Report generation method, apparatus, and terminal device

Abstract

Embodiments of the present application provide a report generation method and device and a terminal device. The method comprises: obtaining a data processing request, the data processing request comprising a plurality of fields and a statistical period; obtaining a first data set in a memory computing platform SAP HANA database supporting enterprise pre-set deployment and cloud deployment mode according to the data processing request, the first data set comprising the plurality of fields, corresponding business data of each field in the statistical period, and a first data type of each business data; processing the first data set through a first calculation view to obtain a target data set, the target data set comprising the plurality of fields, corresponding first target data of each field, a first data type of each first target data, a target data type, and a plurality of second target data corresponding to the target data type; and generating a target report corresponding to the target data set through a second calculation view. The efficiency of data processing is improved.

Description

Technical Field

[0001] This application relates to the field of big data technology, and in particular to a report generation method, apparatus and terminal device. Background Technology

[0002] Businesses can categorize and convert the amounts generated from business transactions to obtain business amount reports corresponding to the number of transactions.

[0003] In related technologies, business data can be processed and reports generated in the following way: Using a pre-configured standard algorithm in the SAP HANA database (System Applications and Products High-Performance Analytic Appliance), which supports both on-premises and cloud deployment modes, preset functions are called to convert the currency of the business amounts corresponding to the business data, resulting in the original currency and target currency amounts for multiple business data points. However, this process is inefficient because each currency conversion requires calling a preset function at the application layer. Summary of the Invention

[0004] This application provides a report generation method, apparatus, and terminal device to solve the problem of low data processing efficiency.

[0005] In a first aspect, embodiments of this application provide a report generation method, including:

[0006] Obtain a data processing request, which includes multiple fields and a statistical time period;

[0007] According to the data processing request, a first data set is obtained from the SAP HANA database, an in-memory computing platform that supports enterprise pre-deployment and cloud deployment modes. The first data set includes the multiple fields, the business data corresponding to each field in the statistical period, and the first data type of each business data.

[0008] The first data set is processed through the first calculation view to obtain a target data set, which includes the plurality of fields, first target data corresponding to each field, first data type of each first target data, target data type, and plurality of second target data corresponding to the target data type;

[0009] The target report corresponding to the target data set is generated through the second calculation view.

[0010] In one possible implementation, the first data set is processed through a first computational view to obtain a target data set, including:

[0011] A first standard table is obtained through the first calculation view. The first standard table includes multiple data types and a first conversion factor between each data type and the target data type.

[0012] Based on the first conversion factor between each data type and the target data type, a left outer join is performed on the first data set and the first standard table to obtain a first joined data set. The first joined data set includes the business data corresponding to each field, the first data type of each business data, and the first conversion factor corresponding to each first type.

[0013] The first connected data set is processed to obtain the target data set.

[0014] In one possible implementation, the first connected data set is processed to obtain the target data set, including:

[0015] In the first connection data set, each business data is multiplied with the corresponding first conversion factor to obtain the first product data corresponding to the target data type;

[0016] The second data set is determined to include business data corresponding to each field, a first data type of each business data, the target data type, and multiple first product data corresponding to the target data type.

[0017] The second data set is processed to obtain the target data set.

[0018] In one possible implementation, processing the second data set to obtain the target data set includes:

[0019] Obtain the second standard table, which includes multiple data types and a second conversion factor corresponding to each data type;

[0020] Based on the second conversion factor corresponding to each data type, a left outer join is performed on the second data set and the second standard table to obtain a second joined data set. The second joined data set includes business data corresponding to each field, a first data type of each business data, the target data type, multiple first product data corresponding to the target data type, and a second conversion factor corresponding to each first data type.

[0021] In the second connected data set, each first product data is multiplied by the second conversion factor corresponding to the first data type to obtain multiple second product data.

[0022] The third data set is determined to include business data corresponding to each field, a first data type for each business data, the target data type, and the multiple second product data.

[0023] The third data set is processed to obtain the target data set.

[0024] In one possible implementation, processing the third data set to obtain the target data set includes:

[0025] Obtain a third standard table, which includes multiple data types and a position factor corresponding to each data type;

[0026] Based on the position factor corresponding to each data type, a left outer join is performed on the third data set and the third standard table to obtain a third joined data set. The third joined data set includes business data corresponding to each field, a first data type of each business data, the target data type, the multiple second product data, and a position factor corresponding to each first data type.

[0027] Based on the position factor corresponding to each data type, each business data is processed to obtain the first target data corresponding to each business data, and each second product data is processed to obtain the second target data corresponding to the target data type.

[0028] The target data set is determined to include the multiple fields, first target data corresponding to each field, a first data type of each first target data, the target data type, and multiple second target data corresponding to the target data type.

[0029] In one possible implementation, each piece of business data is processed to obtain a first target data corresponding to each piece of business data, and each piece of second product data is processed to obtain multiple second target data corresponding to the target data type, including:

[0030] In the third connection data set, obtain the third transformation factor corresponding to each position factor;

[0031] For any given set of business data, the business data is multiplied by the third conversion factor corresponding to the location factor to obtain the first target data;

[0032] For any second product data, the second product data is multiplied by the third transformation factor corresponding to the position factor to obtain the third product data;

[0033] The average value of the multiple third product data is calculated to obtain multiple second target data corresponding to the target data type.

[0034] In one possible implementation, the plurality of third product data are averaged to obtain a plurality of second target data corresponding to the target data type, including:

[0035] For any field, the average of multiple third product data corresponding to the field is calculated according to the statistical period to obtain the second target data corresponding to the target data type.

[0036] Based on the second target data corresponding to each field, determine multiple second target data corresponding to the target data type.

[0037] In one possible implementation, a target report corresponding to the target data set is generated through a second computational view, including:

[0038] The target data set is obtained by performing a database connection operation on the target data set through the second computational view;

[0039] The target connection set is used to generate a target report corresponding to the target data set according to a preset format.

[0040] In one possible implementation, the method further includes:

[0041] Obtain a data query request, the data query request including the target field and the statistical period;

[0042] In the target report, at least one target data corresponding to the target field is determined;

[0043] Based on the at least one target data, generate query results and display the query results.

[0044] Secondly, embodiments of this application provide a report generation apparatus, the apparatus comprising:

[0045] The first acquisition module is used to acquire a data processing request, which includes multiple fields and a statistical time period.

[0046] The second acquisition module is used to acquire a first data set from the SAP HANA database, a memory computing platform that supports enterprise pre-deployment and cloud deployment modes, according to the data processing request. The first data set includes the multiple fields, the business data corresponding to each field in the statistical period, and the first data type of each business data.

[0047] The processing module is used to process the first data set through the first calculation view to obtain a target data set, wherein the target data set includes the plurality of fields, first target data corresponding to each field, a first data type of each first target data, a target data type, and a plurality of second target data corresponding to the target data type;

[0048] The generation module is used to generate a target report corresponding to the target data set through the second calculation view.

[0049] In one possible implementation, the processing module is specifically used for:

[0050] A first standard table is obtained through the first calculation view. The first standard table includes multiple data types and a first conversion factor between each data type and the target data type.

[0051] Based on the first conversion factor between each data type and the target data type, a left outer join is performed on the first data set and the first standard table to obtain a first joined data set. The first joined data set includes the business data corresponding to each field, the first data type of each business data, and the first conversion factor corresponding to each first type.

[0052] The first connected data set is processed to obtain the target data set.

[0053] In one possible implementation, the processing module is specifically used for:

[0054] In the first connection data set, each business data is multiplied with the corresponding first conversion factor to obtain the first product data corresponding to the target data type;

[0055] The second data set is determined to include business data corresponding to each field, a first data type of each business data, the target data type, and multiple first product data corresponding to the target data type.

[0056] The second data set is processed to obtain the target data set.

[0057] In one possible implementation, the processing module is specifically used for:

[0058] Obtain the second standard table, which includes multiple data types and a second conversion factor corresponding to each data type;

[0059] Based on the second conversion factor corresponding to each data type, a left outer join is performed on the second data set and the second standard table to obtain a second joined data set. The second joined data set includes business data corresponding to each field, a first data type of each business data, the target data type, multiple first product data corresponding to the target data type, and a second conversion factor corresponding to each first data type.

[0060] In the second connected data set, each first product data is multiplied by the second conversion factor corresponding to the first data type to obtain multiple second product data.

[0061] The third data set is determined to include business data corresponding to each field, a first data type for each business data, the target data type, and the multiple second product data.

[0062] The third data set is processed to obtain the target data set.

[0063] In one possible implementation, the processing module is specifically used for:

[0064] Obtain a third standard table, which includes multiple data types and a position factor corresponding to each data type;

[0065] Based on the position factor corresponding to each data type, a left outer join is performed on the third data set and the third standard table to obtain a third joined data set. The third joined data set includes business data corresponding to each field, a first data type of each business data, the target data type, the multiple second product data, and a position factor corresponding to each first data type.

[0066] Based on the position factor corresponding to each data type, each business data is processed to obtain the first target data corresponding to each business data, and each second product data is processed to obtain the second target data corresponding to the target data type.

[0067] The target data set is determined to include the multiple fields, first target data corresponding to each field, a first data type of each first target data, the target data type, and multiple second target data corresponding to the target data type.

[0068] In one possible implementation, the processing module is specifically used for:

[0069] In the third connection data set, obtain the third transformation factor corresponding to each position factor;

[0070] For any given set of business data, the business data is multiplied by the third conversion factor corresponding to the location factor to obtain the first target data;

[0071] For any second product data, the second product data is multiplied by the third transformation factor corresponding to the position factor to obtain the third product data;

[0072] The average value of the multiple third product data is calculated to obtain multiple second target data corresponding to the target data type.

[0073] In one possible implementation, the processing module is specifically used for:

[0074] For any field, the average of multiple third product data corresponding to the field is calculated according to the statistical period to obtain the second target data corresponding to the target data type.

[0075] Based on the second target data corresponding to each field, determine multiple second target data corresponding to the target data type.

[0076] In one possible implementation, the generation module is specifically used for:

[0077] The target data set is obtained by performing a database connection operation on the target data set through the second computational view;

[0078] The target connection set is used to generate a target report corresponding to the target data set according to a preset format.

[0079] In one possible implementation, the device further includes a query module.

[0080] The query module is used for:

[0081] Obtain a data query request, the data query request including the target field and the statistical period;

[0082] In the target report, at least one target data corresponding to the target field is determined;

[0083] Based on the at least one target data, generate query results and display the query results.

[0084] Thirdly, embodiments of this application provide a terminal device, including:

[0085] At least one processor; and

[0086] A memory communicatively connected to the at least one processor; wherein,

[0087] The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method described in any of the first aspects.

[0088] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to perform the method described in any one of the first aspects.

[0089] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described in any one of the first aspects.

[0090] The report generation method, apparatus, and terminal device provided in this application embodiment allow the terminal device to retrieve a first data set from the SAP HANA database after receiving a data processing request. The first data set includes business data corresponding to each field and a first data type for each business data. A first computed view processes the first data set to obtain a target data set. The target data set includes first target data corresponding to each field, a first data type for each first target data, a target data type, and multiple second target data corresponding to the target data type. A second computed view generates a target report corresponding to the target data set. In this process, the first data set can be processed using a computed view of the SAP HANA database to obtain the target data set, instead of calling preset functions at the application layer, thus improving data processing efficiency. Attached Figure Description

[0091] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0092] Figure 1 A schematic diagram illustrating the application scenarios provided in the embodiments of this application;

[0093] Figure 2 A flowchart illustrating a report generation method provided in an embodiment of this application;

[0094] Figure 3 This is a schematic diagram illustrating the process of obtaining a data processing request provided in an embodiment of this application;

[0095] Figure 4 A flowchart illustrating another report generation method provided in this application embodiment;

[0096] Figure 5 A schematic diagram illustrating the data query process provided in this application embodiment;

[0097] Figure 6 A schematic diagram illustrating the report generation process provided in this application embodiment;

[0098] Figure 7 This is a schematic diagram of the structure of a report generation device provided in an embodiment of this application;

[0099] Figure 8 This is a schematic diagram of another report generation device provided in an embodiment of this application;

[0100] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application.

[0101] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concepts of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0102] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0103] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0104] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0105] It should be noted that the method and apparatus for generating reports in this application can be used in the field of big data, or in any field other than big data. The application field of the method and apparatus for generating reports in this application is not limited.

[0106] To facilitate understanding, the following will be combined with... Figure 1 The application scenarios applicable to the embodiments of this application will be described.

[0107] Figure 1 This is a schematic diagram illustrating an application scenario provided in an embodiment of this application. Please refer to [link / reference]. Figure 1 The system includes terminal device 101, which can be a computer and includes an SAP HANA database. Users can use the SAP HANA database on terminal device 101 to perform currency conversion on the transaction amounts corresponding to multiple transaction data (transaction data 1, transaction data 2, ..., transaction data N) to obtain the target currency transaction amount for each transaction data. Based on the original currency transaction amounts and target currency transaction amounts corresponding to multiple transaction data, a target report is generated. After the target report is generated by terminal device 101, it can be used to query and retrieve any transaction data from the target report on terminal device 101.

[0108] In related technologies, business data can be processed and reports generated in the following way: Using a pre-defined standard algorithm in the SAP HANA database, a preset function is called to convert the currency of the business amounts corresponding to the business data, resulting in the original currency and target currency business amounts for multiple business data points. However, in this process, because each currency conversion requires calling a preset function at the application layer, the data processing efficiency is low.

[0109] In this embodiment, after receiving a data processing request, the terminal device can retrieve a first data set from the SAP HANA database according to the request. The first data set includes business data corresponding to each field and a first data type for each business data. The first data set is processed through a first computed view to obtain a target data set. The target data set includes first target data corresponding to each field, a first data type for each first target data, the target data type, and multiple second target data corresponding to the target data type. A target report corresponding to the target data set is generated through a second computed view. In the above process, the first data set can be processed using a computed view of the SAP HANA database to obtain the target data set, instead of calling preset functions at the application layer for data processing, thus improving data processing efficiency.

[0110] The method described in this application will now be illustrated through specific embodiments. It should be noted that the following embodiments may exist independently or in combination with each other; identical or similar content will not be repeated in different embodiments.

[0111] Figure 2 This is a flowchart illustrating a report generation method provided in an embodiment of this application. Please refer to [link / reference]. Figure 2 The method may include:

[0112] S201, Obtain data processing request.

[0113] The execution subject of this application embodiment can be a terminal device, or a chip, chip module, or wireless access device disposed in the terminal device. The wireless access device can be implemented by software or by a combination of software and hardware. The terminal device can be a mobile phone, tablet computer, etc.

[0114] The data processing request includes multiple fields and a statistical period. Each field corresponds to a business type. The statistical period can be divided into multiple sub-periods based on user settings. For example, the statistical period may include the sub-periods 2022 / 05 / 01~2022 / 05 / 31 and 2023 / 05 / 01~2023 / 05 / 31.

[0115] Users can set at least one field and a statistical period on the processing page provided by the terminal device. The terminal device generates a data processing request based on the user-defined field and statistical period. Users can also set the data processing period, allowing the terminal device to process data in real time or periodically (e.g., daily, monthly).

[0116] Below, in conjunction with Figure 3 This section explains the process of requesting data processing. Figure 3 This is a schematic diagram illustrating the process of obtaining a data processing request provided in an embodiment of this application. Please refer to... Figure 3This includes interfaces 301 and 302. Interfaces 301 and 302 can be processing pages provided by the terminal device. Referring to interface 301, the user opens the processing page in the application provided by the terminal device. The processing page includes drop-down menus for multiple fields and text input boxes. Referring to interface 302, on the processing page provided by the terminal device, the user selects the revenue for business 1 through the drop-down menu corresponding to field 1, and selects the revenue for business 2 through the drop-down menu corresponding to field 2. The user selects the start time corresponding to the statistical period through the drop-down menu corresponding to date 1, and selects the end time corresponding to the statistical period through the drop-down menu corresponding to date 2, and then clicks "OK". The terminal device responds to the user's input selection operation by generating a data processing request, as shown in Table 1.

[0117] Table 1

[0118] Field 2 Revenue from Business 2 Statistical period 2023 / 05 / 01～2023 / 05 / 31

[0119] S202. Based on the data processing request, retrieve the first data set from the SAP HANA database.

[0120] The first data set includes multiple fields, the business data corresponding to each field within the statistical period, and the first data type of each business data.

[0121] The data type can be the currency corresponding to the business data. For example, if the business amount corresponding to the business data is RMB 100, then the primary data type corresponding to the business data is RMB.

[0122] You can retrieve multiple business data entries containing the field name tsl (original currency amount) and the field name rtcur (original currency type) from the SAP HANA database and generate the first data set.

[0123] For example, based on the data processing request shown in Table 1 above, retrieving multiple business data entries containing the field names tsl and rtcur from the SAP HANA database can be specifically shown in Table 2:

[0124] Table 2

[0125] Revenue of Business 1 from May 1, 2023 to May 31, 2023 Renminbi 500000 Revenue of Business 1 from May 1, 2023 to May 31, 2023 JPY 980731500 Revenue of Business 1 from May 1, 2023 to May 31, 2023 Hong Kong dollars 55295788 Revenue of Business 2 from May 1, 2023 to May 31, 2023 Renminbi 800000 Revenue of Business 2 from May 1, 2023 to May 31, 2023 JPY 1569170500 Revenue of Business 2 from May 1, 2023 to May 31, 2023 Hong Kong dollars 88473260

[0126] Based on the multiple business data shown in Table 2, the first data set can be generated as shown in Table 3:

[0127] Table 3

[0128]

[0129] S203. The first data set is processed through the first calculation view to obtain the target data set.

[0130] The target data set includes multiple fields, the first target data corresponding to each field, the first data type of each first target data, the target data type, and multiple second target data corresponding to the target data type.

[0131] The first data set can be processed through the first computed view to obtain the target data set in the following manner: A first standard table is obtained through the first computed view. The first standard table includes multiple data types and a first conversion factor between each data type and the target data type. Based on the first conversion factor between each data type and the target data type, a left outer join is performed on the first data set and the first standard table to obtain a first joined data set. The first joined data set includes business data corresponding to each field, a first data type for each business data, and a first conversion factor corresponding to each first type. The first joined data set is then processed to obtain the target data set.

[0132] The target data type can be USD. The first conversion factor can be the period-end revaluation exchange rate between the first data type and the target data type.

[0133] For example, based on the first data set shown in Table 3 above, after obtaining the first standard table, the first conversion factor corresponding to each first data type is determined in the first standard table. Based on the first conversion factor between each data type and the target data type, a left outer join is performed on the first data set and the first standard table to obtain the first joined data set, as shown in Table 4.

[0134] Table 4

[0135]

[0136] Assume the target data type is US dollars. Based on the first join data set shown in Table 4 above, perform currency conversion on the business data according to the first conversion factor to obtain the corresponding US dollar amount for each business data. Generate the target data set based on multiple business data sets and the corresponding US dollar amount for each business data set.

[0137] S204. Generate the target report corresponding to the target data set through the second calculation view.

[0138] The target report corresponding to the target data set can be generated through the second calculation view in the following way: perform a database connection operation on the target data set through the second calculation view to obtain the target connection set; generate the target report corresponding to the target data set according to the target connection set in a preset format.

[0139] For example, based on the target data set shown in Table 4 above, insert the target data set into the entity table. Then, in the SAP Bill of Material (BOM) system, create a target report in Web Intelligence format so that users can select the corresponding statistical period for data querying through the query page provided on their terminal devices.

[0140] The report generation method provided in this application embodiment obtains a data processing request. Based on the data processing request, a first data set is obtained from the SAP HANA database. The first data set is processed through a first computed view to obtain a target data set. A target report corresponding to the target data set is generated through a second computed view. In the above process, the first data set can be processed using a computed view of the SAP HANA database to obtain the target data set, instead of calling preset functions at the application layer for data processing, thus improving the efficiency of data processing.

[0141] Based on any of the above embodiments, the following, in conjunction with Figure 4 The detailed process of report generation is explained.

[0142] Figure 4 This is a flowchart illustrating another report generation method provided in an embodiment of this application. Please refer to... Figure 4 The method includes:

[0143] S401, Obtain data processing request.

[0144] It should be noted that the execution process of S401 can be found in S201, and will not be repeated here.

[0145] S402. Based on the data processing request, retrieve the first data set from the SAP HANA database.

[0146] The field corresponding to the statistical period in the first data set can be represented as the year and month in the SAP HANA database as 99999999-TCURR.gdatu. Only fields represented as 99999999-TCURR.gdatu can be used for left outer joins in the first standard table and converted to standard dates.

[0147] The field corresponding to the first conversion factor can be represented in the SAP HANA database as TCURR.kurst = 'P'. Here, P is the period-end revaluation exchange rate.

[0148] The target data type corresponds to the field TCURR.tcurr = 'USD'. Here, USD represents the US dollar.

[0149] For example, the first dataset can be specifically shown in Table 5:

[0150] Table 5

[0151] Revenue of Business 1 from April 1, 2023 to May 31, 2023 Renminbi 500000 Revenue of Business 1 from May 1, 2023 to May 31, 2023 JPY 9807315 Revenue of Business 1 from May 1, 2023 to May 31, 2023 Hong Kong dollars 55295788 Revenue of Business 1 from January 1, 2023 to May 31, 2023 Renminbi 1500000 Revenue of Business 2 from May 1, 2023 to May 31, 2023 Renminbi 800000 Revenue of Business 2 from May 1, 2023 to May 31, 2023 JPY 15691705 Revenue of Business 2 from May 1, 2023 to May 31, 2023 Hong Kong dollars 88473260 Revenue of Business 2 from January 1, 2023 to May 31, 2023 Renminbi 2000000

[0152] S403. Obtain the first standard table through the first calculation view.

[0153] The first standard table includes multiple data types, as well as a first conversion factor between each data type and the target data type.

[0154] For example, the first standard table can be specifically shown in Table 6:

[0155] Table 6

[0156]

[0157] S404. Based on the first conversion factor between each data type and the target data type, perform a left outer join on the first data set and the first standard table to obtain the first joined data set.

[0158] The first connection data set includes the business data corresponding to each field, the first data type of each business data, and the first conversion factor corresponding to each first type.

[0159] For example, based on the first conversion factor between each data type and the target data type, a left outer join is performed on the first data set shown in Table 5 and the first standard table shown in Table 6 to obtain the first joined data set, as shown in Figure 7:

[0160] Table 7

[0161]

[0162] S405. In the first connection data set, each business data is multiplied with the corresponding first conversion factor to obtain the first product data corresponding to the target data type.

[0163] For example, based on the first join data set shown in Table 7 above, for the revenue of field 1 from April 1, 2023 to May 31, 2023, the business data corresponding to the revenue of field 1 from April 1, 2023 to May 31, 2023 is determined to be 500,000. Multiplying the business data by the corresponding first conversion factor yields the first product data corresponding to the target data type: 500,000 * 0.141193 = 70596. Multiplying each business data in the first join data set shown in Table 7 yields the first product data corresponding to the target data type.

[0164] S406. Determine that the second data set includes the business data corresponding to each field, the first data type of each business data, the target data type, and multiple first product data corresponding to the target data type.

[0165] For example, following the example method described above, multiple first product data are obtained. Based on the first concatenation set shown in Table 7 above and the multiple first data, the second data set can be generated as shown in Table 8:

[0166] Table 8

[0167]

[0168] S407. Obtain the second standard table.

[0169] The second standard table includes multiple data types and a second conversion factor for each data type.

[0170] In the SAP HANA database, the second conversion factor can be represented as the reciprocal of the USD amount minus the factor (" / ifnull(TCURF.ffact,1)). Since the business data stored in the SAP HANA database may be integer multiples of the actual amount (typically 10, 100, etc.), it is necessary to determine the second conversion factor corresponding to the business data based on the second standard table to ensure that the business data and its corresponding first product are identical to the actual amount.

[0171] For example, the second standard table can be specifically shown in Table 9:

[0172] Table 9

[0173] Renminbi 1 JPY 0.01 Hong Kong dollars 1 Taiwan Dollar 0.01 GBP 1 Australian Dollar 1

[0174] S408. Based on the second conversion factor corresponding to each data type, perform a left outer join on the second data set and the second standard table to obtain the second joined data set.

[0175] The second connected data set includes the business data corresponding to each field, the first data type of each business data, the target data type, the first product data corresponding to each business data, and the second conversion factor corresponding to each first data type.

[0176] For example, based on the second conversion factor corresponding to each data type, a left outer join is performed on the second data set shown in Table 8 and the second standard table shown in Table 9 to obtain the second joined data set as shown in Table 10:

[0177] Table 10

[0178]

[0179]

[0180] S409. In the second connected data set, each first product data is multiplied with the second conversion factor corresponding to the first data type to obtain multiple second product data.

[0181] For example, based on the second connection data set shown in Table 10 above, for the first product data corresponding to the revenue of field business 1 from 2023 / 04 / 01 to 2023 / 05 / 31, the first product data 70965 is multiplied with the second conversion factor 1 to obtain the second product data 70965.

[0182] S410. Determine that the third data set includes the business data corresponding to each field, the first data type of each business data, the target data type, and multiple second product data.

[0183] For example, based on the second concatenation dataset shown in Table 10 above and the multiple second product datasets obtained by the above method, the third dataset can be generated as shown in Table 11:

[0184] Table 11

[0185]

[0186]

[0187] S411. Process the third data set to obtain the target data set.

[0188] The target data set can be obtained by processing the third data set as follows: Obtain a third standard table, which includes multiple data types and position factors corresponding to each data type; perform a left outer join on the third data set and the third standard table based on the position factors corresponding to each data type to obtain a third joined data set, which includes business data corresponding to each field, a first data type for each business data, a target data type, multiple second product data, and a position factor corresponding to each first data type; process each business data according to the position factors corresponding to each data type to obtain the first target data corresponding to each business data, and process each second product data to obtain the second target data corresponding to the target data type; finally, determine that the target data set includes multiple fields, the first target data corresponding to each field, the first data type of each first target data, the target data type, and multiple second target data corresponding to the target data type.

[0189] The location factor is used to indicate the small unit amount corresponding to the business data. The small unit amount can be jiao or fen.

[0190] Each business data can be processed according to the location factor corresponding to each data type to obtain the first target data corresponding to each business data in the following way: In the third connected data set, obtain the third transformation factor corresponding to each location factor; for any business data, multiply the business data with the third transformation factor corresponding to the location factor to obtain the first target data.

[0191] Each second product data point can be processed in the following way to obtain multiple second target data points corresponding to the target data type: For any second product data point, multiply the second product data point with the third transformation factor corresponding to the position factor to obtain the third product data point; average the multiple third product data points to obtain the second target data point corresponding to the target data type.

[0192] For any given field, the average of multiple third product data corresponding to the field is calculated according to the statistical period to obtain the second target data corresponding to the target data type; based on the second target data corresponding to each field, multiple second target data corresponding to the target data type are determined.

[0193] The location factor for each data type can be determined using the Map and / or IFNULL functions in the SAP HANA database. Specifically, IFNULL is used to determine the location factor of the first data type as 2 if no location factor is found in the standard TCURX table.

[0194] For example, the third transformation factor corresponding to each position factor can be shown in Table 12:

[0195] Table 12

[0196] 0 100 1 10 2 1 3 0.1 4 0.01 Other values ​​besides 0 to 5 1

[0197] For the business data 1569170500 corresponding to the revenue of business 2 in field 2 from 2023 / 05 / 01 to 2023 / 05 / 31 shown in Table 11 above, the third conversion factor corresponding to the position factor is determined to be 0.01. Multiplying the business data 1569170500 by the third conversion factor 0.01 yields the first target data 15691705. The second product data corresponding to the business data 1569170500 is determined to be 107.19. Multiplying the second product data 107.19 by the third conversion factor 0.1 yields the second target data 10.719.

[0198] Based on the above method, the first target data and the third product data corresponding to each field are obtained, and the specific target data set is shown in Table 13:

[0199] Table 13

[0200]

[0201] S412. Generate the target report corresponding to the target data set through the second calculation view.

[0202] For example, based on the target data set shown in Table 13 above, the target report generated through the second calculated view can be as shown in Table 14:

[0203] Table 14

[0204]

[0205] After generating the target report, data can be queried through the page provided by the terminal device. Data querying can be performed as follows: obtain a data query request, which includes the target field and the statistical period; in the target report, determine at least one target data corresponding to the target field; based on at least one target data, generate and display the query results.

[0206] Below, in conjunction with Figure 5 The process of data query is explained. Figure 5 This is a schematic diagram illustrating the data query process provided in an embodiment of this application. Please refer to [link / reference]. Figure 5 This includes interfaces 501 and 502. Interfaces 501 and 502 can be query pages provided by the terminal device.

[0207] Please refer to interface 501. On the query page of interface 501, the user can select the business type, business name, and statistical period and click "OK". The terminal device responds to the user's input selection and generates a data query request. The terminal device determines at least one target data corresponding to the data query request in the target report of the database and generates query results based on at least one target data.

[0208] Please refer to interface 502. The terminal device displays the query results. Users can continue to filter the statistical period or currency on the query page of the terminal device based on the query results. The terminal device responds to the user's selection and updates the displayed query results.

[0209] The report generation method provided in this application embodiment obtains a data processing request. Based on the data processing request, a first data set is obtained from the SAP HANA database. A first standard table is obtained. Based on a first conversion factor between each data type and the target data type, a left outer join is performed on the first data set and the first standard table to obtain a first joined data set. In the first joined data set, each business data is multiplied by its corresponding first conversion factor to obtain a first product data corresponding to the target data type. A second data set is determined to include the business data corresponding to each field, the first data type of each business data, the target data type, and the first product data corresponding to each business data. The second data set is processed to obtain a third data set. The third data set is processed to obtain a target data set. A target report corresponding to the target data set is generated through a second computed view. In the above process, the first data set can be processed using a computed view of the SAP HANA database to obtain the target data set, instead of calling preset functions at the application layer for data processing, thus improving the efficiency of data processing.

[0210] Based on any of the above embodiments, the following, in conjunction with Figure 6 The process of generating reports will be illustrated with examples.

[0211] Figure 6 This is a schematic diagram illustrating the report generation process provided in this application embodiment. Please refer to... Figure 6 This includes terminal device 601. Terminal device 601 can be a computer, and it contains an SAP HANA database. Users can set at least one field and a statistical period on the processing page provided by terminal device 601. The data processing request generated by terminal device 601 based on the user-set field and statistical period is shown in Table 15.

[0212] Table 15

[0213] Field 2 Insurance 2 expenditures Statistical period 2023 / 05 / 01～2023 / 05 / 31

[0214] The first data set obtained by terminal device 601 from the SAP HANA database according to the data processing request shown in Table 15 can be specifically shown in Table 16:

[0215] Table 16

[0216]

[0217] Assuming the target data type is US dollars, terminal device 601 obtains the first standard table through the first computed view of the SAP HANA database. It then performs a left outer join on the first data set and the first standard table, resulting in the first joined data set, as shown in Table 17.

[0218] Table 17

[0219]

[0220] Terminal device 601, through the first computed view of the SAP HANA database, multiplies each business data with its corresponding first transformation factor in the first connection data set shown in Table 17 to obtain the first product data corresponding to the target data type. Based on the first connection data set shown in Table 17 and the multiple first product data, a second data set is generated, as shown in Table 18.

[0221] Table 18

[0222]

[0223] Terminal device 601 obtains the second standard table through the first computed view of the SAP HANA database. The second standard table includes multiple data types and a second transformation factor corresponding to each data type. Based on the second transformation factor corresponding to each data type, a left outer join is performed on the second data set and the second standard table to obtain the second joined data set, as shown in Table 19.

[0224] Table 19

[0225]

[0226]

[0227] Terminal device 601, through the first computed view of the SAP HANA database, multiplies each first product data with the second conversion factor corresponding to the first data type in the second connection data set shown in Table 19 to obtain multiple second product data. Based on the second connection data set shown in Table 19 and the multiple second product data, a third data set is generated, as shown in Table 20.

[0228] Table 20

[0229]

[0230]

[0231] Terminal device 601 obtains the third standard table through the first computed view of the SAP HANA database. Based on the position factor corresponding to each data type, it performs a left outer join on the third data set and the third standard table, resulting in the third joined data set, as shown in Table 21.

[0232] Table 21

[0233]

[0234] Terminal device 601 processes each business data according to the location factor corresponding to each data type through the first computed view of the SAP HANA database to obtain the first target data corresponding to each business data, and processes each second product data to obtain the second target data corresponding to the target data type. Based on the third connection data set shown in Table 21, multiple first target data sets, and multiple second target data sets, the target data set is generated as shown in Table 22.

[0235] Table 22

[0236]

[0237] Terminal device 601 generates the target report corresponding to the target data set shown in Table 22 through the second computed view of the SAP HANA database. The specific target report is shown in Table 23.

[0238] Table 23

[0239]

[0240] After generating the target report shown in Table 23, data can be queried through the page provided by the terminal device 601 to obtain any target data in the target report shown in Table 23.

[0241] The report generation method provided in this application embodiment obtains a data processing request. Based on the data processing request, a first data set is obtained from the SAP HANA database. A first standard table is obtained. Based on a first conversion factor between each data type and the target data type, a left outer join is performed on the first data set and the first standard table to obtain a first joined data set. In the first joined data set, each business data is multiplied by its corresponding first conversion factor to obtain a first product data corresponding to the target data type. A second data set is determined to include the business data corresponding to each field, the first data type of each business data, the target data type, and the first product data corresponding to each business data. The second data set is processed to obtain a third data set. The third data set is processed to obtain a target data set. A target report corresponding to the target data set is generated through a second computed view. In the above process, the first data set can be processed using a computed view of the SAP HANA database to obtain the target data set, instead of calling preset functions at the application layer for data processing, thus improving the efficiency of data processing.

[0242] Figure 7 This is a schematic diagram of a report generation device provided in an embodiment of this application. Please refer to [link / reference]. Figure 7The report generation device 10 may include:

[0243] The first acquisition module 11 is used to acquire a data processing request, the data processing request including multiple fields and a statistical time period;

[0244] The second acquisition module 12 is used to acquire a first data set from the SAP HANA database, a memory computing platform that supports enterprise pre-deployment and cloud deployment modes, according to the data processing request. The first data set includes the multiple fields, the business data corresponding to each field in the statistical period, and the first data type of each business data.

[0245] Processing module 13 is used to process the first data set through a first calculation view to obtain a target data set, the target data set including the plurality of fields, first target data corresponding to each field, first data type of each first target data, target data type and a plurality of second target data corresponding to the target data type;

[0246] The generation module 14 is used to generate a target report corresponding to the target data set through the second calculation view.

[0247] In one possible implementation, the processing module 13 is specifically used for:

[0248] A first standard table is obtained through the first calculation view. The first standard table includes multiple data types and a first conversion factor between each data type and the target data type.

[0249] Based on the first conversion factor between each data type and the target data type, a left outer join is performed on the first data set and the first standard table to obtain a first joined data set. The first joined data set includes the business data corresponding to each field, the first data type of each business data, and the first conversion factor corresponding to each first type.

[0250] The first connected data set is processed to obtain the target data set.

[0251] In one possible implementation, the processing module 13 is specifically used for:

[0252] In the first connection data set, each business data is multiplied with the corresponding first conversion factor to obtain the first product data corresponding to the target data type;

[0253] The second data set is determined to include business data corresponding to each field, a first data type of each business data, the target data type, and multiple first product data corresponding to the target data type.

[0254] The second data set is processed to obtain the target data set.

[0255] In one possible implementation, the processing module 13 is specifically used for:

[0256] Obtain the second standard table, which includes multiple data types and a second conversion factor corresponding to each data type;

[0257] Based on the second conversion factor corresponding to each data type, a left outer join is performed on the second data set and the second standard table to obtain a second joined data set. The second joined data set includes business data corresponding to each field, a first data type of each business data, the target data type, multiple first product data corresponding to the target data type, and a second conversion factor corresponding to each first data type.

[0258] In the second connected data set, each first product data is multiplied by the second conversion factor corresponding to the first data type to obtain multiple second product data.

[0259] The third data set is determined to include business data corresponding to each field, a first data type for each business data, the target data type, and the multiple second product data.

[0260] The third data set is processed to obtain the target data set.

[0261] In one possible implementation, the processing module 13 is specifically used for:

[0262] Obtain a third standard table, which includes multiple data types and a position factor corresponding to each data type;

[0263] Based on the position factor corresponding to each data type, a left outer join is performed on the third data set and the third standard table to obtain a third joined data set. The third joined data set includes business data corresponding to each field, a first data type of each business data, the target data type, the multiple second product data, and a position factor corresponding to each first data type.

[0264] Based on the position factor corresponding to each data type, each business data is processed to obtain the first target data corresponding to each business data, and each second product data is processed to obtain the second target data corresponding to the target data type.

[0265] The target data set is determined to include the multiple fields, first target data corresponding to each field, a first data type of each first target data, the target data type, and multiple second target data corresponding to the target data type.

[0266] In one possible implementation, the processing module 13 is specifically used for:

[0267] In the third connection data set, obtain the third transformation factor corresponding to each position factor;

[0268] For any given set of business data, the business data is multiplied by the third conversion factor corresponding to the location factor to obtain the first target data;

[0269] For any second product data, the second product data is multiplied by the third transformation factor corresponding to the position factor to obtain the third product data;

[0270] The average value of the multiple third product data is calculated to obtain multiple second target data corresponding to the target data type.

[0271] In one possible implementation, the processing module 13 is specifically used for:

[0272] For any field, the average of multiple third product data corresponding to the field is calculated according to the statistical period to obtain the second target data corresponding to the target data type.

[0273] Based on the second target data corresponding to each field, determine multiple second target data corresponding to the target data type.

[0274] In one possible implementation, the generation module 13 is specifically used for:

[0275] The target data set is obtained by performing a database connection operation on the target data set using the second computational graph;

[0276] The target connection set is used to generate a target report corresponding to the target data set according to a preset format.

[0277] The report generation device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0278] Figure 8 This is a schematic diagram of another report generation device provided in an embodiment of this application. Figure 7 Based on the illustrated embodiments, please refer to Figure 8 The report generation device 10 also includes a query module 15.

[0279] The query module 15 is used for:

[0280] Obtain a data query request, the data query request including the target field and the statistical period;

[0281] In the target report, at least one target data corresponding to the target field is determined;

[0282] Based on the at least one target data, generate query results and display the query results.

[0283] The report generation device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0284] Figure 9 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. The terminal device 20 may include a memory 21 and a processor 22. Exemplarily, the memory 21 and the processor 22 are interconnected via a bus 23.

[0285] Memory 21 is used to store program instructions;

[0286] The processor 22 is used to execute the program instructions stored in the memory, so that the terminal device 20 performs the method shown in the above method embodiment.

[0287] The terminal device provided in this application embodiment can execute the technical solution shown in the above method embodiment. Its implementation principle and beneficial effects are similar, and will not be described again here.

[0288] This application provides a computer-readable storage medium storing computer-executable instructions, which are used to implement the above-described method when executed by a processor.

[0289] This application embodiment may also provide a computer program product, including a computer program that, when executed by a processor, can implement the above-described method.

[0290] All or part of the steps in the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above-described method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), random access memory (RAM), flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0291] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processing unit of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0292] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0293] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0294] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

[0295] In this application, the term "comprising" and its variations can refer to non-limiting inclusion; the term "or" and its variations can refer to "and / or". The terms "first", "second", etc., in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

Claims

1. A report generation method, characterized in that, include: Obtain a data processing request, which includes multiple fields and a statistical time period; According to the data processing request, a first data set is obtained from the SAPHANA database, an in-memory computing platform that supports enterprise pre-deployment and cloud deployment modes. The first data set includes the multiple fields, the business data corresponding to each field within the statistical period, and the first data type of each business data. The first data set is processed through the first calculation view to obtain a target data set, which includes the plurality of fields, first target data corresponding to each field, first data type of each first target data, target data type, and plurality of second target data corresponding to the target data type; The target report corresponding to the target data set is generated through the second calculation view; The process of processing the first data set through a first computational view to obtain a target data set includes: obtaining a first standard table through the first computational view, the first standard table including multiple data types and a first conversion factor between each data type and the target data type; performing a left outer join operation on the first data set and the first standard table based on the first conversion factor between each data type and the target data type to obtain a first joined data set, the first joined data set including business data corresponding to each field, a first data type of each business data, and a first conversion factor corresponding to each first data type; multiplying each business data in the first joined data set by its corresponding first conversion factor to obtain a first product data corresponding to the target data type; determining that a second data set includes business data corresponding to each field, a first data type of each business data, the target data type, and multiple first product data corresponding to the target data type; and processing the second data set to obtain the target data set.

2. The method according to claim 1, characterized in that, The second data set is processed to obtain the target data set, including: Obtain the second standard table, which includes multiple data types and a second conversion factor corresponding to each data type; Based on the second conversion factor corresponding to each data type, a left outer join is performed on the second data set and the second standard table to obtain a second joined data set. The second joined data set includes business data corresponding to each field, a first data type of each business data, the target data type, multiple first product data corresponding to the target data type, and a second conversion factor corresponding to each first data type. In the second connected data set, each first product data is multiplied by the second conversion factor corresponding to the first data type to obtain multiple second product data. The third data set is determined to include business data corresponding to each field, a first data type for each business data, the target data type, and the multiple second product data. The third data set is processed to obtain the target data set.

3. The method according to claim 2, characterized in that, The third data set is processed to obtain the target data set, including: Obtain a third standard table, which includes multiple data types and a position factor corresponding to each data type; Based on the position factor corresponding to each data type, a left outer join is performed on the third data set and the third standard table to obtain a third joined data set. The third joined data set includes business data corresponding to each field, a first data type of each business data, the target data type, the multiple second product data, and a position factor corresponding to each first data type. Based on the position factor corresponding to each data type, each business data is processed to obtain the first target data corresponding to each business data, and each second product data is processed to obtain multiple second target data corresponding to the target data type. The target data set is determined to include the multiple fields, first target data corresponding to each field, a first data type of each first target data, the target data type, and multiple second target data corresponding to the target data type.

4. The method according to claim 3, characterized in that, Each piece of business data is processed to obtain the first target data corresponding to each piece of business data, and each piece of second product data is processed to obtain multiple second target data corresponding to the target data type, including: In the third connection data set, obtain the third transformation factor corresponding to each position factor; For any given set of business data, the business data is multiplied by the third conversion factor corresponding to the location factor to obtain the first target data; For any second product data, the second product data is multiplied by the third transformation factor corresponding to the position factor to obtain the third product data; The average value of the multiple third product data is calculated to obtain multiple second target data corresponding to the target data type.

5. The method according to claim 4, characterized in that, The average value of multiple third product data is calculated to obtain multiple second target data corresponding to the target data type, including: For any field, the average of multiple third product data corresponding to the field is calculated according to the statistical period to obtain the second target data corresponding to the target data type. Based on the second target data corresponding to each field, determine multiple second target data corresponding to the target data type.

6. The method according to any one of claims 1-5, characterized in that, The target report corresponding to the target data set is generated through the second calculation view, including: The target data set is obtained by performing a database connection operation on the target data set through the second computational view; The target connection set is used to generate a target report corresponding to the target data set according to a preset format.

7. The method according to any one of claims 1-5, characterized in that, The method further includes: Obtain a data query request, the data query request including the target field and the statistical period; In the target report, at least one target data corresponding to the target field is determined; Based on the at least one target data, generate query results and display the query results.

8. A report generation device, characterized in that, The device includes: The first acquisition module is used to acquire a data processing request, which includes multiple fields and a statistical time period. The second acquisition module is used to acquire a first data set from the SAP HANA database, a memory computing platform that supports enterprise pre-deployment and cloud deployment modes, according to the data processing request. The first data set includes the multiple fields, the business data corresponding to each field in the statistical period, and the first data type of each business data. The processing module is used to process the first data set through the first calculation view to obtain a target data set, wherein the target data set includes the plurality of fields, first target data corresponding to each field, a first data type of each first target data, a target data type, and a plurality of second target data corresponding to the target data type; The generation module is used to generate a target report corresponding to the target data set through the second calculation view; The processing module is specifically configured to: process the first data set through a first computational view to obtain a target data set, including: obtaining a first standard table through the first computational view, the first standard table including multiple data types and a first conversion factor between each data type and the target data type; performing a left outer join operation on the first data set and the first standard table according to the first conversion factor between each data type and the target data type to obtain a first joined data set, the first joined data set including business data corresponding to each field, a first data type of each business data, and a first conversion factor corresponding to each first data type; multiplying each business data with its corresponding first conversion factor in the first joined data set to obtain a first product data corresponding to the target data type; determining that a second data set includes business data corresponding to each field, a first data type of each business data, the target data type, and multiple first product data corresponding to the target data type; and processing the second data set to obtain the target data set.

9. A terminal device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 7.

Images