases are: Step 201: Divide the capacity of a database into multiple complementary databases with the same capacity; Step 202: monitor the remaining capacity of the complementary database storing data in real time; Step 203: Select the capacity of other complementary databases to compensate the complementary database storing data to ensure that the stable storage capacity of the complementary database storing data remains unchanged; The data storage structure of each complementary database is a flexible structure, and the specific implementation steps are: constructing the row-column relationship of the storage space of each complementary database as a flexible relationship; each time a new keyword and a corresponding keyword value are added to the data set, the row interval of the complementary database is extended outward, and at the same time, the column interval of the complementary database is moved upward to ensure that the total number of rows and columns of the storage space remains unchanged.

[0009] In the step 300, the data is stored in the database according to the database storage method, and the specific implementation steps are: The keywords and the key values ​​corresponding to each keyword are used as a data set; The keyword of the data set is used as the column name of the corresponding cloud table, and the key value corresponding to each keyword is mapped to the column where the column name is located in the cloud table; Redundant space is left in the data set for adding new keywords and key values.

[0010] In step 400, the specific implementation steps of data processing for the same keyword in different data sets are: Create a new statistical data collection; Add a new keyword to the statistical data set to indicate the result of processing the same keyword data; Assign the key value data of the same keyword to the key value of the newly added keyword.

[0011] In step 400, the specific implementation steps of performing data processing on different keywords of the same data set are: Adding keywords to represent the results of processing different keyword data in the same data set; The key value data of different keywords in the same data set are processed and assigned to the key value of the newly added keyword.

[0012] In step 500, the specific implementation steps of importing the data set in the database into the cloud table are: Step 501: Establish a data display mode on the cloud table, and couple the data display mode with the operation algorithm of the database; Step 502: Process the selected data set according to the operation algorithm corresponding to the selected data display mode; Step 503: Select a data set that meets the requirements of the running algorithm in the database, and export the selected data set in sequence according to the selected data display mode; Step 504: Integrate the data set of the database into the cloud table in a top-to-bottom order according to a first-out-first-in manner; Step 505: Output the data set in the data display format of the cloud table.

[0013] In step 500, the operation of the cloud table is limited by identity authority. The higher the identity authority level, the greater the scope of operation on the cloud table. The data modified in the cloud table is stored in the database in real time.

[0014] In step 102, the operation command queue includes an array composed of operation command groups, and the array of the operation command queue includes data path symbols, command symbols and content symbols in sequence. The command symbols include delete symbols, insert symbols and replace symbols. By writing data paths, command symbols and content symbols, the snapshot of the online form is modified as specified.

[0015] Beneficial effect: The present invention firstly adopts a universal way to describe the changes of the content of the electronic spreadsheet document, without defining the relevant API for the specific function items, but directly describes the data changes for the specific data path from the perspective of the data structure to achieve the purpose of modifying the data. This can flexibly respond to changes in demand, eliminate the time for API development, greatly reduce the difficulty of developing the real-time collaboration system, and improve the stability and development efficiency of the system. In addition, the present invention first saves the data in the database, and then performs statistical processing on the data in the database according to the operating instructions of the Cloud Grid Table, and then displays the original data and the data statistical results in the Cloud Grid Table. Therefore, ordinary operating users with lower authority levels can only view the data but cannot change the data. Based on the Lightning real-time collaborative technology, the operating users can synchronize and edit with multiple people in real time, which combines the stability and security of the data and prevents others from modifying the data arbitrarily. The database of the present invention is a flexible data structure when saving data, and a complementary connectivity relationship is established between all databases. According to the size of the saved data and the remaining space size of each database, the capacity of other databases is supplemented to the database in use to prevent data from being lost when saved, so that the system has better flexibility and vitality, and a single database has good adaptability, scalability and flexibility. The Cloud Grid Table of the present invention also has users with statistical authority who can flexibly and dynamically project and select database fields to meet the flexibility requirements of the system. DETAILED DESCRIPTION

[0016] The present invention comprises the following steps: Step 100, realizing data synchronization of real-time collaborative online electronic spreadsheets and establishing a database; Step 200: Divide the database into a plurality of complementary databases for accommodating different data, and establish a complementary connectivity relationship among all the complementary databases to achieve capacity compensation; Step 300: split the data into multiple identical keywords, and sequentially store the multiple keywords and the key value corresponding to each keyword as a data set in the complementary database; Step 400: perform data processing on the same keyword in different data sets, and perform summary processing on different keywords in the same data set; Step 500: extract the data set in the database and display it in the form of a cloud table; In step 100, the steps of implementing data synchronization of real-time collaborative online electronic forms include: Step 101, obtaining snapshot data of the table to be modified; Step 102: construct a path symbol, a command symbol, and a content symbol by operating the command queue to modify the JSON format data of the snapshot; Step 103: Implement the parsing logic of the operation command queue obtained in step 102 on the client and the server respectively, send the operation command queue obtained in step 102, realize data synchronization, and use the synchronized data as the database; In step 102, the operation command queue includes an array composed of operation command groups, and the array of the operation command queue includes data path symbols, command symbols and content symbols in sequence. The command symbols include delete symbols, insert symbols and replace symbols. By writing data paths, command symbols and content symbols, the snapshot of the online form is modified as specified.

[0017] In step 200, the capacity of each complementary database is the same, and the capacities of the multiple complementary databases are complementary to each other so that the complementary database in use has a stable storage capacity. The specific implementation steps for establishing the capacity complementary relationship between the multiple complementary databases are: Step 201: Divide the capacity of a database into multiple complementary databases with the same capacity; Step 202: monitor the remaining capacity of the complementary database storing data in real time; Step 203: Select the capacity of other complementary databases to compensate the complementary database storing data to ensure that the stable storage capacity of the complementary database storing data remains unchanged; The data storage structure of each complementary database is a flexible structure, and the specific implementation steps are: constructing the row-column relationship of the storage space of each complementary database as a flexible relationship; each time a new keyword and a corresponding keyword value are added to the data set, the row interval of the complementary database is extended outward, and at the same time, the column interval of the complementary database is moved upward to ensure that the total number of rows and columns of the storage space remains unchanged.

[0018] In the step 300, the data is stored in the database according to the database storage method, and the specific implementation steps are: The keywords and the key values ​​corresponding to each keyword are used as a data set; The keyword of the data set is used as the column name of the corresponding cloud table, and the key value corresponding to each keyword is mapped to the column where the column name is located in the cloud table; Redundant space is left in the data set for adding new keywords and key values.

[0019] In step 400, the specific implementation steps of data processing for the same keyword in different data sets are: Create a new statistical data collection; Add a new keyword to the statistical data set to indicate the result of processing the same keyword data; Assign the key value data of the same keyword to the key value of the newly added keyword.

[0020] In step 400, the specific implementation steps of performing data processing on different keywords of the same data set are: Adding keywords to represent the results of processing different keyword data in the same data set; The key value data of different keywords in the same data set are processed and assigned to the key value of the newly added keyword.

[0021] In step 500, the specific implementation steps of importing the data set in the database into the cloud table are: Step 501: Establish a data display mode on the cloud table, and couple the data display mode with the operation algorithm of the database; Step 502: Process the selected data set according to the operation algorithm corresponding to the selected data display mode; Step 503: Select a data set that meets the requirements of the running algorithm in the database, and export the selected data set in sequence according to the selected data display mode; Step 504: Integrate the data set of the database into the cloud table in a top-to-bottom order according to a first-out-first-in manner; Step 505: Output the data set in the data display format of the cloud table.

[0022] In step 500, the operation of the cloud table is limited by identity authority. The higher the identity authority level, the greater the scope of operation on the cloud table. The data modified in the cloud table is stored in the database in real time.

[0023] The present invention is applicable to the technical field of data synchronization of online electronic forms and cloud processing of databases.

Claims

1. A method for synchronizing and processing cloud table data, characterized in that: The following steps are involved: Step 100, realizing data synchronization of real-time collaborative online electronic spreadsheets and establishing a database; Step 200: Divide the database into a plurality of complementary databases for accommodating different data, and establish a complementary connectivity relationship among all the complementary databases to achieve capacity compensation; Step 300: split the data into multiple identical keywords, and sequentially store the multiple keywords and the key value corresponding to each keyword as a data set in the complementary database; Step 400: perform data processing on the same keyword in different data sets, and perform summary processing on different keywords in the same data set; Step 500: extract the data set in the database and display it in the form of a cloud table; In step 100, the steps of implementing data synchronization of real-time collaborative online electronic forms include: Step 101, obtaining snapshot data of the table to be modified; Step 102: construct a path symbol, a command symbol, and a content symbol by operating the command queue to modify the JSON format data of the snapshot; Step 103: Implement the parsing logic of the operation command queue obtained in step 102 on the client and the server respectively, send the operation command queue obtained in step 102, realize data synchronization, and use the synchronized data as the database; In step 200, the capacity of each complementary database is the same, and the capacities of the multiple complementary databases are complementary to each other so that the complementary database in use has a stable storage capacity. The specific implementation steps for establishing the capacity complementary relationship between the multiple complementary databases are: Step 201: Divide the capacity of a database into multiple complementary databases with the same capacity; Step 202: monitor the remaining capacity of the complementary database storing data in real time; Step 203: Select the capacity of other complementary databases to compensate the complementary database storing data to ensure that the stable storage capacity of the complementary database storing data remains unchanged; The data storage structure of each complementary database is a flexible structure, and the specific implementation steps are: constructing the row-column relationship of the storage space of each complementary database as a flexible relationship; each time a new keyword and a corresponding keyword value are added to the data set, the row interval of the complementary database is extended outward, and at the same time, the column interval of the complementary database is moved upward to ensure that the total number of rows and columns of the storage space remains unchanged.

2. A cloud table data synchronization and processing method according to claim 1, characterized in that: In the step 300, the data is stored in the database according to the database storage method, and the specific implementation steps are: The keywords and the key values ​​corresponding to each keyword are used as a data set; The keyword of the data set is used as the column name of the corresponding cloud table, and the key value corresponding to each keyword is mapped to the column where the column name is located in the cloud table; Redundant space is left in the data set for adding new keywords and key values.

3. A cloud table data synchronization and processing method according to claim 2, characterized in that: In step 400, the specific implementation steps of data processing for the same keyword in different data sets are: Create a new statistical data collection; Add a new keyword to the statistical data set to indicate the result of processing the same keyword data; Assign the key value data of the same keyword to the key value of the newly added keyword.

4. A cloud table data synchronization and processing method according to claim 3, characterized in that: In step 400, the specific implementation steps of performing data processing on different keywords of the same data set are: Adding keywords to represent the results of processing different keyword data in the same data set; The key value data of different keywords in the same data set are processed and assigned to the key value of the newly added keyword.

5. A cloud table data synchronization and processing method according to claim 4, characterized in that: In step 500, the specific implementation steps of importing the data set in the database into the cloud table are: Step 501: Establish a data display mode on the cloud table, and couple the data display mode with the operation algorithm of the database; Step 502: Process the selected data set according to the operation algorithm corresponding to the selected data display mode; Step 503: Select a data set that meets the requirements of the running algorithm in the database, and export the selected data set in sequence according to the selected data display mode; Step 504: Integrate the data set of the database into the cloud table in a top-to-bottom order according to a first-out-first-in manner; Step 505: Output the data set in the data display format of the cloud table.

6. A cloud table data synchronization and processing method according to claim 5, characterized in that: In step 500, the operation of the cloud table is limited by identity authority. The higher the identity authority level, the greater the scope of operation on the cloud table. The data modified in the cloud table is stored in the database in real time.

7. A cloud table data synchronization and processing method according to claim 1, characterized in that: In step 102, the operation command queue includes an array composed of operation command groups, and the array of the operation command queue includes data path symbols, command symbols and content symbols in sequence. The command symbols include delete symbols, insert symbols and replace symbols. By writing data paths, command symbols and content symbols, the snapshot of the online form is modified as specified.