CN112506942A - Data combination query method, system and corresponding equipment and storage medium - Google Patents
Data combination query method, system and corresponding equipment and storage medium Download PDFInfo
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Abstract
The application discloses a data combination query method, a data combination query system, corresponding equipment and a storage medium, wherein the method comprises the following steps: responding to one of the preset query schemes, and generating header data according to the table row display dimension, the table column display dimension and the to-be-queried content contained in the selected query scheme; extracting data from a data source according to the table row display dimension, the table column display dimension and the content to be queried; based on the extracted data, performing grouping processing on data corresponding to the table row display dimension and performing row-to-column processing on the data corresponding to the table row display dimension to generate table body data; splicing the header data and the body data and returning to the foreground; and analyzing and displaying the returned data. The invention can support various and complex multi-dimensional queries and generate and display the query result table in a self-adaptive manner, thereby improving the efficiency and convenience of data combination query.
Description
Technical Field
The present application relates to the field of electronic digital data processing, and in particular, to a method and system for data combination query, and corresponding device and storage medium.
Background
In an accounting business system, when a voucher is input, a subject and necessary auxiliary accounting information (equivalent to more detailed entries) need to be specified, and the auxiliary accounting type supports custom extension.
In practice, there is a large number of cross-table lookup requirements. The cross-table is primarily a query of data in multiple dimensions (e.g., subject and secondary accounting categories). However, because the auxiliary accounting type supports customization and the user requirement is complex, when the subject and the auxiliary accounting generate different column query combinations, additional development and maintenance are required, which is time-consuming and increases the enterprise cost, so that the enterprise production efficiency is low.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a data combination query method, a data combination query system, corresponding equipment and a storage medium, which can support various and complex multi-dimensional queries and generate and display a query result table in a self-adaptive manner, and improve the efficiency and convenience of data combination query.
In a first aspect of the present invention, a data combination query method is provided, including:
responding to one of the preset query schemes, and generating header data according to the table row display dimension, the table column display dimension and the to-be-queried content contained in the selected query scheme;
extracting data from a data source according to the table row display dimension, the table column display dimension and the content to be queried;
based on the extracted data, performing grouping processing on data corresponding to the table row display dimension and performing row-to-column processing on the data corresponding to the table row display dimension to generate table body data;
splicing the header data and the body data and returning to the foreground;
and analyzing and displaying the returned data.
In an embodiment, the method further comprises: and filtering the extracted data according to the data filtering condition contained in the selected query scheme before obtaining the tabular data based on the extracted data.
In an embodiment, generating the header data according to the selected query plan comprises: determining the row number of the header data according to the table row display dimension number contained in the selected query scheme; determining the column number of the header data according to the number of elements displayed by the table row display dimensions, the number of table column display dimensions and the number of contents to be queried, which are contained in the selected query scheme; and splicing to generate xml or json data of the header according to the determined row number and column number.
In an embodiment, the number of rows is determined as: n is N +1, where N is the number of rows of header data, and N is the number of display dimensions of table rows included in the selected query scheme; and the number of columns is determined as: m ═ M1* m2*...*mnA + k, where M is the number of columns of header data, MnAnd displaying the number of elements displayed by the dimension for the nth table row contained in the selected query scheme, wherein a is the number of contents to be queried, and k is the number of dimensions displayed by the table column.
In an embodiment, the header data and the body data are spliced into xml or json data.
In a second aspect of the present invention, there is provided a data combination query system, the system comprising:
the header generation module is used for responding to one of the preset query schemes and generating header data according to the table row display dimension, the table column display dimension and the content to be queried contained in the selected query scheme;
the data extraction module is used for extracting data from a data source according to the table row display dimension, the table column display dimension and the content to be inquired;
the table body generating module is used for grouping the data corresponding to the table row display dimension and performing row-to-column processing on the data corresponding to the table row display dimension based on the extracted data to generate table body data;
the splicing module is used for splicing the header data and the body data and returning to the foreground;
and the display module is used for analyzing and displaying the returned data.
In a third aspect of the invention, a computer device is provided, comprising a processor, a memory and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program realizes the steps of the method according to the first aspect of the invention.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, carries out the steps of the method according to the first aspect of the present invention.
According to the invention, different query schemes can be set according to different service scenes very simply and conveniently as required, each query scheme corresponds to one query scene, and the query schemes can be added, modified or deleted at any time as required. When a user inquires, the user only needs to select one of the preset inquiry schemes (when the existing inquiry scheme does not meet the requirement, the inquiry scheme can be added first, and then the added inquiry scheme is selected), the system can automatically and adaptively generate and display a corresponding inquiry result table according to the selected inquiry scheme, no matter how complex the table is, the independent development and maintenance are not needed, and the freedom degree, the efficiency and the convenience of data combination inquiry are greatly improved. The invention supports various complex business query schemes, supports the query of multi-level headers and is convenient to expand.
Other features and advantages of the present invention will become more apparent from the detailed description of the embodiments of the present invention when taken in conjunction with the accompanying drawings.
Drawings
FIG. 1 is a flow chart of one embodiment of a method according to the present invention;
FIG. 2 is an example of a table of query results obtained using the method of the present invention;
FIG. 3 is another example of a table of query results obtained using the method of the present invention;
FIG. 4 is a block diagram of one embodiment of a system according to the present invention.
For the sake of clarity, the figures are schematic and simplified drawings, which only show details which are necessary for understanding the invention and other details are omitted.
Detailed Description
Embodiments and examples of the present invention will be described in detail below with reference to the accompanying drawings.
The scope of applicability of the present invention will become apparent from the detailed description given hereinafter. It should be understood, however, that the detailed description and the specific examples, while indicating preferred embodiments of the invention, are given by way of illustration only.
FIG. 1 is a flow chart of a preferred embodiment of a data combination query method according to the present invention.
Different query schemes can be preset for different service scenarios. Accounting is used herein as an example. For example, a project account and a department account, which are used to query account information of the department from the perspective of the project and the department, respectively. When setting up a query plan, a subject system such as financial accounting or budget accounting may be selected; data filtering conditions may be set, such as including revocation vouchers, including non-billed vouchers, displaying blank lines, displaying zero value data, and so forth; then selecting/setting query dimensions, and setting whether the query dimensions are displayed in columns or rows in the table for each query dimension; finally, the contents to be inquired are set, such as the balance of the beginning of the year, the initial balance, the amount of the current period, the accumulated amount of the period and/or the balance of the end period. The query plan determines the final display effect of the query result table and the constraints of the query contents, including the combined effect of rows and columns, whether to include the data that is not booked, whether to display the data of empty rows, whether to display the data of empty columns, and the like. When the existing query scheme can not meet the requirement, a new query scheme can be added (set) at any time according to the requirement.
In step S102, when the user selects one of the preset query schemes, the header data is obtained by splicing and assembling the table row display dimensions, the table column display dimensions and the contents to be queried included in the selected query scheme.
For example, the project account refers to a dimension of a project (a kind of auxiliary accounting) and inquires about the annual balance, current generation amount, end balance and the like of different subjects. When setting up a query plan, the items may be set to the column dimension and the subjects to the row dimension. And a basic table (header) can be constructed according to the row and column settings of the query scheme, and comprises basic information such as items, accounting subjects, early balance, current generation amount, final balance and the like.
FIG. 2 illustrates an example of a query plan, i.e., a project ledger query effect. The items are 01 financial funds and 02 non-financial special funds, the subjects are 330101 surplus in the current period, medical surplus, 330102 surplus in the current period, public health surplus, 330103 surplus in the current period, scientific education surplus, and the inquired content is the balance at the end of the 1 month (both loan and loan are included). The subjects are set to be in a row display dimension, the items are set to be in a column display dimension, and finally the data which we want is the end balance. The header data can be generated by splicing according to the rows and columns of the set scheme and the query content.
First, the number of rows of header data may be determined. The number of rows may be determined as N ═ N +1, where N is the number of rows of header data and N is the number of dimensions set to be displayed by rows in the setup scheme, plus 1, since the final effect to be exhibited is to be materialized to the contents of the exposure query. In the business scenario shown in fig. 2, only the accounting subject is set to the line display, so N is 1, and thus the number of lines is calculated to be N-1 + 1-2.
FIG. 3 shows an example of a query effect for another query plan. In the business scenario shown in fig. 3, the payout function categories and items are arranged in a line display, so that N is 2 and the number of lines is calculated as N-2 + 1-3.
Second, the number of columns of header data may be determined. The number of columns can be determined as M ═ M1*m2*...*mnA 2+ k 2, where M is the number of columns of header data, MnAnd displaying the number of elements displayed by the dimension for the nth table row contained in the selected query scheme, wherein a is the number of contents to be queried, and k is the number of dimensions set to be displayed in columns. The elements shown in each row of display dimensions are set when setting up the query plan. In the service scenario shown in fig. 2, only 1 row display dimension is set, and then the specific number of columns is M ═ M1*a*2 +k*2。m1For the element number of the accounting subject, since the accounting subject selects the exhibition 330101, 330102, 330103, m is13. Since the query content only selects the end balance, then a equals 1. Here, a is multiplied by 2 because it is necessary to distinguish between debit and credit. Since only one column display dimension, i.e., item, is set, k is 1. Here, k is multiplied by 2 because of the need to expose the code and name. Thus, the number of columns illustrated in fig. 2 should be M ═ M1*a*2+k*2＝3*1*2+1*2＝8。
In the service scenario shown in fig. 3, 2 row display dimensions are set, and the specific number of columns isM ＝m1*m2*a*2+k*2。m1Number of elements shown for the payout function category (2 elements shown here (education payout and scientific and technical payout)), m2The number of elements exposed for the project (where 3 elements (financial, non-financial specials and other) are set to be exposed), so m1＝2，m23. In addition, 2 column display dimensions (accounting subjects and departments) are set and only the end-of-term balance is selected for the query content, so that a equals 1 and k equals 2. Therefore, the number of columns illustrated in fig. 3 should be M1M 2 1 × 2+ k 2 × 2 × 3 × 1 × 2 × 16.
In other embodiments, a need not be multiplied by 2 if further refinement of the query content is not required. Likewise, k need not be multiplied by 2 if further subdivision of the column display dimension is not required for presentation. In this case, the number of columns can be determined as M ═ M1*m2*...*mn*a+k。
And thirdly, performing corresponding circulation and splicing according to the values of N and M, and assembling to generate xml data of the header.
The corresponding data structure is:
<root>
<tr><td></td>...<td></td></tr>
...
</root>
as described in the above structure, the root tag is < root >, a set of < tr > tags represents a row, and a set of < td > tags represents a lattice, i.e., a specific element. For the example of FIG. 2, the corresponding piece of exemplary code is:
in step S104, data is extracted from the data source according to the table row display dimension, the table column display dimension, and the content to be queried, for example, using SQL.
For the example of fig. 2, the data sources include, for example: acct _ check _ hedger (auxiliary account table) containing data of each dimension (subject, project, department.) and initial balance, current generation amount, cumulative generation amount, final balance, etc.; acct _ void, acct _ void _ detail, and acct _ check _ items, which contain dimension (subject, project, department.) data and most primitive credential data; acct _ subj (accounting subject table) which contains subjects and balance directions.
In this example, the rows and columns each have one dimension, which is the subject and the item, respectively, for a total of two dimensions. The data in the data source contains all dimension data, some of which are not needed for the current query, so a pull-out is needed, and the resulting basic data table # temp _ tb contains the following data: project, accounting subject, end-of-term balance dimensional data.
In step S106, the extracted data is filtered according to the data filtering condition included in the selected query plan. A temporary table may be created based on the elements exposed as column dimensions. Taking the project account as an example, if there are 10 projects in a project, the basic data table such as # temp _ tb would theoretically contain all the project data (unless no business data occurs for a certain project). If the content of the query is not restricted, that is, all the item account data is queried, the result of the query should have 10 pieces of data, because the data of 10 items should be displayed. However, in our system, finer-grained queries are made at the time of scenario setup, i.e., query content can be defined. For example, at the time of scenario setup, only query item a is selected, and at the time of presentation, there are only 1 piece of data. A temporary table such as # temp _ tb1 functions to filter data, define content. In the example shown in FIG. 2, only the project data is presented as columns, so temporary table # temp _ tb1 contains two columns item _ code, item _ name, and inserts the specific element content set in the schema into # temp _ tb1, and so on if multiple column dimensions are set.
In step S108, because the basic data is structured data combined together, the desired display result cannot be directly queried, and the basic data table and the temporary table are associated, for example, a left-out association query is made with # temp _ tb1 and # temp _ tb, a grouping process is performed on data corresponding to the display dimension of the table column, and a row-to-column process is performed on data corresponding to the display dimension of the table row, so as to ensure that the query content is correct, and generate the table body data.
The corresponding data structure is:
<root>
<tr><td></td>...<td></td></tr>
...
</root>
in the example shown in FIG. 2, the corresponding piece of exemplary code is:
in step S110, the generated header data and body data are spliced into xml (or json) data and returned to the foreground.
In step S112, the returned xml (or json) data is analyzed. Tables of query results as a selected query plan may be parsed and displayed using xsl (a template language) or grovey. And when the foreground analyzes the header, performing row combination and column combination according to whether the content in the < td > is consistent.
FIG. 4 shows a block diagram of a preferred embodiment of a data combination query system according to the present invention, the system of this embodiment comprising:
a header generation module 402, configured to, in response to selection of one of the preset query schemes, generate header data according to a table row display dimension, a table column display dimension, and a content to be queried included in the selected query scheme;
a data extraction module 404, configured to extract data from a data source according to the table row display dimension, the table column display dimension, and the content to be queried;
the table body generating module 406 is configured to perform grouping processing on data corresponding to the table row display dimension and perform row-to-column processing on the data corresponding to the table row display dimension based on the extracted data, and generate table body data;
the splicing module 408 is configured to splice the header data and the body data and return to a foreground;
and a display module 410, configured to parse and display the returned data.
In another embodiment, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method embodiment shown and described in connection with fig. 1 or other corresponding method embodiments, which are not described herein again.
In another embodiment, the present invention provides a computer device, which includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the method embodiment shown and described in conjunction with fig. 1 or other corresponding method embodiments when executing the computer program, and details are not repeated herein.
The various embodiments described herein, or certain features, structures, or characteristics thereof, may be combined as suitable in one or more embodiments of the invention. Additionally, in some cases, the order of steps depicted in the flowcharts and/or in the pipelined process may be modified, as appropriate, and need not be performed exactly in the order depicted. In addition, various aspects of the invention may be implemented using software, hardware, firmware, or a combination thereof, and/or other computer implemented modules or devices that perform the described functions. Software implementations of the present invention may include executable code stored in a computer readable medium and executed by one or more processors. The computer-readable medium may include a computer hard drive, ROM, RAM, flash memory, portable computer storage media such as CD-ROM, DVD-ROM, flash drives, and/or other devices with a Universal Serial Bus (USB) interface, and/or any other suitable tangible or non-transitory computer-readable medium or computer memory on which executable code may be stored and executed by a processor. The present invention may be used in conjunction with any suitable operating system.
As used herein, the singular forms "a", "an" and "the" include plural references (i.e., have the meaning "at least one"), unless the context clearly dictates otherwise. It will be further understood that the terms "has," "includes" and/or "including," when used in this specification, specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
The foregoing describes some preferred embodiments of the present invention, but it should be emphasized that the invention is not limited to these embodiments, but can be implemented in other ways within the scope of the inventive subject matter. Various modifications and alterations of this invention will become apparent to those skilled in the art without departing from the spirit and scope of this invention.
Claims (10)
1. A data combination query method is characterized by comprising the following steps:
responding to one of the preset query schemes, and generating header data according to the table row display dimension, the table column display dimension and the to-be-queried content contained in the selected query scheme;
extracting data from a data source according to the table row display dimension, the table column display dimension and the content to be queried;
based on the extracted data, performing grouping processing on data corresponding to the table row display dimension and performing row-to-column processing on the data corresponding to the table row display dimension to generate table body data;
splicing the header data and the body data and returning to the foreground;
and analyzing and displaying the returned data.
2. The method of claim 1, further comprising:
and filtering the extracted data according to the data filtering condition contained in the selected query scheme before obtaining the tabular data based on the extracted data.
3. The method of claim 1, wherein generating header data according to the selected query plan comprises:
determining the row number of the header data according to the table row display dimension number contained in the selected query scheme;
determining the column number of the header data according to the number of elements displayed by the table row display dimensions, the number of table column display dimensions and the number of contents to be queried, which are contained in the selected query scheme;
and splicing to generate xml or json data of the header according to the determined row number and column number.
4. The method of claim 3, wherein the number of rows is determined as: n is N +1, where N is the number of rows of header data, and N is the number of display dimensions of table rows included in the selected query scheme; and the number of columns is determined as: m ═ M1*m2*...*mnA + k, where M is the number of columns of header data, MnAnd displaying the number of elements displayed by the dimension for the nth table row contained in the selected query scheme, wherein a is the number of contents to be queried, and k is the number of dimensions displayed by the table column.
5. The method of claim 1, wherein the header data and the body data are concatenated into xml or json data.
6. A data combination query system, the system comprising:
the header generation module is used for responding to one of the preset query schemes and generating header data according to the table row display dimension, the table column display dimension and the content to be queried contained in the selected query scheme;
the data extraction module is used for extracting data from a data source according to the table row display dimension, the table column display dimension and the content to be inquired;
the table body generating module is used for grouping the data corresponding to the table row display dimension and performing row-to-column processing on the data corresponding to the table row display dimension based on the extracted data to generate table body data;
the splicing module is used for splicing the header data and the body data and returning to the foreground;
and the display module is used for analyzing and displaying the returned data.
7. The system of claim 6, further comprising:
and the filtering module is used for filtering the extracted data according to the data filtering conditions contained in the selected query scheme before the table body data is obtained based on the extracted data.
8. The system of claim 6, wherein the header generation module comprises:
the line number determining submodule is used for determining the line number of the header data according to the display dimension number of the table lines contained in the selected query scheme;
the column number determining submodule is used for determining the column number of the header data according to the number of elements displayed by the table row display dimensions, the number of the table column display dimensions and the number of contents to be queried, which are contained in the selected query scheme;
and the header assembling submodule is used for splicing and generating xml or json data of the header according to the determined row number and column number.
9. A computer device comprising a processor, a memory and a computer program stored on the memory and executable on the processor, wherein the steps of the method according to any of claims 1-5 are implemented when the computer program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.
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