Multi-dimensional data processing method and device, medium and electronic equipment

Abstract

The application discloses a multi-dimensional data processing method and device, a medium and an electronic equipment. The method comprises the following steps: acquiring data, wherein the data comprises at least one existing data table and at least one scattered data; performing extraction on at least one field in the at least one data table based on a table extraction configuration, and mapping the extracted at least one field to a corresponding dimension based on a dimension mapping configuration, so as to form a first multi-dimensional data table, and recording the at least one scattered data into the first multi-dimensional data table; and based on at least one set predetermined index and an index dimension corresponding to each predetermined index, collecting data meeting the index dimension in the first multi-dimensional data table to each predetermined index, so as to generate a first index data table. When multi-dimensional profit and loss needs to be understood, the application can avoid a large amount of repeated development / modification work, and saves human resources and technological construction costs.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a multi-dimensional data processing method, device, electronic device, and computer program product. Background Technology

[0002] Many companies lack detailed financial management and statistical analysis of their business operations, especially multidimensional profit and loss data. They have an urgent need for data, but there is no clear multidimensional profit and loss data available. The data is scattered in manual business ledgers, is inaccurate, and historical data is unavailable, making statistical work difficult. Developing such functions in traditional systems requires extensive development based on business needs, consuming significant human resources and technology investment.

[0003] Typical multi-dimensional profit and loss solutions are developed by batch processing data one by one according to requirements. The process follows the traditional software development process, including requirements analysis, design, development, testing, and maintenance. However, existing solutions have long development cycles, are inflexible in responding to rule changes, and require frequent system updates and deployments. Summary of the Invention

[0004] This application provides a multi-dimensional data processing method, device medium, electronic device, and computer program product.

[0005] In a first aspect, embodiments of this application provide a multi-dimensional data processing method, the method comprising: an acquisition step, acquiring data, the data including at least one existing data table and at least one piece of scattered data; a preprocessing step, extracting at least one field from the at least one data table based on a table extraction configuration, and mapping the extracted at least one field to a corresponding dimension based on a dimension mapping configuration, thereby forming a first multi-dimensional data table, and supplementing the at least one piece of scattered data into the first multi-dimensional data table; and a first generation step, based on at least one predetermined indicator and an indicator dimension corresponding to each predetermined indicator, aggregating the data in the first multi-dimensional data table that conforms to the indicator dimension into each predetermined indicator, thereby generating a first indicator data table.

[0006] In one possible implementation of the first aspect above, it further includes: a total-to-score verification step, in which the first indicator data table and the general ledger data table are reconciled, and the difference data is added to the first multidimensional data table to form a second multidimensional data table; and a second generation step, in which, based on at least one of the predetermined indicators and the indicator dimensions, the data in the second multidimensional data table that conform to the indicator dimensions are aggregated to each of the predetermined indicators, thereby generating the second indicator data table.

[0007] In one possible implementation of the first aspect above, the total reconciliation step further includes: aggregating the values ​​of at least one account in the general ledger data table into reconciliation values ​​of reconciliation indicators; summarizing the values ​​aggregating the reconciliation indicators in the first indicator data table to obtain a summary value, wherein the reconciliation indicator is one of at least one of the predetermined indicators; comparing the reconciliation value and the summary value to obtain a difference; determining the dimension value of each dimension according to the dimension processing method of each dimension; and adding each dimension value and the difference as the difference data to the first multidimensional data table to form a second multidimensional data table.

[0008] In one possible implementation of the first aspect above, in the preprocessing step, the table extraction configuration includes a data table to be extracted, fields, and data filtering conditions, thereby extracting the corresponding data of at least one field in the at least one data table. The dimension mapping configuration includes multiple dimensions and a mapping method for each dimension, thereby mapping the corresponding data of the at least one field to the corresponding dimension according to the mapping method.

[0009] In one possible implementation of the first aspect described above, the preprocessing step further includes adjusting the data in the first multidimensional data table.

[0010] In one possible implementation of the first aspect described above, the proportion method is used to adjust at least one data table entering the first multidimensional data table, and the commission method is used to adjust the at least one piece of scattered data before adding it to the first multidimensional data table.

[0011] Secondly, embodiments of this application provide a multi-dimensional data processing apparatus, comprising: an acquisition unit for acquiring data, the data including at least one existing data table and at least one piece of scattered data; a preprocessing unit for extracting at least one field from the at least one data table based on a table extraction configuration, and mapping the extracted at least one field to a corresponding dimension based on a dimension mapping configuration, thereby forming a first multi-dimensional data table, and supplementing the at least one piece of scattered data into the first multi-dimensional data table; and a first generation unit for aggregating data from the first multi-dimensional data table that conforms to the indicator dimension into each of the predetermined indicators based on at least one set predetermined indicator and the indicator dimension corresponding to each predetermined indicator, thereby generating a first indicator data table. The acquisition unit, preprocessing unit, and first generation unit described above can be implemented by a processor in an electronic device that has these module or unit functions.

[0012] In one possible implementation of the second aspect above, it further includes: a total-to-score verification unit, which performs total-to-score verification between the first indicator data table and the general ledger data table, and adds the difference data to the first multidimensional data table to form a second multidimensional data table; and a second generation unit, which, based on at least one of the predetermined indicators and the indicator dimensions, aggregates the data in the second multidimensional data table that conform to the indicator dimensions to each of the predetermined indicators, thereby generating a second indicator data table.

[0013] In one possible implementation of the second aspect above, the general ledger reconciliation unit further performs the following operations: aggregating the values ​​of at least one account in the general ledger data table into reconciliation values ​​of reconciliation indicators; summarizing the values ​​aggregating the reconciliation indicators in the first indicator data table to obtain a summary value, wherein the reconciliation indicator is at least one of the predetermined indicators; comparing the reconciliation value and the summary value to obtain a difference; determining the dimension value of each dimension according to the dimension processing method of each dimension; and supplementing each dimension value and the difference as the difference data into the first multidimensional data table to form a second multidimensional data table.

[0014] In one possible implementation of the second aspect above, in the preprocessing unit, the table extraction configuration includes a data table to be extracted, fields, and data filtering conditions, thereby extracting the corresponding data of at least one field in the at least one data table. The dimension mapping configuration includes multiple dimensions and a mapping method for each dimension, thereby mapping the corresponding data of the at least one field to the corresponding dimension according to the mapping method.

[0015] In one possible implementation of the second aspect described above, the preprocessing unit further adjusts the data in the first multidimensional data table.

[0016] In one possible implementation of the second aspect described above, the proportion method is used to adjust at least one data table entering the first multidimensional data table, and the commission method is used to adjust the at least one piece of scattered data before adding it to the first multidimensional data table.

[0017] Thirdly, embodiments of this application provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the multi-dimensional data processing method described in the first aspect above.

[0018] Fourthly, embodiments of this application provide an electronic device, including: one or more processors; one or more memories; the one or more memories storing one or more programs, which, when executed by the one or more processors, cause the electronic device to perform the multi-dimensional data processing method described in the first aspect above.

[0019] Fifthly, embodiments of this application provide a computer program product including computer-executable instructions, characterized in that the instructions are executed by a processor to implement the multi-dimensional data processing method in the first aspect.

[0020] In this invention, by utilizing table extraction configuration and dimension mapping configuration, the processing and generation of multidimensional data can be flexibly controlled. Data reliability can be ensured through total score verification, and by generating indicator data tables, the required data can be obtained based on the indicators. Thus, according to this invention, when it is necessary to understand multidimensional profit and loss situations, a large amount of repetitive development / modification work can be avoided, saving human resources and technology construction costs. Attached Figure Description

[0021] Figure 1 According to the first embodiment of this application, a flowchart of a multi-dimensional data processing method is shown;

[0022] Figure 2 A schematic diagram of a multi-dimensional data processing apparatus is shown according to a first embodiment of this application;

[0023] Figure 3 According to a second embodiment of this application, a flowchart of a multi-dimensional data processing method is shown;

[0024] Figure 4 According to a second embodiment of this application, a schematic diagram of a multi-dimensional data processing apparatus is shown;

[0025] Figure 5 A block diagram of an electronic device is shown according to some embodiments of this application. Detailed Implementation

[0026] The illustrative embodiments of this application include, but are not limited to, multi-dimensional data processing methods, apparatuses, media, and electronic devices.

[0027] The acquisition, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0028] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0029] First Embodiment

[0030] like Figure 1 The diagram shown is a flowchart of a multi-dimensional data processing method provided in an embodiment of this application. This method is used in electronic devices, such as computers, servers, and mobile terminals.

[0031] In step S10, data is acquired, which includes at least one existing data table and at least one piece of scattered data. For example, there are 20 data tables and 10 pieces of scattered data. The scattered data is, for example, ledger data.

[0032] In the preprocessing step S11, at least one field from the at least one data table is extracted based on the table extraction configuration, and the extracted at least one field is mapped to the corresponding dimension based on the dimension mapping configuration, thereby forming a first multidimensional data table, and the at least one piece of scattered data is added to the first multidimensional data table.

[0033] The table extraction configuration includes the data table to be extracted, the fields, and the data filtering conditions, so as to extract the corresponding data of at least one field in at least one data table.

[0034] Specifically, the table extraction configuration can specify the source table, the fields to be extracted from the tables, and data filtering conditions. The source table might be the table number to be extracted from, thus determining which of the 20 tables to extract from. Based on this extraction configuration, macros such as CURDATE and YEAR_START are used to extract the required fields from the 20 tables into the first multidimensional data detail table.

[0035] Dimension mapping configuration includes multiple dimensions and the mapping method for each dimension, thereby mapping the data of at least one field to the corresponding dimension according to the mapping method.

[0036] Dimension mapping configurations can include any number of dimensions, such as customers, organizations, departments, indicator categories, products, accounting entities, etc. For example, a dimension mapping configuration could include four dimensions: Dimension 1 is customers, Dimension 2 is organizations, Dimension 3 is departments, and Dimension 4 is indicator categories.

[0037] The mapping methods for each dimension include, for example, inheritance mapping, specified mapping, or rule mapping.

[0038] Inheritance mapping means that the dimension value originates from a field in the source table, that is, from the value of a field in the aforementioned data table. For example, if dimension 1 is mapped using inheritance mapping, then the value of dimension 1 comes from the value of field 3 in data table 2 of the aforementioned 20 data tables.

[0039] Specifying a mapping means that the dimension value of a dimension is assigned a specific value. For example, if the mapping method for dimension 4 is specified as "income", then the dimension value of dimension 4 will be "income".

[0040] Rule mapping refers to the generation of a dimension value through specific mapping relationships between other dimensions.

[0041] For "rule mapping," a detailed description can be provided, specifying which dimensions are used to map to the desired dimension. For example, if the mapping method for dimension 2 is rule mapping, its detailed description could be "associating with dimension 3," meaning that dimension 2 is associated with dimension 3, and the dimension value of dimension 3 (department) is mapped to the dimension value of dimension 2 (organization). For example, the dimension value "Finance Department" of dimension 3 (department) is mapped to the dimension value "BDC" of dimension 2 (organization).

[0042] Understandably, the detailed description of "rule mapping" can be customized according to business needs without restriction.

[0043] Following the above method, the first multidimensional data table can be formed from 20 data tables, as shown in Table 1.

[0044] Table 1

[0045]

[0046] For illustrative purposes, only two data points are shown in Table 1. It is understood that the data in Table 1 can be any number of data points, without restriction.

[0047] The "Source" field in Table 1 indicates the source of this data, where "Interface" indicates that this data comes from the aforementioned 20 data tables.

[0048] In addition, the data in the first multidimensional data table is adjusted. For example, the data table entering the multidimensional data table can be adjusted using a proportional method. The proportional method is, for example, to deduct the amount proportionally for certain dimensions and put it into a temporary pool (splitting out), and then the data from the temporary pool is proportionally distributed to the dimension values ​​of other specified dimensions to be transferred in (splitting in).

[0049] Furthermore, the aforementioned 10 scattered data entries will be added to the first multidimensional data table. A commission-based method can be used to adjust these 10 scattered data entries before adding them to the multidimensional data table. The commission-based method could involve directly specifying which dimensions to deduct amounts from (outward distribution) or which dimensions to add amounts to (inward distribution).

[0050] The first multidimensional data table after the supplementary data entry is shown in Table 2 below, which only shows two supplementary data entries (data 3 and data 4).

[0051] Table 2

[0052]

[0053]

[0054] As can be seen from Table 2, data 3 and data 4 are from "supplementary entries".

[0055] During the process of adjusting and supplementing the data, two fields can be added to the first multidimensional data table: for example, the time of supplementation and the level of supplementation.

[0056] Timing of Supplementary Entry: Since there is a total score verification step later, it is necessary to choose whether the supplemented data should be included in the total score verification. The timing of supplementary entry can be before or after the supplementary entry. Before the supplementary entry means that the supplemented data will be included in the total score verification, while after the supplementary entry means that the supplemented data will not be included in the total score verification.

[0057] Supplementary Data Level: Because of the proportional method, it's optional whether supplementary data should be included in the proportional method's calculation. Therefore, the supplementary data level can be increased to make the supplementary data applicable to lower-level supplementary data results. The default level is 0, meaning supplementary data doesn't affect each other by default.

[0058] In the first generation step S12, at least one predetermined indicator and an indicator dimension corresponding to each predetermined indicator are set, and the data that conforms to the indicator dimension in the first multidimensional data table are collected into each predetermined indicator, thereby generating the first indicator data table.

[0059] For example, the predetermined indicators are "Company A's revenue," "Other companies' revenue," etc. The indicator dimensions corresponding to "Company A's revenue" are, for example, Dimension 1 and Dimension 4. Dimension 1 is the customer, and its values ​​include Company A, Company B, etc. Dimension 4 is the "indicator category," and its values ​​include, for example, "revenue," "expenses," etc. The indicator dimension is then defined by setting Dimension 1's value to "Company A" and Dimension 4's value to "revenue," thus specifying "Company A's revenue" as the indicator dimension.

[0060] Furthermore, each metric dimension is configured with a dimension filtering method, such as "association filtering" or "rule filtering".

[0061] For example, if the dimension filtering method for both dimension 1 and dimension 4 is "association filtering", then the data in the first multidimensional data table that meets the indicator dimension will be aggregated into the predetermined indicator "Company A Revenue". That is, the data with the dimension value of "Company A" in dimension 1 and the dimension value of "revenue" in dimension 4 will be aggregated into the predetermined indicator "Company A Revenue", thereby generating the first indicator data table, as shown in Table 3 below.

[0062] Table 3

[0063]

[0064]

[0065] For example, the dimension filtering method for Dimension 2 (Institutions) is "rule-based filtering," which is suitable for situations where derived dimensions are needed. For instance, if "Institution" has the attribute of "Important Region," based on this attribute, a derived dimension "Important Region" can be added, and a derived indicator "Important Region Revenue" can be generated. In this way, data under "Institutions" belonging to "Important Region" are aggregated into the indicator "Important Region Revenue."

[0066] It is understandable that different dimensions can use different filtering methods, but only one filtering method can be used for the same dimension.

[0067] It is understandable that each indicator has associated dimensions, and indicators and dimensions coexist. It is also understandable that, for illustrative purposes, Table 3 only shows data aggregated under the predetermined indicator "Company A's Revenue," but it may also include data aggregated under the predetermined indicator "Other Companies' Revenue," as well as data aggregated under other predetermined indicators.

[0068] Furthermore, based on the required metrics, reports corresponding to the specified metrics can be generated from the first metric data table. For example, if the required metric for the current business is "Company A's Revenue," all data aggregated under "Company A's Revenue" in the first metric data table (e.g., data 1 and data 3 in Table 3) can be used to generate a report corresponding to "Company A's Revenue." This report can then be displayed through a user interface.

[0069] In this invention, by utilizing table extraction configuration and dimension mapping configuration, the processing and generation of multidimensional data can be flexibly controlled. Furthermore, by generating a first indicator data table, the required data reports can be obtained based on the indicators. Thus, according to this invention, when it is necessary to understand multidimensional profit and loss situations, a large amount of repetitive development / modification work can be avoided, saving human resources and technology construction costs.

[0070] Second Embodiment

[0071] The following joints Figure 4 and Figure 5 The second embodiment of the present invention is described below.

[0072] like Figure 3 As shown, after performing steps S10-S12 in the first embodiment, the method in this embodiment further includes a total-to-score verification step S31, which verifies the total-to-score of the first indicator data table and the general ledger data table, and adds the difference data to the first multidimensional data table to form a second multidimensional data table.

[0073] To ensure data credibility, data is usually benchmarked to maintain consistency with publicly disclosed general ledger data.

[0074] In this embodiment, the general ledger data table is used as the benchmark. To address the potential inconsistency between the first multidimensional data table formed in step S11 and the general ledger data table, and to ensure consistency between the data and disclosure standards in the final generated report, and to improve the cross-referencing between tables, this invention employs a general-to-subsidiary verification step S41.

[0075] Specifically, the values ​​of at least one account in the general ledger data table are aggregated into the verification value of the verification indicator, and the values ​​aggregated into the verification indicator in the first indicator data table are summarized to obtain the summary value, wherein the verification indicator is at least one of the predetermined indicators.

[0076] For example, if the verification indicator is "Company A's Revenue", the values ​​of five items in the general ledger data table are aggregated into the verification value of "Company A's Revenue", for example, 200 yuan. The values ​​aggregated into "Company A's Revenue" in the first indicator data table (Table 3) are summarized. For example, the values ​​of data 1 (100) and data 3 (50) in Table 3 are summarized to obtain the summary value, for example, 150 yuan.

[0077] Understandably, the verification indicator "Company A's revenue" is one of the aforementioned predetermined indicators.

[0078] Comparing the verified value "200 yuan" and the summarized value "150 yuan" yields a difference of "50 yuan". Here, we only obtain the difference of "50 yuan", and we need to determine the dimension value for each dimension corresponding to the difference of "50 yuan" using the following method.

[0079] Specifically, based on the dimensional processing method for each dimension, the dimensional value of each dimension of the difference data is determined. Each dimensional value and the difference are then added to the first multidimensional data table (Table 2) as difference data, forming the second multidimensional data table, as shown in Table 4 below. It can be understood that data 5 in Table 4 represents the difference data.

[0080] Table 4

[0081]

[0082] It is understandable that the dimension processing method for each processing dimension is pre-configured. There are three types of dimension processing methods, such as association processing, specified processing, and rule processing, which will be described in detail below.

[0083] For example, if both dimensions 2 and 4 are processed using "association processing", then the dimension values ​​of dimensions 2 and 4 in the difference data are associated with the dimension values ​​of dimensions 2 and 4 in data 1 and data 3 in Table 3. That is, the dimension value of dimension 2 in the difference data (data 5) is "BDC", and the dimension value of dimension 4 in the difference data (data 5) is "income".

[0084] For example, if the dimension processing method for dimension 1 is "specified processing" and is specified as "Company A", then the dimension value of dimension 1 for the difference data (data 5) is "Company A".

[0085] For example, the dimension processing method for dimension 3 is "rule processing". "Rule processing" can include a specific description, such as "mapping 'General Department' to dimension 3". Then the dimension value of dimension 3 for the difference data (data 5) is "General Department".

[0086] Understandably, following the above method, the dimension value and the value "50" of each dimension of the difference data (data 5) to be added to the first multidimensional data table can be determined, and the second multidimensional data table shown in Table 4 can be generated. Furthermore, as shown in Table 4, the source of data 5 is "difference filling".

[0087] Next, in the second generation step S32, based on at least one predetermined indicator and indicator dimension, the data that conforms to the indicator dimension in the second multidimensional data table are aggregated into each predetermined indicator, thereby generating the second indicator data table.

[0088] Through the overall and sub-ledger verification process, this invention ensures that the second multidimensional data table and the general ledger data table are consistent, thereby making the data and disclosure standards of the final generated report consistent and improving the inter-table reconciliation.

[0089] The second generation step S42 is basically similar to the first generation step S12, except that in the second generation step S42, the data that conforms to the indicator dimension in the second multidimensional data table are aggregated into each predetermined indicator.

[0090] For example, the predetermined indicators are "Company A's revenue," "Other companies' revenue," etc. The indicator dimensions corresponding to "Company A's revenue" are, for example, Dimension 1 and Dimension 4. Dimension 1 is the customer, and its values ​​include Company A, Company B, etc. Dimension 4 is the "indicator category," and its values ​​include, for example, "revenue," "expenses," etc. The indicator dimension is then defined by setting Dimension 1's value to "Company A" and Dimension 4's value to "revenue," thus specifying "Company A's revenue" as the indicator dimension.

[0091] Furthermore, each metric dimension is configured with a dimension filtering method, such as "association filtering" or "rule filtering".

[0092] For example, if the dimension filtering method for both dimension 1 and dimension 4 is "association filtering", then the data in the first multidimensional data table that meets the indicator dimension will be aggregated into the predetermined indicator "Company A Revenue". That is, the data with the dimension value of "Company A" in dimension 1 and the dimension value of "revenue" in dimension 4 will be aggregated into the predetermined indicator "Company A Revenue", thereby generating the second indicator data table, as shown in Table 5 below.

[0093] Table 5

[0094]

[0095] For example, the dimension filtering method for Dimension 2 (Institutions) is "rule-based filtering," which is suitable for situations where derived dimensions are needed. For instance, if "Institution" has the attribute of "Important Region," based on this attribute, a derived dimension "Important Region" can be added, and a derived indicator "Important Region Revenue" can be generated. In this way, data under "Institutions" belonging to "Important Region" are aggregated into the indicator "Important Region Revenue."

[0096] It is understandable that different dimensions can use different filtering methods, but only one filtering method can be used for the same dimension.

[0097] It is understandable that each indicator has associated dimensions, and indicators and dimensions coexist. It is also understandable that, for illustrative purposes, Table 5 only shows data aggregated under the predetermined indicator "Company A's Revenue," but it may also include data aggregated under the predetermined indicator "Other Companies' Revenue," as well as data aggregated under other predetermined indicators.

[0098] Furthermore, based on the required metrics, reports corresponding to the specified metrics can be generated from the first metric data table. For example, if the required metric for the current business is "Company A's Revenue," all data aggregated under "Company A's Revenue" in the first metric data table (e.g., data 1, data 3, and data 5 in Table 5) can be used to generate a report corresponding to "Company A's Revenue." This report can then be displayed through a user interface.

[0099] Understandably, Table 5 includes additional data (5) to supplement the difference compared to Table 3. Therefore, this invention ensures that the data in the final generated report is consistent with the disclosure standards, thus improving the consistency between tables.

[0100] In this invention, by utilizing table extraction configuration and dimension mapping configuration, the processing and generation of multidimensional data can be flexibly controlled. Data reliability can be ensured through total score verification, and by generating indicator data tables, the required data can be obtained based on the indicators. Thus, according to this invention, when it is necessary to understand multidimensional profit and loss situations, a large amount of repetitive development / modification work can be avoided, saving human resources and technology construction costs.

[0101] The present invention also provides a multi-dimensional data processing device 20, such as... Figure 2As shown, the device 20 includes: an acquisition unit 21, which acquires data, the data including at least one existing data table and at least one piece of scattered data; a preprocessing unit 22, which extracts at least one field from the at least one data table based on a table extraction configuration, and maps the extracted at least one field to a corresponding dimension based on a dimension mapping configuration, thereby forming a first multidimensional data table, and adds the at least one piece of scattered data to the first multidimensional data table; and a first generation unit 23, which, based on at least one predetermined indicator and the indicator dimension corresponding to each predetermined indicator, aggregates the data in the first multidimensional data table that conforms to the indicator dimension to each predetermined indicator, thereby generating a first indicator data table.

[0102] It is understandable that the acquisition unit 21, the preprocessing unit 22, and the first generation unit 23 can be... Figure 5 The processor 102 in the electronic device 100 has the functions of these modules or units to implement them.

[0103] The present invention also provides a multi-dimensional data processing device 40, such as... Figure 4 As shown, the device 40 includes: an acquisition unit 41, which acquires data, including at least one existing data table and at least one piece of scattered data; a preprocessing unit 42, which extracts at least one field from the at least one data table based on a table extraction configuration, and maps the extracted at least one field to a corresponding dimension based on a dimension mapping configuration, thereby forming a first multidimensional data table, and adds the at least one piece of scattered data to the first multidimensional data table; a first generation unit 43, which, based on at least one predetermined indicator and the indicator dimension corresponding to each predetermined indicator, aggregates the data in the first multidimensional data table that conforms to the indicator dimension to each predetermined indicator, thereby generating a first indicator data table; a total score verification unit 44, which performs a total score verification between the first indicator data table and the general ledger data table, and adds the difference data to the first multidimensional data table, thereby forming a second multidimensional data table; and a second generation unit 45, which, based on at least one predetermined indicator and the indicator dimension, aggregates the data in the second multidimensional data table that conforms to the indicator dimension to each predetermined indicator, thereby generating a second indicator data table.

[0104] It is understandable that the acquisition unit 41, preprocessing unit 42, first generation unit 43, total score verification unit 44, and second generation unit 45 can be... Figure 5 The processor 102 in the electronic device 100 has the functions of these modules or units to implement them.

[0105] The present invention also provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform [operations]. Figure 1 and Figure 3The multi-dimensional data processing method shown is illustrated.

[0106] The present invention also provides a computer program product, including computer-executable instructions, which are executed by processor 102 to implement the multi-dimensional data processing method of the present invention.

[0107] Now for reference Figure 5 , Figure 5 An example electronic device 1400 according to an embodiment of the present invention is illustrated schematically. In one embodiment, system 1400 may include one or more processors 1404, system control logic 1408 connected to at least one of the processors 1404, system memory 1412 connected to system control logic 1408, non-volatile memory (NVM) 1416 connected to system control logic 1408, and network interface 1420 connected to system control logic 1408.

[0108] In some embodiments, processor 1404 may include one or more single-core or multi-core processors. In some embodiments, processor 1404 may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.). In embodiments where system 1400 employs eNB (Evolved Node B) 101 or RAN (Radio Access Network) controller 102, processor 1404 may be configured to perform various corresponding embodiments, such as... Figure 1 The example shown.

[0109] In some embodiments, system control logic 1408 may include any suitable interface controller to provide any suitable interface to at least one of the processors 1404 and / or any suitable device or component communicating with system control logic 1408.

[0110] In some embodiments, system control logic 1408 may include one or more memory controllers to provide an interface to system memory 1412. System memory 1412 may be used to load and store data and / or instructions. In some embodiments, memory 1412 of system 1400 may include any suitable volatile memory, such as suitable dynamic random access memory (DRAM).

[0111] NVM / memory 1416 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. In some embodiments, NVM / memory 1416 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device, such as at least one of HDD (Hard Disk Drive), CD (Compact Disc) drive, and DVD (Digital Versatile Disc) drive.

[0112] NVM / Memory 1416 may include a portion of the storage resources on the device on which System 1400 is installed, or it may be accessible by the device, but is not necessarily part of the device. For example, NVM / Memory 1416 may be accessed over a network via Network Interface 1420.

[0113] Specifically, system memory 1412 and NVM / memory 1416 may each include a temporary copy and a permanent copy of instruction 1424. Instruction 1424 may include, when executed by at least one of processors 1404, causing electronic device 1400 to perform, as Figure 2 The instructions for the method shown. In some embodiments, instructions 1424, hardware, firmware and / or their software components may additionally / alternatively be located in system control logic 1408, network interface 1420 and / or processor 1404.

[0114] Network interface 1420 may include a transceiver for providing a radio interface to system 1400, thereby enabling communication with any other suitable device (such as a front-end module, antenna, etc.) via one or more networks. In some embodiments, network interface 1420 may be integrated into other components of system 1400. For example, network interface 1420 may be integrated into at least one of processor 1404, system memory 1412, NVM / memory 1416, and firmware device (not shown) with instructions that, when at least one of processor 1404 executes the instructions, electronic device 1400 implements as follows: Figure 1 The method shown.

[0115] The network interface 1420 may further include any suitable hardware and / or firmware to provide a multiple-input multiple-output radio interface. For example, the network interface 1420 may be a network adapter, a wireless network adapter, a telephone modem, and / or a wireless modem.

[0116] In one embodiment, at least one of the processors 1404 may be packaged together with the logic of one or more controllers for system control logic 1408 to form a system-in-package (SiP). In another embodiment, at least one of the processors 1404 may be integrated on the same die with the logic of one or more controllers for system control logic 1408 to form a system-on-a-chip (SoC).

[0117] The electronic device 1400 may further include an input / output (I / O) device 1432. The I / O device 1432 may include a user interface enabling a user to interact with the electronic device 1400; the peripheral component interface is designed to allow peripheral components to also interact with the electronic device 1400. In some embodiments, the electronic device 1400 may also include sensors for determining at least one type of environmental condition and location information related to the electronic device 1400.

[0118] In some embodiments, the user interface may include, but is not limited to, a display (e.g., a liquid crystal display, a touch screen display, etc.), a speaker, a microphone, one or more cameras (e.g., a still image camera and / or a video camera), a flashlight (e.g., a light-emitting diode flash), and a keyboard.

[0119] The various embodiments of the mechanisms disclosed in this application can be implemented in hardware, software, firmware, or a combination of these implementation methods. Embodiments of this application can be implemented as computer programs or program code executable on a programmable system, the programmable system including at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0120] Program code can be applied to input instructions to execute the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, the processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor.

[0121] The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used when needed. In fact, the mechanisms described in this application are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0122] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored thereon on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or through other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine-readable (e.g., computer-readable) form, including but not limited to floppy disks, optical disks, CD-ROMs, magneto-optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic cards or optical cards, flash memory, or tangible machine-readable storage for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in the form of electrical, optical, acoustic, or other propagation signals. Therefore, machine-readable media include any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a machine-readable (e.g., computer-readable) form.

[0123] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Furthermore, the inclusion of structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0124] It should be noted that all units / modules mentioned in the device embodiments of this application are logical units / modules. Physically, a logical unit / module can be a physical unit / module, a part of a physical unit / module, or a combination of multiple physical units / modules. The physical implementation of these logical units / modules themselves is not the most important factor; the combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed in this application. Furthermore, to highlight the innovative aspects of this application, the above-described device embodiments of this application have not introduced units / modules that are not closely related to solving the technical problems proposed in this application. This does not mean that the above-described device embodiments do not contain other units / modules.

[0125] It should be noted that in the examples and description of this patent, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, 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. Without further limitations, 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 said element.

[0126] Although this application has been illustrated and described with reference to certain preferred embodiments thereof, those skilled in the art should understand that various changes in form and detail may be made thereto without departing from the spirit and scope of this application.

Claims

1. A multi-dimensional data processing method, characterized in that, The method includes: The acquisition step involves acquiring data, which includes at least one existing data table and at least one piece of scattered data. The preprocessing step involves extracting at least one field from the at least one data table based on the table extraction configuration, and mapping the extracted at least one field to the corresponding dimension based on the dimension mapping configuration, thereby forming a first multidimensional data table, and supplementing the at least one piece of scattered data into the first multidimensional data table. The first generation step involves, based on at least one predetermined indicator and the indicator dimension corresponding to each predetermined indicator, aggregating the data in the first multidimensional data table that conforms to the indicator dimension into each predetermined indicator, thereby generating a first indicator data table. The total-to-score verification step involves verifying the total-to-score data between the first indicator data table and the general ledger data table, and adding the discrepancies to the first multidimensional data table to form the second multidimensional data table. The second generation step involves, based on at least one of the predetermined indicators and the indicator dimensions, aggregating the data in the second multidimensional data table that conform to the indicator dimensions into each of the predetermined indicators, thereby generating a second indicator data table.

2. The method according to claim 1, characterized in that, The total score verification step further includes: The values ​​of at least one account in the general ledger data table are aggregated into the verification value of the verification indicator. The values ​​aggregated into the verification indicator in the first indicator data table are summarized to obtain the summary value, wherein the verification indicator is at least one of the predetermined indicators. The checked value and the summarized value are compared to obtain the difference; Based on the dimension processing method for each dimension, the dimension value of each dimension is determined, and each dimension value and the difference are added to the first multidimensional data table as the difference data to form the second multidimensional data table.

3. The method according to claim 1, characterized in that, In the preprocessing step, the table extraction configuration includes the data table to be extracted, the fields, and the data filtering conditions, thereby extracting the corresponding data of at least one field in the at least one data table. The dimension mapping configuration includes multiple dimensions and a mapping method for each dimension, thereby mapping the corresponding data of the at least one field to the corresponding dimension according to the mapping method.

4. The method according to claim 1, characterized in that, The preprocessing step further includes adjusting the data in the first multidimensional data table.

5. The method according to claim 4, characterized in that, The proportion method is used to adjust at least one data table that has entered into the first multidimensional data table, and the commission method is used to adjust the at least one piece of scattered data before adding it to the first multidimensional data table.

6. A multi-dimensional data processing device, characterized in that, The device includes: The acquisition unit acquires data, which includes at least one existing data table and at least one piece of scattered data. The preprocessing unit extracts at least one field from the at least one data table based on the table extraction configuration, and maps the extracted at least one field to the corresponding dimension based on the dimension mapping configuration, thereby forming a first multidimensional data table, and supplements the at least one piece of scattered data into the first multidimensional data table. The first generation unit, based on at least one predetermined indicator and the indicator dimension corresponding to each predetermined indicator, aggregates the data that conforms to the indicator dimension in the first multidimensional data table into each predetermined indicator, thereby generating a first indicator data table. The total score verification unit performs a total score verification between the first indicator data table and the general ledger data table, and adds the difference data into the first multidimensional data table to form a second multidimensional data table; The second generation unit, based on at least one of the predetermined indicators and the indicator dimensions, aggregates the data in the second multidimensional data table that conform to the indicator dimensions into each of the predetermined indicators, thereby generating a second indicator data table.

7. The apparatus according to claim 6, characterized in that, The total score verification unit further performs the following operations: The values ​​of at least one account in the general ledger data table are aggregated into the verification value of the verification indicator. The values ​​aggregated into the verification indicator in the first indicator data table are summarized to obtain the summary value, wherein the verification indicator is at least one of the predetermined indicators. The checked value and the summarized value are compared to obtain the difference; Based on the dimension processing method for each dimension, the dimension value of each dimension is determined, and each dimension value and the difference are added to the first multidimensional data table as the difference data to form the second multidimensional data table.

8. The apparatus according to claim 6, characterized in that, In the preprocessing unit, the table extraction configuration includes the data table to be extracted, the fields, and the data filtering conditions, thereby extracting the corresponding data of at least one field in the at least one data table. The dimension mapping configuration includes multiple dimensions and a mapping method for each dimension, thereby mapping the corresponding data of the at least one field to the corresponding dimension according to the mapping method.

9. The apparatus according to claim 6, characterized in that, The preprocessing unit further adjusts the data in the first multidimensional data table.

10. The apparatus according to claim 9, characterized in that, The proportion method is used to adjust at least one data table that has entered into the first multidimensional data table, and the commission method is used to adjust the at least one piece of scattered data before adding it to the first multidimensional data table.

11. A computer-readable storage medium, characterized in that, The storage medium stores instructions that, when executed on a computer, cause the computer to perform the multi-dimensional data processing method according to any one of claims 1 to 5.

12. An electronic device, characterized in that, include: One or more processors; One or more memories; the one or more memories storing one or more programs, which, when executed by the one or more processors, cause the electronic device to perform the multidimensional data processing method of any one of claims 1 to 5.

13. A computer program product comprising computer-executable instructions, characterized in that, The instructions are executed by the processor to implement the multidimensional data processing method according to any one of claims 1 to 5.

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